statxplorer is an R package for downloading tabular data from the Department for Work and Pensions Stat-Xplore API. This package lets you send queries to the Stat-XPlore API and receive the results in a simple and manageable data structure, which includes dataframes of tidy data ready for analysis. The package supports a workflow of designing queries in the Stat-Xplore web interface (or by hand) and then using them to retrieve the data programatically from the API's Table endpoint. Queries can be loaded directly from files when you send a request for the data. Please note that queries that use custom aggregate variables require special handling (see below).
This package has principally been developed to support automation and reproducible research in the House of Commons Library, but may be useful to other researchers who routinely work with DWP data. Let me know if you have any feedback or find any bugs.
Install from GitHub using remotes.
install.packages("remotes")
remotes::install_github("houseofcommonslibrary/statxplorer")This package requires Tidyr 1.0.0 or later.
To use Stat-Xplore you need an API key, which you can download after you register for an account. To use this package you will need to set the API key once for each session. You can do this either by providing the key directly using set_api_key, or loading the key from a file with load_api_key.
library(statxplorer)
# Set API key with a string
set_api_key("APIKEYSTRING")
# Set API key from a file
load_api_key("api_key.txt")
Stat-Xplore queries can be verbose. You can generate them yourself according to the JSON schema, but it is easiest to start by designing a query in Stat-Xplore's web interface and downloading it as a JSON file (choose "Open Data API Query" as the download option in the web interface).
Suppose you have this query stored in a file called uc-households.json, which requests the number of households claiming Universal Credit in August 2018 broken down by family type.
{
"database" : "str:database:UC_Households",
"measures" : [ "str:count:UC_Households:V_F_UC_HOUSEHOLDS" ],
"recodes" : {
"str:field:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE" : {
"map" : [
[ "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:1" ],
[ "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:2" ],
[ "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:3" ],
[ "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:4" ],
[ "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:99" ] ],
"total" : true
},
"str:field:UC_Households:F_UC_DATE:DATE_NAME" : {
"map" : [ [ "str:value:UC_Households:F_UC_DATE:DATE_NAME:C_UC_DATE:201808" ] ],
"total" : false
}
},
"dimensions" : [
[ "str:field:UC_Households:F_UC_DATE:DATE_NAME" ],
[ "str:field:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE" ] ]
}You can download the data for this query using fetch_table, either by passing the query as a string to the fetch_table function, or by loading it from a file with the filename argument.
# Pass the query directly
library(readr)
query <- read_file("uc-households.json")
results <- fetch_table(query)
# Load the query from a file
results <- fetch_table(filename = "uc-households.json")The results of the query are returned as a list with the following elements:
- measures - the names of the measures for each dataset (character)
- fields - the names of categorical variables included in the data (character)
- items - the names of the categories or levels within each field (list)
- uris - the uris of the categories or levels within each field (list)
- dfs - a dataframe for each measure with the data in long form (list)
The results of the query shown above look like this:
$measures
[1] "Households on Universal Credit"
$fields
[1] "Month" "Family Type"
$items
$items$`Month`
[1] "August 2018"
$items$`Family Type`
[1] "Single, no child dependant" "Single, with child dependant(s)"
[3] "Couple, no child dependant" "Couple, with child dependant(s)"
[5] "Unknown or missing family type" "Total"
$uris
$uris$`Month`
[1] "str:value:UC_Households:F_UC_DATE:DATE_NAME:C_UC_DATE:201808"
$uris$`Family Type`
[1] "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:1"
[2] "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:2"
[3] "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:3"
[4] "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:4"
[5] "str:value:UC_Households:V_F_UC_HOUSEHOLDS:HNFAMILY_TYPE:C_UC_FAMILY_TYPE:99"
$dfs
$dfs$`Households on Universal Credit`
# A tibble: 6 x 3
Month `Family Type` `Households on Universal Credit`
<chr> <chr> <dbl>
1 August 2018 Single, no child dependant 625010
2 August 2018 Single, with child dependant(s) 251797
3 August 2018 Couple, no child dependant 35651
4 August 2018 Couple, with child dependant(s) 91236
5 August 2018 Unknown or missing family type 0
6 August 2018 Total 1003697The results are provided as tidy datasets in results$dfs. The dfs list contains one dataframe for each measure that was requested in the query.
Stat-Xplore provides ids for each variable in the URI strings. These URIs sometimes include codes that are externally recognised identifiers as the final token in the string. For example, URIs for geographical areas contain the ids that the Office for National Statistics uses for those areas at the end of the URI. These ids can be helpful for linking the data to other datasets.
