A function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.
Usage
ApiData(
urlToData,
...,
getDataByGET = FALSE,
returnMetaData = FALSE,
returnMetaValues = FALSE,
returnMetaFrames = FALSE,
returnApiQuery = FALSE,
defaultJSONquery = c(1, -2, -1),
verbosePrint = FALSE,
use_factors = FALSE,
urlType = "SSB",
apiPackage = "httr",
dataPackage = "rjstat",
returnDataSet = NULL,
makeNAstatus = TRUE,
responseFormat = "json-stat2"
)
GetApiData(..., getDataByGET = TRUE)
pxwebData(..., apiPackage = "pxweb", dataPackage = "pxweb")
PxData(..., apiPackage = "pxweb", dataPackage = "rjstat")
ApiData1(..., returnDataSet = 1)
ApiData2(..., returnDataSet = 2)
ApiData12(..., returnDataSet = 12)
GetApiData1(..., returnDataSet = 1)
GetApiData2(..., returnDataSet = 2)
GetApiData12(..., returnDataSet = 12)
pxwebData1(..., returnDataSet = 1)
pxwebData2(..., returnDataSet = 2)
pxwebData12(..., returnDataSet = 12)
PxData1(..., returnDataSet = 1)
PxData2(..., returnDataSet = 2)
PxData12(..., returnDataSet = 12)Arguments
- urlToData
url to data or id of SSB data
- ...
specification of JSON query for each variable
- getDataByGET
When TRUE, readymade dataset by GET
- returnMetaData
When TRUE, metadata returned
- returnMetaValues
When TRUE, values from metadata returned
- returnMetaFrames
When TRUE, values and valueTexts from metadata returned as data frames
- returnApiQuery
When TRUE, JSON query returned
- defaultJSONquery
specification for variables not included in ...
- verbosePrint
When TRUE, printing to console
- use_factors
Parameter to
fromJSONstatdefining whether dimension categories should be factors or character objects.- urlType
Parameter defining how url is constructed from id number. Currently two Statistics Norway possibilities: "SSB" (Norwegian) or "SSBen" (English)
- apiPackage
Package used to capture json(-stat) data from API:
"httr"(default) or"pxweb"- dataPackage
Package used to transform json(-stat) data to data frame:
"rjstat"(default) or"pxweb"- returnDataSet
Possible non-NULL values are
1,2and12. Then a single data set is returned as a data frame.1: The first data set2: The second data set12: Both data sets combined
- makeNAstatus
When TRUE and when dataPackage is
"rjstat"and when missing entries invalue, the function tries to add an additional variable, namedNAstatus, with status codes.- responseFormat
Response format to be used when
apiPackageanddataPackageare defaults ("json-stat"or"json-stat2").
Details
Each variable is specified by using the variable name as input parameter. The value can be specified as: TRUE (all), FALSE (eliminated), imaginary value (top), variable indices, original variable id's (values) or variable labels (valueTexts). Reversed indices can be specified as negative values. Indices outside the range are removed. Variables not specified is set to the value of defaultJSONquery whose default means the first and the two last elements.
The value can also be specified as a (unnamed) two-element list corresponding to the two query elements, filter and values. In addition it possible with a single-element list. Then filter is set to 'all'. See examples.
A comment attribute with elements label, source and updated is added to output as a named character vector.
When available, the elements tableid and contents are also included, resulting in a vector with 3 to 5 elements.
Run comment to obtain this information.
Functionality in the package pxweb can be utilized by making use of the parameters
apiPackage and dataPackage
as implemented as the wrappers PxData and pxwebData.
With data sets too large for ordinary downloads, PxData can solve the problem (multiple downloads).
When using pxwebData, data will be downloaded in px-json format instead of json-stat and the output data frame
will be organized differently (ContentsCode categories as separate variables).
Examples
# \donttest{
##### Readymade dataset by GET. Works for readymade datasets and "saved-JSON-stat-query-links".
x <- ApiData("https://data.ssb.no/api/v0/dataset/1066.json?lang=en", getDataByGET = TRUE)
x[[1]] # The label version of the data set
#> industry month
#> 1 Retail trade, except of motor vehicles and motorcycles 2023M09
#> 2 Retail trade, except of motor vehicles and motorcycles 2023M10
#> 3 Retail trade, except of motor vehicles and motorcycles 2023M11
#> 4 Retail trade, except of motor vehicles and motorcycles 2023M12
#> 5 Retail trade, except of motor vehicles and motorcycles 2024M01
#> 6 Retail trade, except of motor vehicles and motorcycles 2024M02
#> 7 Retail trade, except of motor vehicles and motorcycles 2024M03
#> 8 Retail trade, except of motor vehicles and motorcycles 2024M04
#> 9 Retail trade, except of motor vehicles and motorcycles 2024M05
#> 10 Retail trade, except of motor vehicles and motorcycles 2024M06
#> 11 Retail trade, except of motor vehicles and motorcycles 2024M07
#> 12 Retail trade, except of motor vehicles and motorcycles 2024M08
#> 13 Retail trade, except of motor vehicles and motorcycles 2024M09
#> contents value
#> 1 Volume index, seasonally adjusted 92.3
#> 2 Volume index, seasonally adjusted 92.6
#> 3 Volume index, seasonally adjusted 92.9
#> 4 Volume index, seasonally adjusted 92.1
#> 5 Volume index, seasonally adjusted 92.1
#> 6 Volume index, seasonally adjusted 92.2
#> 7 Volume index, seasonally adjusted 92.6
#> 8 Volume index, seasonally adjusted 92.5
#> 9 Volume index, seasonally adjusted 95.9
#> 10 Volume index, seasonally adjusted 91.0
#> 11 Volume index, seasonally adjusted 92.1
#> 12 Volume index, seasonally adjusted 92.2
#> 13 Volume index, seasonally adjusted 91.9
x[[2]] # The id version of the data set
#> NACE Tid ContentsCode value
#> 1 47 2023M09 VolumSesong 92.3
#> 2 47 2023M10 VolumSesong 92.6
#> 3 47 2023M11 VolumSesong 92.9
#> 4 47 2023M12 VolumSesong 92.1
#> 5 47 2024M01 VolumSesong 92.1
#> 6 47 2024M02 VolumSesong 92.2
#> 7 47 2024M03 VolumSesong 92.6
#> 8 47 2024M04 VolumSesong 92.5
#> 9 47 2024M05 VolumSesong 95.9
#> 10 47 2024M06 VolumSesong 91.0
#> 11 47 2024M07 VolumSesong 92.1
#> 12 47 2024M08 VolumSesong 92.2
#> 13 47 2024M09 VolumSesong 91.9
names(x)
#> [1] "07129: The Index of wholesale and retail trade, by industry, month and contents"
#> [2] "dataset"
comment(x)
#> label
#> "07129: The Index of wholesale and retail trade, by industry, month and contents"
#> source
#> "Statistics Norway"
#> updated
#> "2024-10-30T07:00:00Z"
##### As above with single data set output
url <- "https://data.ssb.no/api/v0/dataset/1066.json?lang=en"
x1 <- ApiData1(url, getDataByGET = TRUE) # as x[[1]]
x2 <- ApiData2(url, getDataByGET = TRUE) # as x[[2]]
ApiData12(url, getDataByGET = TRUE) # Combined
#> industry month
#> 1 Retail trade, except of motor vehicles and motorcycles 2023M09
#> 2 Retail trade, except of motor vehicles and motorcycles 2023M10
#> 3 Retail trade, except of motor vehicles and motorcycles 2023M11
#> 4 Retail trade, except of motor vehicles and motorcycles 2023M12
#> 5 Retail trade, except of motor vehicles and motorcycles 2024M01
#> 6 Retail trade, except of motor vehicles and motorcycles 2024M02
#> 7 Retail trade, except of motor vehicles and motorcycles 2024M03
#> 8 Retail trade, except of motor vehicles and motorcycles 2024M04
#> 9 Retail trade, except of motor vehicles and motorcycles 2024M05
#> 10 Retail trade, except of motor vehicles and motorcycles 2024M06
#> 11 Retail trade, except of motor vehicles and motorcycles 2024M07
#> 12 Retail trade, except of motor vehicles and motorcycles 2024M08
#> 13 Retail trade, except of motor vehicles and motorcycles 2024M09
#> contents NACE Tid ContentsCode value
#> 1 Volume index, seasonally adjusted 47 2023M09 VolumSesong 92.3
#> 2 Volume index, seasonally adjusted 47 2023M10 VolumSesong 92.6
#> 3 Volume index, seasonally adjusted 47 2023M11 VolumSesong 92.9
#> 4 Volume index, seasonally adjusted 47 2023M12 VolumSesong 92.1
#> 5 Volume index, seasonally adjusted 47 2024M01 VolumSesong 92.1
#> 6 Volume index, seasonally adjusted 47 2024M02 VolumSesong 92.2
#> 7 Volume index, seasonally adjusted 47 2024M03 VolumSesong 92.6
#> 8 Volume index, seasonally adjusted 47 2024M04 VolumSesong 92.5
#> 9 Volume index, seasonally adjusted 47 2024M05 VolumSesong 95.9
#> 10 Volume index, seasonally adjusted 47 2024M06 VolumSesong 91.0
#> 11 Volume index, seasonally adjusted 47 2024M07 VolumSesong 92.1
#> 12 Volume index, seasonally adjusted 47 2024M08 VolumSesong 92.2
#> 13 Volume index, seasonally adjusted 47 2024M09 VolumSesong 91.9
