vignettes/Downloading-UNPD-data-into-R.Rmd
Downloading-UNPD-data-into-R.RmdDDSQLtools is a package that aims to make downloading data from the UNPD SQL API into R easy. In this vignette we will discuss the different ways in which you can explore and download this data. Make sure you have the latest version of DDSQLtools by installing it from Github:
devtools::install_github("timriffe/DDSQLtools")and load the package:
Before we begin, some of the output might show numbers with scientific notation depending on your R setup (2.56×10 instead of 0.00000256). You can suppress scientific notation by typing this into your R console: options(scipen = 9999).
If you’re already familiar with concepts from the UNPD SQL API, then you’ll only have to interact with the get_recorddata function. For example, here’s how to get data on population by age and sex for Egypt with different options:
egypt_str <- get_recorddata(dataProcessTypeIds = "Census",
indicatorTypeIds = "Population by age and sex",
locIds = "Egypt",
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged")get_recorddata accepts either string names or codes. For example, this will return the same thing as above:
egypt_num <- get_recorddata(dataProcessTypeIds = 2,
indicatorTypeIds = 8,
locIds = 818,
locAreaTypeIds = 2,
subGroupIds = 2,
isComplete = 0)Note that using numeric codes is faster than using string names because we don’t have to make code translations behind the scenes (if you’re unsure what these codes mean, please refer to the section Exploring the UNPD SQL API from R below). If you want to figure out which names to use, each of these arguments has an equivalent lookup function. For example, for figuring out the possible values in locIds, use get_locations(). You can see all arguments and their equivalent lookup functions in the documentation of ?get_recorddata. Do note that these lookup functions have both the 'Name' and a 'ShortName' of each value. The translation will only accept string names from the 'Name' column.
Here are a few other examples:
china <- get_recorddata(dataProcessTypeIds = "Census",
indicatorTypeIds = "Population by age and sex",
locIds = "China",
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged")
#>
#> https://popdiv.dfs.un.org/DemoData/api/locations?
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatortypes?
#>
#> https://popdiv.dfs.un.org/DemoData/api/subgroups?
#>
#> https://popdiv.dfs.un.org/DemoData/api/locareatypes?indicatorTypeIds=8&isComplete=0&locIds=156
#>
#> https://popdiv.dfs.un.org/DemoData/api/dataprocesstypes?indicatorTypeIds=8&isComplete=0&locIds=156
#> If you run the same query again, use the one below (faster):
#> get_recorddata(dataProcessTypeIds = 2, indicatorTypeIds = 8, isComplete = 0, locIds = 156, locAreaTypeIds = 2, subGroupIds = 2)
#> https://popdiv.dfs.un.org/DemoData/api/structureddatarecords?dataProcessTypeIds=2&indicatorTypeIds=8&isComplete=0&locIds=156&locAreaTypeIds=2&subGroupIds=2
head(china)
#> IndicatorName IndicatorID LocName LocID
#> 1 Population by age and sex - abridged 58 China 156
#> 2 Population by age and sex - abridged 58 China 156
#> 3 Population by age and sex - abridged 58 China 156
#> 4 Population by age and sex - abridged 58 China 156
#> 5 Population by age and sex - abridged 58 China 156
#> 6 Population by age and sex - abridged 58 China 156
#> StructuredDataID LocTypeName LocTypeID RegName RegID AreaName AreaID
#> 1 269739808 Country 5 NA 0 NA 0
#> 2 269783627 Country 5 NA 0 NA 0
#> 3 260217995 Country 5 NA 0 NA 0
#> 4 260217996 Country 5 NA 0 NA 0
#> 5 267289442 Country 5 NA 0 NA 0
#> 6 260221703 Country 5 NA 0 NA 0
#> LocAreaTypeName LocAreaTypeID SubGroupTypeName SubGroupTypeID
#> 1 Whole area 2 Total / All groups / Whole group 2
#> 2 Whole area 2 Total / All groups / Whole group 2
#> 3 Whole area 2 Total / All groups / Whole group 2
#> 4 Whole area 2 Total / All groups / Whole group 2
#> 5 Whole area 2 Total / All groups / Whole group 2
#> 6 Whole area 2 Total / All groups / Whole group 2
#> SubGroupID1 SubGroupName SubGroupCombinationID SubGroupTypeSortOrder
#> 1 2 Total or All groups 0 3
#> 2 2 Total or All groups 0 3
#> 3 2 Total or All groups 0 3
#> 4 2 Total or All groups 0 3
#> 5 2 Total or All groups 0 3
#> 6 2 Total or All groups 0 3
#> IndicatorShortName DataCatalogID DataProcessTypeID DataProcessType
#> 1 Population5 274 Census Census
#> 2 Population5 274 Census Census
#> 3 Population5 274 Census Census
#> 4 Population5 274 Census Census
#> 5 Population5 274 Census Census
#> 6 Population5 274 Census Census
#> DataProcessTypeSort DataProcess DataProcessID DataProcessSort
#> 1 1 Census 2 1
#> 2 1 Census 2 1
#> 3 1 Census 2 1
#> 4 1 Census 2 1
#> 5 1 Census 2 1
#> 6 1 Census 2 1
#> DataCatalogName DataCatalogShortName ReferencePeriod ReferenceYearStart
#> 1 China 1953 Census 1953 Census 1953 1953
#> 2 China 1953 Census 1953 Census 1953 1953
#> 3 China 1953 Census 1953 Census 1953 1953
#> 4 China 1953 Census 1953 Census 1953 1953
#> 5 China 1953 Census 1953 Census 1953 1953
#> 6 China 1953 Census 1953 Census 1953 1953
#> ReferenceYearEnd ReferenceYearMid FieldWorkStart FieldWorkEnd
#> 1 1953 1953.5 6/30/1953 12:00:00 AM 6/30/1953 12:00:00 AM
#> 2 1953 1953.5 6/30/1953 12:00:00 AM 6/30/1953 12:00:00 AM
#> 3 1953 1953.5 6/30/1953 12:00:00 AM 6/30/1953 12:00:00 AM
#> 4 1953 1953.5 6/30/1953 12:00:00 AM 6/30/1953 12:00:00 AM
#> 5 1953 1953.5 6/30/1953 12:00:00 AM 6/30/1953 12:00:00 AM
#> 6 1953 1953.5 6/30/1953 12:00:00 AM 6/30/1953 12:00:00 AM
#> FieldWorkMiddle DataCatalogNote DataSourceID
#> 1 1953.496 NA 42162
#> 2 1953.496 NA 42882
#> 3 1953.496 NA 42162
#> 4 1953.496 NA 42162
#> 5 1953.496 NA 42882
#> 6 1953.496 NA 42162
#> DataSourceAuthor DataSourceYear
#> 1 A. Coale 1982
#> 2 National Bureau of Statistics of China 1988
#> 3 A. Coale 1982
#> 4 A. Coale 1982
#> 5 National Bureau of Statistics of China 1988
#> 6 A. Coale 1982
#> DataSourceName
#> 1 Rapid Population Change in China1952–1982
#> 2 Tabulations of Population Census 1953
#> 3 Rapid Population Change in China1952–1982
#> 4 Rapid Population Change in China1952–1982
#> 5 Tabulations of Population Census 1953
#> 6 Rapid Population Change in China1952–1982
#> DataSourceShortName DataSourceSort DataStatusName
#> 1 Rapid Population Change in China1952–1982 0 Final
#> 2 NBS 0 Final
#> 3 Rapid Population Change in China1952–1982 0 Final
#> 4 Rapid Population Change in China1952–1982 0 Final
#> 5 NBS 0 Final
#> 6 Rapid Population Change in China1952–1982 0 Final
#> DataStatusID DataStatusSort StatisticalConceptName StatisticalConceptID
#> 1 1 2 De-facto 2
#> 2 1 2 De-facto 2
#> 3 1 2 De-facto 2
#> 4 1 2 De-facto 2
#> 5 1 2 De-facto 2
#> 6 1 2 De-facto 2
#> StatisticalConceptSort SexName SexID SexSort AgeID AgeUnit AgeStart AgeEnd
#> 1 1 Male 1 1 700 Year 0 -1
#> 2 1 Male 1 1 700 Year 0 -1
#> 3 1 Male 1 1 701 Year 0 5
#> 4 1 Male 1 1 702 Year 5 10
#> 5 1 Male 1 1 702 Year 5 10
#> 6 1 Male 1 1 703 Year 10 15
#> AgeSpan AgeMid AgeLabel AgeSort agesort DataTypeGroupName DataTypeGroupID
#> 1 -1 -1.0 Total 999 0 Direct 3
#> 2 -1 -1.0 Total 999 0 Direct 3
#> 3 5 2.5 0-4 5 0 Direct 3
#> 4 5 7.5 5-9 69 0 Direct 3
#> 5 5 7.5 5-9 69 0 Direct 3
#> 6 5 12.5 10-14 133 0 Direct 3
#> DataTypeName DataTypeID DataTypeSort ModelType
#> 1 Population by age and sex 58 203 Not applicable
#> 2 Population by age and sex 58 203 Not applicable
#> 3 Population by age and sex 58 203 Not applicable
#> 4 Population by age and sex 58 203 Not applicable
#> 5 Population by age and sex 58 203 Not applicable
#> 6 Population by age and sex 58 203 Not applicable
#> ModelPatternFamilyName ModelPatternFamilyID ModelPatternName
#> 1 Not applicable 15 Not applicable (Direct)
#> 2 Not applicable 15 Not applicable (Direct)
#> 3 Not applicable 15 Not applicable (Direct)
#> 4 Not applicable 15 Not applicable (Direct)
#> 5 Not applicable 15 Not applicable (Direct)
#> 6 Not applicable 15 Not applicable (Direct)
#> ModelPatternID DataReliabilityName DataReliabilityID DataReliabilitySort
#> 1 40 Fair 5 2
#> 2 40 Fair 5 2
#> 3 40 Fair 5 2
#> 4 40 Fair 5 2
#> 5 40 Fair 5 2
#> 6 40 Fair 5 2
#> PeriodTypeName PeriodTypeID PeriodGroupName PeriodGroupID PeriodStart
#> 1 CR 3 Not applicable 10 0
#> 2 RP 6 0 433 0
#> 3 CR 3 Not applicable 10 0
#> 4 CR 3 Not applicable 10 0
#> 5 RP 6 0 433 0
#> 6 CR 3 Not applicable 10 0
#> PeriodEnd PeriodSpan PeriodMiddle Weight TimeUnit TimeStart TimeEnd
#> 1 0 0 0 NA year 01/07/1953 01/07/1953
#> 2 0 0 0 NA year 01/07/1953 01/07/1953
#> 3 0 0 0 NA year 01/07/1953 01/07/1953
#> 4 0 0 0 NA year 01/07/1953 01/07/1953
#> 5 0 0 0 NA year 01/07/1953 01/07/1953
#> 6 0 0 0 NA year 01/07/1953 01/07/1953
#> TimeDuration TimeMid TimeLabel FootNoteID id SeriesID DataValue
#> 1 0 1953.5 1953 0 1 78230188493073 301310016
#> 2 0 1953.5 1953 0 1 15911390187176125 291969824
#> 3 0 1953.5 1953 0 1 78230188493073 46777800
#> 4 0 1953.5 1953 0 1 78230188493073 32750400
#> 5 0 1953.5 1953 0 1 15911390187176125 33264940
#> 6 0 1953.5 1953 0 1 78230188493073 29506800
get_recorddata(dataProcessTypeIds = "Census",
indicatorTypeIds = "Births by age of mother",
locIds = "Togo",
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged")
#>
#> https://popdiv.dfs.un.org/DemoData/api/locations?
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatortypes?
#>
#> https://popdiv.dfs.un.org/DemoData/api/subgroups?
#>
#> https://popdiv.dfs.un.org/DemoData/api/locareatypes?indicatorTypeIds=16&isComplete=0&locIds=768
#>
#> https://popdiv.dfs.un.org/DemoData/api/dataprocesstypes?indicatorTypeIds=16&isComplete=0&locIds=768
#> If you run the same query again, use the one below (faster):
#> get_recorddata(dataProcessTypeIds = 2, indicatorTypeIds = 16, isComplete = 0, locIds = 768, locAreaTypeIds = 2, subGroupIds = 2)
#> https://popdiv.dfs.un.org/DemoData/api/structureddatarecords?dataProcessTypeIds=2&indicatorTypeIds=16&isComplete=0&locIds=768&locAreaTypeIds=2&subGroupIds=2
#> IndicatorName
#> 1 Children ever born by age of mother (and by sex of child) - abridged
#> 2 Children ever born by age of mother (and by sex of child) - abridged
#> 3 Children ever born by age of mother (and by sex of child) - abridged
#> 4 Children ever born by age of mother (and by sex of child) - abridged
#> 5 Children ever born by age of mother (and by sex of child) - abridged
#> 6 Children ever born by age of mother (and by sex of child) - abridged
#> 7 Children ever born by age of mother (and by sex of child) - abridged
#> 8 Children ever born by age of mother (and by sex of child) - abridged
#> 9 Children ever born by age of mother (and by sex of child) - abridged
#> 10 Children ever born by age of mother (and by sex of child) - abridged
#> 11 Children ever born by age of mother (and by sex of child) - abridged
#> 12 Children ever born by age of mother (and by sex of child) - abridged
#> 13 Children ever born by age of mother (and by sex of child) - abridged
#> 14 Children ever born by age of mother (and by sex of child) - abridged
#> 15 Children ever born by age of mother (and by sex of child) - abridged
#> 16 Children ever born by age of mother (and by sex of child) - abridged
#> 17 Children ever born by age of mother (and by sex of child) - abridged
#> 18 Children ever born by age of mother (and by sex of child) - abridged
#> 19 Children ever born by age of mother (and by sex of child) - abridged
#> 20 Children ever born by age of mother (and by sex of child) - abridged
#> 21 Children ever born by age of mother (and by sex of child) - abridged
#> 22 Children ever born by age of mother (and by sex of child) - abridged
#> 23 Children ever born by age of mother (and by sex of child) - abridged
#> 24 Children ever born by age of mother (and by sex of child) - abridged
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#> 59 Children ever born by age of mother (and by sex of child) - abridged
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#> 61 Children ever born by age of mother (and by sex of child) - abridged
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#> 76 Children ever born by age of mother (and by sex of child) - abridged
#> 77 Children ever born by age of mother (and by sex of child) - abridged
#> 78 Children ever born by age of mother (and by sex of child) - abridged
#> 79 Children ever born by age of mother (and by sex of child) - abridged
#> 80 Children ever born by age of mother (and by sex of child) - abridged
#> 81 Children ever born by age of mother (and by sex of child) - abridged
#> 82 Children ever born by age of mother (and by sex of child) - abridged
#> 83 Children ever born by age of mother (and by sex of child) - abridged
#> 84 Children ever born by age of mother (and by sex of child) - abridged
#> 85 Children ever born by age of mother (and by sex of child) - abridged
#> 86 Children ever born by age of mother (and by sex of child) - abridged
#> 87 Children ever born by age of mother (and by sex of child) - abridged
#> 88 Children ever born by age of mother (and by sex of child) - abridged
#> 89 Children ever born by age of mother (and by sex of child) - abridged
#> 90 Children ever born by age of mother (and by sex of child) - abridged
#> 91 Children ever born by age of mother (and by sex of child) - abridged
#> 92 Children ever born by age of mother (and by sex of child) - abridged
#> 93 Children ever born by age of mother (and by sex of child) - abridged
#> 94 Children ever born by age of mother (and by sex of child) - abridged
#> 95 Children ever born by age of mother (and by sex of child) - abridged
#> 96 Children ever born by age of mother (and by sex of child) - abridged
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#> 100 Children ever born by age of mother (and by sex of child) - abridged
#> 101 Children ever born by age of mother (and by sex of child) - abridged
#> 102 Children ever born by age of mother (and by sex of child) - abridged
#> 103 Children ever born by age of mother (and by sex of child) - abridged
#> 104 Children ever born by age of mother (and by sex of child) - abridged
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#> 108 Children ever born by age of mother (and by sex of child) - abridged
#> 109 Children ever born by age of mother (and by sex of child) - abridged
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#> 111 Children ever born by age of mother (and by sex of child) - abridged
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#> 113 Children ever born by age of mother (and by sex of child) - abridged
#> 114 Children ever born by age of mother (and by sex of child) - abridged
#> 115 Mean children ever born by age of mother (and by sex of child) - abridged
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#> 199 Mean children ever born by age of mother (and by sex of child) - abridged
#> 200 Mean children ever born by age of mother (and by sex of child) - abridged
#> 201 Mean children ever born by age of mother (and by sex of child) - abridged
#> 202 Mean children ever born by age of mother (and by sex of child) - abridged
#> 203 Mean children ever born by age of mother (and by sex of child) - abridged
#> 204 Mean children ever born by age of mother (and by sex of child) - abridged
#> 205 Mean children ever born by age of mother (and by sex of child) - abridged
#> 206 Mean children ever born by age of mother (and by sex of child) - abridged
#> 207 Mean children ever born by age of mother (and by sex of child) - abridged
#> 208 Mean children ever born by age of mother (and by sex of child) - abridged
#> 209 Mean children ever born by age of mother (and by sex of child) - abridged
#> 210 Mean children ever born by age of mother (and by sex of child) - abridged
#> 211 Mean children ever born by age of mother (and by sex of child) - abridged
#> 212 Mean children ever born by age of mother (and by sex of child) - abridged
#> 213 Mean children ever born by age of mother (and by sex of child) - abridged
#> 214 Mean children ever born by age of mother (and by sex of child) - abridged
#> 215 Mean children ever born by age of mother (and by sex of child) - abridged
#> 216 Mean children ever born by age of mother (and by sex of child) - abridged
#> 217 Mean children ever born by age of mother (and by sex of child) - abridged
#> 218 Mean children ever born by age of mother (and by sex of child) - abridged
#> 219 Mean children ever born by age of mother (and by sex of child) - abridged
#> 220 Mean children ever born by age of mother (and by sex of child) - abridged
#> 221 Mean children ever born by age of mother (and by sex of child) - abridged
#> 222 Mean children ever born by age of mother (and by sex of child) - abridged
#> 223 Mean children ever born by age of mother (and by sex of child) - abridged
#> 224 Mean children ever born by age of mother (and by sex of child) - abridged
#> 225 Mean children ever born by age of mother (and by sex of child) - abridged
#> 226 Mean children ever born by age of mother (and by sex of child) - abridged
#> 227 Mean children ever born by age of mother (and by sex of child) - abridged
#> 228 Mean children ever born by age of mother (and by sex of child) - abridged
#> 229 Mean children ever born by age of mother (and by sex of child) - abridged
#> 230 Mean children ever born by age of mother (and by sex of child) - abridged
#> 231 Mean children ever born by age of mother (and by sex of child) - abridged
#> 232 Mean children ever born by age of mother (and by sex of child) - abridged
#> 233 Mean children ever born by age of mother (and by sex of child) - abridged
#> 234 Mean children ever born by age of mother (and by sex of child) - abridged
#> 235 Mean children ever born by age of mother (and by sex of child) - abridged
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#> 119 319 Togo 768 275961129 Country 5 NA
#> 120 319 Togo 768 275961130 Country 5 NA
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#> 133 319 Togo 768 275961143 Country 5 NA
#> 134 319 Togo 768 275961144 Country 5 NA
#> 135 319 Togo 768 275961145 Country 5 NA
#> 136 319 Togo 768 275961146 Country 5 NA
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#> 52 Total / All groups / Whole group 2 2
#> 53 Total / All groups / Whole group 2 2
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#> 55 Total / All groups / Whole group 2 2
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#> 63 Total / All groups / Whole group 2 2
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#> 65 Total / All groups / Whole group 2 2
#> 66 Total / All groups / Whole group 2 2
#> 67 Total / All groups / Whole group 2 2
#> 68 Total / All groups / Whole group 2 2
#> 69 Total / All groups / Whole group 2 2
#> 70 Total / All groups / Whole group 2 2
#> 71 Total / All groups / Whole group 2 2
#> 72 Total / All groups / Whole group 2 2
#> 73 Total / All groups / Whole group 2 2
#> 74 Total / All groups / Whole group 2 2
#> 75 Total / All groups / Whole group 2 2
#> 76 Total / All groups / Whole group 2 2
#> 77 Total / All groups / Whole group 2 2
#> 78 Total / All groups / Whole group 2 2
#> 79 Total / All groups / Whole group 2 2
#> 80 Total / All groups / Whole group 2 2
#> 81 Total / All groups / Whole group 2 2
#> 82 Total / All groups / Whole group 2 2
#> 83 Total / All groups / Whole group 2 2
#> 84 Total / All groups / Whole group 2 2
#> 85 Total / All groups / Whole group 2 2
#> 86 Total / All groups / Whole group 2 2
#> 87 Total / All groups / Whole group 2 2
#> 88 Total / All groups / Whole group 2 2
#> 89 Total / All groups / Whole group 2 2
#> 90 Total / All groups / Whole group 2 2
#> 91 Total / All groups / Whole group 2 2
#> 92 Total / All groups / Whole group 2 2
#> 93 Total / All groups / Whole group 2 2
#> 94 Total / All groups / Whole group 2 2
#> 95 Total / All groups / Whole group 2 2
#> 96 Total / All groups / Whole group 2 2
#> 97 Total / All groups / Whole group 2 2
#> 98 Total / All groups / Whole group 2 2
#> 99 Total / All groups / Whole group 2 2
#> 100 Total / All groups / Whole group 2 2
#> 101 Total / All groups / Whole group 2 2
#> 102 Total / All groups / Whole group 2 2
#> 103 Total / All groups / Whole group 2 2
#> 104 Total / All groups / Whole group 2 2
#> 105 Total / All groups / Whole group 2 2
#> 106 Total / All groups / Whole group 2 2
#> 107 Total / All groups / Whole group 2 2
#> 108 Total / All groups / Whole group 2 2
#> 109 Total / All groups / Whole group 2 2
#> 110 Total / All groups / Whole group 2 2
#> 111 Total / All groups / Whole group 2 2
#> 112 Total / All groups / Whole group 2 2
#> 113 Total / All groups / Whole group 2 2
#> 114 Total / All groups / Whole group 2 2
#> 115 Total / All groups / Whole group 2 2
#> 116 Total / All groups / Whole group 2 2
#> 117 Total / All groups / Whole group 2 2
#> 118 Total / All groups / Whole group 2 2
#> 119 Total / All groups / Whole group 2 2
#> 120 Total / All groups / Whole group 2 2
#> 121 Total / All groups / Whole group 2 2
#> 122 Total / All groups / Whole group 2 2
#> 123 Total / All groups / Whole group 2 2
#> 124 Total / All groups / Whole group 2 2
#> 125 Total / All groups / Whole group 2 2
#> 126 Total / All groups / Whole group 2 2
#> 127 Total / All groups / Whole group 2 2
#> 128 Total / All groups / Whole group 2 2
#> 129 Total / All groups / Whole group 2 2
#> 130 Total / All groups / Whole group 2 2
#> 131 Total / All groups / Whole group 2 2
#> 132 Total / All groups / Whole group 2 2
#> 133 Total / All groups / Whole group 2 2
#> 134 Total / All groups / Whole group 2 2
#> 135 Total / All groups / Whole group 2 2
#> 136 Total / All groups / Whole group 2 2
#> 137 Total / All groups / Whole group 2 2
#> 138 Total / All groups / Whole group 2 2
#> 139 Total / All groups / Whole group 2 2
#> 140 Total / All groups / Whole group 2 2
#> 141 Total / All groups / Whole group 2 2
#> 142 Total / All groups / Whole group 2 2
#> 143 Total / All groups / Whole group 2 2
#> 144 Total / All groups / Whole group 2 2
#> 145 Total / All groups / Whole group 2 2
#> 146 Total / All groups / Whole group 2 2
#> 147 Total / All groups / Whole group 2 2
#> 148 Total / All groups / Whole group 2 2
#> 149 Total / All groups / Whole group 2 2
#> 150 Total / All groups / Whole group 2 2
#> 151 Total / All groups / Whole group 2 2
#> 152 Total / All groups / Whole group 2 2
#> 153 Total / All groups / Whole group 2 2
#> 154 Total / All groups / Whole group 2 2
#> 155 Total / All groups / Whole group 2 2
#> 156 Total / All groups / Whole group 2 2
#> 157 Total / All groups / Whole group 2 2
#> 158 Total / All groups / Whole group 2 2
#> 159 Total / All groups / Whole group 2 2
#> 160 Total / All groups / Whole group 2 2
#> 161 Total / All groups / Whole group 2 2
#> 162 Total / All groups / Whole group 2 2
#> 163 Total / All groups / Whole group 2 2
#> 164 Total / All groups / Whole group 2 2
#> 165 Total / All groups / Whole group 2 2
#> 166 Total / All groups / Whole group 2 2
#> 167 Total / All groups / Whole group 2 2
#> 168 Total / All groups / Whole group 2 2
#> 169 Total / All groups / Whole group 2 2
#> 170 Total / All groups / Whole group 2 2
#> 171 Total / All groups / Whole group 2 2
#> 172 Total / All groups / Whole group 2 2
#> 173 Total / All groups / Whole group 2 2
#> 174 Total / All groups / Whole group 2 2
#> 175 Total / All groups / Whole group 2 2
#> 176 Total / All groups / Whole group 2 2
#> 177 Total / All groups / Whole group 2 2
#> 178 Total / All groups / Whole group 2 2
#> 179 Total / All groups / Whole group 2 2
#> 180 Total / All groups / Whole group 2 2
#> 181 Total / All groups / Whole group 2 2
#> 182 Total / All groups / Whole group 2 2
#> 183 Total / All groups / Whole group 2 2
#> 184 Total / All groups / Whole group 2 2
#> 185 Total / All groups / Whole group 2 2
#> 186 Total / All groups / Whole group 2 2
#> 187 Total / All groups / Whole group 2 2
#> 188 Total / All groups / Whole group 2 2
#> 189 Total / All groups / Whole group 2 2
#> 190 Total / All groups / Whole group 2 2
#> 191 Total / All groups / Whole group 2 2
#> 192 Total / All groups / Whole group 2 2
#> 193 Total / All groups / Whole group 2 2
#> 194 Total / All groups / Whole group 2 2
#> 195 Total / All groups / Whole group 2 2
#> 196 Total / All groups / Whole group 2 2
#> 197 Total / All groups / Whole group 2 2
#> 198 Total / All groups / Whole group 2 2
#> 199 Total / All groups / Whole group 2 2
#> 200 Total / All groups / Whole group 2 2
#> 201 Total / All groups / Whole group 2 2
#> 202 Total / All groups / Whole group 2 2
#> 203 Total / All groups / Whole group 2 2
#> 204 Total / All groups / Whole group 2 2
#> 205 Total / All groups / Whole group 2 2
#> 206 Total / All groups / Whole group 2 2
#> 207 Total / All groups / Whole group 2 2
#> 208 Total / All groups / Whole group 2 2
#> 209 Total / All groups / Whole group 2 2
#> 210 Total / All groups / Whole group 2 2
#> 211 Total / All groups / Whole group 2 2
#> 212 Total / All groups / Whole group 2 2
#> 213 Total / All groups / Whole group 2 2
#> 214 Total / All groups / Whole group 2 2
#> 215 Total / All groups / Whole group 2 2
#> 216 Total / All groups / Whole group 2 2
#> 217 Total / All groups / Whole group 2 2
#> 218 Total / All groups / Whole group 2 2
#> 219 Total / All groups / Whole group 2 2
#> 220 Total / All groups / Whole group 2 2
#> 221 Total / All groups / Whole group 2 2
#> 222 Total / All groups / Whole group 2 2
#> 223 Total / All groups / Whole group 2 2
#> 224 Total / All groups / Whole group 2 2
#> 225 Total / All groups / Whole group 2 2
#> 226 Total / All groups / Whole group 2 2
#> 227 Total / All groups / Whole group 2 2
#> 228 Total / All groups / Whole group 2 2
#> 229 Total / All groups / Whole group 2 2
#> 230 Total / All groups / Whole group 2 2
#> 231 Total / All groups / Whole group 2 2
#> 232 Total / All groups / Whole group 2 2
#> 233 Total / All groups / Whole group 2 2
#> 234 Total / All groups / Whole group 2 2
#> 235 Total / All groups / Whole group 2 2
#> SubGroupName SubGroupCombinationID SubGroupTypeSortOrder
#> 1 Total or All groups 0 3
#> 2 Total or All groups 0 3
#> 3 Total or All groups 0 3
#> 4 Total or All groups 0 3
#> 5 Total or All groups 0 3
#> 6 Total or All groups 0 3
#> 7 Total or All groups 0 3
#> 8 Total or All groups 0 3
#> 9 Total or All groups 0 3
#> 10 Total or All groups 0 3
#> 11 Total or All groups 0 3
#> 12 Total or All groups 0 3
#> 13 Total or All groups 0 3
#> 14 Total or All groups 0 3
#> 15 Total or All groups 0 3
#> 16 Total or All groups 0 3
#> 17 Total or All groups 0 3
#> 18 Total or All groups 0 3
#> 19 Total or All groups 0 3
#> 20 Total or All groups 0 3
#> 21 Total or All groups 0 3
#> 22 Total or All groups 0 3
#> 23 Total or All groups 0 3
#> 24 Total or All groups 0 3
#> 25 Total or All groups 0 3
#> 26 Total or All groups 0 3
#> 27 Total or All groups 0 3
#> 28 Total or All groups 0 3
#> 29 Total or All groups 0 3
#> 30 Total or All groups 0 3
#> 31 Total or All groups 0 3
#> 32 Total or All groups 0 3
#> 33 Total or All groups 0 3
#> 34 Total or All groups 0 3
#> 35 Total or All groups 0 3
#> 36 Total or All groups 0 3
#> 37 Total or All groups 0 3
#> 38 Total or All groups 0 3
#> 39 Total or All groups 0 3
#> 40 Total or All groups 0 3
#> 41 Total or All groups 0 3
#> 42 Total or All groups 0 3
#> 43 Total or All groups 0 3
#> 44 Total or All groups 0 3
#> 45 Total or All groups 0 3
#> 46 Total or All groups 0 3
#> 47 Total or All groups 0 3
#> 48 Total or All groups 0 3
#> 49 Total or All groups 0 3
#> 50 Total or All groups 0 3
#> 51 Total or All groups 0 3
#> 52 Total or All groups 0 3
#> 53 Total or All groups 0 3
#> 54 Total or All groups 0 3
#> 55 Total or All groups 0 3
#> 56 Total or All groups 0 3
#> 57 Total or All groups 0 3
#> 58 Total or All groups 0 3
#> 59 Total or All groups 0 3
#> 60 Total or All groups 0 3
#> 61 Total or All groups 0 3
#> 62 Total or All groups 0 3
#> 63 Total or All groups 0 3
#> 64 Total or All groups 0 3
#> 65 Total or All groups 0 3
#> 66 Total or All groups 0 3
#> 67 Total or All groups 0 3
#> 68 Total or All groups 0 3
#> 69 Total or All groups 0 3
#> 70 Total or All groups 0 3
#> 71 Total or All groups 0 3
#> 72 Total or All groups 0 3
#> 73 Total or All groups 0 3
#> 74 Total or All groups 0 3
#> 75 Total or All groups 0 3
#> 76 Total or All groups 0 3
#> 77 Total or All groups 0 3
#> 78 Total or All groups 0 3
#> 79 Total or All groups 0 3
#> 80 Total or All groups 0 3
#> 81 Total or All groups 0 3
#> 82 Total or All groups 0 3
#> 83 Total or All groups 0 3
#> 84 Total or All groups 0 3
#> 85 Total or All groups 0 3
#> 86 Total or All groups 0 3
#> 87 Total or All groups 0 3
#> 88 Total or All groups 0 3
#> 89 Total or All groups 0 3
#> 90 Total or All groups 0 3
#> 91 Total or All groups 0 3
#> 92 Total or All groups 0 3
#> 93 Total or All groups 0 3
#> 94 Total or All groups 0 3
#> 95 Total or All groups 0 3
#> 96 Total or All groups 0 3
#> 97 Total or All groups 0 3
#> 98 Total or All groups 0 3
#> 99 Total or All groups 0 3
#> 100 Total or All groups 0 3
#> 101 Total or All groups 0 3
#> 102 Total or All groups 0 3
#> 103 Total or All groups 0 3
#> 104 Total or All groups 0 3
#> 105 Total or All groups 0 3
#> 106 Total or All groups 0 3
#> 107 Total or All groups 0 3
#> 108 Total or All groups 0 3
#> 109 Total or All groups 0 3
#> 110 Total or All groups 0 3
#> 111 Total or All groups 0 3
#> 112 Total or All groups 0 3
#> 113 Total or All groups 0 3
#> 114 Total or All groups 0 3
#> 115 Total or All groups 0 3
#> 116 Total or All groups 0 3
#> 117 Total or All groups 0 3
#> 118 Total or All groups 0 3
#> 119 Total or All groups 0 3
#> 120 Total or All groups 0 3
#> 121 Total or All groups 0 3
#> 122 Total or All groups 0 3
#> 123 Total or All groups 0 3
#> 124 Total or All groups 0 3
#> 125 Total or All groups 0 3
#> 126 Total or All groups 0 3
#> 127 Total or All groups 0 3
#> 128 Total or All groups 0 3
#> 129 Total or All groups 0 3
#> 130 Total or All groups 0 3
