Coder Social home page Coder Social logo

dwpstat's People

Contributors

evanodell avatar francisbarton avatar

Stargazers

avatar avatar avatar avatar

Watchers

avatar avatar avatar

Forkers

joeheywood guyweir

dwpstat's Issues

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]>    175187

Created 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.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.