##### Special output
ApiData("https://data.ssb.no/api/v0/en/table/11419", returnMetaData = TRUE) # meta data
#> [[1]]
#> [[1]]$code
#> [1] "MaaleMetode"
#>
#> [[1]]$text
#> [1] "measuring method"
#>
#> [[1]]$values
#> [1] "02" "01" "051" "061" "10" "11"
#>
#> [[1]]$valueTexts
#> [1] "Average" "Median"
#> [3] "Lower quartile" "Upper quartile"
#> [5] "Number of employments with earnings" "Number of full-time equivalents"
#>
#>
#> [[2]]
#> [[2]]$code
#> [1] "Yrke"
#>
#> [[2]]$text
#> [1] "occupation"
#>
#> [[2]]$values
#> [1] "0-9" "1" "1120" "2" "3" "4" "5" "6" "7" "8"
#> [11] "9"
#>
#> [[2]]$valueTexts
#> [1] "All occupations"
#> [2] "Managers"
#> [3] "Managing directors and chief executives"
#> [4] "Professionals"
#> [5] "Technicians and associate professionals"
#> [6] "Clerical support workers"
#> [7] "Service and sales workers"
#> [8] "Skilled agricultural, forestry and fishery workers"
#> [9] "Craft and related trades workers"
#> [10] "Plant and machine operators and assemblers"
#> [11] "Elementary occupations"
#>
#> [[2]]$elimination
#> [1] TRUE
#>
#>
#> [[3]]
#> [[3]]$code
#> [1] "Sektor"
#>
#> [[3]]$text
#> [1] "sector"
#>
#> [[3]]$values
#> [1] "ALLE" "A+B+D+E" "6500" "6100"
#>
#> [[3]]$valueTexts
#> [1] "Sum all sectors"
#> [2] "Private sector and public enterprises"
#> [3] "Local government"
#> [4] "Central government"
#>
#> [[3]]$elimination
#> [1] TRUE
#>
#>
#> [[4]]
#> [[4]]$code
#> [1] "NACE2007"
#>
#> [[4]]$text
#> [1] "industry (SIC2007)"
#>
#> [[4]]$values
#> [1] "01-03" "03.2" "05_07_08_09.9"
#> [4] "05-09" "06+09.1" "10-12"
#> [7] "10-33" "13-15" "16"
#> [10] "17" "18" "19-21"
#> [13] "22-23" "24" "25"
#> [16] "26-27" "28" "29-30"
#> [19] "31-32" "33" "35-39"
#> [22] "41" "41-43" "42"
#> [25] "43" "45" "45-47"
#> [28] "46" "47" "49.1_49.3"
#> [31] "49.2_49.4" "49-53" "50.1"
#> [34] "50.2" "51" "52"
#> [37] "53" "55" "55-56"
#> [40] "56.1_56.3" "56.2" "58"
#> [43] "58-63" "59" "60"
#> [46] "61" "62" "63"
#> [49] "64.1_65.1-65.3" "64.2-64.9_66.1-66.3" "64-66"
#> [52] "68-75" "68.2" "68.31"
#> [55] "69" "70" "71"
#> [58] "72" "73" "77"
#> [61] "77-82" "78" "79"
#> [64] "80" "81" "81.2"
#> [67] "82" "84" "84.11"
#> [70] "84.12" "84.13" "84.21"
#> [73] "84.22" "84.23" "84.24"
#> [76] "84.25" "84.30" "85"
#> [79] "85.1-85.2" "85.3" "85.4"
#> [82] "85.5-85.6" "86-88" "86"
#> [85] "86.1" "87" "88.1"
#> [88] "88.911" "88.99" "88.993-88.994"
#> [91] "90" "90-99" "91"
#> [94] "93" "94.1" "94.2"
#> [97] "94.9" "95" "96"
#> [100] "97" "99" "00.0"
#> [103] "00" "A" "A-S"
#> [106] "B" "C" "D"
#> [109] "E" "F" "G"
#> [112] "H" "I" "J"
#> [115] "K" "L" "M"
#> [118] "N" "O" "P"
#> [121] "Q" "R" "S"
#> [124] "T" "U"
#>
#> [[4]]$valueTexts
#> [1] "Agriculture, forestry and fishing"
#> [2] "Aquaculture"
#> [3] "Mining"
#> [4] "Mining and quarrying"
#> [5] "Oil and gas extraction incl. support activities"
#> [6] "Manufacture of food products, beverages and tobacco"
#> [7] "Manufacture"
#> [8] "Manufacture of textiles, wearing apparel and leather products"
#> [9] "Wood and wood products"
#> [10] "Paper and paper products"
#> [11] "Printing and reproduction"
#> [12] "Refined petro., chemicals, pharmac."
#> [13] "Rubber, plastic and mineral prod."
#> [14] "Basic metals"
#> [15] "Fabricated metal prod."
#> [16] "Computer and electrical equipment"
#> [17] "Machinery and equipment"
#> [18] "Other workshop industry"
#> [19] "Furniture and manufacturing n.e.c."
#> [20] "Repair, installation of machinery"
#> [21] "Electricity, water supply, sewerage, waste management"
#> [22] "Construction of buildings"
#> [23] "Construction"
#> [24] "Civil engineering"
#> [25] "Specialised construction activities"
#> [26] "Wholesale and retail trade and repair of motor vehicles and motorcycles"
#> [27] "Wholesale and retail trade: repair of motor vehicles and motorcycles"
#> [28] "Wholesale trade, except of motor vehicles and motorcycles"
#> [29] "Retail trade, except of motor vehicles and motorcycles"
#> [30] "Passenger land transport"
#> [31] "Freight land transport"
#> [32] "Transportation and storage"
#> [33] "Sea and coastal passenger water transport"
#> [34] "Sea and coastal freight water transport"
#> [35] "Air transport"
#> [36] "Support activities for transportation"
#> [37] "Postal and courier activities"
#> [38] "Accommodation"
#> [39] "Accommodation and food service activities"
#> [40] "Restaurants and beverage serving activities"
#> [41] "Event catering and other food service activities"
#> [42] "Publishing activities"
#> [43] "Information and communication"
#> [44] "Motion picture, TV, music prod."
#> [45] "Programming, broadcasting activities"
#> [46] "Telecommunications"
#> [47] "Computer programming, consultancy"
#> [48] "Information service activities"
#> [49] "Monetary and insurance intermediation"
#> [50] "Other financial intermediation"
#> [51] "Financial and insurance activities"
#> [52] "Real estate, professional, scientific and technical activities"
#> [53] "Renting and operating of own or leased real estate"
#> [54] "Real estate agencies"
#> [55] "Legal and accounting activities"
#> [56] "Head offices, management consult."
#> [57] "Architecture, engineering activities"
#> [58] "Scientific research and development"
#> [59] "Advertising and market research"
#> [60] "Rental and leasing activities"
#> [61] "Administrative and support service activities"
#> [62] "Employment activities"
#> [63] "Travel agency, tour operators"
#> [64] "Security, investigation activities"
#> [65] "Buildings, landscape service activities"
#> [66] "Cleaning activities"
#> [67] "Business support activities"
#> [68] "Public adm., defence, soc. security"
#> [69] "General public administration activities"
#> [70] "Act. provid. health care, educ. etc."
#> [71] "Regulation of and contribution to more efficient operation of businesses"
#> [72] "Foreign affairs"
#> [73] "Defence activities"
#> [74] "Justice and judicial activities"
#> [75] "Public order and safety activities"
#> [76] "Fire service activities"
#> [77] "Compulsory social security activities"
#> [78] "Education"
#> [79] "Primary education"
#> [80] "Secondary education"
#> [81] "Higher education"
#> [82] "Other education and educational support activities"
#> [83] "Human health and social work activities"
#> [84] "Human health activities"
#> [85] "Hospital activities"
#> [86] "Residential care activities"
#> [87] "Social work activities without accommodation for the elderly and disabled"
#> [88] "Nursery schools"
#> [89] "Other social work activities without accommodation n.e.c."
#> [90] "Vocational rehabilitation activities"
#> [91] "Arts and entertainment activities"
#> [92] "Other service activities"
#> [93] "Libraries, museums, other culture"
#> [94] "Sports, amusement, recreation"
#> [95] "Activities of business, employers and professional membership organisations"
#> [96] "Activities of trade unions"
#> [97] "Activities of other membership organisations"
#> [98] "Repair, personal, household goods"
#> [99] "Other personal service activities"
#> [100] "Households as employers activities"
#> [101] "Extraterritorial organisations and bodies"
#> [102] "Unspecified"
#> [103] "Unspecified"
#> [104] "Agriculture, forestry and fishing"
#> [105] "All industries"
#> [106] "Mining and quarrying"
#> [107] "Manufacturing"
#> [108] "Electricity, gas and steam"
#> [109] "Water supply, sewerage, waste"
#> [110] "Construction"
#> [111] "Wholesale and retail trade: repair of motor vehicles and motorcycles"
#> [112] "Transportation and storage"
#> [113] "Accommodation and food service activities"
#> [114] "Information and communication"
#> [115] "Financial and insurance activities"
#> [116] "Real estate activities"
#> [117] "Professional, scientific and technical activities"
#> [118] "Administrative and support service activities"
#> [119] "Public administration and defence"
#> [120] "Education"
#> [121] "Human health and social work activities"
#> [122] "Arts, entertainment and recreation"
#> [123] "Other service activities"
#> [124] "Activities of household as employers"
#> [125] "Activities of extraterritorial organisations and bodies"
#>
#> [[4]]$elimination
#> [1] TRUE
#>
#>
#> [[5]]
#> [[5]]$code
#> [1] "Kjonn"
#>
#> [[5]]$text
#> [1] "sex"
#>
#> [[5]]$values
#> [1] "0" "2" "1"
#>
#> [[5]]$valueTexts
#> [1] "Both sexes" "Females" "Males"
#>
#> [[5]]$elimination
#> [1] TRUE
#>
#>
#> [[6]]
#> [[6]]$code
#> [1] "AvtaltVanlig"
#>
#> [[6]]$text
#> [1] "contractual/usual working hours per week"
#>
#> [[6]]$values