#> 131 Total or All groups 0 3
#> 132 Total or All groups 0 3
#> 133 Total or All groups 0 3
#> 134 Total or All groups 0 3
#> 135 Total or All groups 0 3
#> 136 Total or All groups 0 3
#> 137 Total or All groups 0 3
#> 138 Total or All groups 0 3
#> 139 Total or All groups 0 3
#> 140 Total or All groups 0 3
#> 141 Total or All groups 0 3
#> 142 Total or All groups 0 3
#> 143 Total or All groups 0 3
#> 144 Total or All groups 0 3
#> 145 Total or All groups 0 3
#> 146 Total or All groups 0 3
#> 147 Total or All groups 0 3
#> 148 Total or All groups 0 3
#> 149 Total or All groups 0 3
#> 150 Total or All groups 0 3
#> 151 Total or All groups 0 3
#> 152 Total or All groups 0 3
#> 153 Total or All groups 0 3
#> 154 Total or All groups 0 3
#> 155 Total or All groups 0 3
#> 156 Total or All groups 0 3
#> 157 Total or All groups 0 3
#> 158 Total or All groups 0 3
#> 159 Total or All groups 0 3
#> 160 Total or All groups 0 3
#> 161 Total or All groups 0 3
#> 162 Total or All groups 0 3
#> 163 Total or All groups 0 3
#> 164 Total or All groups 0 3
#> 165 Total or All groups 0 3
#> 166 Total or All groups 0 3
#> 167 Total or All groups 0 3
#> 168 Total or All groups 0 3
#> 169 Total or All groups 0 3
#> 170 Total or All groups 0 3
#> 171 Total or All groups 0 3
#> 172 Total or All groups 0 3
#> 173 Total or All groups 0 3
#> 174 Total or All groups 0 3
#> 175 Total or All groups 0 3
#> 176 Total or All groups 0 3
#> 177 Total or All groups 0 3
#> 178 Total or All groups 0 3
#> 179 Total or All groups 0 3
#> 180 Total or All groups 0 3
#> 181 Total or All groups 0 3
#> 182 Total or All groups 0 3
#> 183 Total or All groups 0 3
#> 184 Total or All groups 0 3
#> 185 Total or All groups 0 3
#> 186 Total or All groups 0 3
#> 187 Total or All groups 0 3
#> 188 Total or All groups 0 3
#> 189 Total or All groups 0 3
#> 190 Total or All groups 0 3
#> 191 Total or All groups 0 3
#> 192 Total or All groups 0 3
#> 193 Total or All groups 0 3
#> 194 Total or All groups 0 3
#> 195 Total or All groups 0 3
#> 196 Total or All groups 0 3
#> 197 Total or All groups 0 3
#> 198 Total or All groups 0 3
#> 199 Total or All groups 0 3
#> 200 Total or All groups 0 3
#> 201 Total or All groups 0 3
#> 202 Total or All groups 0 3
#> 203 Total or All groups 0 3
#> 204 Total or All groups 0 3
#> 205 Total or All groups 0 3
#> 206 Total or All groups 0 3
#> 207 Total or All groups 0 3
#> 208 Total or All groups 0 3
#> 209 Total or All groups 0 3
#> 210 Total or All groups 0 3
#> 211 Total or All groups 0 3
#> 212 Total or All groups 0 3
#> 213 Total or All groups 0 3
#> 214 Total or All groups 0 3
#> 215 Total or All groups 0 3
#> 216 Total or All groups 0 3
#> 217 Total or All groups 0 3
#> 218 Total or All groups 0 3
#> 219 Total or All groups 0 3
#> 220 Total or All groups 0 3
#> 221 Total or All groups 0 3
#> 222 Total or All groups 0 3
#> 223 Total or All groups 0 3
#> 224 Total or All groups 0 3
#> 225 Total or All groups 0 3
#> 226 Total or All groups 0 3
#> 227 Total or All groups 0 3
#> 228 Total or All groups 0 3
#> 229 Total or All groups 0 3
#> 230 Total or All groups 0 3
#> 231 Total or All groups 0 3
#> 232 Total or All groups 0 3
#> 233 Total or All groups 0 3
#> 234 Total or All groups 0 3
#> 235 Total or All groups 0 3
#> IndicatorShortName DataCatalogID DataProcessTypeID DataProcessType
#> 1 CEB5 5267 Census Census
#> 2 CEB5 5267 Census Census
#> 3 CEB5 5267 Census Census
#> 4 CEB5 5267 Census Census
#> 5 CEB5 5267 Census Census
#> 6 CEB5 5267 Census Census
#> 7 CEB5 5267 Census Census
#> 8 CEB5 5267 Census Census
#> 9 CEB5 5267 Census Census
#> 10 CEB5 5267 Census Census
#> 11 CEB5 5267 Census Census
#> 12 CEB5 5267 Census Census
#> 13 CEB5 5267 Census Census
#> 14 CEB5 5267 Census Census
#> 15 CEB5 5267 Census Census
#> 16 CEB5 5267 Census Census
#> 17 CEB5 5267 Census Census
#> 18 CEB5 5267 Census Census
#> 19 CEB5 5267 Census Census
#> 20 CEB5 5267 Census Census
#> 21 CEB5 5267 Census Census
#> 22 CEB5 5267 Census Census
#> 23 CEB5 5267 Census Census
#> 24 CEB5 5267 Census Census
#> 25 CEB5 5267 Census Census
#> 26 CEB5 5267 Census Census
#> 27 CEB5 5267 Census Census
#> 28 CEB5 5267 Census Census
#> 29 CEB5 5267 Census Census
#> 30 CEB5 5267 Census Census
#> 31 CEB5 5267 Census Census
#> 32 CEB5 5267 Census Census
#> 33 CEB5 5267 Census Census
#> 34 CEB5 5267 Census Census
#> 35 CEB5 5267 Census Census
#> 36 CEB5 5267 Census Census
#> 37 CEB5 5267 Census Census
#> 38 CEB5 5267 Census Census
#> 39 CEB5 5267 Census Census
#> 40 CEB5 5267 Census Census
#> 41 CEB5 5267 Census Census
#> 42 CEB5 5267 Census Census
#> 43 CEB5 5267 Census Census
#> 44 CEB5 5267 Census Census
#> 45 CEB5 5267 Census Census
#> 46 CEB5 5267 Census Census
#> 47 CEB5 5267 Census Census
#> 48 CEB5 5267 Census Census
#> 49 CEB5 5267 Census Census
#> 50 CEB5 5267 Census Census
#> 51 CEB5 5267 Census Census
#> 52 CEB5 5267 Census Census
#> 53 CEB5 5267 Census Census
#> 54 CEB5 5267 Census Census
#> 55 CEB5 5267 Census Census
#> 56 CEB5 5267 Census Census
#> 57 CEB5 5267 Census Census
#> 58 CEB5 2574 Census Census
#> 59 CEB5 2574 Census Census
#> 60 CEB5 2574 Census Census
#> 61 CEB5 2574 Census Census
#> 62 CEB5 2574 Census Census
#> 63 CEB5 2574 Census Census
#> 64 CEB5 2574 Census Census
#> 65 CEB5 2574 Census Census
#> 66 CEB5 2574 Census Census
#> 67 CEB5 2574 Census Census
#> 68 CEB5 2574 Census Census
#> 69 CEB5 2574 Census Census
#> 70 CEB5 2574 Census Census
#> 71 CEB5 2574 Census Census
#> 72 CEB5 2574 Census Census
#> 73 CEB5 2574 Census Census
#> 74 CEB5 2574 Census Census
#> 75 CEB5 2574 Census Census
#> 76 CEB5 2574 Census Census
#> 77 CEB5 2574 Census Census
#> 78 CEB5 2574 Census Census
#> 79 CEB5 2574 Census Census
#> 80 CEB5 2574 Census Census
#> 81 CEB5 2574 Census Census
#> 82 CEB5 2574 Census Census
#> 83 CEB5 2574 Census Census
#> 84 CEB5 2574 Census Census
#> 85 CEB5 2574 Census Census
#> 86 CEB5 2574 Census Census
#> 87 CEB5 2574 Census Census
#> 88 CEB5 2574 Census Census
#> 89 CEB5 2574 Census Census
#> 90 CEB5 2574 Census Census
#> 91 CEB5 2574 Census Census
#> 92 CEB5 2574 Census Census
#> 93 CEB5 2574 Census Census
#> 94 CEB5 2574 Census Census
#> 95 CEB5 2574 Census Census
#> 96 CEB5 2574 Census Census
#> 97 CEB5 2574 Census Census
#> 98 CEB5 2574 Census Census
#> 99 CEB5 2574 Census Census
#> 100 CEB5 2574 Census Census
#> 101 CEB5 2574 Census Census
#> 102 CEB5 2574 Census Census
#> 103 CEB5 2574 Census Census
#> 104 CEB5 2574 Census Census
#> 105 CEB5 2574 Census Census
#> 106 CEB5 2574 Census Census
#> 107 CEB5 2574 Census Census
#> 108 CEB5 2574 Census Census
#> 109 CEB5 2574 Census Census
#> 110 CEB5 2574 Census Census
#> 111 CEB5 2574 Census Census
#> 112 CEB5 2574 Census Census
#> 113 CEB5 2574 Census Census
#> 114 CEB5 2574 Census Census
#> 115 mCEB5 2574 Census Census
#> 116 mCEB5 2574 Census Census
#> 117 mCEB5 2574 Census Census
#> 118 mCEB5 2574 Census Census
#> 119 mCEB5 2574 Census Census
#> 120 mCEB5 2574 Census Census
#> 121 mCEB5 2574 Census Census
#> 122 mCEB5 2574 Census Census
#> 123 mCEB5 2574 Census Census
#> 124 mCEB5 2574 Census Census
#> 125 mCEB5 2574 Census Census
#> 126 mCEB5 2574 Census Census
#> 127 mCEB5 2574 Census Census
#> 128 mCEB5 2574 Census Census
#> 129 mCEB5 2574 Census Census
#> 130 mCEB5 2574 Census Census
#> 131 mCEB5 2574 Census Census
#> 132 mCEB5 2574 Census Census
#> 133 mCEB5 2574 Census Census
#> 134 mCEB5 2574 Census Census
#> 135 mCEB5 2574 Census Census
#> 136 mCEB5 2574 Census Census
#> 137 mCEB5 2574 Census Census
#> 138 mCEB5 2574 Census Census
#> 139 mCEB5 2574 Census Census
#> 140 mCEB5 2574 Census Census
#> 141 mCEB5 2574 Census Census
#> 142 mCEB5 2574 Census Census
#> 143 mCEB5 2574 Census Census
#> 144 mCEB5 2574 Census Census
#> 145 mCEB5 2574 Census Census
#> 146 mCEB5 2574 Census Census
#> 147 mCEB5 2574 Census Census
#> 148 mCEB5 2574 Census Census
#> 149 mCEB5 2574 Census Census
#> 150 mCEB5 2574 Census Census
#> 151 mCEB5 2574 Census Census
#> 152 mCEB5 2574 Census Census
#> 153 mCEB5 2574 Census Census
#> 154 mCEB5 2574 Census Census
#> 155 mCEB5 2574 Census Census
#> 156 mCEB5 2574 Census Census
#> 157 mCEB5 2574 Census Census
#> 158 mCEB5 2574 Census Census
#> 159 mCEB5 2574 Census Census
#> 160 mCEB5 2574 Census Census
#> 161 mCEB5 2574 Census Census
#> 162 mCEB5 2574 Census Census
#> 163 mCEB5 2574 Census Census
#> 164 mCEB5 2574 Census Census
#> 165 mCEB5 2574 Census Census
#> 166 mCEB5 2574 Census Census
#> 167 mCEB5 2574 Census Census
#> 168 mCEB5 2574 Census Census
#> 169 mCEB5 2574 Census Census
#> 170 mCEB5 2574 Census Census
#> 171 mCEB5 2574 Census Census
#> 172 mCEB5 5267 Census Census
#> 173 mCEB5 5267 Census Census
#> 174 mCEB5 5267 Census Census
#> 175 mCEB5 5267 Census Census
#> 176 mCEB5 5267 Census Census
#> 177 mCEB5 5267 Census Census
#> 178 mCEB5 5267 Census Census
#> 179 mCEB5 5267 Census Census
#> 180 mCEB5 5267 Census Census
#> 181 mCEB5 5267 Census Census
#> 182 mCEB5 5267 Census Census
#> 183 mCEB5 5267 Census Census
#> 184 mCEB5 5267 Census Census
#> 185 mCEB5 5267 Census Census
#> 186 mCEB5 5267 Census Census
#> 187 mCEB5 5267 Census Census
#> 188 mCEB5 5267 Census Census
#> 189 mCEB5 5267 Census Census
#> 190 mCEB5 5267 Census Census
#> 191 mCEB5 5267 Census Census
#> 192 mCEB5 5267 Census Census
#> 193 mCEB5 5267 Census Census
#> 194 mCEB5 5267 Census Census
#> 195 mCEB5 5267 Census Census
#> 196 mCEB5 5267 Census Census
#> 197 mCEB5 5267 Census Census
#> 198 mCEB5 5267 Census Census
#> 199 mCEB5 5267 Census Census
#> 200 mCEB5 5267 Census Census
#> 201 mCEB5 5267 Census Census
#> 202 mCEB5 5267 Census Census
#> 203 mCEB5 5267 Census Census
#> 204 mCEB5 5267 Census Census
#> 205 mCEB5 5267 Census Census
#> 206 mCEB5 5267 Census Census
#> 207 mCEB5 5267 Census Census
#> 208 mCEB5 5267 Census Census
#> 209 mCEB5 5267 Census Census
#> 210 mCEB5 5267 Census Census
#> 211 mCEB5 5267 Census Census
#> 212 mCEB5 5267 Census Census
#> 213 mCEB5 5267 Census Census
#> 214 mCEB5 5267 Census Census
#> 215 mCEB5 5267 Census Census
#> 216 mCEB5 5267 Census Census
#> 217 mCEB5 5267 Census Census
#> 218 mCEB5 5267 Census Census
#> 219 mCEB5 5267 Census Census
#> 220 mCEB5 5267 Census Census
#> 221 mCEB5 5267 Census Census
#> 222 mCEB5 5267 Census Census
#> 223 mCEB5 5267 Census Census
#> 224 mCEB5 5267 Census Census
#> 225 mCEB5 5267 Census Census
#> 226 mCEB5 5267 Census Census
#> 227 mCEB5 5267 Census Census
#> 228 mCEB5 5267 Census Census
#> 229 mCEB5 267 Census Census
#> 230 mCEB5 267 Census Census
#> 231 mCEB5 267 Census Census
#> 232 mCEB5 267 Census Census
#> 233 mCEB5 267 Census Census
#> 234 mCEB5 267 Census Census
#> 235 mCEB5 267 Census Census
#> DataProcessTypeSort DataProcess DataProcessID DataProcessSort
#> 1 1 Census 2 1
#> 2 1 Census 2 1
#> 3 1 Census 2 1
#> 4 1 Census 2 1
#> 5 1 Census 2 1
#> 6 1 Census 2 1
#> 7 1 Census 2 1
#> 8 1 Census 2 1
#> 9 1 Census 2 1
#> 10 1 Census 2 1
#> 11 1 Census 2 1
#> 12 1 Census 2 1
#> 13 1 Census 2 1
#> 14 1 Census 2 1
#> 15 1 Census 2 1
#> 16 1 Census 2 1
#> 17 1 Census 2 1
#> 18 1 Census 2 1
#> 19 1 Census 2 1
#> 20 1 Census 2 1
#> 21 1 Census 2 1
#> 22 1 Census 2 1
#> 23 1 Census 2 1
#> 24 1 Census 2 1
#> 25 1 Census 2 1
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#> DataCatalogName DataCatalogShortName ReferencePeriod
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#> 2 Togo 2010 Census 2010 Census 2010
#> 3 Togo 2010 Census 2010 Census 2010
#> 4 Togo 2010 Census 2010 Census 2010
#> 5 Togo 2010 Census 2010 Census 2010
#> 6 Togo 2010 Census 2010 Census 2010
#> 7 Togo 2010 Census 2010 Census 2010
#> 8 Togo 2010 Census 2010 Census 2010
#> 9 Togo 2010 Census 2010 Census 2010
#> 10 Togo 2010 Census 2010 Census 2010
#> 11 Togo 2010 Census 2010 Census 2010
#> 12 Togo 2010 Census 2010 Census 2010
#> 13 Togo 2010 Census 2010 Census 2010
#> 14 Togo 2010 Census 2010 Census 2010
#> 15 Togo 2010 Census 2010 Census 2010
#> 16 Togo 2010 Census 2010 Census 2010
#> 17 Togo 2010 Census 2010 Census 2010
#> 18 Togo 2010 Census 2010 Census 2010
#> 19 Togo 2010 Census 2010 Census 2010
#> 20 Togo 2010 Census 2010 Census 2010
#> 21 Togo 2010 Census 2010 Census 2010
#> 22 Togo 2010 Census 2010 Census 2010
#> 23 Togo 2010 Census 2010 Census 2010
#> 24 Togo 2010 Census 2010 Census 2010
#> 25 Togo 2010 Census 2010 Census 2010
#> 26 Togo 2010 Census 2010 Census 2010
#> 27 Togo 2010 Census 2010 Census 2010
#> 28 Togo 2010 Census 2010 Census 2010
#> 29 Togo 2010 Census 2010 Census 2010
#> 30 Togo 2010 Census 2010 Census 2010
#> 31 Togo 2010 Census 2010 Census 2010
#> 32 Togo 2010 Census 2010 Census 2010
#> 33 Togo 2010 Census 2010 Census 2010
#> 34 Togo 2010 Census 2010 Census 2010
#> 35 Togo 2010 Census 2010 Census 2010
#> 36 Togo 2010 Census 2010 Census 2010
#> 37 Togo 2010 Census 2010 Census 2010
#> 38 Togo 2010 Census 2010 Census 2010
#> 39 Togo 2010 Census 2010 Census 2010
#> 40 Togo 2010 Census 2010 Census 2010
#> 41 Togo 2010 Census 2010 Census 2010
#> 42 Togo 2010 Census 2010 Census 2010
#> 43 Togo 2010 Census 2010 Census 2010
#> 44 Togo 2010 Census 2010 Census 2010
#> 45 Togo 2010 Census 2010 Census 2010
#> 46 Togo 2010 Census 2010 Census 2010
#> 47 Togo 2010 Census 2010 Census 2010
#> 48 Togo 2010 Census 2010 Census 2010
#> 49 Togo 2010 Census 2010 Census 2010
#> 50 Togo 2010 Census 2010 Census 2010
#> 51 Togo 2010 Census 2010 Census 2010
#> 52 Togo 2010 Census 2010 Census 2010
#> 53 Togo 2010 Census 2010 Census 2010
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#> 121 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 122 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 123 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 124 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 125 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 126 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 127 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 128 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 129 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 130 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 131 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 132 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 133 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 134 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 135 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 136 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 137 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 138 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 139 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 140 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 141 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 142 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 143 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 144 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 145 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 146 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 147 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 148 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 149 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 150 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 151 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 152 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 153 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 154 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 155 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 156 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 157 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 158 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 159 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 160 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 161 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 162 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 163 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 164 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 165 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 166 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 167 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 168 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 169 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 170 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 171 Togo 1958-1960 Census 1958-1960 Census 1958-1960
#> 172 Togo 2010 Census 2010 Census 2010
#> 173 Togo 2010 Census 2010 Census 2010
#> 174 Togo 2010 Census 2010 Census 2010
#> 175 Togo 2010 Census 2010 Census 2010
#> 176 Togo 2010 Census 2010 Census 2010
#> 177 Togo 2010 Census 2010 Census 2010
#> 178 Togo 2010 Census 2010 Census 2010
#> 179 Togo 2010 Census 2010 Census 2010
#> 180 Togo 2010 Census 2010 Census 2010
#> 181 Togo 2010 Census 2010 Census 2010
#> 182 Togo 2010 Census 2010 Census 2010
#> 183 Togo 2010 Census 2010 Census 2010
#> 184 Togo 2010 Census 2010 Census 2010
#> 185 Togo 2010 Census 2010 Census 2010
#> 186 Togo 2010 Census 2010 Census 2010
#> 187 Togo 2010 Census 2010 Census 2010
#> 188 Togo 2010 Census 2010 Census 2010
#> 189 Togo 2010 Census 2010 Census 2010
#> 190 Togo 2010 Census 2010 Census 2010
#> 191 Togo 2010 Census 2010 Census 2010
#> 192 Togo 2010 Census 2010 Census 2010
#> 193 Togo 2010 Census 2010 Census 2010
#> 194 Togo 2010 Census 2010 Census 2010
#> 195 Togo 2010 Census 2010 Census 2010
#> 196 Togo 2010 Census 2010 Census 2010
#> 197 Togo 2010 Census 2010 Census 2010
#> 198 Togo 2010 Census 2010 Census 2010
#> 199 Togo 2010 Census 2010 Census 2010
#> 200 Togo 2010 Census 2010 Census 2010
#> 201 Togo 2010 Census 2010 Census 2010
#> 202 Togo 2010 Census 2010 Census 2010
#> 203 Togo 2010 Census 2010 Census 2010
#> 204 Togo 2010 Census 2010 Census 2010
#> 205 Togo 2010 Census 2010 Census 2010
#> 206 Togo 2010 Census 2010 Census 2010
#> 207 Togo 2010 Census 2010 Census 2010
#> 208 Togo 2010 Census 2010 Census 2010
#> 209 Togo 2010 Census 2010 Census 2010
#> 210 Togo 2010 Census 2010 Census 2010
#> 211 Togo 2010 Census 2010 Census 2010
#> 212 Togo 2010 Census 2010 Census 2010
#> 213 Togo 2010 Census 2010 Census 2010
#> 214 Togo 2010 Census 2010 Census 2010
#> 215 Togo 2010 Census 2010 Census 2010
#> 216 Togo 2010 Census 2010 Census 2010
#> 217 Togo 2010 Census 2010 Census 2010
#> 218 Togo 2010 Census 2010 Census 2010
#> 219 Togo 2010 Census 2010 Census 2010
#> 220 Togo 2010 Census 2010 Census 2010
#> 221 Togo 2010 Census 2010 Census 2010
#> 222 Togo 2010 Census 2010 Census 2010
#> 223 Togo 2010 Census 2010 Census 2010
#> 224 Togo 2010 Census 2010 Census 2010
#> 225 Togo 2010 Census 2010 Census 2010
#> 226 Togo 2010 Census 2010 Census 2010
#> 227 Togo 2010 Census 2010 Census 2010
#> 228 Togo 2010 Census 2010 Census 2010
#> 229 Togo 1970 Census 1970 Census 1970
#> 230 Togo 1970 Census 1970 Census 1970
#> 231 Togo 1970 Census 1970 Census 1970
#> 232 Togo 1970 Census 1970 Census 1970
#> 233 Togo 1970 Census 1970 Census 1970
#> 234 Togo 1970 Census 1970 Census 1970
#> 235 Togo 1970 Census 1970 Census 1970
#> ReferenceYearStart ReferenceYearEnd ReferenceYearMid FieldWorkStart
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#> 2 2010 2010 2010.5 11/6/2010 12:00:00 AM
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#> 5 2010 2010 2010.5 11/6/2010 12:00:00 AM
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#> 8 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 9 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 10 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 11 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 12 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 13 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 14 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 15 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 16 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 17 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 18 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 19 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 20 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 21 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 22 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 23 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 24 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 25 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 26 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 27 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 28 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 29 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 30 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 31 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 32 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 33 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 34 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 35 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 36 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 37 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 38 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 39 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 40 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 41 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 42 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 43 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 44 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 45 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 46 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 47 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 48 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 49 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 50 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 51 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 52 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 53 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 54 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 55 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 56 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 57 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 58 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 59 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 60 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 61 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 62 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 63 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 64 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 65 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 66 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 67 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 68 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 69 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 70 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 71 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 72 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 73 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 74 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 75 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 76 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 77 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 78 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 79 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 80 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 81 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 82 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 83 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 84 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 85 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 86 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 87 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 88 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 89 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 90 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 91 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 92 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 93 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 94 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 95 1958 1960 1959.5 11/11/1958 12:00:00 AM
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#> 97 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 98 1958 1960 1959.5 11/11/1958 12:00:00 AM
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#> 100 1958 1960 1959.5 11/11/1958 12:00:00 AM
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#> 115 1958 1960 1959.5 11/11/1958 12:00:00 AM
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#> 121 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 122 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 123 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 124 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 125 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 126 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 127 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 128 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 129 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 130 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 131 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 132 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 133 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 134 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 135 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 136 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 137 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 138 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 139 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 140 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 141 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 142 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 143 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 144 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 145 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 146 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 147 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 148 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 149 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 150 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 151 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 152 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 153 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 154 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 155 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 156 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 157 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 158 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 159 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 160 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 161 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 162 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 163 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 164 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 165 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 166 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 167 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 168 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 169 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 170 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 171 1958 1960 1959.5 11/11/1958 12:00:00 AM
#> 172 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 173 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 174 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 175 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 176 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 177 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 178 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 179 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 180 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 181 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 182 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 183 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 184 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 185 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 186 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 187 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 188 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 189 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 190 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 191 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 192 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 193 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 194 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 195 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 196 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 197 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 198 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 199 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 200 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 201 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 202 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 203 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 204 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 205 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 206 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 207 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 208 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 209 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 210 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 211 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 212 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 213 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 214 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 215 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 216 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 217 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 218 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 219 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 220 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 221 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 222 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 223 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 224 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 225 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 226 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 227 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 228 2010 2010 2010.5 11/6/2010 12:00:00 AM