To make it easier to extract and use these ids, the package has a function called add_codes_for_field. This will attempt to create a new column in each of the results dataframes containing the codes for a given field. To add a set of codes to the dataframes in the results, call add_codes_for_field with the results you want to modify, the name of the field for which you want to add codes, and the name of the column that will contain the codes.
results <- add_codes_for_field(results, field = "Family Type", colname = "Family Type Codes")If the given field does not exist, or the given column name already exists, the function will throw an error.
In any query that uses the standard variables offered by the API, there is a relationship between the structure of the results data and the structure of the metadata about those results that the API returns. The statxplorer package uses this relationship to provide a generic way of handling query results and returning them as tidy data through the fetch_table function.
In addition to the standard variables, the API also lets you combine several variable items within a given field to create custom aggregate variables. So for example, if you have a field that contains items representing five year age bands, you can add together the results of consecutive age bands to create a new variable representing a wider age band. (You can find this option in the web interface under Custom Data.)
This is sometimes convenient but it comes with a cost: it breaks the relationship between the structure of the results data and the structure of the metadata about the results. In particular, the metadata that the API returns does not list the custom variables you have used for items within a field. Instead it lists all of the individual standard variable items you have added together to create the custom variables for that field. This means it is not possible to handle the results of queries that use custom variables in a completely generic way.
However, it is still possible to use fetch_table to process queries that use custom aggregate variables. You just need to provide the function with the missing metadata that it needs to handle the results. You can do this with the custom argument, which takes a named list. Each name in the list is the name of a field that contains custom variables, and each value is a character vector specifying the labels you wish to use for the variable items in that field.
So for example, suppose you have a query that gets data on Housing Benefit claimants by age band, using custom age bands for people aged 16-64 and 65+, along with a total, stored in a file called hb-by-age.json.
{
"database" : "str:database:hb_new",
"measures" : [ "str:count:hb_new:V_F_HB_NEW" ],
"recodes" : {
"str:field:hb_new:F_HB_NEW_DATE:NEW_DATE_NAME" : {
"map" : [ [ "str:value:hb_new:F_HB_NEW_DATE:NEW_DATE_NAME:C_HB_NEW_DATE:201811" ] ],
"total" : false
},
"str:field:hb_new:V_F_HB_NEW:AGE_BAND" : {
"map" : [
["str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:1",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:2",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:3",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:4",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:5",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:6",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:7" ],
["str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:8",
"str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:9" ] ],
"total" : true
}
},
"dimensions" : [ [ "str:field:hb_new:V_F_HB_NEW:AGE_BAND" ], [ "str:field:hb_new:F_HB_NEW_DATE:NEW_DATE_NAME" ] ]
}You would call fetch_table with the correct metadata in the following way:
custom <- list("Age of Claimant (bands only)" = c("16-64", "65+", "Total"))
results <- fetch_table(filename = "hb-by-age.json", custom = custom)The results look like this. Note that the URIs are not modified, and show the component age bands used in the custom variables.
$measures
[1] "Housing Benefit Claimants"
$fields
[1] "Age of Claimant (bands only)" "Month"
$items
$items$`Age of Claimant (bands only)`
[1] "16-64" "65+" "Total"
$items$Month
[1] "201811 (Nov-18)"
$uris
$uris$`Age of Claimant (bands only)`
[1] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:1"
[2] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:2"
[3] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:3"
[4] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:4"
[5] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:5"
[6] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:6"
[7] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:7"
[8] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:8"
[9] "str:value:hb_new:V_F_HB_NEW:AGE_BAND:V_C_AGE_BAND_2:9"
$uris$Month
[1] "str:value:hb_new:F_HB_NEW_DATE:NEW_DATE_NAME:C_HB_NEW_DATE:201811"
$dfs
$dfs$`Housing Benefit Claimants`
# A tibble: 3 x 3
`Age of Claimant (bands only)` Month `Housing Benefit Claimants`
<chr> <chr> <dbl>
1 16-64 201811 (Nov-18) 2688155
2 65+ 201811 (Nov-18) 1239526
3 Total 201811 (Nov-18) 3927676When specifying the custom metadata, you need to use the correct field names for the custom variables, and provide the correct number of labels, listed in the same order that they are defined within the query. As long as the custom argument correctly describes the structure of the variables you have used in your results, fetch_table should do the right thing. But it is up to you to ensure you have described the missing metadata correctly. You should verify that the labels in your results are aligned with the correct data by comparing your query results with the results of an equivalent query shown in the web interface.
Please note that this is currently an experimental feature.
Every now and again the Stat-Xplore API fails to respond to a request that it normally returns correctly. This results in a timeout with the error message Could not connect to Stat-Xplore: the server may be down. This appears to be an issue with the server itself: waiting and trying again later normally resolves this issue. I have reported the problem to the Stat-Xplore team.