#> [1] "0" "5" "6"
#>
#> [[6]]$valueTexts
#> [1] "All employees" "Full-time employees" "Part-time employees"
#>
#> [[6]]$elimination
#> [1] TRUE
#>
#>
#> [[7]]
#> [[7]]$code
#> [1] "ContentsCode"
#>
#> [[7]]$text
#> [1] "contents"
#>
#> [[7]]$values
#> [1] "Manedslonn" "AvtaltManedslonn" "Uregtil" "Bonus"
#> [5] "Overtid" "AlderLA" "AvtArbTid"
#>
#> [[7]]$valueTexts
#> [1] "Monthly earnings (NOK)"
#> [2] "Basic monthly salary (NOK)"
#> [3] "Variable additional allowances (NOK)"
#> [4] "Bonus (NOK)"
#> [5] "Overtime pay (NOK)"
#> [6] "Age (years)"
#> [7] "Contractual working hours per week (hours)"
#>
#>
#> [[8]]
#> [[8]]$code
#> [1] "Tid"
#>
#> [[8]]$text
#> [1] "year"
#>
#> [[8]]$values
#> [1] "2015" "2016" "2017" "2018" "2019" "2020" "2021" "2022" "2023"
#>
#> [[8]]$valueTexts
#> [1] "2015" "2016" "2017" "2018" "2019" "2020" "2021" "2022" "2023"
#>
#> [[8]]$time
#> [1] TRUE
#>
#>
ApiData("https://data.ssb.no/api/v0/en/table/11419", returnMetaValues = TRUE) # meta data values
#> $MaaleMetode
#> [1] "02" "01" "051" "061" "10" "11"
#>
#> $Yrke
#> [1] "0-9" "1" "1120" "2" "3" "4" "5" "6" "7" "8"
#> [11] "9"
#>
#> $Sektor
#> [1] "ALLE" "A+B+D+E" "6500" "6100"
#>
#> $NACE2007
#> [1] "01-03" "03.2" "05_07_08_09.9"
#> [4] "05-09" "06+09.1" "10-12"
#> [7] "10-33" "13-15" "16"
#> [10] "17" "18" "19-21"
#> [13] "22-23" "24" "25"
#> [16] "26-27" "28" "29-30"
#> [19] "31-32" "33" "35-39"
#> [22] "41" "41-43" "42"
#> [25] "43" "45" "45-47"
#> [28] "46" "47" "49.1_49.3"
#> [31] "49.2_49.4" "49-53" "50.1"
#> [34] "50.2" "51" "52"
#> [37] "53" "55" "55-56"
#> [40] "56.1_56.3" "56.2" "58"
#> [43] "58-63" "59" "60"
#> [46] "61" "62" "63"
#> [49] "64.1_65.1-65.3" "64.2-64.9_66.1-66.3" "64-66"
#> [52] "68-75" "68.2" "68.31"
#> [55] "69" "70" "71"
#> [58] "72" "73" "77"
#> [61] "77-82" "78" "79"
#> [64] "80" "81" "81.2"
#> [67] "82" "84" "84.11"
#> [70] "84.12" "84.13" "84.21"
#> [73] "84.22" "84.23" "84.24"
#> [76] "84.25" "84.30" "85"
#> [79] "85.1-85.2" "85.3" "85.4"
#> [82] "85.5-85.6" "86-88" "86"
#> [85] "86.1" "87" "88.1"
#> [88] "88.911" "88.99" "88.993-88.994"
#> [91] "90" "90-99" "91"
#> [94] "93" "94.1" "94.2"
#> [97] "94.9" "95" "96"
#> [100] "97" "99" "00.0"
#> [103] "00" "A" "A-S"
#> [106] "B" "C" "D"
#> [109] "E" "F" "G"
#> [112] "H" "I" "J"
#> [115] "K" "L" "M"
#> [118] "N" "O" "P"
#> [121] "Q" "R" "S"
#> [124] "T" "U"
#>
#> $Kjonn
#> [1] "0" "2" "1"
#>
#> $AvtaltVanlig
#> [1] "0" "5" "6"
#>
#> $ContentsCode
#> [1] "Manedslonn" "AvtaltManedslonn" "Uregtil" "Bonus"
#> [5] "Overtid" "AlderLA" "AvtArbTid"
#>
#> $Tid
#> [1] "2015" "2016" "2017" "2018" "2019" "2020" "2021" "2022" "2023"
#>
#> attr(,"elimination")
#> [1] FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
ApiData("https://data.ssb.no/api/v0/en/table/11419", returnMetaFrames = TRUE) # list of data frames
#> $MaaleMetode
#> values valueTexts
#> 1 02 Average
#> 2 01 Median
#> 3 051 Lower quartile
#> 4 061 Upper quartile
#> 5 10 Number of employments with earnings
#> 6 11 Number of full-time equivalents
#>
#> $Yrke
#> values valueTexts
#> 1 0-9 All occupations
#> 2 1 Managers
#> 3 1120 Managing directors and chief executives
#> 4 2 Professionals
#> 5 3 Technicians and associate professionals
#> 6 4 Clerical support workers
#> 7 5 Service and sales workers
#> 8 6 Skilled agricultural, forestry and fishery workers
#> 9 7 Craft and related trades workers
#> 10 8 Plant and machine operators and assemblers
#> 11 9 Elementary occupations
#>
#> $Sektor
#> values valueTexts
#> 1 ALLE Sum all sectors
#> 2 A+B+D+E Private sector and public enterprises
#> 3 6500 Local government
#> 4 6100 Central government
#>
#> $NACE2007
#> values
#> 1 01-03
#> 2 03.2
#> 3 05_07_08_09.9
#> 4 05-09
#> 5 06+09.1
#> 6 10-12
#> 7 10-33
#> 8 13-15
#> 9 16
#> 10 17
#> 11 18
#> 12 19-21
#> 13 22-23
#> 14 24
#> 15 25
#> 16 26-27
#> 17 28
#> 18 29-30
#> 19 31-32
#> 20 33
#> 21 35-39
#> 22 41
#> 23 41-43
#> 24 42
#> 25 43
#> 26 45
#> 27 45-47
#> 28 46
#> 29 47
#> 30 49.1_49.3
#> 31 49.2_49.4
#> 32 49-53
#> 33 50.1
#> 34 50.2
#> 35 51
#> 36 52
#> 37 53
#> 38 55
#> 39 55-56
#> 40 56.1_56.3
#> 41 56.2
#> 42 58
#> 43 58-63
#> 44 59
#> 45 60
#> 46 61
#> 47 62
#> 48 63
#> 49 64.1_65.1-65.3
#> 50 64.2-64.9_66.1-66.3
#> 51 64-66
#> 52 68-75
#> 53 68.2
#> 54 68.31
#> 55 69
#> 56 70
#> 57 71
#> 58 72
#> 59 73
#> 60 77
#> 61 77-82
#> 62 78
#> 63 79
#> 64 80
#> 65 81
#> 66 81.2
#> 67 82
#> 68 84
#> 69 84.11
#> 70 84.12
#> 71 84.13
#> 72 84.21
#> 73 84.22
#> 74 84.23
#> 75 84.24
#> 76 84.25
#> 77 84.30
#> 78 85
#> 79 85.1-85.2
#> 80 85.3
#> 81 85.4
#> 82 85.5-85.6
#> 83 86-88
#> 84 86
#> 85 86.1
#> 86 87
#> 87 88.1
#> 88 88.911
#> 89 88.99
#> 90 88.993-88.994
#> 91 90
#> 92 90-99
#> 93 91
#> 94 93
#> 95 94.1
#> 96 94.2
#> 97 94.9
#> 98 95
#> 99 96
#> 100 97
#> 101 99
#> 102 00.0
#> 103 00
#> 104 A
#> 105 A-S
#> 106 B
#> 107 C
#> 108 D
#> 109 E
#> 110 F
#> 111 G
#> 112 H
#> 113 I
#> 114 J
#> 115 K
#> 116 L
#> 117 M
#> 118 N
#> 119 O
#> 120 P
#> 121 Q
#> 122 R
#> 123 S
#> 124 T
#> 125 U
#> valueTexts
#> 1 Agriculture, forestry and fishing
#> 2 Aquaculture
#> 3 Mining
#> 4 Mining and quarrying
#> 5 Oil and gas extraction incl. support activities
#> 6 Manufacture of food products, beverages and tobacco
#> 7 Manufacture
#> 8 Manufacture of textiles, wearing apparel and leather products
#> 9 Wood and wood products
#> 10 Paper and paper products
#> 11 Printing and reproduction
#> 12 Refined petro., chemicals, pharmac.
#> 13 Rubber, plastic and mineral prod.
#> 14 Basic metals
#> 15 Fabricated metal prod.
#> 16 Computer and electrical equipment
#> 17 Machinery and equipment
#> 18 Other workshop industry
#> 19 Furniture and manufacturing n.e.c.
#> 20 Repair, installation of machinery
#> 21 Electricity, water supply, sewerage, waste management
#> 22 Construction of buildings
#> 23 Construction
#> 24 Civil engineering
#> 25 Specialised construction activities
#> 26 Wholesale and retail trade and repair of motor vehicles and motorcycles
#> 27 Wholesale and retail trade: repair of motor vehicles and motorcycles
#> 28 Wholesale trade, except of motor vehicles and motorcycles
#> 29 Retail trade, except of motor vehicles and motorcycles
#> 30 Passenger land transport
#> 31 Freight land transport
#> 32 Transportation and storage
#> 33 Sea and coastal passenger water transport
#> 34 Sea and coastal freight water transport
#> 35 Air transport
#> 36 Support activities for transportation
#> 37 Postal and courier activities
#> 38 Accommodation
#> 39 Accommodation and food service activities
#> 40 Restaurants and beverage serving activities
#> 41 Event catering and other food service activities
#> 42 Publishing activities
#> 43 Information and communication
#> 44 Motion picture, TV, music prod.
#> 45 Programming, broadcasting activities
#> 46 Telecommunications
#> 47 Computer programming, consultancy
#> 48 Information service activities
#> 49 Monetary and insurance intermediation
#> 50 Other financial intermediation
#> 51 Financial and insurance activities
#> 52 Real estate, professional, scientific and technical activities
#> 53 Renting and operating of own or leased real estate
#> 54 Real estate agencies
#> 55 Legal and accounting activities
#> 56 Head offices, management consult.
#> 57 Architecture, engineering activities
#> 58 Scientific research and development
#> 59 Advertising and market research
#> 60 Rental and leasing activities
#> 61 Administrative and support service activities
#> 62 Employment activities
#> 63 Travel agency, tour operators
#> 64 Security, investigation activities
#> 65 Buildings, landscape service activities
#> 66 Cleaning activities
#> 67 Business support activities
#> 68 Public adm., defence, soc. security
#> 69 General public administration activities
#> 70 Act. provid. health care, educ. etc.
#> 71 Regulation of and contribution to more efficient operation of businesses
#> 72 Foreign affairs
#> 73 Defence activities
#> 74 Justice and judicial activities
#> 75 Public order and safety activities
#> 76 Fire service activities
#> 77 Compulsory social security activities
#> 78 Education
#> 79 Primary education
#> 80 Secondary education
#> 81 Higher education
#> 82 Other education and educational support activities
#> 83 Human health and social work activities
#> 84 Human health activities
#> 85 Hospital activities
#> 86 Residential care activities
#> 87 Social work activities without accommodation for the elderly and disabled
#> 88 Nursery schools
#> 89 Other social work activities without accommodation n.e.c.