#> 229 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> 230 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> 231 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> 232 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> 233 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> 234 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> 235 1970 1970 1970.5 3/1/1970 12:00:00 AM
#> FieldWorkEnd FieldWorkMiddle DataCatalogNote DataSourceID
#> 1 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 2 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 3 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 4 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 5 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 6 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 7 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 8 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 9 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 10 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 11 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 12 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 13 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 14 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 15 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 16 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 17 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 18 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 19 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 20 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 21 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 22 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 23 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 24 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 25 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 26 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 27 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 28 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 29 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 30 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 31 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 32 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 33 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 34 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 35 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 36 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 37 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 38 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 39 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 40 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 41 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 42 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 43 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 44 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 45 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 46 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 47 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 48 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 49 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 50 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 51 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 52 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 53 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 54 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 55 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 56 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 57 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 58 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 59 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 60 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 61 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 62 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 63 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 64 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 65 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 66 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 67 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 68 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 69 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 70 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 71 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 72 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 73 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 74 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 75 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 76 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 77 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 78 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 79 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 80 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 81 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 82 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 83 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 84 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 85 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 86 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 87 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 88 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 89 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 90 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 91 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 92 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 93 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 94 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 95 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 96 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 97 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 98 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 99 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 100 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 101 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 102 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 103 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 104 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 105 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 106 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 107 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 108 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 109 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 110 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 111 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 112 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 113 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 114 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 115 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 116 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 117 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 118 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 119 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 120 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 121 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 122 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 123 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 124 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 125 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 126 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 127 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 128 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 129 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 130 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 131 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 132 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 133 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 134 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 135 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 136 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 137 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 138 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 139 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 140 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 141 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 142 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 143 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 144 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 145 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 146 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 147 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 148 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 149 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 150 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 151 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 152 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 153 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 154 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 155 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 156 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 157 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 158 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 159 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 160 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 161 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 162 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 163 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 164 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 165 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 166 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 167 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 168 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 169 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 170 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 171 12/15/1960 12:00:00 AM 1959.910 NA 42053
#> 172 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 173 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 174 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 175 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 176 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 177 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 178 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 179 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 180 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 181 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 182 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 183 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 184 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 185 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 186 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 187 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 188 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 189 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 190 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 191 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 192 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 193 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 194 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 195 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 196 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 197 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 198 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 199 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 200 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 201 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 202 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 203 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 204 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 205 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 206 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 207 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 208 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 209 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 210 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 211 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 212 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 213 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 214 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 215 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 216 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 217 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 218 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 219 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 220 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 221 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 222 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 223 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 224 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 225 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 226 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 227 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 228 11/21/2010 12:00:00 AM 2010.870 NA 42053
#> 229 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> 230 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> 231 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> 232 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> 233 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> 234 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> 235 4/30/1970 12:00:00 AM 1970.247 NA 44698
#> DataSourceAuthor DataSourceYear
#> 1 Minnesota Population Center 2020
#> 2 Minnesota Population Center 2020
#> 3 Minnesota Population Center 2020
#> 4 Minnesota Population Center 2020
#> 5 Minnesota Population Center 2020
#> 6 Minnesota Population Center 2020
#> 7 Minnesota Population Center 2020
#> 8 Minnesota Population Center 2020
#> 9 Minnesota Population Center 2020
#> 10 Minnesota Population Center 2020
#> 11 Minnesota Population Center 2020
#> 12 Minnesota Population Center 2020
#> 13 Minnesota Population Center 2020
#> 14 Minnesota Population Center 2020
#> 15 Minnesota Population Center 2020
#> 16 Minnesota Population Center 2020
#> 17 Minnesota Population Center 2020
#> 18 Minnesota Population Center 2020
#> 19 Minnesota Population Center 2020
#> 20 Minnesota Population Center 2020
#> 21 Minnesota Population Center 2020
#> 22 Minnesota Population Center 2020
#> 23 Minnesota Population Center 2020
#> 24 Minnesota Population Center 2020
#> 25 Minnesota Population Center 2020
#> 26 Minnesota Population Center 2020
#> 27 Minnesota Population Center 2020
#> 28 Minnesota Population Center 2020
#> 29 Minnesota Population Center 2020
#> 30 Minnesota Population Center 2020
#> 31 Minnesota Population Center 2020
#> 32 Minnesota Population Center 2020
#> 33 Minnesota Population Center 2020
#> 34 Minnesota Population Center 2020
#> 35 Minnesota Population Center 2020
#> 36 Minnesota Population Center 2020
#> 37 Minnesota Population Center 2020
#> 38 Minnesota Population Center 2020
#> 39 Minnesota Population Center 2020
#> 40 Minnesota Population Center 2020
#> 41 Minnesota Population Center 2020
#> 42 Minnesota Population Center 2020
#> 43 Minnesota Population Center 2020
#> 44 Minnesota Population Center 2020
#> 45 Minnesota Population Center 2020
#> 46 Minnesota Population Center 2020
#> 47 Minnesota Population Center 2020
#> 48 Minnesota Population Center 2020
#> 49 Minnesota Population Center 2020
#> 50 Minnesota Population Center 2020
#> 51 Minnesota Population Center 2020
#> 52 Minnesota Population Center 2020
#> 53 Minnesota Population Center 2020
#> 54 Minnesota Population Center 2020
#> 55 Minnesota Population Center 2020
#> 56 Minnesota Population Center 2020
#> 57 Minnesota Population Center 2020
#> 58 Minnesota Population Center 2020
#> 59 Minnesota Population Center 2020
#> 60 Minnesota Population Center 2020
#> 61 Minnesota Population Center 2020
#> 62 Minnesota Population Center 2020
#> 63 Minnesota Population Center 2020
#> 64 Minnesota Population Center 2020
#> 65 Minnesota Population Center 2020
#> 66 Minnesota Population Center 2020
#> 67 Minnesota Population Center 2020
#> 68 Minnesota Population Center 2020
#> 69 Minnesota Population Center 2020
#> 70 Minnesota Population Center 2020
#> 71 Minnesota Population Center 2020
#> 72 Minnesota Population Center 2020
#> 73 Minnesota Population Center 2020
#> 74 Minnesota Population Center 2020
#> 75 Minnesota Population Center 2020
#> 76 Minnesota Population Center 2020
#> 77 Minnesota Population Center 2020
#> 78 Minnesota Population Center 2020
#> 79 Minnesota Population Center 2020
#> 80 Minnesota Population Center 2020
#> 81 Minnesota Population Center 2020
#> 82 Minnesota Population Center 2020
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#> 229 National Institute of Statistics (INSEED) 1975
#> 230 National Institute of Statistics (INSEED) 1975
#> 231 National Institute of Statistics (INSEED) 1975
#> 232 National Institute of Statistics (INSEED) 1975
#> 233 National Institute of Statistics (INSEED) 1975
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#> 235 National Institute of Statistics (INSEED) 1975
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#> 138 Final 1 2 De-jure
#> 139 Final 1 2 De-jure
#> 140 Final 1 2 De-jure
#> 141 Final 1 2 De-jure
#> 142 Final 1 2 De-jure
#> 143 Final 1 2 De-jure
#> 144 Final 1 2 De-jure
#> 145 Final 1 2 De-jure
#> 146 Final 1 2 De-jure
#> 147 Final 1 2 De-jure
#> 148 Final 1 2 De-jure
#> 149 Final 1 2 De-jure
#> 150 Final 1 2 De-jure
#> 151 Final 1 2 De-jure
#> 152 Final 1 2 De-jure
#> 153 Final 1 2 De-jure
#> 154 Final 1 2 De-jure
#> 155 Final 1 2 De-jure
#> 156 Final 1 2 De-jure
#> 157 Final 1 2 De-jure
#> 158 Final 1 2 De-jure
#> 159 Final 1 2 De-jure
#> 160 Final 1 2 De-jure
#> 161 Final 1 2 De-jure
#> 162 Final 1 2 De-jure
#> 163 Final 1 2 De-jure
#> 164 Final 1 2 De-jure
#> 165 Final 1 2 De-jure
#> 166 Final 1 2 De-jure
#> 167 Final 1 2 De-jure
#> 168 Final 1 2 De-jure
#> 169 Final 1 2 De-jure
#> 170 Final 1 2 De-jure
#> 171 Final 1 2 De-jure
#> 172 Final 1 2 De-jure
#> 173 Final 1 2 De-jure
#> 174 Final 1 2 De-jure
#> 175 Final 1 2 De-jure
#> 176 Final 1 2 De-jure
#> 177 Final 1 2 De-jure
#> 178 Final 1 2 De-jure
#> 179 Final 1 2 De-jure
#> 180 Final 1 2 De-jure
#> 181 Final 1 2 De-jure
#> 182 Final 1 2 De-jure
#> 183 Final 1 2 De-jure
#> 184 Final 1 2 De-jure
#> 185 Final 1 2 De-jure
#> 186 Final 1 2 De-jure
#> 187 Final 1 2 De-jure
#> 188 Final 1 2 De-jure
#> 189 Final 1 2 De-jure
#> 190 Final 1 2 De-jure
#> 191 Final 1 2 De-jure
#> 192 Final 1 2 De-jure
#> 193 Final 1 2 De-jure
#> 194 Final 1 2 De-jure
#> 195 Final 1 2 De-jure
#> 196 Final 1 2 De-jure
#> 197 Final 1 2 De-jure
#> 198 Final 1 2 De-jure
#> 199 Final 1 2 De-jure
#> 200 Final 1 2 De-jure
#> 201 Final 1 2 De-jure
#> 202 Final 1 2 De-jure
#> 203 Final 1 2 De-jure
#> 204 Final 1 2 De-jure
#> 205 Final 1 2 De-jure
#> 206 Final 1 2 De-jure
#> 207 Final 1 2 De-jure
#> 208 Final 1 2 De-jure
#> 209 Final 1 2 De-jure
#> 210 Final 1 2 De-jure
#> 211 Final 1 2 De-jure
#> 212 Final 1 2 De-jure
#> 213 Final 1 2 De-jure
#> 214 Final 1 2 De-jure
#> 215 Final 1 2 De-jure
#> 216 Final 1 2 De-jure
#> 217 Final 1 2 De-jure
#> 218 Final 1 2 De-jure
#> 219 Final 1 2 De-jure
#> 220 Final 1 2 De-jure
#> 221 Final 1 2 De-jure
#> 222 Final 1 2 De-jure
#> 223 Final 1 2 De-jure
#> 224 Final 1 2 De-jure
#> 225 Final 1 2 De-jure
#> 226 Final 1 2 De-jure
#> 227 Final 1 2 De-jure
#> 228 Final 1 2 De-jure
#> 229 Final 1 2 De-facto
#> 230 Final 1 2 De-facto
#> 231 Final 1 2 De-facto
#> 232 Final 1 2 De-facto
#> 233 Final 1 2 De-facto
#> 234 Final 1 2 De-facto
#> 235 Final 1 2 De-facto
#> StatisticalConceptID StatisticalConceptSort SexName SexID SexSort AgeID
#> 1 3 3 Male 1 1 700
#> 2 3 3 Male 1 1 703
#> 3 3 3 Male 1 1 704
#> 4 3 3 Male 1 1 705
#> 5 3 3 Male 1 1 706
#> 6 3 3 Male 1 1 707
#> 7 3 3 Male 1 1 708
#> 8 3 3 Male 1 1 709
#> 9 3 3 Male 1 1 710
#> 10 3 3 Male 1 1 711
#> 11 3 3 Male 1 1 712
#> 12 3 3 Male 1 1 713
#> 13 3 3 Male 1 1 714
#> 14 3 3 Male 1 1 715
#> 15 3 3 Male 1 1 716
#> 16 3 3 Male 1 1 717
#> 17 3 3 Male 1 1 718
#> 18 3 3 Male 1 1 719
#> 19 3 3 Male 1 1 769
#> 20 3 3 Female 2 2 700
#> 21 3 3 Female 2 2 703
#> 22 3 3 Female 2 2 704
#> 23 3 3 Female 2 2 705
#> 24 3 3 Female 2 2 706
#> 25 3 3 Female 2 2 707
#> 26 3 3 Female 2 2 708
#> 27 3 3 Female 2 2 709
#> 28 3 3 Female 2 2 710
#> 29 3 3 Female 2 2 711
#> 30 3 3 Female 2 2 712
#> 31 3 3 Female 2 2 713
#> 32 3 3 Female 2 2 714
#> 33 3 3 Female 2 2 715
#> 34 3 3 Female 2 2 716
#> 35 3 3 Female 2 2 717
#> 36 3 3 Female 2 2 718
#> 37 3 3 Female 2 2 719
#> 38 3 3 Female 2 2 769
#> 39 3 3 Both sexes 3 3 700
#> 40 3 3 Both sexes 3 3 703
#> 41 3 3 Both sexes 3 3 704
#> 42 3 3 Both sexes 3 3 705
#> 43 3 3 Both sexes 3 3 706
#> 44 3 3 Both sexes 3 3 707
#> 45 3 3 Both sexes 3 3 708
#> 46 3 3 Both sexes 3 3 709
#> 47 3 3 Both sexes 3 3 710
#> 48 3 3 Both sexes 3 3 711
#> 49 3 3 Both sexes 3 3 712
#> 50 3 3 Both sexes 3 3 713
#> 51 3 3 Both sexes 3 3 714
#> 52 3 3 Both sexes 3 3 715
#> 53 3 3 Both sexes 3 3 716
#> 54 3 3 Both sexes 3 3 717
#> 55 3 3 Both sexes 3 3 718
#> 56 3 3 Both sexes 3 3 719
#> 57 3 3 Both sexes 3 3 769
#> 58 3 3 Male 1 1 700
#> 59 3 3 Male 1 1 703
#> 60 3 3 Male 1 1 704
#> 61 3 3 Male 1 1 705
#> 62 3 3 Male 1 1 706
#> 63 3 3 Male 1 1 707
#> 64 3 3 Male 1 1 708
#> 65 3 3 Male 1 1 709
#> 66 3 3 Male 1 1 710
#> 67 3 3 Male 1 1 711
#> 68 3 3 Male 1 1 712
#> 69 3 3 Male 1 1 713
#> 70 3 3 Male 1 1 714
#> 71 3 3 Male 1 1 715
#> 72 3 3 Male 1 1 716
#> 73 3 3 Male 1 1 717
#> 74 3 3 Male 1 1 718
#> 75 3 3 Male 1 1 719
#> 76 3 3 Male 1 1 769
#> 77 3 3 Female 2 2 700
#> 78 3 3 Female 2 2 703
#> 79 3 3 Female 2 2 704
#> 80 3 3 Female 2 2 705
#> 81 3 3 Female 2 2 706
#> 82 3 3 Female 2 2 707
#> 83 3 3 Female 2 2 708
#> 84 3 3 Female 2 2 709
#> 85 3 3 Female 2 2 710
#> 86 3 3 Female 2 2 711
#> 87 3 3 Female 2 2 712
#> 88 3 3 Female 2 2 713
#> 89 3 3 Female 2 2 714
#> 90 3 3 Female 2 2 715
#> 91 3 3 Female 2 2 716
#> 92 3 3 Female 2 2 717
#> 93 3 3 Female 2 2 718
#> 94 3 3 Female 2 2 719
#> 95 3 3 Female 2 2 769
#> 96 3 3 Both sexes 3 3 700
#> 97 3 3 Both sexes 3 3 703
#> 98 3 3 Both sexes 3 3 704
#> 99 3 3 Both sexes 3 3 705
#> 100 3 3 Both sexes 3 3 706
#> 101 3 3 Both sexes 3 3 707
#> 102 3 3 Both sexes 3 3 708
#> 103 3 3 Both sexes 3 3 709
#> 104 3 3 Both sexes 3 3 710
#> 105 3 3 Both sexes 3 3 711
#> 106 3 3 Both sexes 3 3 712
#> 107 3 3 Both sexes 3 3 713
#> 108 3 3 Both sexes 3 3 714
#> 109 3 3 Both sexes 3 3 715
#> 110 3 3 Both sexes 3 3 716
#> 111 3 3 Both sexes 3 3 717
#> 112 3 3 Both sexes 3 3 718
#> 113 3 3 Both sexes 3 3 719
#> 114 3 3 Both sexes 3 3 769
#> 115 3 3 Male 1 1 700
#> 116 3 3 Male 1 1 703
#> 117 3 3 Male 1 1 704
#> 118 3 3 Male 1 1 705
#> 119 3 3 Male 1 1 706
#> 120 3 3 Male 1 1 707
#> 121 3 3 Male 1 1 708
#> 122 3 3 Male 1 1 709
#> 123 3 3 Male 1 1 710
#> 124 3 3 Male 1 1 711
#> 125 3 3 Male 1 1 712
#> 126 3 3 Male 1 1 713
#> 127 3 3 Male 1 1 714
#> 128 3 3 Male 1 1 715
#> 129 3 3 Male 1 1 716
#> 130 3 3 Male 1 1 717
#> 131 3 3 Male 1 1 718
#> 132 3 3 Male 1 1 719
#> 133 3 3 Male 1 1 769
#> 134 3 3 Female 2 2 700
#> 135 3 3 Female 2 2 703
#> 136 3 3 Female 2 2 704
#> 137 3 3 Female 2 2 705
#> 138 3 3 Female 2 2 706
#> 139 3 3 Female 2 2 707
#> 140 3 3 Female 2 2 708
#> 141 3 3 Female 2 2 709
#> 142 3 3 Female 2 2 710
#> 143 3 3 Female 2 2 711
#> 144 3 3 Female 2 2 712
#> 145 3 3 Female 2 2 713
#> 146 3 3 Female 2 2 714
#> 147 3 3 Female 2 2 715
#> 148 3 3 Female 2 2 716
#> 149 3 3 Female 2 2 717
#> 150 3 3 Female 2 2 718
#> 151 3 3 Female 2 2 719
#> 152 3 3 Female 2 2 769
#> 153 3 3 Both sexes 3 3 700
#> 154 3 3 Both sexes 3 3 703
#> 155 3 3 Both sexes 3 3 704
#> 156 3 3 Both sexes 3 3 705
#> 157 3 3 Both sexes 3 3 706
#> 158 3 3 Both sexes 3 3 707
#> 159 3 3 Both sexes 3 3 708
#> 160 3 3 Both sexes 3 3 709
#> 161 3 3 Both sexes 3 3 710
#> 162 3 3 Both sexes 3 3 711
#> 163 3 3 Both sexes 3 3 712
#> 164 3 3 Both sexes 3 3 713
#> 165 3 3 Both sexes 3 3 714
#> 166 3 3 Both sexes 3 3 715
#> 167 3 3 Both sexes 3 3 716
#> 168 3 3 Both sexes 3 3 717
#> 169 3 3 Both sexes 3 3 718
#> 170 3 3 Both sexes 3 3 719
#> 171 3 3 Both sexes 3 3 769
#> 172 3 3 Male 1 1 700
#> 173 3 3 Male 1 1 703
#> 174 3 3 Male 1 1 704
#> 175 3 3 Male 1 1 705
#> 176 3 3 Male 1 1 706
#> 177 3 3 Male 1 1 707
#> 178 3 3 Male 1 1 708
#> 179 3 3 Male 1 1 709
#> 180 3 3 Male 1 1 710
#> 181 3 3 Male 1 1 711
#> 182 3 3 Male 1 1 712
#> 183 3 3 Male 1 1 713
#> 184 3 3 Male 1 1 714
#> 185 3 3 Male 1 1 715
#> 186 3 3 Male 1 1 716
#> 187 3 3 Male 1 1 717
#> 188 3 3 Male 1 1 718
#> 189 3 3 Male 1 1 719
#> 190 3 3 Male 1 1 769
#> 191 3 3 Female 2 2 700
#> 192 3 3 Female 2 2 703
#> 193 3 3 Female 2 2 704
#> 194 3 3 Female 2 2 705
#> 195 3 3 Female 2 2 706
#> 196 3 3 Female 2 2 707
#> 197 3 3 Female 2 2 708
#> 198 3 3 Female 2 2 709
#> 199 3 3 Female 2 2 710
#> 200 3 3 Female 2 2 711
#> 201 3 3 Female 2 2 712
#> 202 3 3 Female 2 2 713
#> 203 3 3 Female 2 2 714
#> 204 3 3 Female 2 2 715
#> 205 3 3 Female 2 2 716
#> 206 3 3 Female 2 2 717
#> 207 3 3 Female 2 2 718
#> 208 3 3 Female 2 2 719
#> 209 3 3 Female 2 2 769
#> 210 3 3 Both sexes 3 3 700
#> 211 3 3 Both sexes 3 3 703
#> 212 3 3 Both sexes 3 3 704
#> 213 3 3 Both sexes 3 3 705
#> 214 3 3 Both sexes 3 3 706
#> 215 3 3 Both sexes 3 3 707
#> 216 3 3 Both sexes 3 3 708
#> 217 3 3 Both sexes 3 3 709
#> 218 3 3 Both sexes 3 3 710
#> 219 3 3 Both sexes 3 3 711
#> 220 3 3 Both sexes 3 3 712
#> 221 3 3 Both sexes 3 3 713
#> 222 3 3 Both sexes 3 3 714
#> 223 3 3 Both sexes 3 3 715
#> 224 3 3 Both sexes 3 3 716
#> 225 3 3 Both sexes 3 3 717
#> 226 3 3 Both sexes 3 3 718
#> 227 3 3 Both sexes 3 3 719
#> 228 3 3 Both sexes 3 3 769
#> 229 2 1 Both sexes 3 3 704
#> 230 2 1 Both sexes 3 3 705
#> 231 2 1 Both sexes 3 3 706
#> 232 2 1 Both sexes 3 3 707
#> 233 2 1 Both sexes 3 3 708
#> 234 2 1 Both sexes 3 3 709
#> 235 2 1 Both sexes 3 3 710
#> AgeUnit AgeStart AgeEnd AgeSpan AgeMid AgeLabel AgeSort agesort
#> 1 Year 0 -1 -1 -1.0 Total 999 0
#> 2 Year 10 15 5 12.5 10-14 133 0
#> 3 Year 15 20 5 17.5 15-19 190 0
#> 4 Year 20 25 5 22.5 20-24 263 0
#> 5 Year 25 30 5 27.5 25-29 306 0
#> 6 Year 30 35 5 32.5 30-34 330 0
#> 7 Year 35 40 5 37.5 35-39 354 0
#> 8 Year 40 45 5 42.5 40-44 374 0
#> 9 Year 45 50 5 47.5 45-49 394 0
#> 10 Year 50 55 5 52.5 50-54 421 0
#> 11 Year 55 60 5 57.5 55-59 440 0
#> 12 Year 60 65 5 62.5 60-64 459 0
#> 13 Year 65 70 5 67.5 65-69 482 0
#> 14 Year 70 75 5 72.5 70-74 503 0
#> 15 Year 75 80 5 77.5 75-79 519 0
#> 16 Year 80 85 5 82.5 80-84 534 0
#> 17 Year 85 90 5 87.5 85-89 548 0
#> 18 Year 90 95 5 92.5 90-94 561 0
#> 19 Year 95 0 -1 97.5 95+ 574 0
#> 20 Year 0 -1 -1 -1.0 Total 999 0
#> 21 Year 10 15 5 12.5 10-14 133 0
#> 22 Year 15 20 5 17.5 15-19 190 0
#> 23 Year 20 25 5 22.5 20-24 263 0
#> 24 Year 25 30 5 27.5 25-29 306 0
#> 25 Year 30 35 5 32.5 30-34 330 0
#> 26 Year 35 40 5 37.5 35-39 354 0
#> 27 Year 40 45 5 42.5 40-44 374 0
#> 28 Year 45 50 5 47.5 45-49 394 0
#> 29 Year 50 55 5 52.5 50-54 421 0
#> 30 Year 55 60 5 57.5 55-59 440 0
#> 31 Year 60 65 5 62.5 60-64 459 0
#> 32 Year 65 70 5 67.5 65-69 482 0
#> 33 Year 70 75 5 72.5 70-74 503 0
#> 34 Year 75 80 5 77.5 75-79 519 0
#> 35 Year 80 85 5 82.5 80-84 534 0
#> 36 Year 85 90 5 87.5 85-89 548 0
#> 37 Year 90 95 5 92.5 90-94 561 0
#> 38 Year 95 0 -1 97.5 95+ 574 0
#> 39 Year 0 -1 -1 -1.0 Total 999 0
#> 40 Year 10 15 5 12.5 10-14 133 0
#> 41 Year 15 20 5 17.5 15-19 190 0
#> 42 Year 20 25 5 22.5 20-24 263 0
#> 43 Year 25 30 5 27.5 25-29 306 0
#> 44 Year 30 35 5 32.5 30-34 330 0
#> 45 Year 35 40 5 37.5 35-39 354 0
#> 46 Year 40 45 5 42.5 40-44 374 0
#> 47 Year 45 50 5 47.5 45-49 394 0
#> 48 Year 50 55 5 52.5 50-54 421 0
#> 49 Year 55 60 5 57.5 55-59 440 0
#> 50 Year 60 65 5 62.5 60-64 459 0
#> 51 Year 65 70 5 67.5 65-69 482 0
#> 52 Year 70 75 5 72.5 70-74 503 0
#> 53 Year 75 80 5 77.5 75-79 519 0
#> 54 Year 80 85 5 82.5 80-84 534 0
#> 55 Year 85 90 5 87.5 85-89 548 0
#> 56 Year 90 95 5 92.5 90-94 561 0
#> 57 Year 95 0 -1 97.5 95+ 574 0
#> 58 Year 0 -1 -1 -1.0 Total 999 0
#> 59 Year 10 15 5 12.5 10-14 133 0
#> 60 Year 15 20 5 17.5 15-19 190 0
#> 61 Year 20 25 5 22.5 20-24 263 0
#> 62 Year 25 30 5 27.5 25-29 306 0
#> 63 Year 30 35 5 32.5 30-34 330 0
#> 64 Year 35 40 5 37.5 35-39 354 0
#> 65 Year 40 45 5 42.5 40-44 374 0
#> 66 Year 45 50 5 47.5 45-49 394 0
#> 67 Year 50 55 5 52.5 50-54 421 0
