evanodell / dwpstat Goto Github PK
View Code? Open in Web Editor NEWAccess 'Stat-Xplore' data on the UK benefits system
License: Other
dwpstat's Issues
Add recoding options
The API has an option for recodes, e.g. specific geographies. Documentation gives an example constructed in JSON.
Guidance on how to pull data within specific geographies
I'm not clear how to restrict the stat-xplore data download to specific areas (in particular, here, all LSOAs within a specific Local Authority).
I'm not clear how to use the recodes feature, basically!
Here's a reprex of my rather rudimentary code so far.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(magrittr)
library(tibble)
library(purrr)
#>
#> Attaching package: 'purrr'
#> The following object is masked from 'package:magrittr':
#>
#> set_names
library(dwpstat)
lad19cd <- "E07000170" # Ashfield
lsoa_ids <- c("E01027925", "E01027926", "E01027927", "E01027928", "E01027929",
"E01027930", "E01027931", "E01027932", "E01027933", "E01027934",
"E01027935", "E01027936", "E01027937", "E01027938", "E01027939",
"E01027940", "E01027941", "E01027942", "E01027943", "E01027944",
"E01027945", "E01027946", "E01027947", "E01027948", "E01027949",
"E01027950", "E01027951", "E01027952", "E01027953", "E01027954",
"E01027955", "E01027956", "E01027957", "E01027958", "E01027959",
"E01027960", "E01027961", "E01027962", "E01027963", "E01027964",
"E01027965", "E01027966", "E01027967", "E01027968", "E01027969",
"E01027970", "E01027971", "E01027972", "E01027973", "E01027974",
"E01027975", "E01027976", "E01027977", "E01027978", "E01027979",
"E01027980", "E01027981", "E01027982", "E01027983", "E01027984",
"E01027985", "E01027986", "E01027987", "E01027988", "E01027989",
"E01027990", "E01027991", "E01027992", "E01027993", "E01027994",
"E01027995", "E01027996", "E01027997", "E01027998")
uc_database_id <- dwp_schema(
dwp_schema() %>%
filter(label == "Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)
uc_database_id
#> [1] "str:database:UC_Households"
uc_measures_id <- dwp_schema(
dwp_schema(
dwp_schema() %>%
filter(label == "Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)
uc_measures_id
#> [1] "str:count:UC_Households:V_F_UC_HOUSEHOLDS"
uc_month_id <- dwp_schema(
dwp_schema(
dwp_schema() %>%
filter(label == "Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)) %>%
filter(label == "Month") %>%
pull(id)
uc_month_id
#> [1] "str:field:UC_Households:F_UC_DATE:DATE_NAME"
uc_geog_la_id <- dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema() %>%
filter(label == "Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)) %>%
filter(label == "Geography (residence-based)") %>%
pull(id)) %>%
filter(label == "National - Regional - LA - OAs") %>%
pull(id)) %>%
filter(label == "Local Authority") %>%
pull(id) %>%
paste0(., ":", lad19cd)
uc_geog_la_id
#> [1] "str:valueset:UC_Households:V_F_UC_HOUSEHOLDS:COA_CODE:V_C_MASTERGEOG11_LA_TO_REGION:E07000170"
uc_geog_lsoa_id <- dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema() %>%
filter(label == "Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)) %>%
filter(label == "Geography (residence-based)") %>%
pull(id)) %>%
filter(label == "National - Regional - LA - OAs") %>%
pull(id)) %>%
filter(label == "Lower Layer Super Output Areas") %>%
pull(id)
uc_geog_lsoa_id
#> [1] "str:valueset:UC_Households:V_F_UC_HOUSEHOLDS:COA_CODE:V_C_MASTERGEOG11_LSOA_TO_MSOA"
uc_geographies_data <- dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema(
dwp_schema() %>%
filter(label == "Universal Credit") %>%
pull(id)) %>%
filter(label == "Households on Universal Credit") %>%
pull(id)) %>%
filter(label == "Geography (residence-based)") %>%
pull(id)) %>%
filter(label == "National - Regional - LA - OAs") %>%
pull(id)) %>%
filter(label == "Lower Layer Super Output Areas") %>%
pull(id))
head(uc_geographies_data)
#> # A tibble: 6 x 4
#> id label location type
#> <chr> <chr> <chr> <chr>
#> 1 str:value:UC_Households:V_F_~ Wakefield ~ https://stat-xplore.dwp.gov.u~ VALUE
#> 2 str:value:UC_Households:V_F_~ Dunnikier ~ https://stat-xplore.dwp.gov.u~ VALUE
#> 3 str:value:UC_Households:V_F_~ Kingston u~ https://stat-xplore.dwp.gov.u~ VALUE
#> 4 str:value:UC_Households:V_F_~ Barnsley 0~ https://stat-xplore.dwp.gov.u~ VALUE
#> 5 str:value:UC_Households:V_F_~ Dunoon - 05 https://stat-xplore.dwp.gov.u~ VALUE
#> 6 str:value:UC_Households:V_F_~ Torbay 017B https://stat-xplore.dwp.gov.u~ VALUE.
# this gets total national numbers of claimants
# how do I add in a geography filter?
uc_data_pull <- dwp_get_data(
database = uc_database_id,
measures = uc_measures_id,
column = uc_month_id)
uc_data_tibble <- tibble(
month = uc_data_pull %>%
pluck("fields", "items", 1, "labels"),
claimants = uc_data_pull %>%
pluck("cubes", "str:count:UC_Households:V_F_UC_HOUSEHOLDS", "values"))
head(uc_data_tibble)
#> # A tibble: 6 x 2
#> month claimants
#> <list> <dbl>
#> 1 <chr [1]> 98139
#> 2 <chr [1]> 111297
#> 3 <chr [1]> 124950
#> 4 <chr [1]> 141142
#> 5 <chr [1]> 155999
#> 6 <chr [1]> 175187Created on 2020-02-11 by the reprex package (v0.3.0)
Matching data array to label array
The API currently returns data labels in one array and the actual data in a separate array. A function to match the two arrays together would make data returned with dwpstat easier to work with.
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