#> 90 Vocational rehabilitation activities
#> 91 Arts and entertainment activities
#> 92 Other service activities
#> 93 Libraries, museums, other culture
#> 94 Sports, amusement, recreation
#> 95 Activities of business, employers and professional membership organisations
#> 96 Activities of trade unions
#> 97 Activities of other membership organisations
#> 98 Repair, personal, household goods
#> 99 Other personal service activities
#> 100 Households as employers activities
#> 101 Extraterritorial organisations and bodies
#> 102 Unspecified
#> 103 Unspecified
#> 104 Agriculture, forestry and fishing
#> 105 All industries
#> 106 Mining and quarrying
#> 107 Manufacturing
#> 108 Electricity, gas and steam
#> 109 Water supply, sewerage, waste
#> 110 Construction
#> 111 Wholesale and retail trade: repair of motor vehicles and motorcycles
#> 112 Transportation and storage
#> 113 Accommodation and food service activities
#> 114 Information and communication
#> 115 Financial and insurance activities
#> 116 Real estate activities
#> 117 Professional, scientific and technical activities
#> 118 Administrative and support service activities
#> 119 Public administration and defence
#> 120 Education
#> 121 Human health and social work activities
#> 122 Arts, entertainment and recreation
#> 123 Other service activities
#> 124 Activities of household as employers
#> 125 Activities of extraterritorial organisations and bodies
#>
#> $Kjonn
#> values valueTexts
#> 1 0 Both sexes
#> 2 2 Females
#> 3 1 Males
#>
#> $AvtaltVanlig
#> values valueTexts
#> 1 0 All employees
#> 2 5 Full-time employees
#> 3 6 Part-time employees
#>
#> $ContentsCode
#> values valueTexts
#> 1 Manedslonn Monthly earnings (NOK)
#> 2 AvtaltManedslonn Basic monthly salary (NOK)
#> 3 Uregtil Variable additional allowances (NOK)
#> 4 Bonus Bonus (NOK)
#> 5 Overtid Overtime pay (NOK)
#> 6 AlderLA Age (years)
#> 7 AvtArbTid Contractual working hours per week (hours)
#>
#> $Tid
#> values valueTexts
#> 1 2015 2015
#> 2 2016 2016
#> 3 2017 2017
#> 4 2018 2018
#> 5 2019 2019
#> 6 2020 2020
#> 7 2021 2021
#> 8 2022 2022
#> 9 2023 2023
#>
#> attr(,"text")
#> MaaleMetode
#> "measuring method"
#> Yrke
#> "occupation"
#> Sektor
#> "sector"
#> NACE2007
#> "industry (SIC2007)"
#> Kjonn
#> "sex"
#> AvtaltVanlig
#> "contractual/usual working hours per week"
#> ContentsCode
#> "contents"
#> Tid
#> "year"
#> attr(,"elimination")
#> MaaleMetode Yrke Sektor NACE2007 Kjonn AvtaltVanlig
#> FALSE TRUE TRUE TRUE TRUE TRUE
#> ContentsCode Tid
#> FALSE FALSE
#> attr(,"time")
#> MaaleMetode Yrke Sektor NACE2007 Kjonn AvtaltVanlig
#> FALSE FALSE FALSE FALSE FALSE FALSE
#> ContentsCode Tid
#> FALSE TRUE
ApiData("https://data.ssb.no/api/v0/en/table/11419", returnApiQuery = TRUE) # query using defaults
#> {
#> "query": [
#> {
#> "code": "MaaleMetode",
#> "selection": {
#> "filter": "item",
#> "values": ["02", "10", "11"]
#> }
#> },
#> {
#> "code": "Yrke",
#> "selection": {
#> "filter": "item",
#> "values": ["0-9", "8", "9"]
#> }
#> },
#> {
#> "code": "Sektor",
#> "selection": {
#> "filter": "item",
#> "values": ["ALLE", "6500", "6100"]
#> }
#> },
#> {
#> "code": "NACE2007",
#> "selection": {
#> "filter": "item",
#> "values": ["01-03", "T", "U"]
#> }
#> },
#> {
#> "code": "Kjonn",
#> "selection": {
#> "filter": "item",
#> "values": ["0", "2", "1"]
#> }
#> },
#> {
#> "code": "AvtaltVanlig",
#> "selection": {
#> "filter": "item",
#> "values": ["0", "5", "6"]
#> }
#> },
#> {
#> "code": "ContentsCode",
#> "selection": {
#> "filter": "item",
#> "values": ["Manedslonn", "AlderLA", "AvtArbTid"]
#> }
#> },
#> {
#> "code": "Tid",
#> "selection": {
#> "filter": "item",
#> "values": ["2015", "2022", "2023"]
#> }
#> }
#> ],
#> "response": {
#> "format": "json-stat2"
#> }
#> }
##### Ordinary use (makeNAstatus is in use in first two examples)
# NACE2007 as imaginary value (top 10), ContentsCode as TRUE (all), Tid is default
x <- ApiData("https://data.ssb.no/api/v0/en/table/11419", NACE2007 = 10i, ContentsCode = TRUE)
# Two specified and the last is default (as above) - in Norwegian change en to no in url
x <- ApiData("https://data.ssb.no/api/v0/no/table/11419", NACE2007 = 10i, ContentsCode = TRUE)
# Number of residents (bosatte) last year, each region
x <- ApiData("https://data.ssb.no/api/v0/en/table/04861", Region = TRUE,
ContentsCode = "Bosatte", Tid = 1i)
# Number of residents (bosatte) each year, total
ApiData("https://data.ssb.no/api/v0/en/table/04861", Region = FALSE,
ContentsCode = "Bosatte", Tid = TRUE)
#> $`04861: Area and population of urban settlements, by contents and year`
#> contents year value
#> 1 Number of residents 2000 3396382
#> 2 Number of residents 2002 3474623
#> 3 Number of residents 2003 3514417
#> 4 Number of residents 2004 3536454
#> 5 Number of residents 2005 3560137
#> 6 Number of residents 2006 3607813
#> 7 Number of residents 2007 3655391
#> 8 Number of residents 2008 3722786
#> 9 Number of residents 2009 3780068
#> 10 Number of residents 2011 3899115
#> 11 Number of residents 2012 3958038
#> 12 Number of residents 2013 4050626
#> 13 Number of residents 2014 4114414
#> 14 Number of residents 2015 4172782
#> 15 Number of residents 2016 4229827
#> 16 Number of residents 2017 4283166
#> 17 Number of residents 2018 4327937
#> 18 Number of residents 2019 4368614
#> 19 Number of residents 2020 4416981
#> 20 Number of residents 2021 4443243
#> 21 Number of residents 2022 4485236
#> 22 Number of residents 2023 4554562
#> 23 Number of residents 2024 4619969
#>
#> $dataset
#> ContentsCode Tid value
#> 1 Bosatte 2000 3396382
#> 2 Bosatte 2002 3474623
#> 3 Bosatte 2003 3514417
#> 4 Bosatte 2004 3536454
#> 5 Bosatte 2005 3560137
#> 6 Bosatte 2006 3607813
#> 7 Bosatte 2007 3655391
#> 8 Bosatte 2008 3722786
#> 9 Bosatte 2009 3780068
#> 10 Bosatte 2011 3899115
#> 11 Bosatte 2012 3958038
#> 12 Bosatte 2013 4050626
#> 13 Bosatte 2014 4114414
#> 14 Bosatte 2015 4172782
#> 15 Bosatte 2016 4229827
#> 16 Bosatte 2017 4283166
#> 17 Bosatte 2018 4327937
#> 18 Bosatte 2019 4368614
#> 19 Bosatte 2020 4416981
#> 20 Bosatte 2021 4443243
#> 21 Bosatte 2022 4485236
#> 22 Bosatte 2023 4554562
#> 23 Bosatte 2024 4619969
#>
# Some years
ApiData("https://data.ssb.no/api/v0/en/table/04861", Region = FALSE,
ContentsCode = "Bosatte", Tid = c(1, 5, -1))
#> $`04861: Area and population of urban settlements, by contents and year`
#> contents year value
#> 1 Number of residents 2000 3396382
#> 2 Number of residents 2005 3560137
#> 3 Number of residents 2024 4619969
#>
#> $dataset
#> ContentsCode Tid value
#> 1 Bosatte 2000 3396382
#> 2 Bosatte 2005 3560137
#> 3 Bosatte 2024 4619969
#>
# Two selected regions
ApiData("https://data.ssb.no/api/v0/en/table/04861", Region = c("1103", "0301"),
ContentsCode = 2, Tid = c(1, -1))
#> $`04861: Area and population of urban settlements, by region, contents and year`
#> region contents year value
#> 1 Oslo municipality Number of residents 2000 504348
#> 2 Oslo municipality Number of residents 2024 714630
#> 3 Stavanger Number of residents 2000 106804
#> 4 Stavanger Number of residents 2024 142897
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Bosatte 2000 504348
#> 2 0301 Bosatte 2024 714630
#> 3 1103 Bosatte 2000 106804
#> 4 1103 Bosatte 2024 142897
#>