#> 68 Year 55 60 5 57.5 55-59 440 0
#> 69 Year 60 65 5 62.5 60-64 459 0
#> 70 Year 65 70 5 67.5 65-69 482 0
#> 71 Year 70 75 5 72.5 70-74 503 0
#> 72 Year 75 80 5 77.5 75-79 519 0
#> 73 Year 80 85 5 82.5 80-84 534 0
#> 74 Year 85 90 5 87.5 85-89 548 0
#> 75 Year 90 95 5 92.5 90-94 561 0
#> 76 Year 95 0 -1 97.5 95+ 574 0
#> 77 Year 0 -1 -1 -1.0 Total 999 0
#> 78 Year 10 15 5 12.5 10-14 133 0
#> 79 Year 15 20 5 17.5 15-19 190 0
#> 80 Year 20 25 5 22.5 20-24 263 0
#> 81 Year 25 30 5 27.5 25-29 306 0
#> 82 Year 30 35 5 32.5 30-34 330 0
#> 83 Year 35 40 5 37.5 35-39 354 0
#> 84 Year 40 45 5 42.5 40-44 374 0
#> 85 Year 45 50 5 47.5 45-49 394 0
#> 86 Year 50 55 5 52.5 50-54 421 0
#> 87 Year 55 60 5 57.5 55-59 440 0
#> 88 Year 60 65 5 62.5 60-64 459 0
#> 89 Year 65 70 5 67.5 65-69 482 0
#> 90 Year 70 75 5 72.5 70-74 503 0
#> 91 Year 75 80 5 77.5 75-79 519 0
#> 92 Year 80 85 5 82.5 80-84 534 0
#> 93 Year 85 90 5 87.5 85-89 548 0
#> 94 Year 90 95 5 92.5 90-94 561 0
#> 95 Year 95 0 -1 97.5 95+ 574 0
#> 96 Year 0 -1 -1 -1.0 Total 999 0
#> 97 Year 10 15 5 12.5 10-14 133 0
#> 98 Year 15 20 5 17.5 15-19 190 0
#> 99 Year 20 25 5 22.5 20-24 263 0
#> 100 Year 25 30 5 27.5 25-29 306 0
#> 101 Year 30 35 5 32.5 30-34 330 0
#> 102 Year 35 40 5 37.5 35-39 354 0
#> 103 Year 40 45 5 42.5 40-44 374 0
#> 104 Year 45 50 5 47.5 45-49 394 0
#> 105 Year 50 55 5 52.5 50-54 421 0
#> 106 Year 55 60 5 57.5 55-59 440 0
#> 107 Year 60 65 5 62.5 60-64 459 0
#> 108 Year 65 70 5 67.5 65-69 482 0
#> 109 Year 70 75 5 72.5 70-74 503 0
#> 110 Year 75 80 5 77.5 75-79 519 0
#> 111 Year 80 85 5 82.5 80-84 534 0
#> 112 Year 85 90 5 87.5 85-89 548 0
#> 113 Year 90 95 5 92.5 90-94 561 0
#> 114 Year 95 0 -1 97.5 95+ 574 0
#> 115 Year 0 -1 -1 -1.0 Total 999 0
#> 116 Year 10 15 5 12.5 10-14 133 0
#> 117 Year 15 20 5 17.5 15-19 190 0
#> 118 Year 20 25 5 22.5 20-24 263 0
#> 119 Year 25 30 5 27.5 25-29 306 0
#> 120 Year 30 35 5 32.5 30-34 330 0
#> 121 Year 35 40 5 37.5 35-39 354 0
#> 122 Year 40 45 5 42.5 40-44 374 0
#> 123 Year 45 50 5 47.5 45-49 394 0
#> 124 Year 50 55 5 52.5 50-54 421 0
#> 125 Year 55 60 5 57.5 55-59 440 0
#> 126 Year 60 65 5 62.5 60-64 459 0
#> 127 Year 65 70 5 67.5 65-69 482 0
#> 128 Year 70 75 5 72.5 70-74 503 0
#> 129 Year 75 80 5 77.5 75-79 519 0
#> 130 Year 80 85 5 82.5 80-84 534 0
#> 131 Year 85 90 5 87.5 85-89 548 0
#> 132 Year 90 95 5 92.5 90-94 561 0
#> 133 Year 95 0 -1 97.5 95+ 574 0
#> 134 Year 0 -1 -1 -1.0 Total 999 0
#> 135 Year 10 15 5 12.5 10-14 133 0
#> 136 Year 15 20 5 17.5 15-19 190 0
#> 137 Year 20 25 5 22.5 20-24 263 0
#> 138 Year 25 30 5 27.5 25-29 306 0
#> 139 Year 30 35 5 32.5 30-34 330 0
#> 140 Year 35 40 5 37.5 35-39 354 0
#> 141 Year 40 45 5 42.5 40-44 374 0
#> 142 Year 45 50 5 47.5 45-49 394 0
#> 143 Year 50 55 5 52.5 50-54 421 0
#> 144 Year 55 60 5 57.5 55-59 440 0
#> 145 Year 60 65 5 62.5 60-64 459 0
#> 146 Year 65 70 5 67.5 65-69 482 0
#> 147 Year 70 75 5 72.5 70-74 503 0
#> 148 Year 75 80 5 77.5 75-79 519 0
#> 149 Year 80 85 5 82.5 80-84 534 0
#> 150 Year 85 90 5 87.5 85-89 548 0
#> 151 Year 90 95 5 92.5 90-94 561 0
#> 152 Year 95 0 -1 97.5 95+ 574 0
#> 153 Year 0 -1 -1 -1.0 Total 999 0
#> 154 Year 10 15 5 12.5 10-14 133 0
#> 155 Year 15 20 5 17.5 15-19 190 0
#> 156 Year 20 25 5 22.5 20-24 263 0
#> 157 Year 25 30 5 27.5 25-29 306 0
#> 158 Year 30 35 5 32.5 30-34 330 0
#> 159 Year 35 40 5 37.5 35-39 354 0
#> 160 Year 40 45 5 42.5 40-44 374 0
#> 161 Year 45 50 5 47.5 45-49 394 0
#> 162 Year 50 55 5 52.5 50-54 421 0
#> 163 Year 55 60 5 57.5 55-59 440 0
#> 164 Year 60 65 5 62.5 60-64 459 0
#> 165 Year 65 70 5 67.5 65-69 482 0
#> 166 Year 70 75 5 72.5 70-74 503 0
#> 167 Year 75 80 5 77.5 75-79 519 0
#> 168 Year 80 85 5 82.5 80-84 534 0
#> 169 Year 85 90 5 87.5 85-89 548 0
#> 170 Year 90 95 5 92.5 90-94 561 0
#> 171 Year 95 0 -1 97.5 95+ 574 0
#> 172 Year 0 -1 -1 -1.0 Total 999 0
#> 173 Year 10 15 5 12.5 10-14 133 0
#> 174 Year 15 20 5 17.5 15-19 190 0
#> 175 Year 20 25 5 22.5 20-24 263 0
#> 176 Year 25 30 5 27.5 25-29 306 0
#> 177 Year 30 35 5 32.5 30-34 330 0
#> 178 Year 35 40 5 37.5 35-39 354 0
#> 179 Year 40 45 5 42.5 40-44 374 0
#> 180 Year 45 50 5 47.5 45-49 394 0
#> 181 Year 50 55 5 52.5 50-54 421 0
#> 182 Year 55 60 5 57.5 55-59 440 0
#> 183 Year 60 65 5 62.5 60-64 459 0
#> 184 Year 65 70 5 67.5 65-69 482 0
#> 185 Year 70 75 5 72.5 70-74 503 0
#> 186 Year 75 80 5 77.5 75-79 519 0
#> 187 Year 80 85 5 82.5 80-84 534 0
#> 188 Year 85 90 5 87.5 85-89 548 0
#> 189 Year 90 95 5 92.5 90-94 561 0
#> 190 Year 95 0 -1 97.5 95+ 574 0
#> 191 Year 0 -1 -1 -1.0 Total 999 0
#> 192 Year 10 15 5 12.5 10-14 133 0
#> 193 Year 15 20 5 17.5 15-19 190 0
#> 194 Year 20 25 5 22.5 20-24 263 0
#> 195 Year 25 30 5 27.5 25-29 306 0
#> 196 Year 30 35 5 32.5 30-34 330 0
#> 197 Year 35 40 5 37.5 35-39 354 0
#> 198 Year 40 45 5 42.5 40-44 374 0
#> 199 Year 45 50 5 47.5 45-49 394 0
#> 200 Year 50 55 5 52.5 50-54 421 0
#> 201 Year 55 60 5 57.5 55-59 440 0
#> 202 Year 60 65 5 62.5 60-64 459 0
#> 203 Year 65 70 5 67.5 65-69 482 0
#> 204 Year 70 75 5 72.5 70-74 503 0
#> 205 Year 75 80 5 77.5 75-79 519 0
#> 206 Year 80 85 5 82.5 80-84 534 0
#> 207 Year 85 90 5 87.5 85-89 548 0
#> 208 Year 90 95 5 92.5 90-94 561 0
#> 209 Year 95 0 -1 97.5 95+ 574 0
#> 210 Year 0 -1 -1 -1.0 Total 999 0
#> 211 Year 10 15 5 12.5 10-14 133 0
#> 212 Year 15 20 5 17.5 15-19 190 0
#> 213 Year 20 25 5 22.5 20-24 263 0
#> 214 Year 25 30 5 27.5 25-29 306 0
#> 215 Year 30 35 5 32.5 30-34 330 0
#> 216 Year 35 40 5 37.5 35-39 354 0
#> 217 Year 40 45 5 42.5 40-44 374 0
#> 218 Year 45 50 5 47.5 45-49 394 0
#> 219 Year 50 55 5 52.5 50-54 421 0
#> 220 Year 55 60 5 57.5 55-59 440 0
#> 221 Year 60 65 5 62.5 60-64 459 0
#> 222 Year 65 70 5 67.5 65-69 482 0
#> 223 Year 70 75 5 72.5 70-74 503 0
#> 224 Year 75 80 5 77.5 75-79 519 0
#> 225 Year 80 85 5 82.5 80-84 534 0
#> 226 Year 85 90 5 87.5 85-89 548 0
#> 227 Year 90 95 5 92.5 90-94 561 0
#> 228 Year 95 0 -1 97.5 95+ 574 0
#> 229 Year 15 20 5 17.5 15-19 190 0
#> 230 Year 20 25 5 22.5 20-24 263 0
#> 231 Year 25 30 5 27.5 25-29 306 0
#> 232 Year 30 35 5 32.5 30-34 330 0
#> 233 Year 35 40 5 37.5 35-39 354 0
#> 234 Year 40 45 5 42.5 40-44 374 0
#> 235 Year 45 50 5 47.5 45-49 394 0
#> DataTypeGroupName DataTypeGroupID
#> 1 Direct 3
#> 2 Direct 3
#> 3 Direct 3
#> 4 Direct 3
#> 5 Direct 3
#> 6 Direct 3
#> 7 Direct 3
#> 8 Direct 3
#> 9 Direct 3
#> 10 Direct 3
#> 11 Direct 3
#> 12 Direct 3
#> 13 Direct 3
#> 14 Direct 3
#> 15 Direct 3
#> 16 Direct 3
#> 17 Direct 3
#> 18 Direct 3
#> 19 Direct 3
#> 20 Direct 3
#> 21 Direct 3
#> 22 Direct 3
#> 23 Direct 3
#> 24 Direct 3
#> 25 Direct 3
#> 26 Direct 3
#> 27 Direct 3
#> 28 Direct 3
#> 29 Direct 3
#> 30 Direct 3
#> 31 Direct 3
#> 32 Direct 3
#> 33 Direct 3
#> 34 Direct 3
#> 35 Direct 3
#> 36 Direct 3
#> 37 Direct 3
#> 38 Direct 3
#> 39 Direct 3
#> 40 Direct 3
#> 41 Direct 3
#> 42 Direct 3
#> 43 Direct 3
#> 44 Direct 3
#> 45 Direct 3
#> 46 Direct 3
#> 47 Direct 3
#> 48 Direct 3
#> 49 Direct 3
#> 50 Direct 3
#> 51 Direct 3
#> 52 Direct 3
#> 53 Direct 3
#> 54 Direct 3
#> 55 Direct 3
#> 56 Direct 3
#> 57 Direct 3
#> 58 Direct 3
#> 59 Direct 3
#> 60 Direct 3
#> 61 Direct 3
#> 62 Direct 3
#> 63 Direct 3
#> 64 Direct 3
#> 65 Direct 3
#> 66 Direct 3
#> 67 Direct 3
#> 68 Direct 3
#> 69 Direct 3
#> 70 Direct 3
#> 71 Direct 3
#> 72 Direct 3
#> 73 Direct 3
#> 74 Direct 3
#> 75 Direct 3
#> 76 Direct 3
#> 77 Direct 3
#> 78 Direct 3
#> 79 Direct 3
#> 80 Direct 3
#> 81 Direct 3
#> 82 Direct 3
#> 83 Direct 3
#> 84 Direct 3
#> 85 Direct 3
#> 86 Direct 3
#> 87 Direct 3
#> 88 Direct 3
#> 89 Direct 3
#> 90 Direct 3
#> 91 Direct 3
#> 92 Direct 3
#> 93 Direct 3
#> 94 Direct 3
#> 95 Direct 3
#> 96 Direct 3
#> 97 Direct 3
#> 98 Direct 3
#> 99 Direct 3
#> 100 Direct 3
#> 101 Direct 3
#> 102 Direct 3
#> 103 Direct 3
#> 104 Direct 3
#> 105 Direct 3
#> 106 Direct 3
#> 107 Direct 3
#> 108 Direct 3
#> 109 Direct 3
#> 110 Direct 3
#> 111 Direct 3
#> 112 Direct 3
#> 113 Direct 3
#> 114 Direct 3
#> 115 Direct 3
#> 116 Direct 3
#> 117 Direct 3
#> 118 Direct 3
#> 119 Direct 3
#> 120 Direct 3
#> 121 Direct 3
#> 122 Direct 3
#> 123 Direct 3
#> 124 Direct 3
#> 125 Direct 3
#> 126 Direct 3
#> 127 Direct 3
#> 128 Direct 3
#> 129 Direct 3
#> 130 Direct 3
#> 131 Direct 3
#> 132 Direct 3
#> 133 Direct 3
#> 134 Direct 3
#> 135 Direct 3
#> 136 Direct 3
#> 137 Direct 3
#> 138 Direct 3
#> 139 Direct 3
#> 140 Direct 3
#> 141 Direct 3
#> 142 Direct 3
#> 143 Direct 3
#> 144 Direct 3
#> 145 Direct 3
#> 146 Direct 3
#> 147 Direct 3
#> 148 Direct 3
#> 149 Direct 3
#> 150 Direct 3
#> 151 Direct 3
#> 152 Direct 3
#> 153 Direct 3
#> 154 Direct 3
#> 155 Direct 3
#> 156 Direct 3
#> 157 Direct 3
#> 158 Direct 3
#> 159 Direct 3
#> 160 Direct 3
#> 161 Direct 3
#> 162 Direct 3
#> 163 Direct 3
#> 164 Direct 3
#> 165 Direct 3
#> 166 Direct 3
#> 167 Direct 3
#> 168 Direct 3
#> 169 Direct 3
#> 170 Direct 3
#> 171 Direct 3
#> 172 Direct 3
#> 173 Direct 3
#> 174 Direct 3
#> 175 Direct 3
#> 176 Direct 3
#> 177 Direct 3
#> 178 Direct 3
#> 179 Direct 3
#> 180 Direct 3
#> 181 Direct 3
#> 182 Direct 3
#> 183 Direct 3
#> 184 Direct 3
#> 185 Direct 3
#> 186 Direct 3
#> 187 Direct 3
#> 188 Direct 3
#> 189 Direct 3
#> 190 Direct 3
#> 191 Direct 3
#> 192 Direct 3
#> 193 Direct 3
#> 194 Direct 3
#> 195 Direct 3
#> 196 Direct 3
#> 197 Direct 3
#> 198 Direct 3
#> 199 Direct 3
#> 200 Direct 3
#> 201 Direct 3
#> 202 Direct 3
#> 203 Direct 3
#> 204 Direct 3
#> 205 Direct 3
#> 206 Direct 3
#> 207 Direct 3
#> 208 Direct 3
#> 209 Direct 3
#> 210 Direct 3
#> 211 Direct 3
#> 212 Direct 3
#> 213 Direct 3
#> 214 Direct 3
#> 215 Direct 3
#> 216 Direct 3
#> 217 Direct 3
#> 218 Direct 3
#> 219 Direct 3
#> 220 Direct 3
#> 221 Direct 3
#> 222 Direct 3
#> 223 Direct 3
#> 224 Direct 3
#> 225 Direct 3
#> 226 Direct 3
#> 227 Direct 3
#> 228 Direct 3
#> 229 Direct 3
#> 230 Direct 3
#> 231 Direct 3
#> 232 Direct 3
#> 233 Direct 3
#> 234 Direct 3
#> 235 Direct 3
#> DataTypeName DataTypeID DataTypeSort
#> 1 Population by age and sex (sample tabulation) 141 204
#> 2 Population by age and sex (sample tabulation) 141 204
#> 3 Population by age and sex (sample tabulation) 141 204
#> 4 Population by age and sex (sample tabulation) 141 204
#> 5 Population by age and sex (sample tabulation) 141 204
#> 6 Population by age and sex (sample tabulation) 141 204
#> 7 Population by age and sex (sample tabulation) 141 204
#> 8 Population by age and sex (sample tabulation) 141 204
#> 9 Population by age and sex (sample tabulation) 141 204
#> 10 Population by age and sex (sample tabulation) 141 204
#> 11 Population by age and sex (sample tabulation) 141 204
#> 12 Population by age and sex (sample tabulation) 141 204
#> 13 Population by age and sex (sample tabulation) 141 204
#> 14 Population by age and sex (sample tabulation) 141 204
#> 15 Population by age and sex (sample tabulation) 141 204
#> 16 Population by age and sex (sample tabulation) 141 204
#> 17 Population by age and sex (sample tabulation) 141 204
#> 18 Population by age and sex (sample tabulation) 141 204
#> 19 Population by age and sex (sample tabulation) 141 204
#> 20 Population by age and sex (sample tabulation) 141 204
#> 21 Population by age and sex (sample tabulation) 141 204
#> 22 Population by age and sex (sample tabulation) 141 204
#> 23 Population by age and sex (sample tabulation) 141 204
#> 24 Population by age and sex (sample tabulation) 141 204
#> 25 Population by age and sex (sample tabulation) 141 204
#> 26 Population by age and sex (sample tabulation) 141 204
#> 27 Population by age and sex (sample tabulation) 141 204
#> 28 Population by age and sex (sample tabulation) 141 204
#> 29 Population by age and sex (sample tabulation) 141 204
#> 30 Population by age and sex (sample tabulation) 141 204
#> 31 Population by age and sex (sample tabulation) 141 204
#> 32 Population by age and sex (sample tabulation) 141 204
#> 33 Population by age and sex (sample tabulation) 141 204
#> 34 Population by age and sex (sample tabulation) 141 204
#> 35 Population by age and sex (sample tabulation) 141 204
#> 36 Population by age and sex (sample tabulation) 141 204
#> 37 Population by age and sex (sample tabulation) 141 204
#> 38 Population by age and sex (sample tabulation) 141 204
#> 39 Population by age and sex (sample tabulation) 141 204
#> 40 Population by age and sex (sample tabulation) 141 204
#> 41 Population by age and sex (sample tabulation) 141 204
#> 42 Population by age and sex (sample tabulation) 141 204
#> 43 Population by age and sex (sample tabulation) 141 204
#> 44 Population by age and sex (sample tabulation) 141 204
#> 45 Population by age and sex (sample tabulation) 141 204
#> 46 Population by age and sex (sample tabulation) 141 204
#> 47 Population by age and sex (sample tabulation) 141 204
#> 48 Population by age and sex (sample tabulation) 141 204
#> 49 Population by age and sex (sample tabulation) 141 204
#> 50 Population by age and sex (sample tabulation) 141 204
#> 51 Population by age and sex (sample tabulation) 141 204
#> 52 Population by age and sex (sample tabulation) 141 204
#> 53 Population by age and sex (sample tabulation) 141 204
#> 54 Population by age and sex (sample tabulation) 141 204
#> 55 Population by age and sex (sample tabulation) 141 204
#> 56 Population by age and sex (sample tabulation) 141 204
#> 57 Population by age and sex (sample tabulation) 141 204
#> 58 Population by age and sex (sample tabulation) 141 204
#> 59 Population by age and sex (sample tabulation) 141 204
#> 60 Population by age and sex (sample tabulation) 141 204
#> 61 Population by age and sex (sample tabulation) 141 204
#> 62 Population by age and sex (sample tabulation) 141 204
#> 63 Population by age and sex (sample tabulation) 141 204
#> 64 Population by age and sex (sample tabulation) 141 204
#> 65 Population by age and sex (sample tabulation) 141 204
#> 66 Population by age and sex (sample tabulation) 141 204
#> 67 Population by age and sex (sample tabulation) 141 204
#> 68 Population by age and sex (sample tabulation) 141 204
#> 69 Population by age and sex (sample tabulation) 141 204
#> 70 Population by age and sex (sample tabulation) 141 204
#> 71 Population by age and sex (sample tabulation) 141 204
#> 72 Population by age and sex (sample tabulation) 141 204
#> 73 Population by age and sex (sample tabulation) 141 204
#> 74 Population by age and sex (sample tabulation) 141 204
#> 75 Population by age and sex (sample tabulation) 141 204
#> 76 Population by age and sex (sample tabulation) 141 204
#> 77 Population by age and sex (sample tabulation) 141 204
#> 78 Population by age and sex (sample tabulation) 141 204
#> 79 Population by age and sex (sample tabulation) 141 204
#> 80 Population by age and sex (sample tabulation) 141 204
#> 81 Population by age and sex (sample tabulation) 141 204
#> 82 Population by age and sex (sample tabulation) 141 204
#> 83 Population by age and sex (sample tabulation) 141 204
#> 84 Population by age and sex (sample tabulation) 141 204
#> 85 Population by age and sex (sample tabulation) 141 204
#> 86 Population by age and sex (sample tabulation) 141 204
#> 87 Population by age and sex (sample tabulation) 141 204
#> 88 Population by age and sex (sample tabulation) 141 204
#> 89 Population by age and sex (sample tabulation) 141 204
#> 90 Population by age and sex (sample tabulation) 141 204
#> 91 Population by age and sex (sample tabulation) 141 204
#> 92 Population by age and sex (sample tabulation) 141 204
#> 93 Population by age and sex (sample tabulation) 141 204
#> 94 Population by age and sex (sample tabulation) 141 204
#> 95 Population by age and sex (sample tabulation) 141 204
#> 96 Population by age and sex (sample tabulation) 141 204
#> 97 Population by age and sex (sample tabulation) 141 204
#> 98 Population by age and sex (sample tabulation) 141 204
#> 99 Population by age and sex (sample tabulation) 141 204
#> 100 Population by age and sex (sample tabulation) 141 204
#> 101 Population by age and sex (sample tabulation) 141 204
#> 102 Population by age and sex (sample tabulation) 141 204
#> 103 Population by age and sex (sample tabulation) 141 204
#> 104 Population by age and sex (sample tabulation) 141 204
#> 105 Population by age and sex (sample tabulation) 141 204
#> 106 Population by age and sex (sample tabulation) 141 204
#> 107 Population by age and sex (sample tabulation) 141 204
#> 108 Population by age and sex (sample tabulation) 141 204
#> 109 Population by age and sex (sample tabulation) 141 204
#> 110 Population by age and sex (sample tabulation) 141 204
#> 111 Population by age and sex (sample tabulation) 141 204
#> 112 Population by age and sex (sample tabulation) 141 204
#> 113 Population by age and sex (sample tabulation) 141 204
#> 114 Population by age and sex (sample tabulation) 141 204
#> 115 Population by age and sex (sample tabulation) 141 204
#> 116 Population by age and sex (sample tabulation) 141 204
#> 117 Population by age and sex (sample tabulation) 141 204
#> 118 Population by age and sex (sample tabulation) 141 204
#> 119 Population by age and sex (sample tabulation) 141 204
#> 120 Population by age and sex (sample tabulation) 141 204
#> 121 Population by age and sex (sample tabulation) 141 204
#> 122 Population by age and sex (sample tabulation) 141 204
#> 123 Population by age and sex (sample tabulation) 141 204
#> 124 Population by age and sex (sample tabulation) 141 204
#> 125 Population by age and sex (sample tabulation) 141 204
#> 126 Population by age and sex (sample tabulation) 141 204
#> 127 Population by age and sex (sample tabulation) 141 204
#> 128 Population by age and sex (sample tabulation) 141 204
#> 129 Population by age and sex (sample tabulation) 141 204
#> 130 Population by age and sex (sample tabulation) 141 204
#> 131 Population by age and sex (sample tabulation) 141 204
#> 132 Population by age and sex (sample tabulation) 141 204
#> 133 Population by age and sex (sample tabulation) 141 204
#> 134 Population by age and sex (sample tabulation) 141 204
#> 135 Population by age and sex (sample tabulation) 141 204
#> 136 Population by age and sex (sample tabulation) 141 204
#> 137 Population by age and sex (sample tabulation) 141 204
#> 138 Population by age and sex (sample tabulation) 141 204
#> 139 Population by age and sex (sample tabulation) 141 204
#> 140 Population by age and sex (sample tabulation) 141 204
#> 141 Population by age and sex (sample tabulation) 141 204
#> 142 Population by age and sex (sample tabulation) 141 204
#> 143 Population by age and sex (sample tabulation) 141 204
#> 144 Population by age and sex (sample tabulation) 141 204
#> 145 Population by age and sex (sample tabulation) 141 204
#> 146 Population by age and sex (sample tabulation) 141 204
#> 147 Population by age and sex (sample tabulation) 141 204
#> 148 Population by age and sex (sample tabulation) 141 204
#> 149 Population by age and sex (sample tabulation) 141 204
#> 150 Population by age and sex (sample tabulation) 141 204
#> 151 Population by age and sex (sample tabulation) 141 204
#> 152 Population by age and sex (sample tabulation) 141 204
#> 153 Population by age and sex (sample tabulation) 141 204
#> 154 Population by age and sex (sample tabulation) 141 204
#> 155 Population by age and sex (sample tabulation) 141 204
#> 156 Population by age and sex (sample tabulation) 141 204
#> 157 Population by age and sex (sample tabulation) 141 204
#> 158 Population by age and sex (sample tabulation) 141 204
#> 159 Population by age and sex (sample tabulation) 141 204
#> 160 Population by age and sex (sample tabulation) 141 204
#> 161 Population by age and sex (sample tabulation) 141 204
#> 162 Population by age and sex (sample tabulation) 141 204
#> 163 Population by age and sex (sample tabulation) 141 204
#> 164 Population by age and sex (sample tabulation) 141 204
#> 165 Population by age and sex (sample tabulation) 141 204
#> 166 Population by age and sex (sample tabulation) 141 204
#> 167 Population by age and sex (sample tabulation) 141 204
#> 168 Population by age and sex (sample tabulation) 141 204
#> 169 Population by age and sex (sample tabulation) 141 204
#> 170 Population by age and sex (sample tabulation) 141 204
#> 171 Population by age and sex (sample tabulation) 141 204
#> 172 Population by age and sex (sample tabulation) 141 204
#> 173 Population by age and sex (sample tabulation) 141 204
#> 174 Population by age and sex (sample tabulation) 141 204
#> 175 Population by age and sex (sample tabulation) 141 204
#> 176 Population by age and sex (sample tabulation) 141 204
#> 177 Population by age and sex (sample tabulation) 141 204
#> 178 Population by age and sex (sample tabulation) 141 204
#> 179 Population by age and sex (sample tabulation) 141 204
#> 180 Population by age and sex (sample tabulation) 141 204
#> 181 Population by age and sex (sample tabulation) 141 204
#> 182 Population by age and sex (sample tabulation) 141 204
#> 183 Population by age and sex (sample tabulation) 141 204
#> 184 Population by age and sex (sample tabulation) 141 204
#> 185 Population by age and sex (sample tabulation) 141 204
#> 186 Population by age and sex (sample tabulation) 141 204
#> 187 Population by age and sex (sample tabulation) 141 204
#> 188 Population by age and sex (sample tabulation) 141 204
#> 189 Population by age and sex (sample tabulation) 141 204
#> 190 Population by age and sex (sample tabulation) 141 204
#> 191 Population by age and sex (sample tabulation) 141 204
#> 192 Population by age and sex (sample tabulation) 141 204
#> 193 Population by age and sex (sample tabulation) 141 204
#> 194 Population by age and sex (sample tabulation) 141 204
#> 195 Population by age and sex (sample tabulation) 141 204
#> 196 Population by age and sex (sample tabulation) 141 204
#> 197 Population by age and sex (sample tabulation) 141 204
#> 198 Population by age and sex (sample tabulation) 141 204
#> 199 Population by age and sex (sample tabulation) 141 204
#> 200 Population by age and sex (sample tabulation) 141 204
#> 201 Population by age and sex (sample tabulation) 141 204
#> 202 Population by age and sex (sample tabulation) 141 204
#> 203 Population by age and sex (sample tabulation) 141 204
#> 204 Population by age and sex (sample tabulation) 141 204
#> 205 Population by age and sex (sample tabulation) 141 204
#> 206 Population by age and sex (sample tabulation) 141 204
#> 207 Population by age and sex (sample tabulation) 141 204
#> 208 Population by age and sex (sample tabulation) 141 204
#> 209 Population by age and sex (sample tabulation) 141 204
#> 210 Population by age and sex (sample tabulation) 141 204
#> 211 Population by age and sex (sample tabulation) 141 204
#> 212 Population by age and sex (sample tabulation) 141 204
#> 213 Population by age and sex (sample tabulation) 141 204
#> 214 Population by age and sex (sample tabulation) 141 204
#> 215 Population by age and sex (sample tabulation) 141 204
#> 216 Population by age and sex (sample tabulation) 141 204
#> 217 Population by age and sex (sample tabulation) 141 204
#> 218 Population by age and sex (sample tabulation) 141 204
#> 219 Population by age and sex (sample tabulation) 141 204
#> 220 Population by age and sex (sample tabulation) 141 204
#> 221 Population by age and sex (sample tabulation) 141 204
#> 222 Population by age and sex (sample tabulation) 141 204
#> 223 Population by age and sex (sample tabulation) 141 204
#> 224 Population by age and sex (sample tabulation) 141 204
#> 225 Population by age and sex (sample tabulation) 141 204
#> 226 Population by age and sex (sample tabulation) 141 204
#> 227 Population by age and sex (sample tabulation) 141 204
#> 228 Population by age and sex (sample tabulation) 141 204
#> 229 Direct 16 11
#> 230 Direct 16 11
#> 231 Direct 16 11
#> 232 Direct 16 11
#> 233 Direct 16 11
#> 234 Direct 16 11
#> 235 Direct 16 11
#> ModelType ModelPatternFamilyName ModelPatternFamilyID
#> 1 Not applicable Not applicable 15
#> 2 Not applicable Not applicable 15
#> 3 Not applicable Not applicable 15
#> 4 Not applicable Not applicable 15
#> 5 Not applicable Not applicable 15
#> 6 Not applicable Not applicable 15
#> 7 Not applicable Not applicable 15
#> 8 Not applicable Not applicable 15
#> 9 Not applicable Not applicable 15
#> 10 Not applicable Not applicable 15
#> 11 Not applicable Not applicable 15
#> 12 Not applicable Not applicable 15
#> 13 Not applicable Not applicable 15
#> 14 Not applicable Not applicable 15
#> 15 Not applicable Not applicable 15
#> 16 Not applicable Not applicable 15
#> 17 Not applicable Not applicable 15
#> 18 Not applicable Not applicable 15
#> 19 Not applicable Not applicable 15
#> 20 Not applicable Not applicable 15
#> 21 Not applicable Not applicable 15
#> 22 Not applicable Not applicable 15
#> 23 Not applicable Not applicable 15
#> 24 Not applicable Not applicable 15
#> 25 Not applicable Not applicable 15
#> 26 Not applicable Not applicable 15
#> 27 Not applicable Not applicable 15
#> 28 Not applicable Not applicable 15
#> 29 Not applicable Not applicable 15
#> 30 Not applicable Not applicable 15
#> 31 Not applicable Not applicable 15
#> 32 Not applicable Not applicable 15
#> 33 Not applicable Not applicable 15
#> 34 Not applicable Not applicable 15
#> 35 Not applicable Not applicable 15
#> 36 Not applicable Not applicable 15
#> 37 Not applicable Not applicable 15
#> 38 Not applicable Not applicable 15
#> 39 Not applicable Not applicable 15
#> 40 Not applicable Not applicable 15
#> 41 Not applicable Not applicable 15
#> 42 Not applicable Not applicable 15
#> 43 Not applicable Not applicable 15
#> 44 Not applicable Not applicable 15
#> 45 Not applicable Not applicable 15
#> 46 Not applicable Not applicable 15
#> 47 Not applicable Not applicable 15
#> 48 Not applicable Not applicable 15
#> 49 Not applicable Not applicable 15
#> 50 Not applicable Not applicable 15
#> 51 Not applicable Not applicable 15
#> 52 Not applicable Not applicable 15
#> 53 Not applicable Not applicable 15
#> 54 Not applicable Not applicable 15
#> 55 Not applicable Not applicable 15
#> 56 Not applicable Not applicable 15
#> 57 Not applicable Not applicable 15
#> 58 Not applicable Not applicable 15
#> 59 Not applicable Not applicable 15
#> 60 Not applicable Not applicable 15
#> 61 Not applicable Not applicable 15
#> 62 Not applicable Not applicable 15
#> 63 Not applicable Not applicable 15
#> 64 Not applicable Not applicable 15
#> 65 Not applicable Not applicable 15
#> 66 Not applicable Not applicable 15
#> 67 Not applicable Not applicable 15
#> 68 Not applicable Not applicable 15
#> 69 Not applicable Not applicable 15
#> 70 Not applicable Not applicable 15
#> 71 Not applicable Not applicable 15
#> 72 Not applicable Not applicable 15
#> 73 Not applicable Not applicable 15
#> 74 Not applicable Not applicable 15
#> 75 Not applicable Not applicable 15
#> 76 Not applicable Not applicable 15
#> 77 Not applicable Not applicable 15
#> 78 Not applicable Not applicable 15
#> 79 Not applicable Not applicable 15
#> 80 Not applicable Not applicable 15
#> 81 Not applicable Not applicable 15
#> 82 Not applicable Not applicable 15
#> 83 Not applicable Not applicable 15
#> 84 Not applicable Not applicable 15
#> 85 Not applicable Not applicable 15
#> 86 Not applicable Not applicable 15
#> 87 Not applicable Not applicable 15
#> 88 Not applicable Not applicable 15
#> 89 Not applicable Not applicable 15
#> 90 Not applicable Not applicable 15
#> 91 Not applicable Not applicable 15
#> 92 Not applicable Not applicable 15
#> 93 Not applicable Not applicable 15
#> 94 Not applicable Not applicable 15
#> 95 Not applicable Not applicable 15
#> 96 Not applicable Not applicable 15
#> 97 Not applicable Not applicable 15
#> 98 Not applicable Not applicable 15
#> 99 Not applicable Not applicable 15
#> 100 Not applicable Not applicable 15
#> 101 Not applicable Not applicable 15
#> 102 Not applicable Not applicable 15
#> 103 Not applicable Not applicable 15
#> 104 Not applicable Not applicable 15
#> 105 Not applicable Not applicable 15
#> 106 Not applicable Not applicable 15
#> 107 Not applicable Not applicable 15
#> 108 Not applicable Not applicable 15
#> 109 Not applicable Not applicable 15
#> 110 Not applicable Not applicable 15
#> 111 Not applicable Not applicable 15
#> 112 Not applicable Not applicable 15
#> 113 Not applicable Not applicable 15
#> 114 Not applicable Not applicable 15
#> 115 Not applicable Not applicable 15
#> 116 Not applicable Not applicable 15
#> 117 Not applicable Not applicable 15
#> 118 Not applicable Not applicable 15
#> 119 Not applicable Not applicable 15
#> 120 Not applicable Not applicable 15
#> 121 Not applicable Not applicable 15
#> 122 Not applicable Not applicable 15
#> 123 Not applicable Not applicable 15
#> 124 Not applicable Not applicable 15
#> 125 Not applicable Not applicable 15
#> 126 Not applicable Not applicable 15
#> 127 Not applicable Not applicable 15
#> 128 Not applicable Not applicable 15
#> 129 Not applicable Not applicable 15
#> 130 Not applicable Not applicable 15
#> 131 Not applicable Not applicable 15
#> 132 Not applicable Not applicable 15
#> 133 Not applicable Not applicable 15
#> 134 Not applicable Not applicable 15
#> 135 Not applicable Not applicable 15
#> 136 Not applicable Not applicable 15
#> 137 Not applicable Not applicable 15
#> 138 Not applicable Not applicable 15
#> 139 Not applicable Not applicable 15
#> 140 Not applicable Not applicable 15
#> 141 Not applicable Not applicable 15
#> 142 Not applicable Not applicable 15
#> 143 Not applicable Not applicable 15
#> 144 Not applicable Not applicable 15
#> 145 Not applicable Not applicable 15
#> 146 Not applicable Not applicable 15
#> 147 Not applicable Not applicable 15
#> 148 Not applicable Not applicable 15
#> 149 Not applicable Not applicable 15
#> 150 Not applicable Not applicable 15
#> 151 Not applicable Not applicable 15
#> 152 Not applicable Not applicable 15
#> 153 Not applicable Not applicable 15