##### Using id instead of url, unnamed input and verbosePrint
ApiData(4861, c("1103", "0301"), 1, c(1, -1)) # same as below
#> $`04861: Areal og befolkning i tettsteder, etter region, statistikkvariabel og år`
#> region statistikkvariabel år value
#> 1 Oslo Areal av tettsted (km²) 2000 132.90
#> 2 Oslo Areal av tettsted (km²) 2024 130.31
#> 3 Stavanger Areal av tettsted (km²) 2000 41.85
#> 4 Stavanger Areal av tettsted (km²) 2024 44.34
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Areal 2000 132.90
#> 2 0301 Areal 2024 130.31
#> 3 1103 Areal 2000 41.85
#> 4 1103 Areal 2024 44.34
#>
ApiData(4861, Region = c("1103", "0301"), ContentsCode=2, Tid=c(1, -1))
#> $`04861: Areal og befolkning i tettsteder, etter region, statistikkvariabel og år`
#> region statistikkvariabel år value
#> 1 Oslo Bosatte 2000 504348
#> 2 Oslo Bosatte 2024 714630
#> 3 Stavanger Bosatte 2000 106804
#> 4 Stavanger Bosatte 2024 142897
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Bosatte 2000 504348
#> 2 0301 Bosatte 2024 714630
#> 3 1103 Bosatte 2000 106804
#> 4 1103 Bosatte 2024 142897
#>
names(ApiData(4861,returnMetaFrames = TRUE)) # these names from metadata assumed two lines above
#> [1] "Region" "ContentsCode" "Tid"
ApiData("4861", c("1103", "0301"), 1, c(1, -1), urlType="SSBen")
#> $`04861: Area and population of urban settlements, by region, contents and year`
#> region contents year value
#> 1 Oslo municipality Area of urban settlements (km²) 2000 132.90
#> 2 Oslo municipality Area of urban settlements (km²) 2024 130.31
#> 3 Stavanger Area of urban settlements (km²) 2000 41.85
#> 4 Stavanger Area of urban settlements (km²) 2024 44.34
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Areal 2000 132.90
#> 2 0301 Areal 2024 130.31
#> 3 1103 Areal 2000 41.85
#> 4 1103 Areal 2024 44.34
#>
ApiData("01222", c("1103", "0301"), c(4, 9:11), 2i, verbosePrint = TRUE)
#> $Region
#> [1] "0" "31" "3101" "3103" "3105" "3107" "3110" "3112" "3114"
#> [10] "3116" "3118" "3120" "3122" "3124" "32" "3201" "3203" "3205"
#> [19] "3207" "3209" "3212" "3214" "3216" "3218" "3220" "3222" "3224"
#> [28] "3226" "3228" "3230" "3232" "3234" "3236" "3238" "3240" "3242"
#> [37] "30" "01" "3001" "3002" "3003" "3004" "3005" "3006" "3007"
#> [46] "3011" "3012" "3013" "3014" "3015" "3016" "3017" "3018" "3019"
#> [55] "3020" "3021" "3022" "3023" "3024" "3025" "3026" "3027" "3028"
#> [64] "3029" "3030" "3031" "3032" "3033" "3034" "3035" "3036" "3037"
#> [73] "3038" "3039" "3040" "3041" "3042" "3043" "3044" "3045" "3046"
#> [82] "3047" "3048" "3049" "3050" "3051" "3052" "3053" "3054" "0101"
#> [91] "0102" "0103" "0104" "0105" "0106" "0111" "0113" "0114" "0115"
#> [100] "0116" "0117" "0118" "0119" "0121" "0122" "0123" "0124" "0125"
#> [109] "0127" "0128" "0130" "0131" "0133" "0134" "0135" "0136" "0137"
#> [118] "0138" "0199" "02" "0211" "0213" "0214" "0215" "0216" "0217"
#> [127] "0219" "0220" "0221" "0226" "0227" "0228" "0229" "0230" "0231"
#> [136] "0233" "0234" "0235" "0236" "0237" "0238" "0239" "0299" "03"
#> [145] "0301" "33" "0399" "3301" "3303" "3305" "3310" "3312" "3314"
#> [154] "3316" "3318" "3320" "3322" "3324" "3326" "3328" "3330" "3332"
#> [163] "3334" "3336" "3338" "34" "04" "3401" "3403" "3405" "3407"
#> [172] "3411" "3412" "3413" "3414" "3415" "3416" "3417" "3418" "3419"
#> [181] "3420" "3421" "3422" "3423" "3424" "3425" "3426" "3427" "3428"
#> [190] "3429" "3430" "3431" "3432" "3433" "3434" "3435" "3436" "3437"
#> [199] "3438" "3439" "3440" "3441" "3442" "3443" "3446" "3447" "3448"
#> [208] "3449" "3450" "3451" "3452" "3453" "3454" "0401" "0402" "0403"
#> [217] "0412" "0414" "0415" "0417" "0418" "0419" "0420" "0423" "0425"
#> [226] "0426" "0427" "0428" "0429" "0430" "0432" "0434" "0435" "0436"
#> [235] "0437" "0438" "0439" "0441" "0499" "05" "0501" "0502" "0511"
#> [244] "0512" "0513" "0514" "0515" "0516" "0517" "0518" "0519" "0520"
#> [253] "0521" "0522" "0528" "0529" "0532" "0533" "0534" "0536" "0538"
#> [262] "0540" "0541" "0542" "0543" "0544" "0545" "0599" "06" "38"
#> [271] "3801" "3802" "3803" "3804" "3805" "3806" "3807" "3808" "3811"
#> [280] "3812" "3813" "3814" "3815" "3816" "3817" "3818" "3819" "3820"
#> [289] "3821" "3822" "3823" "3824" "3825" "0601" "0602" "0604" "0605"
#> [298] "0612" "0615" "0616" "0617" "0618" "0619" "0620" "0621" "0622"
#> [307] "0623" "0624" "0625" "0626" "0627" "0628" "0631" "0632" "0633"
#> [316] "39" "40" "0699" "3901" "3903" "3905" "3907" "3909" "3911"
#> [325] "4001" "4003" "4005" "4010" "4012" "4014" "4016" "4018" "4020"
#> [334] "4022" "4024" "4026" "4028" "4030" "4032" "4034" "4036" "07"
#> [343] "0701" "0702" "0703" "0704" "0705" "0706" "0707" "0708" "0709"
#> [352] "0710" "0711" "0712" "0713" "0714" "0715" "0716" "0716u" "0717"
#> [361] "0718" "0719" "0720" "0721" "0722" "0723" "0724" "0725" "0726"
#> [370] "0727" "0728" "0729" "0799" "08" "0805" "0806" "0807" "0811"
#> [379] "0814" "0815" "0817" "0819" "0821" "0822" "0826" "0827" "0828"
#> [388] "0829" "0830" "0831" "0833" "0834" "0899" "42" "09" "4201"
#> [397] "4202" "4203" "4204" "4205" "4206" "4207" "4211" "4212" "4213"
#> [406] "4214" "4215" "4216" "4217" "4218" "4219" "4220" "4221" "4222"
#> [415] "4223" "4224" "4225" "4226" "4227" "4228" "0901" "0903" "0904"
#> [424] "0906" "0911" "0912" "0914" "0918" "0919" "0920" "0921" "0922"
#> [433] "0923" "0924" "0926" "0928" "0929" "0932" "0933" "0935" "0937"
#> [442] "0938" "0940" "0941" "0999" "10" "1001" "1002" "1003" "1004"
#> [451] "1014" "1017" "1018" "1021" "1026" "1027" "1029" "1032" "1034"
#> [460] "1037" "1046" "1099" "11" "1101" "1102" "1103" "1106" "1108"
#> [469] "1111" "1112" "1114" "1119" "1120" "1121" "1122" "1124" "1127"
#> [478] "1129" "1130" "1133" "1134" "1135" "1141" "1142" "1144" "1145"
#> [487] "1146" "1149" "1151" "1154" "1159" "1160" "1199" "46" "12"
#> [496] "4601" "4602" "4611" "4612" "4613" "4614" "4615" "4616" "4617"
#> [505] "4618" "4619" "4620" "4621" "4622" "4623" "4624" "4625" "4626"
#> [514] "4627" "4628" "4629" "4630" "4631" "4632" "4633" "4634" "4635"
#> [523] "4636" "4637" "4638" "4639" "4640" "4641" "4642" "4643" "4644"
#> [532] "4645" "4646" "4647" "4648" "4649" "4650" "4651" "1201" "1211"
#> [541] "1214" "1216" "1219" "1221" "1222" "1223" "1224" "1227" "1228"
#> [550] "1230" "1231" "1232" "1233" "1234" "1235" "1238" "1241" "1242"
#> [559] "1243" "1244" "1245" "1246" "1247" "1248" "1249" "1250" "1251"
#> [568] "1252" "1253" "1255" "1256" "1259" "1260" "1263" "1264" "1265"
#> [577] "1266" "1299" "13" "1301" "14" "1401" "1411" "1412" "1413"
#> [586] "1416" "1417" "1418" "1419" "1420" "1421" "1422" "1424" "1426"
#> [595] "1428" "1429" "1430" "1431" "1432" "1433" "1438" "1439" "1441"
#> [604] "1443" "1444" "1445" "1448" "1449" "1499" "15" "1501" "1502"
#> [613] "1503" "1504" "1505" "1506" "1507" "1508" "1511" "1514" "1515"
#> [622] "1516" "1517" "1519" "1520" "1523" "1524" "1525" "1526" "1527"
#> [631] "1528" "1529" "1531" "1532" "1534" "1535" "1539" "1543" "1545"
#> [640] "1546" "1547" "1548" "1551" "1554" "1556" "1557" "1560" "1563"
#> [649] "1566" "1567" "1569" "1571" "1572" "1573" "1576" "1577" "1578"
#> [658] "1579" "1580" "1599" "50" "16" "5001" "5004" "5005" "5006"
#> [667] "5007" "5011" "5012" "5013" "5014" "5015" "5016" "5017" "5018"
#> [676] "5019" "5020" "5021" "5022" "5023" "5024" "5025" "5026" "5027"
#> [685] "5028" "5029" "5030" "5031" "5032" "5033" "5034" "5035" "5036"