#> 154 Not applicable Not applicable 15
#> 155 Not applicable Not applicable 15
#> 156 Not applicable Not applicable 15
#> 157 Not applicable Not applicable 15
#> 158 Not applicable Not applicable 15
#> 159 Not applicable Not applicable 15
#> 160 Not applicable Not applicable 15
#> 161 Not applicable Not applicable 15
#> 162 Not applicable Not applicable 15
#> 163 Not applicable Not applicable 15
#> 164 Not applicable Not applicable 15
#> 165 Not applicable Not applicable 15
#> 166 Not applicable Not applicable 15
#> 167 Not applicable Not applicable 15
#> 168 Not applicable Not applicable 15
#> 169 Not applicable Not applicable 15
#> 170 Not applicable Not applicable 15
#> 171 Not applicable Not applicable 15
#> 172 Not applicable Not applicable 15
#> 173 Not applicable Not applicable 15
#> 174 Not applicable Not applicable 15
#> 175 Not applicable Not applicable 15
#> 176 Not applicable Not applicable 15
#> 177 Not applicable Not applicable 15
#> 178 Not applicable Not applicable 15
#> 179 Not applicable Not applicable 15
#> 180 Not applicable Not applicable 15
#> 181 Not applicable Not applicable 15
#> 182 Not applicable Not applicable 15
#> 183 Not applicable Not applicable 15
#> 184 Not applicable Not applicable 15
#> 185 Not applicable Not applicable 15
#> 186 Not applicable Not applicable 15
#> 187 Not applicable Not applicable 15
#> 188 Not applicable Not applicable 15
#> 189 Not applicable Not applicable 15
#> 190 Not applicable Not applicable 15
#> 191 Not applicable Not applicable 15
#> 192 Not applicable Not applicable 15
#> 193 Not applicable Not applicable 15
#> 194 Not applicable Not applicable 15
#> 195 Not applicable Not applicable 15
#> 196 Not applicable Not applicable 15
#> 197 Not applicable Not applicable 15
#> 198 Not applicable Not applicable 15
#> 199 Not applicable Not applicable 15
#> 200 Not applicable Not applicable 15
#> 201 Not applicable Not applicable 15
#> 202 Not applicable Not applicable 15
#> 203 Not applicable Not applicable 15
#> 204 Not applicable Not applicable 15
#> 205 Not applicable Not applicable 15
#> 206 Not applicable Not applicable 15
#> 207 Not applicable Not applicable 15
#> 208 Not applicable Not applicable 15
#> 209 Not applicable Not applicable 15
#> 210 Not applicable Not applicable 15
#> 211 Not applicable Not applicable 15
#> 212 Not applicable Not applicable 15
#> 213 Not applicable Not applicable 15
#> 214 Not applicable Not applicable 15
#> 215 Not applicable Not applicable 15
#> 216 Not applicable Not applicable 15
#> 217 Not applicable Not applicable 15
#> 218 Not applicable Not applicable 15
#> 219 Not applicable Not applicable 15
#> 220 Not applicable Not applicable 15
#> 221 Not applicable Not applicable 15
#> 222 Not applicable Not applicable 15
#> 223 Not applicable Not applicable 15
#> 224 Not applicable Not applicable 15
#> 225 Not applicable Not applicable 15
#> 226 Not applicable Not applicable 15
#> 227 Not applicable Not applicable 15
#> 228 Not applicable Not applicable 15
#> 229 Not applicable Not applicable 15
#> 230 Not applicable Not applicable 15
#> 231 Not applicable Not applicable 15
#> 232 Not applicable Not applicable 15
#> 233 Not applicable Not applicable 15
#> 234 Not applicable Not applicable 15
#> 235 Not applicable Not applicable 15
#> ModelPatternName ModelPatternID DataReliabilityName
#> 1 Not applicable (Direct) 40 Fair
#> 2 Not applicable (Direct) 40 Fair
#> 3 Not applicable (Direct) 40 Fair
#> 4 Not applicable (Direct) 40 Fair
#> 5 Not applicable (Direct) 40 Fair
#> 6 Not applicable (Direct) 40 Fair
#> 7 Not applicable (Direct) 40 Fair
#> 8 Not applicable (Direct) 40 Fair
#> 9 Not applicable (Direct) 40 Fair
#> 10 Not applicable (Direct) 40 Fair
#> 11 Not applicable (Direct) 40 Fair
#> 12 Not applicable (Direct) 40 Fair
#> 13 Not applicable (Direct) 40 Fair
#> 14 Not applicable (Direct) 40 Fair
#> 15 Not applicable (Direct) 40 Fair
#> 16 Not applicable (Direct) 40 Fair
#> 17 Not applicable (Direct) 40 Fair
#> 18 Not applicable (Direct) 40 Fair
#> 19 Not applicable (Direct) 40 Fair
#> 20 Not applicable (Direct) 40 Fair
#> 21 Not applicable (Direct) 40 Fair
#> 22 Not applicable (Direct) 40 Fair
#> 23 Not applicable (Direct) 40 Fair
#> 24 Not applicable (Direct) 40 Fair
#> 25 Not applicable (Direct) 40 Fair
#> 26 Not applicable (Direct) 40 Fair
#> 27 Not applicable (Direct) 40 Fair
#> 28 Not applicable (Direct) 40 Fair
#> 29 Not applicable (Direct) 40 Fair
#> 30 Not applicable (Direct) 40 Fair
#> 31 Not applicable (Direct) 40 Fair
#> 32 Not applicable (Direct) 40 Fair
#> 33 Not applicable (Direct) 40 Fair
#> 34 Not applicable (Direct) 40 Fair
#> 35 Not applicable (Direct) 40 Fair
#> 36 Not applicable (Direct) 40 Fair
#> 37 Not applicable (Direct) 40 Fair
#> 38 Not applicable (Direct) 40 Fair
#> 39 Not applicable (Direct) 40 Fair
#> 40 Not applicable (Direct) 40 Fair
#> 41 Not applicable (Direct) 40 Fair
#> 42 Not applicable (Direct) 40 Fair
#> 43 Not applicable (Direct) 40 Fair
#> 44 Not applicable (Direct) 40 Fair
#> 45 Not applicable (Direct) 40 Fair
#> 46 Not applicable (Direct) 40 Fair
#> 47 Not applicable (Direct) 40 Fair
#> 48 Not applicable (Direct) 40 Fair
#> 49 Not applicable (Direct) 40 Fair
#> 50 Not applicable (Direct) 40 Fair
#> 51 Not applicable (Direct) 40 Fair
#> 52 Not applicable (Direct) 40 Fair
#> 53 Not applicable (Direct) 40 Fair
#> 54 Not applicable (Direct) 40 Fair
#> 55 Not applicable (Direct) 40 Fair
#> 56 Not applicable (Direct) 40 Fair
#> 57 Not applicable (Direct) 40 Fair
#> 58 Not applicable (Direct) 40 Fair
#> 59 Not applicable (Direct) 40 Fair
#> 60 Not applicable (Direct) 40 Fair
#> 61 Not applicable (Direct) 40 Fair
#> 62 Not applicable (Direct) 40 Fair
#> 63 Not applicable (Direct) 40 Fair
#> 64 Not applicable (Direct) 40 Fair
#> 65 Not applicable (Direct) 40 Fair
#> 66 Not applicable (Direct) 40 Fair
#> 67 Not applicable (Direct) 40 Fair
#> 68 Not applicable (Direct) 40 Fair
#> 69 Not applicable (Direct) 40 Fair
#> 70 Not applicable (Direct) 40 Fair
#> 71 Not applicable (Direct) 40 Fair
#> 72 Not applicable (Direct) 40 Fair
#> 73 Not applicable (Direct) 40 Fair
#> 74 Not applicable (Direct) 40 Fair
#> 75 Not applicable (Direct) 40 Fair
#> 76 Not applicable (Direct) 40 Fair
#> 77 Not applicable (Direct) 40 Fair
#> 78 Not applicable (Direct) 40 Fair
#> 79 Not applicable (Direct) 40 Fair
#> 80 Not applicable (Direct) 40 Fair
#> 81 Not applicable (Direct) 40 Fair
#> 82 Not applicable (Direct) 40 Fair
#> 83 Not applicable (Direct) 40 Fair
#> 84 Not applicable (Direct) 40 Fair
#> 85 Not applicable (Direct) 40 Fair
#> 86 Not applicable (Direct) 40 Fair
#> 87 Not applicable (Direct) 40 Fair
#> 88 Not applicable (Direct) 40 Fair
#> 89 Not applicable (Direct) 40 Fair
#> 90 Not applicable (Direct) 40 Fair
#> 91 Not applicable (Direct) 40 Fair
#> 92 Not applicable (Direct) 40 Fair
#> 93 Not applicable (Direct) 40 Fair
#> 94 Not applicable (Direct) 40 Fair
#> 95 Not applicable (Direct) 40 Fair
#> 96 Not applicable (Direct) 40 Fair
#> 97 Not applicable (Direct) 40 Fair
#> 98 Not applicable (Direct) 40 Fair
#> 99 Not applicable (Direct) 40 Fair
#> 100 Not applicable (Direct) 40 Fair
#> 101 Not applicable (Direct) 40 Fair
#> 102 Not applicable (Direct) 40 Fair
#> 103 Not applicable (Direct) 40 Fair
#> 104 Not applicable (Direct) 40 Fair
#> 105 Not applicable (Direct) 40 Fair
#> 106 Not applicable (Direct) 40 Fair
#> 107 Not applicable (Direct) 40 Fair
#> 108 Not applicable (Direct) 40 Fair
#> 109 Not applicable (Direct) 40 Fair
#> 110 Not applicable (Direct) 40 Fair
#> 111 Not applicable (Direct) 40 Fair
#> 112 Not applicable (Direct) 40 Fair
#> 113 Not applicable (Direct) 40 Fair
#> 114 Not applicable (Direct) 40 Fair
#> 115 Not applicable (Direct) 40 Fair
#> 116 Not applicable (Direct) 40 Fair
#> 117 Not applicable (Direct) 40 Fair
#> 118 Not applicable (Direct) 40 Fair
#> 119 Not applicable (Direct) 40 Fair
#> 120 Not applicable (Direct) 40 Fair
#> 121 Not applicable (Direct) 40 Fair
#> 122 Not applicable (Direct) 40 Fair
#> 123 Not applicable (Direct) 40 Fair
#> 124 Not applicable (Direct) 40 Fair
#> 125 Not applicable (Direct) 40 Fair
#> 126 Not applicable (Direct) 40 Fair
#> 127 Not applicable (Direct) 40 Fair
#> 128 Not applicable (Direct) 40 Fair
#> 129 Not applicable (Direct) 40 Fair
#> 130 Not applicable (Direct) 40 Fair
#> 131 Not applicable (Direct) 40 Fair
#> 132 Not applicable (Direct) 40 Fair
#> 133 Not applicable (Direct) 40 Fair
#> 134 Not applicable (Direct) 40 Fair
#> 135 Not applicable (Direct) 40 Fair
#> 136 Not applicable (Direct) 40 Fair
#> 137 Not applicable (Direct) 40 Fair
#> 138 Not applicable (Direct) 40 Fair
#> 139 Not applicable (Direct) 40 Fair
#> 140 Not applicable (Direct) 40 Fair
#> 141 Not applicable (Direct) 40 Fair
#> 142 Not applicable (Direct) 40 Fair
#> 143 Not applicable (Direct) 40 Fair
#> 144 Not applicable (Direct) 40 Fair
#> 145 Not applicable (Direct) 40 Fair
#> 146 Not applicable (Direct) 40 Fair
#> 147 Not applicable (Direct) 40 Fair
#> 148 Not applicable (Direct) 40 Fair
#> 149 Not applicable (Direct) 40 Fair
#> 150 Not applicable (Direct) 40 Fair
#> 151 Not applicable (Direct) 40 Fair
#> 152 Not applicable (Direct) 40 Fair
#> 153 Not applicable (Direct) 40 Fair
#> 154 Not applicable (Direct) 40 Fair
#> 155 Not applicable (Direct) 40 Fair
#> 156 Not applicable (Direct) 40 Fair
#> 157 Not applicable (Direct) 40 Fair
#> 158 Not applicable (Direct) 40 Fair
#> 159 Not applicable (Direct) 40 Fair
#> 160 Not applicable (Direct) 40 Fair
#> 161 Not applicable (Direct) 40 Fair
#> 162 Not applicable (Direct) 40 Fair
#> 163 Not applicable (Direct) 40 Fair
#> 164 Not applicable (Direct) 40 Fair
#> 165 Not applicable (Direct) 40 Fair
#> 166 Not applicable (Direct) 40 Fair
#> 167 Not applicable (Direct) 40 Fair
#> 168 Not applicable (Direct) 40 Fair
#> 169 Not applicable (Direct) 40 Fair
#> 170 Not applicable (Direct) 40 Fair
#> 171 Not applicable (Direct) 40 Fair
#> 172 Not applicable (Direct) 40 Fair
#> 173 Not applicable (Direct) 40 Fair
#> 174 Not applicable (Direct) 40 Fair
#> 175 Not applicable (Direct) 40 Fair
#> 176 Not applicable (Direct) 40 Fair
#> 177 Not applicable (Direct) 40 Fair
#> 178 Not applicable (Direct) 40 Fair
#> 179 Not applicable (Direct) 40 Fair
#> 180 Not applicable (Direct) 40 Fair
#> 181 Not applicable (Direct) 40 Fair
#> 182 Not applicable (Direct) 40 Fair
#> 183 Not applicable (Direct) 40 Fair
#> 184 Not applicable (Direct) 40 Fair
#> 185 Not applicable (Direct) 40 Fair
#> 186 Not applicable (Direct) 40 Fair
#> 187 Not applicable (Direct) 40 Fair
#> 188 Not applicable (Direct) 40 Fair
#> 189 Not applicable (Direct) 40 Fair
#> 190 Not applicable (Direct) 40 Fair
#> 191 Not applicable (Direct) 40 Fair
#> 192 Not applicable (Direct) 40 Fair
#> 193 Not applicable (Direct) 40 Fair
#> 194 Not applicable (Direct) 40 Fair
#> 195 Not applicable (Direct) 40 Fair
#> 196 Not applicable (Direct) 40 Fair
#> 197 Not applicable (Direct) 40 Fair
#> 198 Not applicable (Direct) 40 Fair
#> 199 Not applicable (Direct) 40 Fair
#> 200 Not applicable (Direct) 40 Fair
#> 201 Not applicable (Direct) 40 Fair
#> 202 Not applicable (Direct) 40 Fair
#> 203 Not applicable (Direct) 40 Fair
#> 204 Not applicable (Direct) 40 Fair
#> 205 Not applicable (Direct) 40 Fair
#> 206 Not applicable (Direct) 40 Fair
#> 207 Not applicable (Direct) 40 Fair
#> 208 Not applicable (Direct) 40 Fair
#> 209 Not applicable (Direct) 40 Fair
#> 210 Not applicable (Direct) 40 Fair
#> 211 Not applicable (Direct) 40 Fair
#> 212 Not applicable (Direct) 40 Fair
#> 213 Not applicable (Direct) 40 Fair
#> 214 Not applicable (Direct) 40 Fair
#> 215 Not applicable (Direct) 40 Fair
#> 216 Not applicable (Direct) 40 Fair
#> 217 Not applicable (Direct) 40 Fair
#> 218 Not applicable (Direct) 40 Fair
#> 219 Not applicable (Direct) 40 Fair
#> 220 Not applicable (Direct) 40 Fair
#> 221 Not applicable (Direct) 40 Fair
#> 222 Not applicable (Direct) 40 Fair
#> 223 Not applicable (Direct) 40 Fair
#> 224 Not applicable (Direct) 40 Fair
#> 225 Not applicable (Direct) 40 Fair
#> 226 Not applicable (Direct) 40 Fair
#> 227 Not applicable (Direct) 40 Fair
#> 228 Not applicable (Direct) 40 Fair
#> 229 Not applicable (Direct) 40 Fair
#> 230 Not applicable (Direct) 40 Fair
#> 231 Not applicable (Direct) 40 Fair
#> 232 Not applicable (Direct) 40 Fair
#> 233 Not applicable (Direct) 40 Fair
#> 234 Not applicable (Direct) 40 Fair
#> 235 Not applicable (Direct) 40 Fair
#> DataReliabilityID DataReliabilitySort PeriodTypeName PeriodTypeID
#> 1 5 2 RP 6
#> 2 5 2 RP 6
#> 3 5 2 RP 6
#> 4 5 2 RP 6
#> 5 5 2 RP 6
#> 6 5 2 RP 6
#> 7 5 2 RP 6
#> 8 5 2 RP 6
#> 9 5 2 RP 6
#> 10 5 2 RP 6
#> 11 5 2 RP 6
#> 12 5 2 RP 6
#> 13 5 2 RP 6
#> 14 5 2 RP 6
#> 15 5 2 RP 6
#> 16 5 2 RP 6
#> 17 5 2 RP 6
#> 18 5 2 RP 6
#> 19 5 2 RP 6
#> 20 5 2 RP 6
#> 21 5 2 RP 6
#> 22 5 2 RP 6
#> 23 5 2 RP 6
#> 24 5 2 RP 6
#> 25 5 2 RP 6
#> 26 5 2 RP 6
#> 27 5 2 RP 6
#> 28 5 2 RP 6
#> 29 5 2 RP 6
#> 30 5 2 RP 6
#> 31 5 2 RP 6
#> 32 5 2 RP 6
#> 33 5 2 RP 6
#> 34 5 2 RP 6
#> 35 5 2 RP 6
#> 36 5 2 RP 6
#> 37 5 2 RP 6
#> 38 5 2 RP 6
#> 39 5 2 RP 6
#> 40 5 2 RP 6
#> 41 5 2 RP 6
#> 42 5 2 RP 6
#> 43 5 2 RP 6
#> 44 5 2 RP 6
#> 45 5 2 RP 6
#> 46 5 2 RP 6
#> 47 5 2 RP 6
#> 48 5 2 RP 6
#> 49 5 2 RP 6
#> 50 5 2 RP 6
#> 51 5 2 RP 6
#> 52 5 2 RP 6
#> 53 5 2 RP 6
#> 54 5 2 RP 6
#> 55 5 2 RP 6
#> 56 5 2 RP 6
#> 57 5 2 RP 6
#> 58 5 2 RP 6
#> 59 5 2 RP 6
#> 60 5 2 RP 6
#> 61 5 2 RP 6
#> 62 5 2 RP 6
#> 63 5 2 RP 6
#> 64 5 2 RP 6
#> 65 5 2 RP 6
#> 66 5 2 RP 6
#> 67 5 2 RP 6
#> 68 5 2 RP 6
#> 69 5 2 RP 6
#> 70 5 2 RP 6
#> 71 5 2 RP 6
#> 72 5 2 RP 6
#> 73 5 2 RP 6
#> 74 5 2 RP 6
#> 75 5 2 RP 6
#> 76 5 2 RP 6
#> 77 5 2 RP 6
#> 78 5 2 RP 6
#> 79 5 2 RP 6
#> 80 5 2 RP 6
#> 81 5 2 RP 6
#> 82 5 2 RP 6
#> 83 5 2 RP 6
#> 84 5 2 RP 6
#> 85 5 2 RP 6
#> 86 5 2 RP 6
#> 87 5 2 RP 6
#> 88 5 2 RP 6
#> 89 5 2 RP 6
#> 90 5 2 RP 6
#> 91 5 2 RP 6
#> 92 5 2 RP 6
#> 93 5 2 RP 6
#> 94 5 2 RP 6
#> 95 5 2 RP 6
#> 96 5 2 RP 6
#> 97 5 2 RP 6
#> 98 5 2 RP 6
#> 99 5 2 RP 6
#> 100 5 2 RP 6
#> 101 5 2 RP 6
#> 102 5 2 RP 6
#> 103 5 2 RP 6
#> 104 5 2 RP 6
#> 105 5 2 RP 6
#> 106 5 2 RP 6
#> 107 5 2 RP 6
#> 108 5 2 RP 6
#> 109 5 2 RP 6
#> 110 5 2 RP 6
#> 111 5 2 RP 6
#> 112 5 2 RP 6
#> 113 5 2 RP 6
#> 114 5 2 RP 6
#> 115 5 2 RP 6
#> 116 5 2 RP 6
#> 117 5 2 RP 6
#> 118 5 2 RP 6
#> 119 5 2 RP 6
#> 120 5 2 RP 6
#> 121 5 2 RP 6
#> 122 5 2 RP 6
#> 123 5 2 RP 6
#> 124 5 2 RP 6
#> 125 5 2 RP 6
#> 126 5 2 RP 6
#> 127 5 2 RP 6
#> 128 5 2 RP 6
#> 129 5 2 RP 6
#> 130 5 2 RP 6
#> 131 5 2 RP 6
#> 132 5 2 RP 6
#> 133 5 2 RP 6
#> 134 5 2 RP 6
#> 135 5 2 RP 6
#> 136 5 2 RP 6
#> 137 5 2 RP 6
#> 138 5 2 RP 6
#> 139 5 2 RP 6
#> 140 5 2 RP 6
#> 141 5 2 RP 6
#> 142 5 2 RP 6
#> 143 5 2 RP 6
#> 144 5 2 RP 6
#> 145 5 2 RP 6
#> 146 5 2 RP 6
#> 147 5 2 RP 6
#> 148 5 2 RP 6
#> 149 5 2 RP 6
#> 150 5 2 RP 6
#> 151 5 2 RP 6
#> 152 5 2 RP 6
#> 153 5 2 RP 6
#> 154 5 2 RP 6
#> 155 5 2 RP 6
#> 156 5 2 RP 6
#> 157 5 2 RP 6
#> 158 5 2 RP 6
#> 159 5 2 RP 6
#> 160 5 2 RP 6
#> 161 5 2 RP 6
#> 162 5 2 RP 6
#> 163 5 2 RP 6
#> 164 5 2 RP 6
#> 165 5 2 RP 6
#> 166 5 2 RP 6
#> 167 5 2 RP 6
#> 168 5 2 RP 6
#> 169 5 2 RP 6
#> 170 5 2 RP 6
#> 171 5 2 RP 6
#> 172 5 2 RP 6
#> 173 5 2 RP 6
#> 174 5 2 RP 6
#> 175 5 2 RP 6
#> 176 5 2 RP 6
#> 177 5 2 RP 6
#> 178 5 2 RP 6
#> 179 5 2 RP 6
#> 180 5 2 RP 6
#> 181 5 2 RP 6
#> 182 5 2 RP 6
#> 183 5 2 RP 6
#> 184 5 2 RP 6
#> 185 5 2 RP 6
#> 186 5 2 RP 6
#> 187 5 2 RP 6
#> 188 5 2 RP 6
#> 189 5 2 RP 6
#> 190 5 2 RP 6
#> 191 5 2 RP 6
#> 192 5 2 RP 6
#> 193 5 2 RP 6
#> 194 5 2 RP 6
#> 195 5 2 RP 6
#> 196 5 2 RP 6
#> 197 5 2 RP 6
#> 198 5 2 RP 6
#> 199 5 2 RP 6
#> 200 5 2 RP 6
#> 201 5 2 RP 6
#> 202 5 2 RP 6
#> 203 5 2 RP 6
#> 204 5 2 RP 6
#> 205 5 2 RP 6
#> 206 5 2 RP 6
#> 207 5 2 RP 6
#> 208 5 2 RP 6
#> 209 5 2 RP 6
#> 210 5 2 RP 6
#> 211 5 2 RP 6
#> 212 5 2 RP 6
#> 213 5 2 RP 6
#> 214 5 2 RP 6
#> 215 5 2 RP 6
#> 216 5 2 RP 6
#> 217 5 2 RP 6
#> 218 5 2 RP 6
#> 219 5 2 RP 6
#> 220 5 2 RP 6
#> 221 5 2 RP 6
#> 222 5 2 RP 6
#> 223 5 2 RP 6
#> 224 5 2 RP 6
#> 225 5 2 RP 6
#> 226 5 2 RP 6
#> 227 5 2 RP 6
#> 228 5 2 RP 6
#> 229 5 2 CR 3
#> 230 5 2 CR 3
#> 231 5 2 CR 3
#> 232 5 2 CR 3
#> 233 5 2 CR 3
#> 234 5 2 CR 3
#> 235 5 2 CR 3
#> PeriodGroupName PeriodGroupID PeriodStart PeriodEnd PeriodSpan PeriodMiddle
#> 1 0 433 0 0 0 0
#> 2 0 433 0 0 0 0
#> 3 0 433 0 0 0 0
#> 4 0 433 0 0 0 0
#> 5 0 433 0 0 0 0
#> 6 0 433 0 0 0 0
#> 7 0 433 0 0 0 0
#> 8 0 433 0 0 0 0
#> 9 0 433 0 0 0 0
#> 10 0 433 0 0 0 0
#> 11 0 433 0 0 0 0
#> 12 0 433 0 0 0 0
#> 13 0 433 0 0 0 0
#> 14 0 433 0 0 0 0
#> 15 0 433 0 0 0 0
#> 16 0 433 0 0 0 0
#> 17 0 433 0 0 0 0
#> 18 0 433 0 0 0 0
#> 19 0 433 0 0 0 0
#> 20 0 433 0 0 0 0
#> 21 0 433 0 0 0 0
#> 22 0 433 0 0 0 0
#> 23 0 433 0 0 0 0
#> 24 0 433 0 0 0 0
#> 25 0 433 0 0 0 0
#> 26 0 433 0 0 0 0
#> 27 0 433 0 0 0 0
#> 28 0 433 0 0 0 0
#> 29 0 433 0 0 0 0
#> 30 0 433 0 0 0 0
#> 31 0 433 0 0 0 0
#> 32 0 433 0 0 0 0
#> 33 0 433 0 0 0 0
#> 34 0 433 0 0 0 0
#> 35 0 433 0 0 0 0
#> 36 0 433 0 0 0 0
#> 37 0 433 0 0 0 0
#> 38 0 433 0 0 0 0
#> 39 0 433 0 0 0 0
#> 40 0 433 0 0 0 0
#> 41 0 433 0 0 0 0
#> 42 0 433 0 0 0 0
#> 43 0 433 0 0 0 0
#> 44 0 433 0 0 0 0
#> 45 0 433 0 0 0 0
#> 46 0 433 0 0 0 0
#> 47 0 433 0 0 0 0
#> 48 0 433 0 0 0 0
#> 49 0 433 0 0 0 0
#> 50 0 433 0 0 0 0
#> 51 0 433 0 0 0 0
#> 52 0 433 0 0 0 0
#> 53 0 433 0 0 0 0
#> 54 0 433 0 0 0 0
#> 55 0 433 0 0 0 0
#> 56 0 433 0 0 0 0
#> 57 0 433 0 0 0 0
#> 58 0 433 0 0 0 0
#> 59 0 433 0 0 0 0
#> 60 0 433 0 0 0 0
#> 61 0 433 0 0 0 0
#> 62 0 433 0 0 0 0
#> 63 0 433 0 0 0 0
#> 64 0 433 0 0 0 0
#> 65 0 433 0 0 0 0
#> 66 0 433 0 0 0 0
#> 67 0 433 0 0 0 0
#> 68 0 433 0 0 0 0
#> 69 0 433 0 0 0 0
#> 70 0 433 0 0 0 0
#> 71 0 433 0 0 0 0
#> 72 0 433 0 0 0 0
#> 73 0 433 0 0 0 0
#> 74 0 433 0 0 0 0
#> 75 0 433 0 0 0 0
#> 76 0 433 0 0 0 0
#> 77 0 433 0 0 0 0
#> 78 0 433 0 0 0 0
#> 79 0 433 0 0 0 0
#> 80 0 433 0 0 0 0
#> 81 0 433 0 0 0 0
#> 82 0 433 0 0 0 0
#> 83 0 433 0 0 0 0
#> 84 0 433 0 0 0 0
#> 85 0 433 0 0 0 0
#> 86 0 433 0 0 0 0
#> 87 0 433 0 0 0 0
#> 88 0 433 0 0 0 0
#> 89 0 433 0 0 0 0
#> 90 0 433 0 0 0 0
#> 91 0 433 0 0 0 0
#> 92 0 433 0 0 0 0
#> 93 0 433 0 0 0 0
#> 94 0 433 0 0 0 0
#> 95 0 433 0 0 0 0
#> 96 0 433 0 0 0 0
#> 97 0 433 0 0 0 0
#> 98 0 433 0 0 0 0
#> 99 0 433 0 0 0 0
#> 100 0 433 0 0 0 0
#> 101 0 433 0 0 0 0
#> 102 0 433 0 0 0 0
#> 103 0 433 0 0 0 0
#> 104 0 433 0 0 0 0
#> 105 0 433 0 0 0 0
#> 106 0 433 0 0 0 0
#> 107 0 433 0 0 0 0
#> 108 0 433 0 0 0 0
#> 109 0 433 0 0 0 0
#> 110 0 433 0 0 0 0
#> 111 0 433 0 0 0 0
#> 112 0 433 0 0 0 0
#> 113 0 433 0 0 0 0
#> 114 0 433 0 0 0 0
#> 115 0 433 0 0 0 0
#> 116 0 433 0 0 0 0
#> 117 0 433 0 0 0 0
#> 118 0 433 0 0 0 0
#> 119 0 433 0 0 0 0
#> 120 0 433 0 0 0 0
#> 121 0 433 0 0 0 0
#> 122 0 433 0 0 0 0
#> 123 0 433 0 0 0 0
#> 124 0 433 0 0 0 0
#> 125 0 433 0 0 0 0
#> 126 0 433 0 0 0 0
#> 127 0 433 0 0 0 0
#> 128 0 433 0 0 0 0
#> 129 0 433 0 0 0 0
#> 130 0 433 0 0 0 0
#> 131 0 433 0 0 0 0
#> 132 0 433 0 0 0 0
#> 133 0 433 0 0 0 0
#> 134 0 433 0 0 0 0
#> 135 0 433 0 0 0 0
#> 136 0 433 0 0 0 0
#> 137 0 433 0 0 0 0
#> 138 0 433 0 0 0 0
#> 139 0 433 0 0 0 0
#> 140 0 433 0 0 0 0
#> 141 0 433 0 0 0 0
#> 142 0 433 0 0 0 0
#> 143 0 433 0 0 0 0
#> 144 0 433 0 0 0 0
#> 145 0 433 0 0 0 0
#> 146 0 433 0 0 0 0
#> 147 0 433 0 0 0 0
#> 148 0 433 0 0 0 0
#> 149 0 433 0 0 0 0
#> 150 0 433 0 0 0 0
#> 151 0 433 0 0 0 0
#> 152 0 433 0 0 0 0
#> 153 0 433 0 0 0 0
#> 154 0 433 0 0 0 0
#> 155 0 433 0 0 0 0
#> 156 0 433 0 0 0 0
#> 157 0 433 0 0 0 0
#> 158 0 433 0 0 0 0
#> 159 0 433 0 0 0 0
#> 160 0 433 0 0 0 0
#> 161 0 433 0 0 0 0
#> 162 0 433 0 0 0 0
#> 163 0 433 0 0 0 0
#> 164 0 433 0 0 0 0
#> 165 0 433 0 0 0 0
#> 166 0 433 0 0 0 0
#> 167 0 433 0 0 0 0
#> 168 0 433 0 0 0 0
#> 169 0 433 0 0 0 0
#> 170 0 433 0 0 0 0
#> 171 0 433 0 0 0 0
#> 172 0 433 0 0 0 0
#> 173 0 433 0 0 0 0
#> 174 0 433 0 0 0 0
#> 175 0 433 0 0 0 0
#> 176 0 433 0 0 0 0
#> 177 0 433 0 0 0 0
#> 178 0 433 0 0 0 0
#> 179 0 433 0 0 0 0
#> 180 0 433 0 0 0 0
#> 181 0 433 0 0 0 0
#> 182 0 433 0 0 0 0
#> 183 0 433 0 0 0 0
#> 184 0 433 0 0 0 0
#> 185 0 433 0 0 0 0
#> 186 0 433 0 0 0 0
#> 187 0 433 0 0 0 0
#> 188 0 433 0 0 0 0
#> 189 0 433 0 0 0 0
#> 190 0 433 0 0 0 0
#> 191 0 433 0 0 0 0
#> 192 0 433 0 0 0 0
#> 193 0 433 0 0 0 0
#> 194 0 433 0 0 0 0
#> 195 0 433 0 0 0 0
#> 196 0 433 0 0 0 0
#> 197 0 433 0 0 0 0
#> 198 0 433 0 0 0 0
#> 199 0 433 0 0 0 0
#> 200 0 433 0 0 0 0
#> 201 0 433 0 0 0 0
#> 202 0 433 0 0 0 0
#> 203 0 433 0 0 0 0
#> 204 0 433 0 0 0 0
#> 205 0 433 0 0 0 0
#> 206 0 433 0 0 0 0
#> 207 0 433 0 0 0 0
#> 208 0 433 0 0 0 0
#> 209 0 433 0 0 0 0
#> 210 0 433 0 0 0 0
#> 211 0 433 0 0 0 0
#> 212 0 433 0 0 0 0
#> 213 0 433 0 0 0 0
#> 214 0 433 0 0 0 0
#> 215 0 433 0 0 0 0
#> 216 0 433 0 0 0 0
#> 217 0 433 0 0 0 0
#> 218 0 433 0 0 0 0
#> 219 0 433 0 0 0 0
#> 220 0 433 0 0 0 0
#> 221 0 433 0 0 0 0
#> 222 0 433 0 0 0 0
#> 223 0 433 0 0 0 0
#> 224 0 433 0 0 0 0
#> 225 0 433 0 0 0 0
#> 226 0 433 0 0 0 0
#> 227 0 433 0 0 0 0
#> 228 0 433 0 0 0 0
#> 229 Not applicable 10 0 0 0 0
#> 230 Not applicable 10 0 0 0 0
#> 231 Not applicable 10 0 0 0 0
#> 232 Not applicable 10 0 0 0 0
#> 233 Not applicable 10 0 0 0 0
#> 234 Not applicable 10 0 0 0 0
#> 235 Not applicable 10 0 0 0 0
#> Weight TimeUnit TimeStart TimeEnd TimeDuration TimeMid TimeLabel
#> 1 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 2 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 3 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 4 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 5 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 6 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 7 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 8 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 9 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 10 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 11 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 12 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 13 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 14 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 15 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 16 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 17 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 18 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 19 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 20 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 21 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 22 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 23 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 24 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 25 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 26 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 27 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 28 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 29 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 30 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 31 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 32 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 33 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 34 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 35 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 36 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 37 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 38 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 39 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 40 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 41 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 42 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 43 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 44 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 45 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 46 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 47 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 48 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 49 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 50 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 51 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 52 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 53 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 54 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 55 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 56 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 57 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 58 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 59 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 60 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 61 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 62 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 63 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 64 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 65 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 66 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 67 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 68 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 69 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 70 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 71 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 72 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 73 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 74 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 75 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 76 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 77 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 78 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 79 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 80 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 81 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 82 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 83 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 84 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 85 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 86 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 87 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 88 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 89 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 90 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 91 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 92 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 93 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 94 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 95 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 96 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 97 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 98 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 99 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 100 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 101 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 102 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 103 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 104 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 105 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 106 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 107 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 108 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 109 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 110 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 111 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 112 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 113 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 114 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 115 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 116 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 117 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 118 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 119 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 120 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 121 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 122 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 123 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 124 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 125 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 126 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 127 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 128 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 129 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 130 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 131 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 132 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 133 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 134 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 135 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 136 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 137 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 138 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 139 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 140 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 141 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 142 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 143 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 144 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 145 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 146 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 147 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 148 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 149 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 150 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 151 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 152 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 153 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 154 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 155 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 156 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 157 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 158 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 159 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 160 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 161 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 162 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 163 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 164 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 165 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 166 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 167 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 168 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 169 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 170 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 171 NA year 29/11/1959 29/11/1959 0 1959.912 1959