#> [694] "5037" "5038" "5039" "5040" "5041" "5042" "5043" "5044" "5045"
#> [703] "5046" "5047" "5048" "5049" "5050" "5051" "5052" "5053" "5054"
#> [712] "5055" "5056" "5057" "5058" "5059" "5060" "5061" "1601" "1612"
#> [721] "1613" "1617" "1620" "1621" "1622" "1624" "1627" "1630" "1632"
#> [730] "1633" "1634" "1635" "1636" "1638" "1640" "1644" "1645" "1648"
#> [739] "1653" "1657" "1662" "1663" "1664" "1665" "1699" "17" "1702"
#> [748] "1703" "1711" "1714" "1717" "1718" "1719" "1721" "1723" "1724"
#> [757] "1725" "1729" "1736" "1738" "1739" "1740" "1742" "1743" "1744"
#> [766] "1748" "1749" "1750" "1751" "1755" "1756" "1799" "18" "1804"
#> [775] "1805" "1806" "1811" "1812" "1813" "1814" "1815" "1816" "1818"
#> [784] "1820" "1822" "1824" "1825" "1826" "1827" "1828" "1832" "1833"
#> [793] "1834" "1835" "1836" "1837" "1838" "1839" "1840" "1841" "1842"
#> [802] "1843" "1845" "1848" "1849" "1850" "1851" "1852" "1853" "1854"
#> [811] "1855" "1856" "1857" "1858" "1859" "1860" "1865" "1866" "1867"
#> [820] "1868" "1870" "1871" "1874" "1875" "55" "56" "1899" "5501"
#> [829] "5503" "5510" "5512" "5514" "5516" "5518" "5520" "5522" "5524"
#> [838] "5526" "5528" "5530" "5532" "5534" "5536" "5538" "5540" "5542"
#> [847] "5544" "5546" "5601" "5603" "5605" "5607" "5610" "5612" "5614"
#> [856] "5616" "5618" "5620" "5622" "5624" "5626" "5628" "5630" "5632"
#> [865] "5634" "5636" "54" "19" "5401" "5402" "5403" "5404" "5405"
#> [874] "5406" "5411" "5412" "5413" "5414" "5415" "5416" "5417" "5418"
#> [883] "5419" "5420" "5421" "5422" "5423" "5424" "5425" "5426" "5427"
#> [892] "5428" "5429" "5430" "5432" "5433" "5434" "5435" "5436" "5437"
#> [901] "5438" "5439" "5440" "5441" "5442" "5443" "5444" "1901" "1902"
#> [910] "1903" "1911" "1913" "1915" "1917" "1919" "1920" "1921" "1922"
#> [919] "1923" "1924" "1925" "1926" "1927" "1928" "1929" "1931" "1933"
#> [928] "1936" "1938" "1939" "1940" "1941" "1942" "1943" "1999" "20"
#> [937] "2001" "2002" "2003" "2004" "2011" "2012" "2014" "2015" "2016"
#> [946] "2017" "2018" "2019" "2020" "2021" "2022" "2023" "2024" "2025"
#> [955] "2027" "2028" "2030" "2099" "21" "2111" "2112" "2115" "2121"
#> [964] "2131" "2199" "22" "2211" "2299" "23" "2300" "2311" "2321"
#> [973] "2399" "25" "26" "88" "99" "9999"
#>
#> $ContentsCode
#> [1] "Folketallet1" "Fodte2" "Dode3"
#> [4] "Fodselsoverskudd4" "Innvandring5" "Utvandring6"
#> [7] "Tilflytting7" "Fraflytting8" "Nettoinnflytting9"
#> [10] "Folketilvekst10" "Folketallet11"
#>
#> $Tid
#> [1] "1997K4" "1998K1" "1998K2" "1998K3" "1998K4" "1999K1" "1999K2" "1999K3"
#> [9] "1999K4" "2000K1" "2000K2" "2000K3" "2000K4" "2001K1" "2001K2" "2001K3"
#> [17] "2001K4" "2002K1" "2002K2" "2002K3" "2002K4" "2003K1" "2003K2" "2003K3"
#> [25] "2003K4" "2004K1" "2004K2" "2004K3" "2004K4" "2005K1" "2005K2" "2005K3"
#> [33] "2005K4" "2006K1" "2006K2" "2006K3" "2006K4" "2007K1" "2007K2" "2007K3"
#> [41] "2007K4" "2008K1" "2008K2" "2008K3" "2008K4" "2009K1" "2009K2" "2009K3"
#> [49] "2009K4" "2010K1" "2010K2" "2010K3" "2010K4" "2011K1" "2011K2" "2011K3"
#> [57] "2011K4" "2012K1" "2012K2" "2012K3" "2012K4" "2013K1" "2013K2" "2013K3"
#> [65] "2013K4" "2014K1" "2014K2" "2014K3" "2014K4" "2015K1" "2015K2" "2015K3"
#> [73] "2015K4" "2016K1" "2016K2" "2016K3" "2016K4" "2017K1" "2017K2" "2017K3"
#> [81] "2017K4" "2018K1" "2018K2" "2018K3" "2018K4" "2019K1" "2019K2" "2019K3"
#> [89] "2019K4" "2020K1" "2020K2" "2020K3" "2020K4" "2021K1" "2021K2" "2021K3"
#> [97] "2021K4" "2022K1" "2022K2" "2022K3" "2022K4" "2023K1" "2023K2" "2023K3"
#> [105] "2023K4" "2024K1" "2024K2"
#>
#> attr(,"elimination")
#> [1] TRUE FALSE FALSE
#>
#>
#>
#> $`01222: Befolkning og kvartalsvise endringar, etter region, statistikkvariabel og kvartal`
#> region statistikkvariabel kvartal value
#> 1 Oslo Fødselsoverskot 2024K1 1131
#> 2 Oslo Fødselsoverskot 2024K2 1531
#> 3 Oslo Nettoinnflytting, inkl. inn- og utvandring 2024K1 1011
#> 4 Oslo Nettoinnflytting, inkl. inn- og utvandring 2024K2 -815
#> 5 Oslo Folkevekst 2024K1 2142
#> 6 Oslo Folkevekst 2024K2 716
#> 7 Oslo Befolkning ved utgangen av kvartalet 2024K1 719852
#> 8 Oslo Befolkning ved utgangen av kvartalet 2024K2 720568
#> 9 Stavanger Fødselsoverskot 2024K1 135
#> 10 Stavanger Fødselsoverskot 2024K2 129
#> 11 Stavanger Nettoinnflytting, inkl. inn- og utvandring 2024K1 147
#> 12 Stavanger Nettoinnflytting, inkl. inn- og utvandring 2024K2 -10
#> 13 Stavanger Folkevekst 2024K1 282
#> 14 Stavanger Folkevekst 2024K2 119
#> 15 Stavanger Befolkning ved utgangen av kvartalet 2024K1 149330
#> 16 Stavanger Befolkning ved utgangen av kvartalet 2024K2 149449
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Fodselsoverskudd4 2024K1 1131
#> 2 0301 Fodselsoverskudd4 2024K2 1531
#> 3 0301 Nettoinnflytting9 2024K1 1011
#> 4 0301 Nettoinnflytting9 2024K2 -815
#> 5 0301 Folketilvekst10 2024K1 2142
#> 6 0301 Folketilvekst10 2024K2 716
#> 7 0301 Folketallet11 2024K1 719852
#> 8 0301 Folketallet11 2024K2 720568
#> 9 1103 Fodselsoverskudd4 2024K1 135
#> 10 1103 Fodselsoverskudd4 2024K2 129
#> 11 1103 Nettoinnflytting9 2024K1 147
#> 12 1103 Nettoinnflytting9 2024K2 -10
#> 13 1103 Folketilvekst10 2024K1 282
#> 14 1103 Folketilvekst10 2024K2 119
#> 15 1103 Folketallet11 2024K1 149330
#> 16 1103 Folketallet11 2024K2 149449
#>
ApiData(1066, getDataByGET = TRUE, urlType="SSB")
#> $`07129: Varehandelsindeksen, etter næring, måned og statistikkvariabel`
#> næring måned statistikkvariabel
#> 1 Detaljhandel, unntatt salg av motorvogner 2023M09 Volumindeks, sesongjustert
#> 2 Detaljhandel, unntatt salg av motorvogner 2023M10 Volumindeks, sesongjustert
#> 3 Detaljhandel, unntatt salg av motorvogner 2023M11 Volumindeks, sesongjustert
#> 4 Detaljhandel, unntatt salg av motorvogner 2023M12 Volumindeks, sesongjustert
#> 5 Detaljhandel, unntatt salg av motorvogner 2024M01 Volumindeks, sesongjustert
#> 6 Detaljhandel, unntatt salg av motorvogner 2024M02 Volumindeks, sesongjustert
#> 7 Detaljhandel, unntatt salg av motorvogner 2024M03 Volumindeks, sesongjustert
#> 8 Detaljhandel, unntatt salg av motorvogner 2024M04 Volumindeks, sesongjustert
#> 9 Detaljhandel, unntatt salg av motorvogner 2024M05 Volumindeks, sesongjustert
#> 10 Detaljhandel, unntatt salg av motorvogner 2024M06 Volumindeks, sesongjustert
#> 11 Detaljhandel, unntatt salg av motorvogner 2024M07 Volumindeks, sesongjustert
#> 12 Detaljhandel, unntatt salg av motorvogner 2024M08 Volumindeks, sesongjustert
#> 13 Detaljhandel, unntatt salg av motorvogner 2024M09 Volumindeks, sesongjustert
#> value
#> 1 92.3
#> 2 92.6
#> 3 92.9
#> 4 92.1
#> 5 92.1
#> 6 92.2
#> 7 92.6
#> 8 92.5
#> 9 95.9
#> 10 91.0
#> 11 92.1
#> 12 92.2
#> 13 91.9
#>
#> $dataset
#> NACE Tid ContentsCode value
#> 1 47 2023M09 VolumSesong 92.3
#> 2 47 2023M10 VolumSesong 92.6
#> 3 47 2023M11 VolumSesong 92.9
#> 4 47 2023M12 VolumSesong 92.1
#> 5 47 2024M01 VolumSesong 92.1
#> 6 47 2024M02 VolumSesong 92.2
#> 7 47 2024M03 VolumSesong 92.6
#> 8 47 2024M04 VolumSesong 92.5
#> 9 47 2024M05 VolumSesong 95.9
#> 10 47 2024M06 VolumSesong 91.0
#> 11 47 2024M07 VolumSesong 92.1
#> 12 47 2024M08 VolumSesong 92.2
#> 13 47 2024M09 VolumSesong 91.9
#>
ApiData(1066, getDataByGET = TRUE, urlType="SSBen")
#> $`07129: The Index of wholesale and retail trade, by industry, month and contents`
#> industry month
#> 1 Retail trade, except of motor vehicles and motorcycles 2023M09
#> 2 Retail trade, except of motor vehicles and motorcycles 2023M10
#> 3 Retail trade, except of motor vehicles and motorcycles 2023M11
#> 4 Retail trade, except of motor vehicles and motorcycles 2023M12
#> 5 Retail trade, except of motor vehicles and motorcycles 2024M01
#> 6 Retail trade, except of motor vehicles and motorcycles 2024M02
#> 7 Retail trade, except of motor vehicles and motorcycles 2024M03
#> 8 Retail trade, except of motor vehicles and motorcycles 2024M04
#> 9 Retail trade, except of motor vehicles and motorcycles 2024M05
#> 10 Retail trade, except of motor vehicles and motorcycles 2024M06
#> 11 Retail trade, except of motor vehicles and motorcycles 2024M07
#> 12 Retail trade, except of motor vehicles and motorcycles 2024M08
#> 13 Retail trade, except of motor vehicles and motorcycles 2024M09
#> contents value
#> 1 Volume index, seasonally adjusted 92.3
#> 2 Volume index, seasonally adjusted 92.6
#> 3 Volume index, seasonally adjusted 92.9
#> 4 Volume index, seasonally adjusted 92.1
#> 5 Volume index, seasonally adjusted 92.1
#> 6 Volume index, seasonally adjusted 92.2
#> 7 Volume index, seasonally adjusted 92.6
#> 8 Volume index, seasonally adjusted 92.5
#> 9 Volume index, seasonally adjusted 95.9
#> 10 Volume index, seasonally adjusted 91.0
#> 11 Volume index, seasonally adjusted 92.1
#> 12 Volume index, seasonally adjusted 92.2
#> 13 Volume index, seasonally adjusted 91.9
#>
#> $dataset
#> NACE Tid ContentsCode value
#> 1 47 2023M09 VolumSesong 92.3
#> 2 47 2023M10 VolumSesong 92.6
#> 3 47 2023M11 VolumSesong 92.9
#> 4 47 2023M12 VolumSesong 92.1
#> 5 47 2024M01 VolumSesong 92.1
#> 6 47 2024M02 VolumSesong 92.2
#> 7 47 2024M03 VolumSesong 92.6
#> 8 47 2024M04 VolumSesong 92.5
#> 9 47 2024M05 VolumSesong 95.9
#> 10 47 2024M06 VolumSesong 91.0
#> 11 47 2024M07 VolumSesong 92.1
#> 12 47 2024M08 VolumSesong 92.2
#> 13 47 2024M09 VolumSesong 91.9
#>
# }
##### Advanced use using list. See details above. Try returnApiQuery=TRUE on the same examples.
ApiData(4861, Region = list("03*"), ContentsCode = 1, Tid = 5i) # "all" can be dropped from the list
#> $`04861: Areal og befolkning i tettsteder, etter region, statistikkvariabel og år`
#> region statistikkvariabel år value
#> 1 Oslo Areal av tettsted (km²) 2020 130.49
#> 2 Oslo Areal av tettsted (km²) 2021 130.47
#> 3 Oslo Areal av tettsted (km²) 2022 130.57
#> 4 Oslo Areal av tettsted (km²) 2023 130.46
#> 5 Oslo Areal av tettsted (km²) 2024 130.31
#> 6 Uoppgitt komm. Oslo Areal av tettsted (km²) 2020 0.00
#> 7 Uoppgitt komm. Oslo Areal av tettsted (km²) 2021 0.00
#> 8 Uoppgitt komm. Oslo Areal av tettsted (km²) 2022 0.00
#> 9 Uoppgitt komm. Oslo Areal av tettsted (km²) 2023 0.00
#> 10 Uoppgitt komm. Oslo Areal av tettsted (km²) 2024 0.00
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Areal 2020 130.49
#> 2 0301 Areal 2021 130.47
#> 3 0301 Areal 2022 130.57
#> 4 0301 Areal 2023 130.46
#> 5 0301 Areal 2024 130.31
#> 6 0399 Areal 2020 0.00
#> 7 0399 Areal 2021 0.00
#> 8 0399 Areal 2022 0.00
#> 9 0399 Areal 2023 0.00
#> 10 0399 Areal 2024 0.00
#>
ApiData(4861, Region = list("all", "03*"), ContentsCode = 1, Tid = 5i) # same as above
#> $`04861: Areal og befolkning i tettsteder, etter region, statistikkvariabel og år`
#> region statistikkvariabel år value
#> 1 Oslo Areal av tettsted (km²) 2020 130.49
#> 2 Oslo Areal av tettsted (km²) 2021 130.47
#> 3 Oslo Areal av tettsted (km²) 2022 130.57
#> 4 Oslo Areal av tettsted (km²) 2023 130.46
#> 5 Oslo Areal av tettsted (km²) 2024 130.31
#> 6 Uoppgitt komm. Oslo Areal av tettsted (km²) 2020 0.00
#> 7 Uoppgitt komm. Oslo Areal av tettsted (km²) 2021 0.00
#> 8 Uoppgitt komm. Oslo Areal av tettsted (km²) 2022 0.00
#> 9 Uoppgitt komm. Oslo Areal av tettsted (km²) 2023 0.00
#> 10 Uoppgitt komm. Oslo Areal av tettsted (km²) 2024 0.00
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Areal 2020 130.49
#> 2 0301 Areal 2021 130.47
#> 3 0301 Areal 2022 130.57
#> 4 0301 Areal 2023 130.46
#> 5 0301 Areal 2024 130.31
#> 6 0399 Areal 2020 0.00
#> 7 0399 Areal 2021 0.00
#> 8 0399 Areal 2022 0.00
#> 9 0399 Areal 2023 0.00
#> 10 0399 Areal 2024 0.00
#>
ApiData(04861, Region = list("item", c("1103", "0301")), ContentsCode = 1, Tid = 5i)
#> $`04861: Areal og befolkning i tettsteder, etter region, statistikkvariabel og år`
#> region statistikkvariabel år value
#> 1 Oslo Areal av tettsted (km²) 2020 130.49
#> 2 Oslo Areal av tettsted (km²) 2021 130.47
#> 3 Oslo Areal av tettsted (km²) 2022 130.57
#> 4 Oslo Areal av tettsted (km²) 2023 130.46
#> 5 Oslo Areal av tettsted (km²) 2024 130.31
#> 6 Stavanger Areal av tettsted (km²) 2020 44.13
#> 7 Stavanger Areal av tettsted (km²) 2021 44.22
#> 8 Stavanger Areal av tettsted (km²) 2022 44.45
#> 9 Stavanger Areal av tettsted (km²) 2023 44.21
#> 10 Stavanger Areal av tettsted (km²) 2024 44.34
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Areal 2020 130.49
#> 2 0301 Areal 2021 130.47
#> 3 0301 Areal 2022 130.57
#> 4 0301 Areal 2023 130.46
#> 5 0301 Areal 2024 130.31
#> 6 1103 Areal 2020 44.13
#> 7 1103 Areal 2021 44.22
#> 8 1103 Areal 2022 44.45
#> 9 1103 Areal 2023 44.21
#> 10 1103 Areal 2024 44.34
#>
##### Using data from SCB to illustrate returnMetaFrames
urlSCB <- "https://api.scb.se/OV0104/v1/doris/sv/ssd/BE/BE0101/BE0101A/BefolkningNy"
mf <- ApiData(urlSCB, returnMetaFrames = TRUE)
names(mf) # All the variable names
#> [1] "Region" "Civilstand" "Alder" "Kon" "ContentsCode"
#> [6] "Tid"
attr(mf, "text") # Corresponding text information as attribute
#> Region Civilstand Alder Kon
#> "region" "civilstånd" "ålder" "kön"
#> ContentsCode Tid
#> "tabellinnehåll" "år"
mf$ContentsCode # Data frame for the fifth variable (alternatively mf[[5]])
#> values valueTexts
#> 1 BE0101N1 Folkmängd
#> 2 BE0101N2 Folkökning
attr(mf,"elimination") # Finding variables that can be eliminated
#> Region Civilstand Alder Kon ContentsCode Tid
#> TRUE TRUE TRUE TRUE FALSE FALSE
ApiData(urlSCB, # Eliminating all variables that can be eliminated (line below)
Region = FALSE, Civilstand = FALSE, Alder = FALSE, Kon = FALSE,
ContentsCode = "BE0101N1", # Selecting a single ContentsCode by text input
Tid = TRUE) # Choosing all possible values of Tid.