#> 172 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 173 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 174 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 175 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 176 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 177 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 178 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 179 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 180 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 181 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 182 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 183 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 184 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 185 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 186 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 187 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 188 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 189 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 190 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 191 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 192 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 193 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 194 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 195 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 196 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 197 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 198 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 199 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 200 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 201 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 202 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 203 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 204 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 205 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 206 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 207 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 208 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 209 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 210 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 211 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 212 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 213 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 214 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 215 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 216 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 217 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 218 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 219 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 220 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 221 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 222 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 223 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 224 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 225 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 226 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 227 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 228 NA year 15/11/2010 15/11/2010 0 2010.873 2010
#> 229 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> 230 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> 231 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> 232 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> 233 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> 234 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> 235 NA year 01/04/1970 01/04/1970 0 1970.253 1970
#> FootNoteID id SeriesID DataValue
#> 1 0 1 1312031646109151 3.192747e+06
#> 2 0 1 1312031646109151 1.060850e+03
#> 3 0 1 1312031646109151 3.172075e+04
#> 4 0 1 1312031646109151 1.694427e+05
#> 5 0 1 1312031646109151 3.281656e+05
#> 6 0 1 1312031646109151 4.086363e+05
#> 7 0 1 1312031646109151 4.124887e+05
#> 8 0 1 1312031646109151 3.907710e+05
#> 9 0 1 1312031646109151 3.143484e+05
#> 10 0 1 1312031646109151 2.729399e+05
#> 11 0 1 1312031646109151 1.862644e+05
#> 12 0 1 1312031646109151 1.932039e+05
#> 13 0 1 1312031646109151 1.285396e+05
#> 14 0 1 1312031646109151 1.292026e+05
#> 15 0 1 1312031646109151 6.793989e+04
#> 16 0 1 1312031646109151 6.848128e+04
#> 17 0 1 1312031646109151 2.983607e+04
#> 18 0 1 1312031646109151 2.294431e+04
#> 19 0 1 1312031646109151 3.676065e+04
#> 20 0 1 1312031646109151 3.101880e+06
#> 21 0 1 1312031646109151 1.512650e+03
#> 22 0 1 1312031646109151 3.235423e+04
#> 23 0 1 1312031646109151 1.635887e+05
#> 24 0 1 1312031646109151 3.162787e+05
#> 25 0 1 1312031646109151 3.977883e+05
#> 26 0 1 1312031646109151 3.978662e+05
#> 27 0 1 1312031646109151 3.776520e+05
#> 28 0 1 1312031646109151 3.045633e+05
#> 29 0 1 1312031646109151 2.684752e+05
#> 30 0 1 1312031646109151 1.772662e+05
#> 31 0 1 1312031646109151 1.851407e+05
#> 32 0 1 1312031646109151 1.251035e+05
#> 33 0 1 1312031646109151 1.249406e+05
#> 34 0 1 1312031646109151 6.911118e+04
#> 35 0 1 1312031646109151 7.022582e+04
#> 36 0 1 1312031646109151 2.983551e+04
#> 37 0 1 1312031646109151 2.443320e+04
#> 38 0 1 1312031646109151 3.574463e+04
#> 39 0 1 1312031646109151 6.296704e+06
#> 40 0 1 1312031646109151 2.573500e+03
#> 41 0 1 1312031646109151 6.407498e+04
#> 42 0 1 1312031646109151 3.330314e+05
#> 43 0 1 1312031646109151 6.444442e+05
#> 44 0 1 1312031646109151 8.063912e+05
#> 45 0 1 1312031646109151 8.103870e+05
#> 46 0 1 1312031646109151 7.685518e+05
#> 47 0 1 1312031646109151 6.192353e+05
#> 48 0 1 1312031646109151 5.417362e+05
#> 49 0 1 1312031646109151 3.638022e+05
#> 50 0 1 1312031646109151 3.787138e+05
#> 51 0 1 1312031646109151 2.537719e+05
#> 52 0 1 1312031646109151 2.542716e+05
#> 53 0 1 1312031646109151 1.371259e+05
#> 54 0 1 1312031646109151 1.388140e+05
#> 55 0 1 1312031646109151 5.975710e+04
#> 56 0 1 1312031646109151 4.739889e+04
#> 57 0 1 1312031646109151 7.262293e+04
#> 58 0 1 34168176510155 5.435000e+04
#> 59 0 1 34168176510155 3.000000e+01
#> 60 0 1 34168176510155 1.030000e+03
#> 61 0 1 34168176510155 5.110000e+03
#> 62 0 1 34168176510155 1.018000e+04
#> 63 0 1 34168176510155 7.650000e+03
#> 64 0 1 34168176510155 7.350000e+03
#> 65 0 1 34168176510155 4.770000e+03
#> 66 0 1 34168176510155 4.290000e+03
#> 67 0 1 34168176510155 3.600000e+03
#> 68 0 1 34168176510155 2.620000e+03
#> 69 0 1 34168176510155 2.020000e+03
#> 70 0 1 34168176510155 1.850000e+03
#> 71 0 1 34168176510155 1.510000e+03
#> 72 0 1 34168176510155 1.210000e+03
#> 73 0 1 34168176510155 5.100000e+02
#> 74 0 1 34168176510155 2.400000e+02
#> 75 0 1 34168176510155 1.400000e+02
#> 76 0 1 34168176510155 2.400000e+02
#> 77 0 1 34168176510155 5.106000e+04
#> 78 0 1 34168176510155 4.000000e+01
#> 79 0 1 34168176510155 8.500000e+02
#> 80 0 1 34168176510155 4.930000e+03
#> 81 0 1 34168176510155 9.670000e+03
#> 82 0 1 34168176510155 7.330000e+03
#> 83 0 1 34168176510155 6.530000e+03
#> 84 0 1 34168176510155 4.550000e+03
#> 85 0 1 34168176510155 4.180000e+03
#> 86 0 1 34168176510155 3.410000e+03
#> 87 0 1 34168176510155 2.460000e+03
#> 88 0 1 34168176510155 1.710000e+03
#> 89 0 1 34168176510155 1.790000e+03
#> 90 0 1 34168176510155 1.470000e+03
#> 91 0 1 34168176510155 1.260000e+03
#> 92 0 1 34168176510155 4.200000e+02
#> 93 0 1 34168176510155 2.600000e+02
#> 94 0 1 34168176510155 1.200000e+02
#> 95 0 1 34168176510155 8.000000e+01
#> 96 0 1 34168176510155 1.054100e+05
#> 97 0 1 34168176510155 7.000000e+01
#> 98 0 1 34168176510155 1.880000e+03
#> 99 0 1 34168176510155 1.004000e+04
#> 100 0 1 34168176510155 1.985000e+04
#> 101 0 1 34168176510155 1.498000e+04
#> 102 0 1 34168176510155 1.388000e+04
#> 103 0 1 34168176510155 9.320000e+03
#> 104 0 1 34168176510155 8.470000e+03
#> 105 0 1 34168176510155 7.010000e+03
#> 106 0 1 34168176510155 5.080000e+03
#> 107 0 1 34168176510155 3.730000e+03
#> 108 0 1 34168176510155 3.640000e+03
#> 109 0 1 34168176510155 2.980000e+03
#> 110 0 1 34168176510155 2.470000e+03
#> 111 0 1 34168176510155 9.300000e+02
#> 112 0 1 34168176510155 5.000000e+02
#> 113 0 1 34168176510155 2.600000e+02
#> 114 0 1 34168176510155 3.200000e+02
#> 115 0 1 2519725071138 1.271345e+00
#> 116 0 1 2519725071138 8.287293e-03
#> 117 0 1 2519725071138 1.722408e-01
#> 118 0 1 2519725071138 7.146853e-01
#> 119 0 1 2519725071138 1.348344e+00
#> 120 0 1 2519725071138 1.688742e+00
#> 121 0 1 2519725071138 2.036011e+00
#> 122 0 1 2519725071138 2.110620e+00
#> 123 0 1 2519725071138 2.294118e+00
#> 124 0 1 2519725071138 2.337662e+00
#> 125 0 1 2519725071138 2.165289e+00
#> 126 0 1 2519725071138 2.493827e+00
#> 127 0 1 2519725071138 2.202381e+00
#> 128 0 1 2519725071138 2.188406e+00
#> 129 0 1 2519725071138 2.200000e+00
#> 130 0 1 2519725071138 2.125000e+00
#> 131 0 1 2519725071138 1.846154e+00
#> 132 0 1 2519725071138 1.400000e+00
#> 133 0 1 2519725071138 3.428571e+00
#> 134 0 1 2519725071138 1.194386e+00
#> 135 0 1 2519725071138 1.104972e-02
#> 136 0 1 2519725071138 1.421405e-01
#> 137 0 1 2519725071138 6.895105e-01
#> 138 0 1 2519725071138 1.280795e+00
#> 139 0 1 2519725071138 1.618102e+00
#> 140 0 1 2519725071138 1.808864e+00
#> 141 0 1 2519725071138 2.013274e+00
#> 142 0 1 2519725071138 2.235294e+00
#> 143 0 1 2519725071138 2.214286e+00
#> 144 0 1 2519725071138 2.033058e+00
#> 145 0 1 2519725071138 2.111111e+00
#> 146 0 1 2519725071138 2.130952e+00
#> 147 0 1 2519725071138 2.130435e+00
#> 148 0 1 2519725071138 2.290909e+00
#> 149 0 1 2519725071138 1.750000e+00
#> 150 0 1 2519725071138 2.000000e+00
#> 151 0 1 2519725071138 1.200000e+00
#> 152 0 1 2519725071138 1.142857e+00
#> 153 0 1 2519725071138 2.465731e+00
#> 154 0 1 2519725071138 1.933702e-02
#> 155 0 1 2519725071138 3.143813e-01
#> 156 0 1 2519725071138 1.404196e+00
#> 157 0 1 2519725071138 2.629139e+00
#> 158 0 1 2519725071138 3.306843e+00
#> 159 0 1 2519725071138 3.844875e+00
#> 160 0 1 2519725071138 4.123894e+00
#> 161 0 1 2519725071138 4.529412e+00
#> 162 0 1 2519725071138 4.551948e+00
#> 163 0 1 2519725071138 4.198347e+00
#> 164 0 1 2519725071138 4.604939e+00
#> 165 0 1 2519725071138 4.333333e+00
#> 166 0 1 2519725071138 4.318841e+00
#> 167 0 1 2519725071138 4.490909e+00
#> 168 0 1 2519725071138 3.875000e+00
#> 169 0 1 2519725071138 3.846154e+00
#> 170 0 1 2519725071138 2.600000e+00
#> 171 0 1 2519725071138 4.571429e+00
#> 172 0 1 176841346248 1.504541e+00
#> 173 0 1 176841346248 5.024983e-03
#> 174 0 1 176841346248 1.081311e-01
#> 175 0 1 176841346248 5.772252e-01
#> 176 0 1 176841346248 1.168485e+00
#> 177 0 1 176841346248 1.754577e+00
#> 178 0 1 176841346248 2.222506e+00
#> 179 0 1 176841346248 2.565960e+00
#> 180 0 1 176841346248 2.721360e+00
#> 181 0 1 176841346248 2.886049e+00
#> 182 0 1 176841346248 3.008301e+00
#> 183 0 1 176841346248 3.297256e+00
#> 184 0 1 176841346248 3.333534e+00
#> 185 0 1 176841346248 3.475916e+00
#> 186 0 1 176841346248 3.349489e+00
#> 187 0 1 176841346248 3.412135e+00
#> 188 0 1 176841346248 3.485510e+00
#> 189 0 1 176841346248 3.433066e+00
#> 190 0 1 176841346248 3.499984e+00
#> 191 0 1 176841346248 1.461721e+00
#> 192 0 1 176841346248 7.165047e-03
#> 193 0 1 176841346248 1.102905e-01
#> 194 0 1 176841346248 5.572830e-01
#> 195 0 1 176841346248 1.126160e+00
#> 196 0 1 176841346248 1.707999e+00
#> 197 0 1 176841346248 2.143719e+00
#> 198 0 1 176841346248 2.479815e+00
#> 199 0 1 176841346248 2.636649e+00
#> 200 0 1 176841346248 2.838839e+00
#> 201 0 1 176841346248 2.862972e+00
#> 202 0 1 176841346248 3.159648e+00
#> 203 0 1 176841346248 3.244421e+00
#> 204 0 1 176841346248 3.361258e+00
#> 205 0 1 176841346248 3.407234e+00
#> 206 0 1 176841346248 3.499058e+00
#> 207 0 1 176841346248 3.485445e+00
#> 208 0 1 176841346248 3.655842e+00
#> 209 0 1 176841346248 3.403249e+00
#> 210 0 1 176841346248 2.967241e+00
#> 211 0 1 176841346248 1.219003e-02
#> 212 0 1 176841346248 2.184216e-01
#> 213 0 1 176841346248 1.134508e+00
#> 214 0 1 176841346248 2.294645e+00
#> 215 0 1 176841346248 3.462432e+00
#> 216 0 1 176841346248 4.366398e+00
#> 217 0 1 176841346248 5.046621e+00
#> 218 0 1 176841346248 5.360811e+00
#> 219 0 1 176841346248 5.728282e+00
#> 220 0 1 176841346248 5.875661e+00
#> 221 0 1 176841346248 6.463204e+00
#> 222 0 1 176841346248 6.581295e+00
#> 223 0 1 176841346248 6.840631e+00
#> 224 0 1 176841346248 6.760412e+00
#> 225 0 1 176841346248 6.916520e+00
#> 226 0 1 176841346248 6.980945e+00
#> 227 0 1 176841346248 7.092107e+00
#> 228 0 1 176841346248 6.914435e+00
#> 229 0 1 171145198225219254 2.500000e-01
#> 230 0 1 171145198225219254 1.420000e+00
#> 231 0 1 171145198225219254 2.940000e+00
#> 232 0 1 171145198225219254 4.120000e+00
#> 233 0 1 171145198225219254 4.980000e+00
#> 234 0 1 171145198225219254 5.440000e+00
#> 235 0 1 171145198225219254 5.680000e+00See the error? This means that using "Census" for this set of options is unavailable. We need to check which other data processes have data for these options (for example, using get_dataprocesstype()). Since this is a trial and error process, we’ve nailed it down to "Survey: cross-sectional survey (generic/national)":
togo <- get_recorddata(dataProcessTypeIds = "Survey: cross-sectional survey (generic/national)",
indicatorTypeIds = "Births by age of mother",
locIds = "Togo",
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged")
#>
#> https://popdiv.dfs.un.org/DemoData/api/locations?
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatortypes?
#>
#> https://popdiv.dfs.un.org/DemoData/api/subgroups?
#>
#> https://popdiv.dfs.un.org/DemoData/api/locareatypes?indicatorTypeIds=16&isComplete=0&locIds=768
#>
#> https://popdiv.dfs.un.org/DemoData/api/dataprocesstypes?indicatorTypeIds=16&isComplete=0&locIds=768
#> If you run the same query again, use the one below (faster):
#> get_recorddata(dataProcessTypeIds = 11, indicatorTypeIds = 16, isComplete = 0, locIds = 768, locAreaTypeIds = 2, subGroupIds = 2)
#> https://popdiv.dfs.un.org/DemoData/api/structureddatarecords?dataProcessTypeIds=11&indicatorTypeIds=16&isComplete=0&locIds=768&locAreaTypeIds=2&subGroupIds=2
head(togo)
#> IndicatorName IndicatorID LocName LocID
#> 1 Births by age of mother (and sex of child) 170 Togo 768
#> 2 Births by age of mother (and sex of child) 170 Togo 768
#> 3 Births by age of mother (and sex of child) 170 Togo 768
#> 4 Births by age of mother (and sex of child) 170 Togo 768
#> 5 Births by age of mother (and sex of child) 170 Togo 768
#> 6 Births by age of mother (and sex of child) 170 Togo 768
#> StructuredDataID LocTypeName LocTypeID RegName RegID AreaName AreaID
#> 1 205820005 Country 5 NA 0 NA 0
#> 2 205820006 Country 5 NA 0 NA 0
#> 3 205820007 Country 5 NA 0 NA 0
#> 4 205820008 Country 5 NA 0 NA 0
#> 5 205820009 Country 5 NA 0 NA 0
#> 6 205820010 Country 5 NA 0 NA 0
#> LocAreaTypeName LocAreaTypeID SubGroupTypeName SubGroupTypeID
#> 1 Whole area 2 Total / All groups / Whole group 2
#> 2 Whole area 2 Total / All groups / Whole group 2
#> 3 Whole area 2 Total / All groups / Whole group 2
#> 4 Whole area 2 Total / All groups / Whole group 2
#> 5 Whole area 2 Total / All groups / Whole group 2
#> 6 Whole area 2 Total / All groups / Whole group 2
#> SubGroupID1 SubGroupName SubGroupCombinationID SubGroupTypeSortOrder
#> 1 2 Total or All groups 0 3
#> 2 2 Total or All groups 0 3
#> 3 2 Total or All groups 0 3
#> 4 2 Total or All groups 0 3
#> 5 2 Total or All groups 0 3
#> 6 2 Total or All groups 0 3
#> IndicatorShortName DataCatalogID DataProcessTypeID DataProcessType
#> 1 Births 690 Survey Survey
#> 2 Births 690 Survey Survey
#> 3 Births 690 Survey Survey
#> 4 Births 690 Survey Survey
#> 5 Births 690 Survey Survey
#> 6 Births 690 Survey Survey
#> DataProcessTypeSort DataProcess DataProcessID DataProcessSort
#> 1 3 Survey 15 37
#> 2 3 Survey 15 37
#> 3 3 Survey 15 37
#> 4 3 Survey 15 37
#> 5 3 Survey 15 37
#> 6 3 Survey 15 37
#> DataCatalogName DataCatalogShortName ReferencePeriod
#> 1 Togo 1961 Enquête démographique 1961 ED 1961
#> 2 Togo 1961 Enquête démographique 1961 ED 1961
#> 3 Togo 1961 Enquête démographique 1961 ED 1961
#> 4 Togo 1961 Enquête démographique 1961 ED 1961
#> 5 Togo 1961 Enquête démographique 1961 ED 1961
#> 6 Togo 1961 Enquête démographique 1961 ED 1961
#> ReferenceYearStart ReferenceYearEnd ReferenceYearMid FieldWorkStart
#> 1 1961 1961 1961.5 12/11/1961 12:00:00 AM
#> 2 1961 1961 1961.5 12/11/1961 12:00:00 AM
#> 3 1961 1961 1961.5 12/11/1961 12:00:00 AM
#> 4 1961 1961 1961.5 12/11/1961 12:00:00 AM
#> 5 1961 1961 1961.5 12/11/1961 12:00:00 AM
#> 6 1961 1961 1961.5 12/11/1961 12:00:00 AM
#> FieldWorkEnd FieldWorkMiddle DataCatalogNote DataSourceID
#> 1 12/11/1961 12:00:00 AM 1961.945 18
#> 2 12/11/1961 12:00:00 AM 1961.945 18
#> 3 12/11/1961 12:00:00 AM 1961.945 18
#> 4 12/11/1961 12:00:00 AM 1961.945 18
#> 5 12/11/1961 12:00:00 AM 1961.945 18
#> 6 12/11/1961 12:00:00 AM 1961.945 18
#> DataSourceAuthor DataSourceYear DataSourceName DataSourceShortName
#> 1 UNSD 1965 Demographic Yearbook DYB
#> 2 UNSD 1965 Demographic Yearbook DYB
#> 3 UNSD 1965 Demographic Yearbook DYB
#> 4 UNSD 1965 Demographic Yearbook DYB
#> 5 UNSD 1965 Demographic Yearbook DYB
#> 6 UNSD 1965 Demographic Yearbook DYB
#> DataSourceSort DataStatusName DataStatusID DataStatusSort
#> 1 20 Final 1 2
#> 2 20 Final 1 2
#> 3 20 Final 1 2
#> 4 20 Final 1 2
#> 5 20 Final 1 2
#> 6 20 Final 1 2
#> StatisticalConceptName StatisticalConceptID StatisticalConceptSort SexName
#> 1 Year of occurrence 5 4 Male
#> 2 Year of occurrence 5 4 Male
#> 3 Year of occurrence 5 4 Male
#> 4 Year of occurrence 5 4 Male
#> 5 Year of occurrence 5 4 Male
#> 6 Year of occurrence 5 4 Male
#> SexID SexSort AgeID AgeUnit AgeStart AgeEnd AgeSpan AgeMid AgeLabel AgeSort
#> 1 1 1 304 Year 15 20 5 17.5 15-19 126
#> 2 1 1 305 Year 20 25 5 22.5 20-24 172
#> 3 1 1 306 Year 25 30 5 27.5 25-29 195
#> 4 1 1 307 Year 30 35 5 32.5 30-34 211
#> 5 1 1 308 Year 35 40 5 37.5 35-39 232
#> 6 1 1 309 Year 40 45 5 42.5 40-44 249
#> agesort DataTypeGroupName DataTypeGroupID DataTypeName DataTypeID
#> 1 0 Direct 3 Recent births 62
#> 2 0 Direct 3 Recent births 62
#> 3 0 Direct 3 Recent births 62
#> 4 0 Direct 3 Recent births 62
#> 5 0 Direct 3 Recent births 62
#> 6 0 Direct 3 Recent births 62
#> DataTypeSort ModelType ModelPatternFamilyName ModelPatternFamilyID
#> 1 320 Not applicable Not applicable 15
#> 2 320 Not applicable Not applicable 15
#> 3 320 Not applicable Not applicable 15
#> 4 320 Not applicable Not applicable 15
#> 5 320 Not applicable Not applicable 15
#> 6 320 Not applicable Not applicable 15
#> ModelPatternName ModelPatternID DataReliabilityName DataReliabilityID
#> 1 Not applicable (Direct) 40 Fair 5
#> 2 Not applicable (Direct) 40 Fair 5
#> 3 Not applicable (Direct) 40 Fair 5
#> 4 Not applicable (Direct) 40 Fair 5
#> 5 Not applicable (Direct) 40 Fair 5
#> 6 Not applicable (Direct) 40 Fair 5
#> DataReliabilitySort PeriodTypeName PeriodTypeID PeriodGroupName PeriodGroupID
#> 1 2 RP 6 < 1 67
#> 2 2 RP 6 < 1 67
#> 3 2 RP 6 < 1 67
#> 4 2 RP 6 < 1 67
#> 5 2 RP 6 < 1 67
#> 6 2 RP 6 < 1 67
#> PeriodStart PeriodEnd PeriodSpan PeriodMiddle Weight TimeUnit TimeStart
#> 1 0 1 1 0.5 0.4320109 year 01/01/1961
#> 2 0 1 1 0.5 0.4320109 year 01/01/1961
#> 3 0 1 1 0.5 0.4320109 year 01/01/1961
#> 4 0 1 1 0.5 0.4320109 year 01/01/1961
#> 5 0 1 1 0.5 0.4320109 year 01/01/1961
#> 6 0 1 1 0.5 0.4320109 year 01/01/1961
#> TimeEnd TimeDuration TimeMid TimeLabel FootNoteID id SeriesID
#> 1 31/12/1961 1 1961.5 1961 0 1 157202914869165
#> 2 31/12/1961 1 1961.5 1961 0 1 157202914869165
#> 3 31/12/1961 1 1961.5 1961 0 1 157202914869165
#> 4 31/12/1961 1 1961.5 1961 0 1 157202914869165
#> 5 31/12/1961 1 1961.5 1961 0 1 157202914869165
#> 6 31/12/1961 1 1961.5 1961 0 1 157202914869165
#> DataValue
#> 1 3390
#> 2 10675
#> 3 12215
#> 4 7510
#> 5 4900
#> 6 1890You can also request uncertainty estimates with includeUncertainty = TRUE
oman <- get_recorddata(dataProcessTypeIds = "Register",
indicatorTypeIds = "Deaths by age and sex",
locIds = "Oman",
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged",
includeUncertainty = TRUE)
#>
#> https://popdiv.dfs.un.org/DemoData/api/locations?
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatortypes?
#>
#> https://popdiv.dfs.un.org/DemoData/api/subgroups?
#>
#> https://popdiv.dfs.un.org/DemoData/api/locareatypes?indicatorTypeIds=20&isComplete=0&locIds=512
#>
#> https://popdiv.dfs.un.org/DemoData/api/dataprocesstypes?indicatorTypeIds=20&isComplete=0&locIds=512
#> If you run the same query again, use the one below (faster):
#> get_recorddata(dataProcessTypeIds = 9, indicatorTypeIds = 20, isComplete = 0, locIds = 512, locAreaTypeIds = 2, subGroupIds = 2, includeUncertainty = 1)
#> https://popdiv.dfs.un.org/DemoData/api/structureddatarecords?dataProcessTypeIds=9&indicatorTypeIds=20&isComplete=0&locIds=512&locAreaTypeIds=2&subGroupIds=2&includeUncertainty=true
head(oman)
#> IndicatorName IndicatorID LocName LocID StructuredDataID
#> 1 Deaths by age and sex - abridged 194 Oman 512 245581366
#> 2 Deaths by age and sex - abridged 194 Oman 512 245581367
#> 3 Deaths by age and sex - abridged 194 Oman 512 245581368
#> 4 Deaths by age and sex - abridged 194 Oman 512 245581369
#> 5 Deaths by age and sex - abridged 194 Oman 512 245581370
#> 6 Deaths by age and sex - abridged 194 Oman 512 245581371
#> LocTypeName LocTypeID RegName RegID AreaName AreaID LocAreaTypeName
#> 1 Country 5 NA 0 NA 0 Whole area
#> 2 Country 5 NA 0 NA 0 Whole area
#> 3 Country 5 NA 0 NA 0 Whole area
#> 4 Country 5 NA 0 NA 0 Whole area
#> 5 Country 5 NA 0 NA 0 Whole area
#> 6 Country 5 NA 0 NA 0 Whole area
#> LocAreaTypeID SubGroupTypeName SubGroupTypeID SubGroupID1
#> 1 2 Total / All groups / Whole group 2 2
#> 2 2 Total / All groups / Whole group 2 2
#> 3 2 Total / All groups / Whole group 2 2
#> 4 2 Total / All groups / Whole group 2 2
#> 5 2 Total / All groups / Whole group 2 2
#> 6 2 Total / All groups / Whole group 2 2
#> SubGroupName SubGroupCombinationID SubGroupTypeSortOrder
#> 1 Total or All groups 0 3
#> 2 Total or All groups 0 3
#> 3 Total or All groups 0 3
#> 4 Total or All groups 0 3
#> 5 Total or All groups 0 3
#> 6 Total or All groups 0 3
#> IndicatorShortName DataCatalogID DataProcessTypeID DataProcessType
#> 1 Deaths5 4896 Register Register
#> 2 Deaths5 4896 Register Register
#> 3 Deaths5 4896 Register Register
#> 4 Deaths5 4896 Register Register
#> 5 Deaths5 4896 Register Register
#> 6 Deaths5 4896 Register Register
#> DataProcessTypeSort DataProcess DataProcessID DataProcessSort
#> 1 6 VR 36 4
#> 2 6 VR 36 4
#> 3 6 VR 36 4
#> 4 6 VR 36 4
#> 5 6 VR 36 4
#> 6 6 VR 36 4
#> DataCatalogName DataCatalogShortName ReferencePeriod ReferenceYearStart
#> 1 Vital Registration Register 1980- 1980
#> 2 Vital Registration Register 1980- 1980
#> 3 Vital Registration Register 1980- 1980
#> 4 Vital Registration Register 1980- 1980
#> 5 Vital Registration Register 1980- 1980
#> 6 Vital Registration Register 1980- 1980
#> ReferenceYearEnd ReferenceYearMid FieldWorkStart FieldWorkEnd FieldWorkMiddle
#> 1 NA NA NA NA NA
#> 2 NA NA NA NA NA
#> 3 NA NA NA NA NA
#> 4 NA NA NA NA NA
#> 5 NA NA NA NA NA
#> 6 NA NA NA NA NA
#> DataCatalogNote DataSourceID DataSourceAuthor DataSourceYear
#> 1 NA 56 UNSD 2003
#> 2 NA 56 UNSD 2003
#> 3 NA 56 UNSD 2003
#> 4 NA 56 UNSD 2003
#> 5 NA 56 UNSD 2003
#> 6 NA 56 UNSD 2003
#> DataSourceName DataSourceShortName DataSourceSort HasUncertaintyRecord
#> 1 Demographic Yearbook DYB 20 FALSE
#> 2 Demographic Yearbook DYB 20 FALSE
#> 3 Demographic Yearbook DYB 20 FALSE
#> 4 Demographic Yearbook DYB 20 FALSE
#> 5 Demographic Yearbook DYB 20 FALSE
#> 6 Demographic Yearbook DYB 20 FALSE
#> StandardErrorValue ConfidenceInterval ConfidenceIntervalLowerBound
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> 6 0 0 0
#> ConfidenceIntervalUpperBound DataStatusName DataStatusID DataStatusSort
#> 1 0 Final 1 2
#> 2 0 Final 1 2
#> 3 0 Final 1 2
#> 4 0 Final 1 2
#> 5 0 Final 1 2
#> 6 0 Final 1 2
#> StatisticalConceptName StatisticalConceptID StatisticalConceptSort SexName
#> 1 Year of occurrence 5 4 Both sexes
#> 2 Year of occurrence 5 4 Both sexes
#> 3 Year of occurrence 5 4 Both sexes
#> 4 Year of occurrence 5 4 Both sexes
#> 5 Year of occurrence 5 4 Both sexes
#> 6 Year of occurrence 5 4 Both sexes
#> SexID SexSort AgeID AgeUnit AgeStart AgeEnd AgeSpan AgeMid AgeLabel AgeSort
#> 1 3 3 330 Year 0 1 1 0.5 < 1 1
#> 2 3 3 331 Year 1 5 4 3.0 1-4 4
#> 3 3 3 398 Year 0 -1 -1 -1.0 Total 999
#> 4 3 3 3154 Year 5 15 10 10.0 5-14 51
#> 5 3 3 3242 Year 15 45 30 30.0 15-44 134
#> 6 3 3 3263 Year 45 0 -1 72.5 45+ 260
#> agesort DataTypeGroupName DataTypeGroupID DataTypeName DataTypeID
#> 1 0 Direct 3 Direct 16
#> 2 0 Direct 3 Direct 16
#> 3 0 Direct 3 Direct 16
#> 4 0 Direct 3 Direct 16
#> 5 0 Direct 3 Direct 16
#> 6 0 Direct 3 Direct 16
#> DataTypeSort ModelType ModelPatternFamilyName ModelPatternFamilyID
#> 1 11 Not applicable Not applicable 15
#> 2 11 Not applicable Not applicable 15
#> 3 11 Not applicable Not applicable 15
#> 4 11 Not applicable Not applicable 15
#> 5 11 Not applicable Not applicable 15
#> 6 11 Not applicable Not applicable 15
#> ModelPatternName ModelPatternID DataReliabilityName DataReliabilityID
#> 1 Not applicable (Direct) 40 Low 4
#> 2 Not applicable (Direct) 40 Low 4
#> 3 Not applicable (Direct) 40 Low 4
#> 4 Not applicable (Direct) 40 Low 4
#> 5 Not applicable (Direct) 40 Low 4
#> 6 Not applicable (Direct) 40 Low 4
#> DataReliabilitySort PeriodTypeName PeriodTypeID PeriodGroupName PeriodGroupID
#> 1 3 CR 3 Not applicable 10
#> 2 3 CR 3 Not applicable 10
#> 3 3 CR 3 Not applicable 10
#> 4 3 CR 3 Not applicable 10
#> 5 3 CR 3 Not applicable 10
#> 6 3 CR 3 Not applicable 10
#> PeriodStart PeriodEnd PeriodSpan PeriodMiddle Weight TimeUnit TimeStart
#> 1 0 0 0 0 NA year 01/01/1999
#> 2 0 0 0 0 NA year 01/01/1999
#> 3 0 0 0 0 NA year 01/01/1999
#> 4 0 0 0 0 NA year 01/01/1999
#> 5 0 0 0 0 NA year 01/01/1999
#> 6 0 0 0 0 NA year 01/01/1999
#> TimeEnd TimeDuration TimeMid TimeLabel FootNoteID id SeriesID
#> 1 31/12/1999 1 1999.5 1999 0 1 113218950115123
#> 2 31/12/1999 1 1999.5 1999 0 1 113218950115123
#> 3 31/12/1999 1 1999.5 1999 0 1 113218950115123
#> 4 31/12/1999 1 1999.5 1999 0 1 113218950115123
#> 5 31/12/1999 1 1999.5 1999 0 1 113218950115123
#> 6 31/12/1999 1 1999.5 1999 0 1 113218950115123
#> DataValue
#> 1 386
#> 2 56
#> 3 2440
#> 4 67
#> 5 286
#> 6 1645By default, the results from get_recorddata exclude any uncertainty columns such as StandardErrorValue, ConfidenceInterval, etc. By setting includeUncertainty = TRUE, the resulting data frame adds new fields which have uncertainty for the requested values. These fields are all next to each other so by limiting the data to the fields next to StandardErrorValue, the user can quickly see the uncertainty columns.
get_recorddataadditional allows to use additional arguments such as dataTypeGroupIds and dataTypeGroupId2s.For a list of all arguments you can use, see here. For example:
## For dataTypeGroupIds
dt <-
get_recorddataadditional(
dataTypeGroupIds = "Direct", # or 3
indicatorTypeIds = 8,
isComplete = 0,
locIds = 818,
locAreaTypeIds = 2,
subGroupIds = 2
)
## For dataTypeGroupId2s
dt <-
get_recorddataadditional(
dataTypeGroupId2s = "Population (sample tabulation)", # or 11
indicatorTypeIds = 8,
isComplete = 0,
locIds = 818,
locAreaTypeIds = 2,
subGroupIds = 2
)get_datatypes can show you which dataTypeIds are within the umbrella of aggregated categories such as dataTypeGroupIds and dataTypeGroupId2s.
Currently the IP of the server is automatically set (to https://popdiv.dfs.un.org/DemoData/api/) but can be changed simply by adding options(unpd_server = "your server") to the beginning of your script:
# Change this to the desired server. This is the server
# which is set by default.
options(unpd_server = "https://popdiv.dfs.un.org/DemoData/api/")
get_recorddata(dataProcessTypeIds = "Register",
indicatorTypeIds = "Deaths by age and sex",
locIds = "Oman",
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged")Due to the nature of the UNPD API, it’s not possible to download over 30 countries in the same API request. For example, if we wanted to download data on these countries:
myLocations <- c(28, 492, 570, 674, 308, 96, 196, 8,
376, 662, 670, 642, 84, 188, 442, 100,
192, 170, 414, 616, 320, 480, 218,
818, 222, 300, 558, 40, 52, 348, 470,
620, 702, 858, 56, 780, 388, 246, 352,
591, 144, 862)
get_recorddata(dataProcessTypeIds = "Register",
indicatorTypeIds = "Deaths by age and sex",
locIds = myLocations,
locAreaTypeIds = "Whole area",
subGroupIds = "Total or All groups",
isComplete = "Abridged",
startYear = 1920,
endYear = 2020)You’ll get an error saying that DDSQLtools could not connect to the API. When that happens you can loop over chunks of countries to get it faster. The first thing you want to do is to copy the optimized query printed by get_recorddata that already translated the string codes to numbers.