#> $`Folkmängd efter tabellinnehåll och år`
#> tabellinnehåll år value
#> 1 Folkmängd 1968 7931193
#> 2 Folkmängd 1969 8004270
#> 3 Folkmängd 1970 8081142
#> 4 Folkmängd 1971 8115165
#> 5 Folkmängd 1972 8129129
#> 6 Folkmängd 1973 8144428
#> 7 Folkmängd 1974 8176691
#> 8 Folkmängd 1975 8208442
#> 9 Folkmängd 1976 8236179
#> 10 Folkmängd 1977 8267116
#> 11 Folkmängd 1978 8284437
#> 12 Folkmängd 1979 8303010
#> 13 Folkmängd 1980 8317937
#> 14 Folkmängd 1981 8323033
#> 15 Folkmängd 1982 8327484
#> 16 Folkmängd 1983 8330573
#> 17 Folkmängd 1984 8342621
#> 18 Folkmängd 1985 8358139
#> 19 Folkmängd 1986 8381515
#> 20 Folkmängd 1987 8414083
#> 21 Folkmängd 1988 8458888
#> 22 Folkmängd 1989 8527036
#> 23 Folkmängd 1990 8590630
#> 24 Folkmängd 1991 8644119
#> 25 Folkmängd 1992 8692013
#> 26 Folkmängd 1993 8745109
#> 27 Folkmängd 1994 8816381
#> 28 Folkmängd 1995 8837496
#> 29 Folkmängd 1996 8844499
#> 30 Folkmängd 1997 8847625
#> 31 Folkmängd 1998 8854322
#> 32 Folkmängd 1999 8861426
#> 33 Folkmängd 2000 8882792
#> 34 Folkmängd 2001 8909128
#> 35 Folkmängd 2002 8940788
#> 36 Folkmängd 2003 8975670
#> 37 Folkmängd 2004 9011392
#> 38 Folkmängd 2005 9047752
#> 39 Folkmängd 2006 9113257
#> 40 Folkmängd 2007 9182927
#> 41 Folkmängd 2008 9256347
#> 42 Folkmängd 2009 9340682
#> 43 Folkmängd 2010 9415570
#> 44 Folkmängd 2011 9482855
#> 45 Folkmängd 2012 9555893
#> 46 Folkmängd 2013 9644864
#> 47 Folkmängd 2014 9747355
#> 48 Folkmängd 2015 9851017
#> 49 Folkmängd 2016 9995153
#> 50 Folkmängd 2017 10120242
#> 51 Folkmängd 2018 10230185
#> 52 Folkmängd 2019 10327589
#> 53 Folkmängd 2020 10379295
#> 54 Folkmängd 2021 10452326
#> 55 Folkmängd 2022 10521556
#> 56 Folkmängd 2023 10551707
#>
#> $dataset
#> ContentsCode Tid value
#> 1 BE0101N1 1968 7931193
#> 2 BE0101N1 1969 8004270
#> 3 BE0101N1 1970 8081142
#> 4 BE0101N1 1971 8115165
#> 5 BE0101N1 1972 8129129
#> 6 BE0101N1 1973 8144428
#> 7 BE0101N1 1974 8176691
#> 8 BE0101N1 1975 8208442
#> 9 BE0101N1 1976 8236179
#> 10 BE0101N1 1977 8267116
#> 11 BE0101N1 1978 8284437
#> 12 BE0101N1 1979 8303010
#> 13 BE0101N1 1980 8317937
#> 14 BE0101N1 1981 8323033
#> 15 BE0101N1 1982 8327484
#> 16 BE0101N1 1983 8330573
#> 17 BE0101N1 1984 8342621
#> 18 BE0101N1 1985 8358139
#> 19 BE0101N1 1986 8381515
#> 20 BE0101N1 1987 8414083
#> 21 BE0101N1 1988 8458888
#> 22 BE0101N1 1989 8527036
#> 23 BE0101N1 1990 8590630
#> 24 BE0101N1 1991 8644119
#> 25 BE0101N1 1992 8692013
#> 26 BE0101N1 1993 8745109
#> 27 BE0101N1 1994 8816381
#> 28 BE0101N1 1995 8837496
#> 29 BE0101N1 1996 8844499
#> 30 BE0101N1 1997 8847625
#> 31 BE0101N1 1998 8854322
#> 32 BE0101N1 1999 8861426
#> 33 BE0101N1 2000 8882792
#> 34 BE0101N1 2001 8909128
#> 35 BE0101N1 2002 8940788
#> 36 BE0101N1 2003 8975670
#> 37 BE0101N1 2004 9011392
#> 38 BE0101N1 2005 9047752
#> 39 BE0101N1 2006 9113257
#> 40 BE0101N1 2007 9182927
#> 41 BE0101N1 2008 9256347
#> 42 BE0101N1 2009 9340682
#> 43 BE0101N1 2010 9415570
#> 44 BE0101N1 2011 9482855
#> 45 BE0101N1 2012 9555893
#> 46 BE0101N1 2013 9644864
#> 47 BE0101N1 2014 9747355
#> 48 BE0101N1 2015 9851017
#> 49 BE0101N1 2016 9995153
#> 50 BE0101N1 2017 10120242
#> 51 BE0101N1 2018 10230185
#> 52 BE0101N1 2019 10327589
#> 53 BE0101N1 2020 10379295
#> 54 BE0101N1 2021 10452326
#> 55 BE0101N1 2022 10521556
#> 56 BE0101N1 2023 10551707
#>
##### Using data from Statfi to illustrate use of input by variable labels (valueTexts)
urlStatfi <- "https://pxdata.stat.fi/PXWeb/api/v1/en/StatFin/kuol/statfin_kuol_pxt_12au.px"
ApiData(urlStatfi, returnMetaFrames = TRUE)$Tiedot
#> values valueTexts
#> 1 vm01 Live births
#> 2 vm11 Deaths
#> 3 luonvalisays Natural increase
#> 4 vm43_tulo Intermunicipal in-migration
#> 5 vm43_lahto Intermunicipal out-migration
#> 6 vm43_netto Intermunicipal net migration
#> 7 vm44 Intramunicipal migration
#> 8 vm41 Immigration to Finland
#> 9 vm41_nordic Immigration to Finland from Nordic countries
#> 10 vm41_eu Immigration to Finland from EU countries
#> 11 vm42 Emigration from Finland
#> 12 vm42_nordic Emigration from Finland to Nordic countries
#> 13 vm42_eu Emigration from Finland to EU countries
#> 14 vm4142 Net migration
#> 15 koknetmuutto Total net migration
#> 16 vm2126 Marriages
#> 17 vm3136 Divorces
#> 18 valisays Population increase
#> 19 vakorjaus Population correction
#> 20 kokmuutos Total change
#> 21 vaesto Population
ApiData(urlStatfi, Alue = FALSE, Vuosi = TRUE, Tiedot = "Population") # same as Tiedot = 21
#> $`Vital statistics by Year and Information`
#> Year Information value
#> 1 1990 Population 4998478
#> 2 1991 Population 5029002
#> 3 1992 Population 5054982
#> 4 1993 Population 5077912
#> 5 1994 Population 5098754
#> 6 1995 Population 5116826
#> 7 1996 Population 5132320
#> 8 1997 Population 5147349
#> 9 1998 Population 5159646
#> 10 1999 Population 5171302
#> 11 2000 Population 5181115
#> 12 2001 Population 5194901
#> 13 2002 Population 5206295
#> 14 2003 Population 5219732
#> 15 2004 Population 5236611
#> 16 2005 Population 5255580
#> 17 2006 Population 5276955
#> 18 2007 Population 5300484
#> 19 2008 Population 5326314
#> 20 2009 Population 5351427
#> 21 2010 Population 5375276
#> 22 2011 Population 5401267
#> 23 2012 Population 5426674
#> 24 2013 Population 5451270
#> 25 2014 Population 5471753
#> 26 2015 Population 5487308
#> 27 2016 Population 5503297
#> 28 2017 Population 5513130
#> 29 2018 Population 5517919
#> 30 2019 Population 5525292
#> 31 2020 Population 5533793
#> 32 2021 Population 5548241
#> 33 2022 Population 5563970
#> 34 2023 Population 5603851
#>
#> $dataset
#> Vuosi Tiedot value
#> 1 1990 vaesto 4998478
#> 2 1991 vaesto 5029002
#> 3 1992 vaesto 5054982
#> 4 1993 vaesto 5077912
#> 5 1994 vaesto 5098754
#> 6 1995 vaesto 5116826
#> 7 1996 vaesto 5132320
#> 8 1997 vaesto 5147349
#> 9 1998 vaesto 5159646
#> 10 1999 vaesto 5171302
#> 11 2000 vaesto 5181115
#> 12 2001 vaesto 5194901
#> 13 2002 vaesto 5206295
#> 14 2003 vaesto 5219732
#> 15 2004 vaesto 5236611
#> 16 2005 vaesto 5255580
#> 17 2006 vaesto 5276955
#> 18 2007 vaesto 5300484
#> 19 2008 vaesto 5326314
#> 20 2009 vaesto 5351427
#> 21 2010 vaesto 5375276
#> 22 2011 vaesto 5401267
#> 23 2012 vaesto 5426674
#> 24 2013 vaesto 5451270
#> 25 2014 vaesto 5471753
#> 26 2015 vaesto 5487308
#> 27 2016 vaesto 5503297
#> 28 2017 vaesto 5513130
#> 29 2018 vaesto 5517919
#> 30 2019 vaesto 5525292
#> 31 2020 vaesto 5533793
#> 32 2021 vaesto 5548241
#> 33 2022 vaesto 5563970
#> 34 2023 vaesto 5603851
#>
##### Wrappers PxData and pxwebData
# Exact same output as ApiData
PxData(4861, Region = "0301", ContentsCode = TRUE, Tid = c(1, -1))
#> $`04861: Areal og befolkning i tettsteder, etter region, statistikkvariabel og år`
#> region statistikkvariabel år value
#> 1 Oslo Areal av tettsted (km²) 2000 132.90
#> 2 Oslo Areal av tettsted (km²) 2024 130.31
#> 3 Oslo Bosatte 2000 504348.00
#> 4 Oslo Bosatte 2024 714630.00
#>
#> $dataset
#> Region ContentsCode Tid value
#> 1 0301 Areal 2000 132.90
#> 2 0301 Areal 2024 130.31
#> 3 0301 Bosatte 2000 504348.00
#> 4 0301 Bosatte 2024 714630.00
#>
# Data organized differently
pxwebData(4861, Region = "0301", ContentsCode = TRUE, Tid = c(1, -1))
#> [[1]]
#> region år Areal av tettsted (km²) Bosatte
#> 1 Oslo 2000 132.90 504348
#> 2 Oslo 2024 130.31 714630
#>
#> [[2]]
#> Region Tid Areal Bosatte
#> 1 0301 2000 132.90 504348
#> 2 0301 2024 130.31 714630
#>
# Large query. ApiData will not work.
if(FALSE){ # This query is "commented out"
z <- PxData("https://api.scb.se/OV0104/v1/doris/sv/ssd/BE/BE0101/BE0101A/BefolkningNy",
Region = TRUE, Civilstand = TRUE, Alder = 1:10, Kon = FALSE,
ContentsCode = "BE0101N1", Tid = 1:10, verbosePrint = TRUE)
}
##### Small example where makeNAstatus is in use
ApiData("04469", Tid = "2020", ContentsCode = 1, Alder = TRUE, Region = "3011")
#> $`04469: Bebuarar i bustader kommunen disponerer til pleie- og omsorgsformål, etter region, alder, statistikkvariabel og år`
#> region alder statistikkvariabel år value NAstatus
#> 1 Hvaler (2020-2023) Alder i alt Bebuarar i bustader 2020 36 <NA>
#> 2 Hvaler (2020-2023) Under 67 år Bebuarar i bustader 2020 NA :
#> 3 Hvaler (2020-2023) 67-74 år Bebuarar i bustader 2020 NA :
#> 4 Hvaler (2020-2023) 75-79 år Bebuarar i bustader 2020 NA :
#> 5 Hvaler (2020-2023) 80-84 år Bebuarar i bustader 2020 NA :
#> 6 Hvaler (2020-2023) 85-89 år Bebuarar i bustader 2020 NA :
#> 7 Hvaler (2020-2023) 90 år eller eldre Bebuarar i bustader 2020 NA :
#> 8 Hvaler (2020-2023) Uoppgitt alder Bebuarar i bustader 2020 NA ..
#>
#> $dataset
#> Region Alder ContentsCode Tid value NAstatus
#> 1 3011 999 Beboere 2020 36 <NA>
#> 2 3011 0-66 Beboere 2020 NA :
#> 3 3011 67-74 Beboere 2020 NA :
#> 4 3011 75-79 Beboere 2020 NA :
#> 5 3011 80-84 Beboere 2020 NA :
#> 6 3011 85-89 Beboere 2020 NA :
#> 7 3011 090+ Beboere 2020 NA :
#> 8 3011 888 Beboere 2020 NA ..
#>