After that, you need to loop through groups of countries and make requests for each group separately. Below is the same example using base R and the tidyverse. On average, downloading these countries takes between 25 to 30 minutes.
myLocations <- c(28, 492, 570, 674, 308, 96, 196, 8,
376, 662, 670, 642, 84, 188, 442, 100,
192, 170, 414, 616, 320, 480, 218,
818, 222, 300, 558, 40, 52, 348, 470,
620, 702, 858, 56, 780, 388, 246, 352,
591, 144, 862)
# Here replace with number of desired chunk of countries
n_chunks <- 3
chunk_groups <- rep(1:n_chunks, length.out = length(myLocations))
cnty_groups <- split(myLocations, chunk_groups)
# Loop through each chunk of countries with `lapply`
deaths <- lapply(cnty_groups, function(x) {
res <- get_recorddata(dataProcessTypeIds = 9,
startYear = 1920,
endYear = 2020,
indicatorTypeIds = 20,
isComplete = 0,
locIds = x,
locAreaTypeIds = 2,
subGroupIds = 2)
# Informative message to keep track of which chunk
# of countries have been completed.
cat("Countries", paste0(x, collapse = ", "), "done")
res
})
# Row-bind all the data frames into one data frame
deaths <- do.call(rbind, deaths)
head(deaths)
library(tidyverse)
myLocations <- c(28, 492, 570, 674, 308, 96, 196, 8,
376, 662, 670, 642, 84, 188, 442, 100,
192, 170, 414, 616, 320, 480, 218,
818, 222, 300, 558, 40, 52, 348, 470,
620, 702, 858, 56, 780, 388, 246, 352,
591, 144, 862)
n_chunks <- 3
chunk_groups <- rep(1:n_chunks, length.out = length(myLocations))
cnty_groups <- split(myLocations, chunk_groups)
# Loop through each location with `purrr`
deaths <- map(cnty_groups, ~ {
res <- get_recorddata(dataProcessTypeIds = 9,
startYear = 1920,
endYear = 2020,
indicatorTypeIds = 20,
isComplete = 0,
locIds = .x,
locAreaTypeIds = 2,
subGroupIds = 2)
cat("Countries", paste0(x, collapse = ", "), " done")
res
})
deaths <-
deaths %>%
bind_rows() %>%
as_tibble()
deathsWhenever you download some data, you’ll see that there are several columns that end with Name. This columns contain, for example, the name of the location (LocName) or the name of the subgroup (SubGroupName). To be able to see the equivalent ID of these names, there are two different strategies using the haven package.
First, let’s get some data:
res <- get_recorddata(
dataProcessTypeIds = 2, # Census
indicatorTypeIds = 8, # Population by age and sex - abridged
locIds = 818, # Egypt
locAreaTypeIds = 2, # Whole area
subGroupIds = 2, # Total or All groups
isComplete = 0,
collapse_id_name = TRUE
)
#>
#> https://popdiv.dfs.un.org/DemoData/api/structureddatarecords?dataProcessTypeIds=2&indicatorTypeIds=8&isComplete=0&locIds=818&locAreaTypeIds=2&subGroupIds=2You can use print_labels from haven on the column of interest and it will show you the ID:
library(haven)
print_labels(res$LocName)
#>
#> Labels:
#> value label
#> Egypt 818If you want to flip the labels for the entire data frame such that the ID is current value and not the name, you can do that with as_factor:
Using the tidyverse, the solution from base R applies as well. However, if you convert your data frame to a tibble, it will automatically show you the labels in the data frame.
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
#> ✔ tibble 3.1.6 ✔ dplyr 1.0.7
#> ✔ tidyr 1.1.4 ✔ stringr 1.4.0
#> ✔ readr 2.1.1 ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
res_tb <- res %>% as_tibble()
# See the code next to the name? No need to print it
# separately, `tibble` print boths things together.
res_tb %>%
select(ends_with("Name"))
#> # A tibble: 800 × 23
#> IndicatorName LocName LocTypeName RegName AreaName LocAreaTypeName
#> <chr+lbl> <chr+lbl> <chr+lbl> <dbl+l> <dbl+lb> <chr+lbl>
#> 1 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 2 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 3 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 4 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 5 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 6 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 7 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 8 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 9 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> 10 Population by … [58] Egypt [818] Country [5] NA NA Whole area [2]
#> # … with 790 more rows, and 17 more variables: SubGroupTypeName <chr+lbl>,
#> # SubGroupName <chr>, IndicatorShortName <chr>, DataCatalogName <chr>,
#> # DataCatalogShortName <chr>, DataSourceName <chr>,
#> # DataSourceShortName <chr>, DataStatusName <chr+lbl>,
#> # StatisticalConceptName <chr+lbl>, SexName <chr+lbl>,
#> # DataTypeGroupName <chr+lbl>, DataTypeName <chr>,
#> # ModelPatternFamilyName <chr+lbl>, ModelPatternName <chr+lbl>, …If you’re interested in keeping either the codes or the names, you can do that with as_factor:
res_tb %>%
# You can extract just the ID
as_factor(levels = "labels") %>%
select(ends_with("Name"))
#> # A tibble: 800 × 23
#> IndicatorName LocName LocTypeName RegName AreaName LocAreaTypeName
#> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 58 818 5 NA NA 2
#> 2 58 818 5 NA NA 2
#> 3 58 818 5 NA NA 2
#> 4 58 818 5 NA NA 2
#> 5 58 818 5 NA NA 2
#> 6 58 818 5 NA NA 2
#> 7 58 818 5 NA NA 2
#> 8 58 818 5 NA NA 2
#> 9 58 818 5 NA NA 2
#> 10 58 818 5 NA NA 2
#> # … with 790 more rows, and 17 more variables: SubGroupTypeName <fct>,
#> # SubGroupName <chr>, IndicatorShortName <chr>, DataCatalogName <chr>,
#> # DataCatalogShortName <chr>, DataSourceName <chr>,
#> # DataSourceShortName <chr>, DataStatusName <fct>,
#> # StatisticalConceptName <fct>, SexName <fct>, DataTypeGroupName <fct>,
#> # DataTypeName <chr>, ModelPatternFamilyName <fct>, ModelPatternName <fct>,
#> # DataReliabilityName <fct>, PeriodTypeName <fct>, PeriodGroupName <fct>
res_tb %>%
# Or just the values
as_factor(levels = "values") %>%
select(ends_with("Name"))
#> # A tibble: 800 × 23
#> IndicatorName LocName LocTypeName RegName AreaName LocAreaTypeName
#> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 Population by age and s… Egypt Country NA NA Whole area
#> 2 Population by age and s… Egypt Country NA NA Whole area
#> 3 Population by age and s… Egypt Country NA NA Whole area
#> 4 Population by age and s… Egypt Country NA NA Whole area
#> 5 Population by age and s… Egypt Country NA NA Whole area
#> 6 Population by age and s… Egypt Country NA NA Whole area
#> 7 Population by age and s… Egypt Country NA NA Whole area
#> 8 Population by age and s… Egypt Country NA NA Whole area
#> 9 Population by age and s… Egypt Country NA NA Whole area
#> 10 Population by age and s… Egypt Country NA NA Whole area
#> # … with 790 more rows, and 17 more variables: SubGroupTypeName <fct>,
#> # SubGroupName <chr>, IndicatorShortName <chr>, DataCatalogName <chr>,
#> # DataCatalogShortName <chr>, DataSourceName <chr>,
#> # DataSourceShortName <chr>, DataStatusName <fct>,
#> # StatisticalConceptName <fct>, SexName <fct>, DataTypeGroupName <fct>,
#> # DataTypeName <chr>, ModelPatternFamilyName <fct>, ModelPatternName <fct>,
#> # DataReliabilityName <fct>, PeriodTypeName <fct>, PeriodGroupName <fct>Since get_recorddataadditional is a cousing of get_recorddatal with additional options to specify, all of the above applies to get_recorddataadditional.
The previous examples assume you’re familiar with the names or codes of the parameters of the API: locations, subgroups, etc… Whenever you don’t know these values, you need to resort to the list of available values. Each parameter defined above has an equivalent get_* function to extract all possible values. The current list is:
get_dataprocesstype()
get_indicatortypes()
get_locations()
get_locationtypes()
get_subgroups()
"Abridged", "Complete" or "Total"
For example, to see all possible indicator types:
Here we can see both the code and the name of the indicator. Both values are accepted by get_recorddata. Another example is getting the list of location types. get_locationtypes() will give you those:
get_locationtypes()
#>
#> https://popdiv.dfs.un.org/DemoData/api/locareatypes?
#> PK_LocAreaTypeID Name
#> 1 0 Unknown location type/category
#> 2 1 Not applicable
#> 3 2 Whole area
#> 4 3 Urban
#> 5 4 Rural
#> 6 5 Semi-rural
#> 7 6 Semi-urban
#> 8 20 Reporting Areas
#> 9 30 Part
#> 10 35 Selected Urban and Rural Areas
#> 11 41 Selected Rural Areas
#> 12 51 Selected Urban Areas
#> 13 70 Cities
#> 14 71 Big cities
#> 15 72 Small cities
#> 16 73 Townships
#> 17 74 Hsiangs
#> 18 75 Refugee Camp
#> ShortName LongName SortOrder
#> 1 Unknown location type/category Unknown location type/category 1
#> 2 Not applicable Not applicable 2
#> 3 Whole area Whole area 3
#> 4 Urban areas Urban areas 4
#> 5 Rural areas Rural areas 5
#> 6 Semi-rural Semi-rural 6
#> 7 Semi-urban Semi-urban 7
#> 8 Reporting Areas Reporting Areas 8
#> 9 Part Part 9
#> 10 Selected Urban and Rural Areas Selected Urban and Rural Areas 10
#> 11 Selected Rural Areas Selected Rural Areas 11
#> 12 Selected Urban Areas Selected Urban Areas 12
#> 13 Cities Cities 13
#> 14 Big cities Big cities 14
#> 15 Small cities Small cities 15
#> 16 Townships Townships 16
#> 17 Hsiangs Hsiangs 17
#> 18 Refugee Camp Refugee Camp 18
#> Description isActive isDefault
#> 1 FALSE FALSE
#> 2 FALSE FALSE
#> 3 Formerly 23 TRUE TRUE
#> 4 Formerly 16 TRUE FALSE
#> 5 Formerly 17 TRUE FALSE
#> 6 Formerly 21 FALSE FALSE
#> 7 Formerly 22 FALSE FALSE
#> 8 WHO-DB subdiv=A20 FALSE FALSE
#> 9 WHO-DB subdiv=A30 FALSE FALSE
#> 10 WHO-DB subdiv=A35 FALSE FALSE
#> 11 WHO-DB subdiv=A41 FALSE FALSE
#> 12 WHO-DB subdiv=A51 FALSE FALSE
#> 13 WHO-DB subdiv=A70 FALSE FALSE
#> 14 FALSE FALSE
#> 15 FALSE FALSE
#> 16 FALSE FALSE
#> 17 FALSE FALSE
#> 18 FALSE FALSEIn order to search for a specific combination of values ("Egypt", "Deaths by age and sex", etc…), the process is only possible through trial and error. For example, if we wanted to explore Deaths by age and sex for Oman, we’d have to explore whether there is data for Semi-rural (locAreaTypeIds) and Christian Orthodox (subGroupIds).
For now, let’s try this example for "Semi-rural":
get_recorddata(dataProcessTypeIds = "Register",
indicatorTypeIds = "Deaths by age and sex",
locIds = "Oman",
locAreaTypeIds = "Semi-rural",
subGroupIds = "Total or All groups",
isComplete = "Abridged")
#>
#> https://popdiv.dfs.un.org/DemoData/api/locations?
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatortypes?
#>
#> https://popdiv.dfs.un.org/DemoData/api/subgroups?
#>
#> https://popdiv.dfs.un.org/DemoData/api/locareatypes?indicatorTypeIds=20&isComplete=0&locIds=512
#> Error in fun(vec, ...): Area Type(s) 'Semi-rural' not found. Check get_locationtypes()This error is just saying that the combination of data that you’ve specified is not available (in this case, Semi-rural is not available for register data on deaths by age and sex in Oman for all groups abridged).
Figuring out which specific set of values returns what the user want is a tedious process. If the user doesn’t know the specific combination of values by memory, the most optimal strategy that we advise is to explore the available values in the front end of the API and then use these in the request.
Let’s do one example. Follow the hyperlink and click on "Get Started - Select Indicator & Country". Then select "Deaths by age and sex" -> "Oman". All possible data sets for Oman and "Deaths by age and sex" will appear automatically. We can filter down the options in the left-most menu and use these values to populate our get_recorddata call.
Looking at the possible values of "Type of location", only "Whole area" is available. This means that we can’t specify "Semi-rural" (as we saw in the example above). Looking at "Type of subgroup", the only available group is Total or All groups, meaning that we can’t specify "Christian Orthodox" either.
Using this strategy is much easier and less time consuming that trying out different values.
To explore all possible indicatorIds, indicatorTypeIds and components, get_iitypes can be of use. Although get_indicators and get_indicatortypes are very helpful for looking at the entire list of indicators, a user often wants to combine the two. That’s where get_iitypes comes in. For example:
all_iitypes <- get_iitypes()
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatorindicatortypes?
head(all_iitypes)
#> PK_IndicatorID DemoBaseIndicatorID SortOrder IsActive IsInput IsOutput
#> 1 13 2314 4 TRUE FALSE TRUE
#> 2 14 2313 8 TRUE FALSE TRUE
#> 3 21 4317 12 TRUE FALSE TRUE
#> 4 27 4392 16 FALSE FALSE TRUE
#> 5 29 4393 20 FALSE FALSE TRUE
#> 6 52 15 24 TRUE TRUE TRUE
#> Name ShortName
#> 1 Surface Area (square km) SurfaceArea
#> 2 Population density PopDensity
#> 3 Percentage urban PercentUrban
#> 4 Population in urban agglomerations PopUrbanAgg
#> 5 Population in capital cities PopCapital
#> 6 Total population by sex TPopulation
#> LongName PrintName
#> 1 Surface Area (square km) Surface Area (square km)
#> 2 Population density Population density (persons per square km)
#> 3 Percentage urban Percentage urban
#> 4 Population in urban agglomerations Population in urban agglomerations
#> 5 Population in capital cities Population in capital cities
#> 6 Total population by sex Total population by sex
#> Description
#> 1 Total surface area, comprising land area and inland waters (assumed to consist of major rivers and lakes) and excluding only Polar Regions and uninhabited islands. The surface area given is the most recent estimate available. They are presented in square kilometres, a conversion factor of 2.589988 having been applied to surface areas originally reported in square miles.
#> 2 Number of persons per square Kilometer.
#> 3 Urban population as a percentage of the total population.
#> 4 Population living in urban settlements contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the suburban areas lying outside of but being adjacent to the city boundaries.
#> 5 Population living in specific cities designated as a capital cities as reported by the country or area. The city can be the seat of the government as determined by the country. Few countries designate more than one city to be a capital city with a specific title function (e.g., administrative and/or legislative capital).
#> 6 Total number of persons by sex
#> UnitScaling UnitShortLabel UnitLongLabel VariableType FormatString
#> 1 1 per sq. km per square kilometer absolute ### ### ##0
#> 2 1 per sq. km per square kilometer relative ### ### ##0
#> 3 100 per cent per cent relative ##0.0
#> 4 1 persons persons absolute ### ### ##0
#> 5 1 persons persons absolute ### ### ##0
#> 6 1 persons persons absolute ### ### ##0
#> DomainID ComponentID IndicatorTypeID IndicatorClassID dimTime dimAge AgeUnit
#> 1 1 1 2 2 FALSE FALSE Year
#> 2 1 1 2 2 TRUE FALSE Year
#> 3 3 1 4 2 TRUE FALSE Year
#> 4 3 1 6 2 TRUE FALSE Year
#> 5 3 1 6 2 TRUE FALSE Year
#> 6 1 1 7 2 TRUE FALSE Year
#> AgeObservationPlanID AgeID AgeStart AgeSpan IsComplete dimSex SexID
#> 1 2 1 -9 -9 2 FALSE 9
#> 2 2 700 0 -1 2 FALSE 3
#> 3 2 700 0 -1 2 FALSE 3
#> 4 2 700 0 -1 2 FALSE 3
#> 5 2 700 0 -1 2 FALSE 3
#> 6 2 700 0 -1 2 TRUE NA
#> DefaultSubGroupID AgeEnd MinValue MaxValue IndicatorType.PK_IndicatorTypeID
#> 1 2 -9 0 NA 2
#> 2 2 -1 0 NA 2
#> 3 2 -1 0 100 4
#> 4 2 -1 0 NA 6
#> 5 2 -1 0 NA 6
#> 6 2 -1 0 1412480000 7
#> IndicatorType.ComponentID IndicatorType.ComponentName
#> 1 1 Population
#> 2 1 Population
#> 3 1 Population
#> 4 1 Population
#> 5 1 Population
#> 6 1 Population
#> IndicatorType.Name IndicatorType.ShortName
#> 1 Geographic distribution NA
#> 2 Geographic distribution NA
#> 3 Urban-Rural distribution: Rates NA
#> 4 Population in urban locations NA
#> 5 Population in urban locations NA
#> 6 Population by sex NA
#> IndicatorType.SortOrder IndicatorType.Description
#> 1 4 NA
#> 2 4 NA
#> 3 6 NA
#> 4 8 NA
#> 5 8 NA
#> 6 9 NAThe output is all IndicatorTypeID, together with the IndicatorIds and ComponentIDs. The advantage of get_iitypes is that you can select any of then with the function:
get_iitypes(indicatorIds = 239)
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatorindicatortypes?indicatorIds=239
#> PK_IndicatorID DemoBaseIndicatorID SortOrder IsActive IsInput IsOutput
#> 1 239 16 288 TRUE TRUE TRUE
#> Name ShortName LongName
#> 1 Under-five mortality (5q0) U5MR Under-five mortality rate by sex (5q0)
#> PrintName
#> 1 Under-five mortality by sex, U5MR (5q0)
#> Description UnitScaling
#> 1 Probability of dying between birth and the fifth birthday. 1
#> UnitShortLabel UnitLongLabel VariableType FormatString DomainID ComponentID
#> 1 per live birth per live birth relative #0.000 1 4
#> IndicatorTypeID IndicatorClassID dimTime dimAge AgeUnit AgeObservationPlanID
#> 1 22 3 TRUE FALSE Year 5
#> AgeID AgeStart AgeSpan IsComplete dimSex SexID DefaultSubGroupID AgeEnd
#> 1 301 0 5 2 TRUE NA 2 5
#> MinValue MaxValue IndicatorType.PK_IndicatorTypeID IndicatorType.ComponentID
#> 1 0 1 22 4
#> IndicatorType.ComponentName IndicatorType.Name IndicatorType.ShortName
#> 1 Mortality Under-five mortality NA
#> IndicatorType.SortOrder IndicatorType.Description
#> 1 24 NA
get_iitypes(indicatorTypeIds = 2)
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatorindicatortypes?indicatorTypeIds=2
#> PK_IndicatorID DemoBaseIndicatorID SortOrder IsActive IsInput IsOutput
#> 1 13 2314 4 TRUE FALSE TRUE
#> 2 14 2313 8 TRUE FALSE TRUE
#> 3 360 NA 624 TRUE FALSE TRUE
#> 4 359 NA 628 TRUE FALSE TRUE
#> 5 152 NA 636 TRUE FALSE TRUE
#> 6 402 NA 640 TRUE FALSE TRUE
#> 7 403 NA 644 TRUE FALSE TRUE
#> 8 405 NA 652 TRUE FALSE TRUE
#> 9 406 NA 656 TRUE FALSE TRUE
#> 10 407 NA 660 TRUE FALSE TRUE
#> 11 364 NA 688 TRUE FALSE TRUE
#> 12 365 NA 692 TRUE FALSE TRUE
#> Name ShortName
#> 1 Surface Area (square km) SurfaceArea
#> 2 Population density PopDensity
#> 3 Total population change PopChange
#> 4 Crude rate of natural change of population NatChangeRT
#> 5 Crude rate of total population change PopChangeRT
#> 6 Population turnover PopT
#> 7 Natural turnover NatT
#> 8 Crude rate of population turnover PopTRT
#> 9 Crude rate of natural turnover NatTRT
#> 10 Crude Rate of migration turnover plus statistical adjustment MigTRT
#> 11 Contraceptive prevalence any methods CUany
#> 12 Contraceptive prevalence by method CUmethods
#> LongName
#> 1 Surface Area (square km)
#> 2 Population density
#> 3 Total population change
#> 4 Crude rate of natural change of population
#> 5 Crude rate of total population change
#> 6 Sum of population changes (population turnover)
#> 7 Sum of births and deaths (natural turnover)
#> 8 Crude rate of sum of population changes (crude rate of population turnover)
#> 9 Crude rate of sum of births and deaths (crude rate of natural turnover)
#> 10 Crude rate of the sum of immigration and emigration plus statistical adjustment (Crude Rate of migration turnover plus statistical adjustment)
#> 11 Contraceptive prevalence any methods
#> 12 Contraceptive prevalence by method
#> PrintName
#> 1 Surface Area (square km)
#> 2 Population density (persons per square km)
#> 3 Total population change
#> 4 Crude rate of natural change of population
#> 5 Crude rate of total population change
#> 6 Sum of population changes (population turnover)
#> 7 Sum of births and deaths (natural turnover)
#> 8 Crude rate of sum of population changes (crude rate of population turnover)
#> 9 Crude rate of sum of births and deaths (crude rate of natural turnover)
#> 10 Crude rate of the sum of immigration and emigration plus statistical adjustment (Crude Rate of migration turnover plus statistical adjustment)
#> 11 Contraceptive prevalence any methods
#> 12 Contraceptive prevalence by method
#> Description
#> 1 Total surface area, comprising land area and inland waters (assumed to consist of major rivers and lakes) and excluding only Polar Regions and uninhabited islands. The surface area given is the most recent estimate available. They are presented in square kilometres, a conversion factor of 2.589988 having been applied to surface areas originally reported in square miles.
#> 2 Number of persons per square Kilometer.
#> 3 The difference between the size of the population at the end and the beginning of the period Specifically, it is the difference in population size on 1 January of two consecutive years. A positive population change is also referred to as population growth. A negative population change is also referred to as population decline. The population change consists of two components: natural change and net migration.
#> 4 The crude rate of natural change is the ratio of the natural change during the year (live births minus deaths) to the average population in that year. The value is expressed per 1 000 persons.
#> 5 The crude rate of total change is the ratio of the population change during the year (the difference between the population sizes on 1 January of two consecutive years) to the average population in that year. The value is expressed per 1 000 persons.
#> 6 The total magnitude of changes in membership experienced by a population. It shows the 'gross' effect of the population components, by adding up gains (births and immigration) and losses (deaths and emigration) in a population, as compare to the population change which shows their 'net' effect. The population turnover consists of two components: natural turnover and migration turnover plus statistical adjustment.
#> 7 The sum of births and deaths numbers.
#> 8 Ratio of the population changes to the average population in a given year.
#> 9 Ratio of the sum of births and deaths to the average population in a given year.
#> 10 Ratio of the sum of immigration and emigration plus statistical adjustment to the average population in a given year.
#> 11 Contraceptive prevalence any methods
#> 12 Contraceptive prevalence by method
#> UnitScaling UnitShortLabel UnitLongLabel VariableType
#> 1 1 per sq. km per square kilometer absolute
#> 2 1 per sq. km per square kilometer relative
#> 3 1 persons persons absolute
#> 4 1000 per 1,000 population per 1,000 population relative
#> 5 1000 per 1,000 population per 1,000 population relative
#> 6 1 persons persons absolute
#> 7 1 persons persons absolute
#> 8 1000 per 1,000 population per 1,000 population relative
#> 9 1000 per 1,000 population per 1,000 population relative
#> 10 1000 per 1,000 population per 1,000 population relative
#> 11 100 per cent per cent relative
#> 12 100 per cent per cent relative
#> FormatString DomainID ComponentID IndicatorTypeID IndicatorClassID dimTime
#> 1 ### ### ##0 1 1 2 2 FALSE
#> 2 ### ### ##0 1 1 2 2 TRUE
#> 3 ### ### ##0 1 8 2 3 TRUE
#> 4 ##0.0 1 8 2 3 TRUE
#> 5 ##0.0 1 8 2 3 TRUE
#> 6 ### ### ##0 1 8 2 3 TRUE
#> 7 ### ### ##0 1 8 2 3 TRUE
#> 8 ##0.0 1 8 2 3 TRUE
#> 9 ##0.0 1 8 2 3 TRUE
#> 10 ##0.0 1 7 2 3 TRUE
#> 11 ##0.0 7 9 2 2 TRUE
#> 12 ##0.0 7 9 2 2 TRUE
#> dimAge AgeUnit AgeObservationPlanID AgeID AgeStart AgeSpan IsComplete dimSex
#> 1 FALSE Year 2 1 -9 -9 2 FALSE
#> 2 FALSE Year 2 700 0 -1 2 FALSE
#> 3 FALSE Year 5 398 0 -1 2 FALSE
#> 4 FALSE Year 5 398 0 -1 2 FALSE
#> 5 FALSE Year 5 398 0 -1 2 FALSE
#> 6 FALSE Year 5 398 0 -1 2 FALSE
#> 7 FALSE Year 5 398 0 -1 2 FALSE
#> 8 FALSE Year 5 398 0 -1 2 FALSE
#> 9 FALSE Year 5 398 0 -1 2 FALSE
#> 10 FALSE Year 5 398 0 -1 2 FALSE
#> 11 TRUE Year 5 3152 15 35 0 TRUE
#> 12 TRUE Year 5 3152 15 35 0 TRUE
#> SexID DefaultSubGroupID AgeEnd MinValue MaxValue
#> 1 9 2 -9 0 NA
#> 2 3 2 -1 0 NA
#> 3 3 2 -1 NA NA
#> 4 3 2 -1 NA NA
#> 5 3 2 -1 NA NA
#> 6 3 2 -1 NA NA
#> 7 3 2 -1 NA NA
#> 8 3 2 -1 NA NA
#> 9 3 2 -1 NA NA
#> 10 3 2 -1 NA NA
#> 11 2 2 50 0 100
#> 12 2 2 50 0 100
#> IndicatorType.PK_IndicatorTypeID IndicatorType.ComponentID
#> 1 2 1
#> 2 2 1
#> 3 2 1
#> 4 2 1
#> 5 2 1
#> 6 2 1
#> 7 2 1
#> 8 2 1
#> 9 2 1
#> 10 2 1
#> 11 2 1
#> 12 2 1
#> IndicatorType.ComponentName IndicatorType.Name IndicatorType.ShortName
#> 1 Population Geographic distribution NA
#> 2 Population Geographic distribution NA
#> 3 Population Geographic distribution NA
#> 4 Population Geographic distribution NA
#> 5 Population Geographic distribution NA
#> 6 Population Geographic distribution NA
#> 7 Population Geographic distribution NA
#> 8 Population Geographic distribution NA
#> 9 Population Geographic distribution NA
#> 10 Population Geographic distribution NA
#> 11 Population Geographic distribution NA
#> 12 Population Geographic distribution NA
#> IndicatorType.SortOrder IndicatorType.Description
#> 1 4 NA
#> 2 4 NA
#> 3 4 NA
#> 4 4 NA
#> 5 4 NA
#> 6 4 NA
#> 7 4 NA
#> 8 4 NA
#> 9 4 NA
#> 10 4 NA
#> 11 4 NA
#> 12 4 NAor ComponentIds:
get_iitypes(componentIds = 4)
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatorindicatortypes?componentIds=4
#> PK_IndicatorID DemoBaseIndicatorID SortOrder IsActive IsInput IsOutput
#> 1 100 NA 68 TRUE TRUE TRUE
#> 2 101 NA 72 TRUE TRUE TRUE
#> 3 102 NA 76 TRUE TRUE TRUE
#> 4 105 NA 80 TRUE TRUE TRUE
#> 5 106 NA 84 TRUE TRUE TRUE
#> 6 107 NA 88 TRUE TRUE TRUE
#> 7 188 1062 200 TRUE TRUE TRUE
#> 8 194 6 204 TRUE TRUE TRUE
#> 9 195 NA 208 FALSE TRUE TRUE
#> 10 196 NA 212 TRUE TRUE TRUE
#> 11 314 NA 216 TRUE FALSE TRUE
#> 12 315 NA 220 FALSE FALSE FALSE
#> 13 316 NA 224 TRUE TRUE TRUE
#> 14 197 NA 228 FALSE FALSE FALSE
#> 15 198 NA 232 FALSE FALSE FALSE
#> 16 217 1065 240 TRUE TRUE TRUE
#> 17 218 NA 244 TRUE FALSE TRUE
#> 18 219 NA 248 FALSE TRUE TRUE
#> 19 220 NA 252 FALSE TRUE TRUE
#> 20 229 9 256 TRUE TRUE TRUE
#> 21 221 NA 260 FALSE TRUE TRUE
#> 22 222 NA 264 FALSE TRUE TRUE
#> 23 226 NA 268 FALSE TRUE TRUE
#> 24 223 NA 272 FALSE TRUE TRUE
#> 25 224 NA 276 FALSE TRUE TRUE
#> 26 225 NA 280 FALSE TRUE TRUE
#> 27 234 NA 284 FALSE TRUE TRUE
#> 28 239 16 288 TRUE TRUE TRUE
#> 29 323 NA 292 TRUE FALSE TRUE
#> 30 327 NA 296 TRUE FALSE TRUE
#> 31 372 NA 300 TRUE FALSE TRUE
#> 32 324 NA 304 TRUE FALSE TRUE
#> 33 373 NA 308 TRUE FALSE TRUE
#> 34 325 NA 312 TRUE FALSE TRUE
#> 35 326 NA 316 TRUE FALSE TRUE
#> 36 328 NA 320 TRUE TRUE TRUE
#> 37 332 NA 324 TRUE FALSE TRUE
#> 38 370 NA 328 TRUE FALSE TRUE
#> 39 329 NA 332 TRUE TRUE TRUE
#> 40 371 NA 336 TRUE FALSE TRUE
#> 41 330 NA 340 TRUE FALSE TRUE
#> 42 331 NA 344 TRUE FALSE TRUE
#> Name
#> 1 Population by age and sex, and parental survival status - abridged
#> 2 Proportion of the population with mother alive by age and sex - abridged
#> 3 Proportion of the population with father alive by age and sex - abridged
#> 4 Population by age and sex, and sibling survival status - abridged
#> 5 Proportion of the population with sisters alive by age and sex - abridged
#> 6 Proportion of the population with brothers alive by age and sex - abridged
#> 7 Total deaths by sex
#> 8 Deaths by age and sex - abridged
#> 9 Deaths by age and sex - complete
#> 10 Completeness of death registration
#> 11 Infant and child deaths by sex and age
#> 12 Infant and child deaths by sex and age - unweighted
#> 13 Completeness of infant and child deaths registration
#> 14 Pregnancy-related deaths by age - abridged
#> 15 Total deaths due to conflict and natural disasters
#> 16 Crude death rate
#> 17 Crude death rate due to conflict and natural disasters
#> 18 Age-specific death rate - abridged
#> 19 Age-specific death rate - complete
#> 20 Infant mortality (1q0)
#> 21 Neonatal mortality
#> 22 Early neonatal mortality
#> 23 Late neonatal mortality
#> 24 Late foetal mortality
#> 25 Perinatal mortality
#> 26 Postneonatal mortality
#> 27 Child mortality (4q1)
#> 28 Under-five mortality (5q0)
#> 29 Children dead by age of mother (and by sex of child) - abridged
#> 30 Children dead by age of mother (and by sex of child), unweighted - abridged
#> 31 Children dead by age of mother (and by sex of child) - complete
#> 32 Mean children dead by age of mother (and by sex of child) - abridged
#> 33 Mean children dead by age of mother (and by sex of child) - complete
#> 34 Proportion children dead by age of mother (and by sex of child) - abridged
#> 35 Sex ratio children dead by age of mother - abridged
#> 36 Children surviving by age of mother (and by sex of child) - abridged
#> 37 Children surviving by age of mother (and by sex of child), unweighted - abridged
#> 38 Children surviving by age of mother (and by sex of child) - complete
#> 39 Mean children surviving by age of mother (and by sex of child) - abridged
#> 40 Mean children surviving by age of mother (and by sex of child) - complete
#> 41 Proportion children surviving by age of mother (and by sex of child) - abridged
#> 42 Sex ratio children surviving by age of mother - abridged
#> ShortName
#> 1 PopParentalSurv
#> 2 pMotherAlive
#> 3 pFatherAlive
#> 4 PopSiblingSurv
#> 5 pSisterAlive
#> 6 pBrotherAlive
#> 7 Tdeaths
#> 8 Deaths5
#> 9 Deaths1
#> 10 DeathsReg
#> 11 icDeaths
#> 12 icDeathsu
#> 13 icDeathsReg
#> 14 PregnancyDeaths
#> 15 CrisisTdeaths
#> 16 CDR
#> 17 CrisisCDR
#> 18 Mx
#> 19 Mx
#> 20 IMR
#> 21 NNM
#> 22 ENNM
#> 23 LNNM
#> 24 LFoeMort
#> 25 PeriMort
#> 26 PNNM
#> 27 CMR
#> 28 U5MR
#> 29 CD5
#> 30 CD5u
#> 31 CD1
#> 32 mCD5
#> 33 mCD1
#> 34 pCD5
#> 35 srCD5
#> 36 CS5
#> 37 CS5u
#> 38 CS1
#> 39 mCS5
#> 40 mCS1
#> 41 pCS5
#> 42 srCS5
#> LongName
#> 1 Population by age and sex, and parental survival status - abridged
#> 2 Proportion of the population with mother alive by age and sex - abridged
#> 3 Proportion of the population with father alive by age and sex - abridged
#> 4 Population by age and sex, and sibling survival status - abridged
#> 5 Proportion of the population with sisters alive by age and sex - abridged
#> 6 Proportion of the population with brothers alive by age and sex - abridged
#> 7 Total deaths by sex
#> 8 Deaths by age and sex - abridged
#> 9 Deaths by age and sex - complete
#> 10 Completeness of death registration
#> 11 Infant and child deaths by sex and age
#> 12 Infant and child deaths by sex and age - unweighted
#> 13 Completeness of infant and child deaths registration
#> 14 Pregnancy-related deaths by age - abridged
#> 15 Deaths due to conflict and natural disasters
#> 16 Crude death rate
#> 17 Crude death rate due to conflict and natural disasters
#> 18 Age-specific death rate - abridged
#> 19 Age-specific death rate - complete
#> 20 Infant mortality rate (1q0)
#> 21 Neonatal mortality rate
#> 22 Early neonatal mortality rate
#> 23 Late neonatal mortality rate
#> 24 Late foetal mortality rate
#> 25 Perinatal mortality rate
#> 26 Postneonatal mortality rate
#> 27 Child mortality rate by sex (4q1)
#> 28 Under-five mortality rate by sex (5q0)
#> 29 Children dead by age of mother (and by sex of child) - abridged
#> 30 Children dead by age of mother (and by sex of child), unweighted - abridged
#> 31 Children dead by age of mother (and by sex of child) - complete
#> 32 Mean children dead by age of mother (and by sex of child) - abridged
#> 33 Mean children dead by age of mother (and by sex of child) - complete
#> 34 Proportion children dead by age of mother (and by sex of child) - abridged
#> 35 Sex ratio children dead by age of mother - abridged
#> 36 Children surviving by age of mother (and by sex of child) - abridged
#> 37 Children surviving by age of mother (and by sex of child), unweighted - abridged
#> 38 Children surviving by age of mother (and by sex of child) - complete
#> 39 Mean children surviving by age of mother (and by sex of child) - abridged
#> 40 Mean children surviving by age of mother (and by sex of child) - complete
#> 41 Proportion children surviving by age of mother (and by sex of child) - abridged
#> 42 Sex ratio children surviving by age of mother - abridged
#> PrintName
#> 1 Population by age and sex, and parental survival status - abridged
#> 2 Proportion of the population with mother alive by age and sex - abridged
#> 3 Proportion of the population with father alive by age and sex - abridged
#> 4 Population by age and sex, and sibling survival status - abridged
#> 5 Proportion of the population with sisters alive by age and sex - abridged
#> 6 Proportion of the population with brothers alive by age and sex - abridged
#> 7 Total deaths by sex
#> 8 Deaths by age and sex - abridged
#> 9 Deaths by age and sex - complete
#> 10 Completeness of death registration
#> 11 Infant and child deaths by sex and age (months)
#> 12 Infant and child deaths by sex and age (months) - unweighted
#> 13 Completeness of infant and child deaths registration
#> 14 Pregnancy-related deaths by age - abridged
#> 15 Deaths due to conflict and natural disasters
#> 16 Crude death rate (deaths per 1,000 population)
#> 17 Crude death rate due to conflict and natural disasters (deaths per 1,000 population)
#> 18 Age-specific death rate - abridged
#> 19 Age-specific death rate - complete
#> 20 Infant mortality rate (1q0)
#> 21 Neonatal mortality rate
#> 22 Early neonatal mortality rate
#> 23 Late neonatal mortality rate
#> 24 Late foetal mortality rate
#> 25 Perinatal mortality rate
#> 26 Postneonatal mortality rate
#> 27 Child mortality rate by sex (4q1)
#> 28 Under-five mortality by sex, U5MR (5q0)
#> 29 Children dead by age of mother (and by sex of child) - abridged
#> 30 Children dead by age of mother (and by sex of child), unweighted - abridged
#> 31 Children dead by age of mother (and by sex of child) - complete
#> 32 Mean children dead by age of mother (and by sex of child) - abridged
#> 33 Mean children dead by age of mother (and by sex of child) - complete
#> 34 Proportion children dead by age of mother (and by sex of child) - abridged
#> 35 Sex ratio children dead by age of mother - abridged
#> 36 Children surviving by age of mother (and by sex of child) - abridged
#> 37 Children surviving by age of mother (and by sex of child), unweighted - abridged
#> 38 Children surviving by age of mother (and by sex of child) - complete
#> 39 Mean children surviving by age of mother (and by sex of child) - abridged
#> 40 Mean children surviving by age of mother (and by sex of child) - complete
#> 41 Proportion children surviving by age of mother (and by sex of child) - abridged
#> 42 Sex ratio children surviving by age of mother - abridged
#> Description
#> 1 Number of persons by age groups and by sex, and parental survival status
#> 2 Proportion of the population with mother alive by age groups and by sex
#> 3 Proportion of the population with father alive by age groups and by sex
#> 4 Number of persons by age groups and by sex, and sibling survival status
#> 5 Proportion of the population with sisters alive by age groups and by sex
#> 6 Proportion of the population with brothers alive by age groups and by sex
#> 7 Number of deaths over a given period.
#> 8 Number of deaths by age groups and by sex over a given period.
#> 9 Number of deaths by single age and by sex over a given period.
#> 10 Percentage of actual deaths recorded in the civil registration system.
#> 11 Number of deaths of infants and children who have not yet completed 5 year of life by age (months) and by sex over a given period.
#> 12 Number of deaths of infants and children who have not yet completed 5 year of life by age (months) and by sex over a given period - unweighted sample.
#> 13 Percentage of actual infant and child deaths recorded in the civil registration system.
#> 14 Pregnancy-related deaths by age - abridged
#> 15 Deaths due to conflict and natural disasters
#> 16 Number of deaths over a given period divided by the person-years lived by the population over that period.
#> 17 Number of deaths due to conflict and natural disasters over a given period divided by the person-years lived by the population over that period.
#> 18 Number of deaths by age groups and by sex divided by the number of person-years of exposure of the same age group and sex (for a specified time period).
#> 19 Number of deaths by single age and by sex divided by the number of person-years of exposure of the same age and sex (for a specified time period).
#> 20 Probability of dying before the first birthday.
#> 21 Probability of dying within the first month of life.
#> 22 Probability of dying within the first week of life.
#> 23 Probability of dying within the first 8-28 days of life.
#> 24 Ratio of the number of still births during the year to the number of total births (live births + still births) in that year.
#> 25 Ratio of the number of deaths of children under one week and the stillbirths during the year, to the number of births in that year (including still births).
#> 26 Difference between infant and neonatal mortality.
#> 27 Probability of dying between the first and fifth birthdays.
#> 28 Probability of dying between birth and the fifth birthday.
#> 29 Number of children dead by age groups of mother (and by sex of child)
#> 30 Number of children dead by single age of mother (and by sex of child)
#> 31 Number of children dead by age groups of mother (and by sex of child) - unweighted sample
#> 32 Mean number of children dead by age groups of mother (and by sex of child)
#> 33 Mean number of children dead by single age of mother (and by sex of child)
#> 34 Parity/Fertility Ratio by age of mother where P refers to the average parity (cumulated lifetime fertility) of a cohort of women up to a given age, and F is the cumulated current (period) fertility up to that same age.
#> 35 Number of male children dead per female children dead by age of mother (M/F)
#> 36 Number of children surviving by age groups of mother (and by sex of child)
#> 37 Number of children surviving by single age of mother (and by sex of child)
#> 38 Number of children surviving by age groups of mother (and by sex of child) - unweighted sample
#> 39 Mean number of children surviving by age groups of mother (and by sex of child)
#> 40 Mean number of children surviving by single age of mother (and by sex of child)
#> 41 Parity/Fertility Ratio by age of mother where P refers to the average parity (cumulated lifetime fertility) of a cohort of women up to a given age, and F is the cumulated current (period) fertility up to that same age.
#> 42 Number of male children surviving per female children surviving by age of mother (M/F)
#> UnitScaling UnitShortLabel
#> 1 1 persons
#> 2 1 persons
#> 3 1 persons
#> 4 1 persons
#> 5 1 persons
#> 6 1 persons
#> 7 1 persons
#> 8 1 persons
#> 9 1 persons
#> 10 100 per 100 deaths
#> 11 1 children
#> 12 1 children
#> 13 100 per 100 under-five deaths
#> 14 1 persons
#> 15 1 persons
#> 16 1000 per 1,000 population
#> 17 1000 per 1,000 population
#> 18 1 per persons
#> 19 1 per persons
#> 20 1 per live birth
#> 21 1 per live birth
#> 22 1 per live birth
#> 23 1 per live birth
#> 24 1 per live birth
#> 25 1 per live birth
#> 26 1 per child surviving to 1 month of age
#> 27 1 per child surviving to 12 months of age
#> 28 1 per live birth
#> 29 1 children
#> 30 1 children
#> 31 1 children
#> 32 1 per woman
#> 33 1 per woman
#> 34 1 per woman
#> 35 1 per female ever born
#> 36 1 children
#> 37 1 children
#> 38 1 children
#> 39 1 per woman
#> 40 1 per woman
#> 41 1 per woman
#> 42 1 per female ever born
#> UnitLongLabel VariableType FormatString DomainID
#> 1 persons absolute ### ### ##0 1
#> 2 persons absolute ### ### ##0 1
#> 3 persons absolute ### ### ##0 1
#> 4 persons absolute ### ### ##0 1
#> 5 persons absolute ### ### ##0 1
#> 6 persons absolute ### ### ##0 1
#> 7 persons absolute ### ### ##0 1
#> 8 persons absolute ### ### ##0 1
#> 9 persons absolute ### ### ##0 1
#> 10 per 100 deaths relative ##0.0 1
#> 11 children absolute ### ### ##0 1
#> 12 children absolute ### ### ##0 1
#> 13 per 100 under-five deaths relative ##0.0 1
#> 14 persons absolute ### ### ##0 1
#> 15 persons absolute ### ### ##0 1
#> 16 per 1,000 population relative ##0.0 1
#> 17 per 1,000 population relative ##0.0 1
#> 18 per persons relative #0.00000000 1
#> 19 per persons relative #0.00000000 1
#> 20 per live birth relative #0.000 1
#> 21 per live birth relative #0.000 1
#> 22 per live birth relative #0.000 1
#> 23 per live birth relative #0.000 1
#> 24 per live birth relative #0.000 1
#> 25 per live birth relative #0.000 1
#> 26 per child surviving to 1 month of age relative #0.000 1
#> 27 per child surviving to 12 months of age relative #0.000 1
#> 28 per live birth relative #0.000 1
#> 29 children absolute ### ### ##0 1
#> 30 children absolute ### ### ##0 1
#> 31 children absolute ### ### ##0 1
#> 32 per woman relative #0.000 1
#> 33 per woman relative #0.000 1
#> 34 per woman relative #0.000 1
#> 35 per female ever born relative #0.000 1
#> 36 children absolute ### ### ##0 1
#> 37 children absolute ### ### ##0 1
#> 38 children absolute ### ### ##0 1
#> 39 per woman relative #0.000 1
#> 40 per woman relative #0.000 1
#> 41 per woman relative #0.000 1
#> 42 per female ever born relative #0.000 1
#> ComponentID IndicatorTypeID IndicatorClassID dimTime dimAge AgeUnit
#> 1 4 41 2 TRUE TRUE Year
#> 2 4 41 2 TRUE TRUE Year
#> 3 4 41 2 TRUE TRUE Year
#> 4 4 42 2 TRUE TRUE Year
#> 5 4 42 2 TRUE TRUE Year
#> 6 4 42 2 TRUE TRUE Year
#> 7 4 19 3 TRUE FALSE Year
#> 8 4 20 3 TRUE TRUE Year
#> 9 4 20 3 TRUE TRUE Year
#> 10 4 20 3 TRUE FALSE Year
#> 11 4 20 3 TRUE TRUE Day
#> 12 4 20 3 TRUE TRUE Day
#> 13 4 20 3 TRUE FALSE Day
#> 14 4 43 3 TRUE TRUE Year
#> 15 4 19 3 TRUE FALSE Year
#> 16 4 24 3 TRUE FALSE Year
#> 17 4 24 3 TRUE FALSE Year
#> 18 4 25 3 TRUE TRUE Year
#> 19 4 25 3 TRUE TRUE Year
#> 20 4 21 3 TRUE FALSE Year
#> 21 4 26 3 TRUE FALSE Day
#> 22 4 26 3 TRUE FALSE Day
#> 23 4 26 3 TRUE FALSE Day
#> 24 4 26 3 TRUE FALSE Day
#> 25 4 26 3 TRUE FALSE Day
#> 26 4 27 3 TRUE FALSE Month
#> 27 4 28 3 TRUE FALSE Year
#> 28 4 22 3 TRUE FALSE Year
#> 29 4 38 2 TRUE TRUE Year
#> 30 4 38 2 TRUE TRUE Year
#> 31 4 38 2 TRUE TRUE Year
#> 32 4 38 2 TRUE TRUE Year
#> 33 4 38 2 TRUE TRUE Year
#> 34 4 38 2 TRUE TRUE Year
#> 35 4 38 2 TRUE TRUE Year
#> 36 4 38 2 TRUE TRUE Year
#> 37 4 38 2 TRUE TRUE Year
#> 38 4 38 2 TRUE TRUE Year
#> 39 4 38 2 TRUE TRUE Year
#> 40 4 38 2 TRUE TRUE Year
#> 41 4 38 2 TRUE TRUE Year
#> 42 4 38 2 TRUE TRUE Year
#> AgeObservationPlanID AgeID AgeStart AgeSpan IsComplete dimSex SexID
#> 1 2 701 0 5 0 TRUE NA
#> 2 2 701 0 5 0 TRUE NA
#> 3 2 701 0 5 0 TRUE NA
#> 4 2 704 15 5 0 TRUE NA
#> 5 2 704 15 5 0 TRUE NA
#> 6 2 704 15 5 0 TRUE NA
#> 7 5 398 0 -1 2 TRUE NA
#> 8 5 301 0 5 0 TRUE NA
#> 9 5 330 0 1 1 TRUE NA
#> 10 5 398 0 -1 2 FALSE 3
#> 11 5 NA 0 NA 0 TRUE NA
#> 12 5 NA 0 NA 0 TRUE NA
#> 13 5 NA 0 -1 2 FALSE 3
#> 14 5 304 15 5 0 FALSE 2
#> 15 5 398 0 -1 2 TRUE NA
#> 16 5 398 0 -1 2 FALSE 3
#> 17 5 398 0 -1 2 FALSE 3
#> 18 5 301 0 5 0 TRUE NA
#> 19 5 330 0 1 1 TRUE NA
#> 20 5 330 0 1 2 TRUE NA
#> 21 5 NA 0 28 2 TRUE NA
#> 22 5 30522 0 7 2 TRUE NA
#> 23 5 NA 8 28 2 TRUE NA
#> 24 5 NA -84 84 2 TRUE NA
#> 25 5 NA -84 91 2 TRUE NA
#> 26 5 30489 1 11 2 TRUE NA
#> 27 5 331 1 4 2 TRUE NA
#> 28 5 301 0 5 2 TRUE NA
#> 29 2 703 10 5 0 TRUE NA
#> 30 2 703 10 5 0 TRUE NA
#> 31 2 2011 10 1 1 TRUE NA
#> 32 2 703 10 5 0 TRUE NA
#> 33 2 2011 10 1 1 TRUE NA
#> 34 2 703 10 5 0 TRUE NA
#> 35 2 703 10 5 0 TRUE NA
#> 36 2 703 10 5 0 TRUE NA
#> 37 2 703 10 5 0 TRUE NA
#> 38 2 2011 10 1 1 TRUE NA
#> 39 2 703 10 5 0 TRUE NA
#> 40 2 2011 10 1 1 TRUE NA
#> 41 2 703 10 5 0 TRUE NA
#> 42 2 703 10 5 0 TRUE NA
#> DefaultSubGroupID AgeEnd MinValue MaxValue IndicatorType.PK_IndicatorTypeID
#> 1 2 80 0 NA 41
#> 2 2 80 0 NA 41
#> 3 2 80 0 NA 41
#> 4 2 80 0 NA 42
#> 5 2 80 0 NA 42
#> 6 2 80 0 NA 42
#> 7 2 -1 0 NA 19
#> 8 2 50 0 NA 20
#> 9 2 50 0 NA 20
#> 10 2 -1 0 100 20
#> 11 2 NA 0 NA 20
#> 12 2 NA 0 NA 20
#> 13 2 -1 0 100 20
#> 14 2 50 0 NA 43
#> 15 2 -1 0 NA 19
#> 16 2 -1 0 100 24
#> 17 2 -1 0 100 24
#> 18 2 50 0 2 25
#> 19 2 50 0 2 25
#> 20 2 1 0 1 21
#> 21 2 28 0 1 26
#> 22 2 7 0 1 26
#> 23 2 21 0 1 26
#> 24 2 0 0 1 26
#> 25 2 7 0 1 26
#> 26 2 12 0 1 27
#> 27 2 5 0 1 28
#> 28 2 5 0 1 22
#> 29 2 80 0 NA 38
#> 30 2 80 0 NA 38
#> 31 2 80 0 NA 38
#> 32 2 80 0 20 38
#> 33 2 80 0 20 38
#> 34 2 80 0 1 38
#> 35 2 80 0 NA 38
#> 36 2 50 0 NA 38
#> 37 2 80 0 NA 38
#> 38 2 80 0 NA 38
#> 39 2 50 0 20 38
#> 40 2 80 0 20 38
#> 41 2 80 0 20 38
#> 42 2 80 0 1 38
#> IndicatorType.ComponentID IndicatorType.ComponentName
#> 1 4 Mortality
#> 2 4 Mortality
#> 3 4 Mortality
#> 4 4 Mortality
#> 5 4 Mortality
#> 6 4 Mortality
#> 7 4 Mortality
#> 8 4 Mortality
#> 9 4 Mortality
#> 10 4 Mortality
#> 11 4 Mortality
#> 12 4 Mortality
#> 13 4 Mortality
#> 14 4 Mortality
#> 15 4 Mortality
#> 16 4 Mortality
#> 17 4 Mortality
#> 18 4 Mortality
#> 19 4 Mortality
#> 20 4 Mortality
#> 21 4 Mortality
#> 22 4 Mortality
#> 23 4 Mortality
#> 24 4 Mortality
#> 25 4 Mortality
#> 26 4 Mortality
#> 27 4 Mortality
#> 28 4 Mortality
#> 29 4 Mortality
#> 30 4 Mortality
#> 31 4 Mortality
#> 32 4 Mortality
#> 33 4 Mortality
#> 34 4 Mortality
#> 35 4 Mortality
#> 36 4 Mortality
#> 37 4 Mortality
#> 38 4 Mortality
#> 39 4 Mortality
#> 40 4 Mortality
#> 41 4 Mortality
#> 42 4 Mortality
#> IndicatorType.Name IndicatorType.ShortName
#> 1 Parental orphanhood NA
#> 2 Parental orphanhood NA
#> 3 Parental orphanhood NA
#> 4 Sibling survival NA
#> 5 Sibling survival NA
#> 6 Sibling survival NA
#> 7 Deaths by sex NA
#> 8 Deaths by age and sex NA
#> 9 Deaths by age and sex NA
#> 10 Deaths by age and sex NA
#> 11 Deaths by age and sex NA
#> 12 Deaths by age and sex NA
#> 13 Deaths by age and sex NA
#> 14 Maternal mortality NA
#> 15 Deaths by sex NA
#> 16 Total mortality: Rates NA
#> 17 Total mortality: Rates NA
#> 18 Mortality rates by age and sex NA
#> 19 Mortality rates by age and sex NA
#> 20 Infant mortality NA
#> 21 Neonatal mortality NA
#> 22 Neonatal mortality NA
#> 23 Neonatal mortality NA
#> 24 Neonatal mortality NA
#> 25 Neonatal mortality NA
#> 26 Postneonatal mortality NA
#> 27 Child mortality (age 1-4) NA
#> 28 Under-five mortality NA
#> 29 Children deaths by age of mother NA
#> 30 Children deaths by age of mother NA
#> 31 Children deaths by age of mother NA
#> 32 Children deaths by age of mother NA
#> 33 Children deaths by age of mother NA
#> 34 Children deaths by age of mother NA
#> 35 Children deaths by age of mother NA
#> 36 Children deaths by age of mother NA
#> 37 Children deaths by age of mother NA
#> 38 Children deaths by age of mother NA
#> 39 Children deaths by age of mother NA
#> 40 Children deaths by age of mother NA
#> 41 Children deaths by age of mother NA
#> 42 Children deaths by age of mother NA
#> IndicatorType.SortOrder IndicatorType.Description
#> 1 34 NA
#> 2 34 NA
#> 3 34 NA
#> 4 35 NA
#> 5 35 NA
#> 6 35 NA
#> 7 20 NA
#> 8 21 NA
#> 9 21 NA
#> 10 21 NA
#> 11 21 NA
#> 12 21 NA
#> 13 21 NA
#> 14 36 NA
#> 15 20 NA
#> 16 26 NA
#> 17 26 NA
#> 18 27 NA
#> 19 27 NA
#> 20 23 NA
#> 21 28 NA
#> 22 28 NA
#> 23 28 NA
#> 24 28 NA
#> 25 28 NA
#> 26 29 NA
#> 27 30 NA
#> 28 24 NA
#> 29 22 NA
#> 30 22 NA
#> 31 22 NA
#> 32 22 NA
#> 33 22 NA
#> 34 22 NA
#> 35 22 NA
#> 36 22 NA
#> 37 22 NA
#> 38 22 NA
#> 39 22 NA
#> 40 22 NA
#> 41 22 NA
#> 42 22 NAAlthough this is handy for exploration, what’s even more handy is the combination of parameters:
get_iitypes(componentIds = 4, indicatorTypeIds = 38)
#>
#> https://popdiv.dfs.un.org/DemoData/api/indicatorindicatortypes?indicatorTypeIds=38&componentIds=4
#> PK_IndicatorID DemoBaseIndicatorID SortOrder IsActive IsInput IsOutput
#> 1 323 NA 292 TRUE FALSE TRUE
#> 2 327 NA 296 TRUE FALSE TRUE
#> 3 372 NA 300 TRUE FALSE TRUE
#> 4 324 NA 304 TRUE FALSE TRUE
#> 5 373 NA 308 TRUE FALSE TRUE
#> 6 325 NA 312 TRUE FALSE TRUE
#> 7 326 NA 316 TRUE FALSE TRUE
#> 8 328 NA 320 TRUE TRUE TRUE
#> 9 332 NA 324 TRUE FALSE TRUE
#> 10 370 NA 328 TRUE FALSE TRUE
#> 11 329 NA 332 TRUE TRUE TRUE
#> 12 371 NA 336 TRUE FALSE TRUE
#> 13 330 NA 340 TRUE FALSE TRUE
#> 14 331 NA 344 TRUE FALSE TRUE
#> Name
#> 1 Children dead by age of mother (and by sex of child) - abridged
#> 2 Children dead by age of mother (and by sex of child), unweighted - abridged
#> 3 Children dead by age of mother (and by sex of child) - complete
#> 4 Mean children dead by age of mother (and by sex of child) - abridged
#> 5 Mean children dead by age of mother (and by sex of child) - complete
#> 6 Proportion children dead by age of mother (and by sex of child) - abridged
#> 7 Sex ratio children dead by age of mother - abridged
#> 8 Children surviving by age of mother (and by sex of child) - abridged
#> 9 Children surviving by age of mother (and by sex of child), unweighted - abridged
#> 10 Children surviving by age of mother (and by sex of child) - complete
#> 11 Mean children surviving by age of mother (and by sex of child) - abridged
#> 12 Mean children surviving by age of mother (and by sex of child) - complete
#> 13 Proportion children surviving by age of mother (and by sex of child) - abridged
#> 14 Sex ratio children surviving by age of mother - abridged
#> ShortName
#> 1 CD5
#> 2 CD5u
#> 3 CD1
#> 4 mCD5
#> 5 mCD1
#> 6 pCD5
#> 7 srCD5
#> 8 CS5
#> 9 CS5u
#> 10 CS1
#> 11 mCS5
#> 12 mCS1
#> 13 pCS5
#> 14 srCS5
#> LongName
#> 1 Children dead by age of mother (and by sex of child) - abridged
#> 2 Children dead by age of mother (and by sex of child), unweighted - abridged
#> 3 Children dead by age of mother (and by sex of child) - complete
#> 4 Mean children dead by age of mother (and by sex of child) - abridged
#> 5 Mean children dead by age of mother (and by sex of child) - complete
#> 6 Proportion children dead by age of mother (and by sex of child) - abridged
#> 7 Sex ratio children dead by age of mother - abridged
#> 8 Children surviving by age of mother (and by sex of child) - abridged
#> 9 Children surviving by age of mother (and by sex of child), unweighted - abridged
#> 10 Children surviving by age of mother (and by sex of child) - complete
#> 11 Mean children surviving by age of mother (and by sex of child) - abridged
#> 12 Mean children surviving by age of mother (and by sex of child) - complete
#> 13 Proportion children surviving by age of mother (and by sex of child) - abridged
#> 14 Sex ratio children surviving by age of mother - abridged
#> PrintName
#> 1 Children dead by age of mother (and by sex of child) - abridged
#> 2 Children dead by age of mother (and by sex of child), unweighted - abridged
#> 3 Children dead by age of mother (and by sex of child) - complete
#> 4 Mean children dead by age of mother (and by sex of child) - abridged
#> 5 Mean children dead by age of mother (and by sex of child) - complete
#> 6 Proportion children dead by age of mother (and by sex of child) - abridged
#> 7 Sex ratio children dead by age of mother - abridged
#> 8 Children surviving by age of mother (and by sex of child) - abridged
#> 9 Children surviving by age of mother (and by sex of child), unweighted - abridged
#> 10 Children surviving by age of mother (and by sex of child) - complete
#> 11 Mean children surviving by age of mother (and by sex of child) - abridged
#> 12 Mean children surviving by age of mother (and by sex of child) - complete
#> 13 Proportion children surviving by age of mother (and by sex of child) - abridged
#> 14 Sex ratio children surviving by age of mother - abridged
#> Description
#> 1 Number of children dead by age groups of mother (and by sex of child)
#> 2 Number of children dead by single age of mother (and by sex of child)
#> 3 Number of children dead by age groups of mother (and by sex of child) - unweighted sample
#> 4 Mean number of children dead by age groups of mother (and by sex of child)
#> 5 Mean number of children dead by single age of mother (and by sex of child)
#> 6 Parity/Fertility Ratio by age of mother where P refers to the average parity (cumulated lifetime fertility) of a cohort of women up to a given age, and F is the cumulated current (period) fertility up to that same age.
#> 7 Number of male children dead per female children dead by age of mother (M/F)
#> 8 Number of children surviving by age groups of mother (and by sex of child)
#> 9 Number of children surviving by single age of mother (and by sex of child)
#> 10 Number of children surviving by age groups of mother (and by sex of child) - unweighted sample
#> 11 Mean number of children surviving by age groups of mother (and by sex of child)
#> 12 Mean number of children surviving by single age of mother (and by sex of child)
#> 13 Parity/Fertility Ratio by age of mother where P refers to the average parity (cumulated lifetime fertility) of a cohort of women up to a given age, and F is the cumulated current (period) fertility up to that same age.
#> 14 Number of male children surviving per female children surviving by age of mother (M/F)
#> UnitScaling UnitShortLabel UnitLongLabel VariableType
#> 1 1 children children absolute
#> 2 1 children children absolute
#> 3 1 children children absolute
#> 4 1 per woman per woman relative
#> 5 1 per woman per woman relative
#> 6 1 per woman per woman relative
#> 7 1 per female ever born per female ever born relative
#> 8 1 children children absolute
#> 9 1 children children absolute
#> 10 1 children children absolute
#> 11 1 per woman per woman relative
#> 12 1 per woman per woman relative
#> 13 1 per woman per woman relative
#> 14 1 per female ever born per female ever born relative
#> FormatString DomainID ComponentID IndicatorTypeID IndicatorClassID dimTime
#> 1 ### ### ##0 1 4 38 2 TRUE
#> 2 ### ### ##0 1 4 38 2 TRUE
#> 3 ### ### ##0 1 4 38 2 TRUE
#> 4 #0.000 1 4 38 2 TRUE
#> 5 #0.000 1 4 38 2 TRUE
#> 6 #0.000 1 4 38 2 TRUE
#> 7 #0.000 1 4 38 2 TRUE
#> 8 ### ### ##0 1 4 38 2 TRUE
#> 9 ### ### ##0 1 4 38 2 TRUE
#> 10 ### ### ##0 1 4 38 2 TRUE
#> 11 #0.000 1 4 38 2 TRUE
#> 12 #0.000 1 4 38 2 TRUE
#> 13 #0.000 1 4 38 2 TRUE
#> 14 #0.000 1 4 38 2 TRUE
#> dimAge AgeUnit AgeObservationPlanID AgeID AgeStart AgeSpan IsComplete dimSex
#> 1 TRUE Year 2 703 10 5 0 TRUE
#> 2 TRUE Year 2 703 10 5 0 TRUE
#> 3 TRUE Year 2 2011 10 1 1 TRUE
#> 4 TRUE Year 2 703 10 5 0 TRUE
#> 5 TRUE Year 2 2011 10 1 1 TRUE
#> 6 TRUE Year 2 703 10 5 0 TRUE
#> 7 TRUE Year 2 703 10 5 0 TRUE
#> 8 TRUE Year 2 703 10 5 0 TRUE
#> 9 TRUE Year 2 703 10 5 0 TRUE
#> 10 TRUE Year 2 2011 10 1 1 TRUE
#> 11 TRUE Year 2 703 10 5 0 TRUE
#> 12 TRUE Year 2 2011 10 1 1 TRUE
#> 13 TRUE Year 2 703 10 5 0 TRUE
#> 14 TRUE Year 2 703 10 5 0 TRUE
#> SexID DefaultSubGroupID AgeEnd MinValue MaxValue
#> 1 NA 2 80 0 NA
#> 2 NA 2 80 0 NA
#> 3 NA 2 80 0 NA
#> 4 NA 2 80 0 20
#> 5 NA 2 80 0 20
#> 6 NA 2 80 0 1
#> 7 NA 2 80 0 NA
#> 8 NA 2 50 0 NA
#> 9 NA 2 80 0 NA
#> 10 NA 2 80 0 NA
#> 11 NA 2 50 0 20
#> 12 NA 2 80 0 20
#> 13 NA 2 80 0 20
#> 14 NA 2 80 0 1
#> IndicatorType.PK_IndicatorTypeID IndicatorType.ComponentID
#> 1 38 4
#> 2 38 4
#> 3 38 4
#> 4 38 4
#> 5 38 4
#> 6 38 4
#> 7 38 4
#> 8 38 4
#> 9 38 4
#> 10 38 4
#> 11 38 4
#> 12 38 4
#> 13 38 4
#> 14 38 4
#> IndicatorType.ComponentName IndicatorType.Name
#> 1 Mortality Children deaths by age of mother
#> 2 Mortality Children deaths by age of mother
#> 3 Mortality Children deaths by age of mother
#> 4 Mortality Children deaths by age of mother
#> 5 Mortality Children deaths by age of mother
#> 6 Mortality Children deaths by age of mother
#> 7 Mortality Children deaths by age of mother
#> 8 Mortality Children deaths by age of mother
#> 9 Mortality Children deaths by age of mother
#> 10 Mortality Children deaths by age of mother
#> 11 Mortality Children deaths by age of mother
#> 12 Mortality Children deaths by age of mother
#> 13 Mortality Children deaths by age of mother
#> 14 Mortality Children deaths by age of mother
#> IndicatorType.ShortName IndicatorType.SortOrder IndicatorType.Description
#> 1 NA 22 NA
#> 2 NA 22 NA
#> 3 NA 22 NA
#> 4 NA 22 NA
#> 5 NA 22 NA
#> 6 NA 22 NA
#> 7 NA 22 NA
#> 8 NA 22 NA
#> 9 NA 22 NA
#> 10 NA 22 NA
#> 11 NA 22 NA
#> 12 NA 22 NA
#> 13 NA 22 NA
#> 14 NA 22 NAThis approach to exploring indicators is much more rich that selecting indicators manually with the get_* functions.