nomisr is for accessing UK official statistics from the Nomis database through R. Nomis contains data from the Census, the Labour Force Survey, DWP benefit statistics and other economic and demographic data, and is maintained on behalf of the Office for National Statistics by the University of Durham.
The nomisr package provides functions to find what data is available, the variables and query options for different datasets and a function for downloading data. nomisr returns data in tibble format. Most of the data available through nomisr is based around statistical geographies, with a handful of exceptions.
The package is for demographers, economists, geographers, public health researchers and any other researchers who are interested in geographic factors. The package aims to aid reproducibility, reduce the need to manually download area profiles, and allow easy linking of different datasets covering the same geographic area.
Installation
nomisr is available on CRAN:
install.packages("nomisr")You can install the development version nomisr from github with:
# install.packages("devtools")
devtools::install_github("ropensci/nomisr")Using nomisr
nomisr contains functions to search for datasets, identify the query options for different datasets and retrieve data from queries, all done with tibbles, to take advantage of how tibble manages list-columns. The use of metadata queries, rather than simply downloading all available data, is useful to avoid overwhelming the rate limits of the API.
There are nomisr vignette introduction has details on all available functions and basic demonstrations of their use.
The example below demonstrates a workflow to retrieve the latest data on Jobseeker’s Allowance with rates and proportions, on a national level, with all male claimants and workforce.
library(nomisr)
jobseekers_search <- nomis_search(name = "*Jobseeker*")
tibble::glimpse(jobseekers_search)
#> Rows: 17
#> Columns: 14
#> $ agencyid <chr> "NOMIS", "NOMIS", "NOMIS", "NOMIS…
#> $ id <chr> "NM_1_1", "NM_4_1", "NM_8_1", "NM…
#> $ uri <chr> "Nm-1d1", "Nm-4d1", "Nm-8d1", "Nm…
#> $ version <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ annotations.annotation <list> [<data.frame[10 x 2]>], [<data.f…
#> $ components.attribute <list> [<data.frame[7 x 4]>], [<data.fr…
#> $ components.dimension <list> [<data.frame[5 x 3]>], [<data.fr…
#> $ components.primarymeasure.conceptref <chr> "OBS_VALUE", "OBS_VALUE", "OBS_VA…
#> $ components.timedimension.codelist <chr> "CL_1_1_TIME", "CL_4_1_TIME", "CL…
#> $ components.timedimension.conceptref <chr> "TIME", "TIME", "TIME", "TIME", "…
#> $ description.value <chr> "Records the number of people cla…
#> $ description.lang <chr> "en", "en", NA, "en", "en", "en",…
#> $ name.value <chr> "Jobseeker's Allowance with rates…
#> $ name.lang <chr> "en", "en", "en", "en", "en", "en…
jobseekers_measures <- nomis_get_metadata("NM_1_1", "measures")
tibble::glimpse(jobseekers_measures)
#> Rows: 4
#> Columns: 3
#> $ id <chr> "20100", "20201", "20202", "20203"
#> $ label.en <chr> "claimants", "workforce", "active", "residence"
#> $ description.en <chr> "claimants", "workforce", "active", "residence"
jobseekers_geography <- nomis_get_metadata("NM_1_1", "geography", "TYPE")
tail(jobseekers_geography)
#> # A tibble: 6 × 3
#> id label.en description.en
#> <chr> <chr> <chr>
#> 1 TYPE490 government office regions tec / lec based government office …
#> 2 TYPE491 government office regions (former inc. Merseyside) government office …
#> 3 TYPE492 standard statistical regions standard statistic…
#> 4 TYPE496 pre-1996 local authority districts pre-1996 local aut…
#> 5 TYPE498 pre-1996 counties / scottish regions pre-1996 counties …
#> 6 TYPE499 countries countries
jobseekers_sex <- nomis_get_metadata("NM_1_1", "sex", "TYPE")
tibble::glimpse(jobseekers_sex)
#> Rows: 3
#> Columns: 4
#> $ id <chr> "5", "6", "7"
#> $ parentCode <chr> "7", "7", NA
#> $ label.en <chr> "Male", "Female", "Total"
#> $ description.en <chr> "Male", "Female", "Total"
z <- nomis_get_data(id = "NM_1_1", time = "latest", geography = "TYPE499",
measures=c(20100, 20201), sex=5)
#> No encoding supplied: defaulting to UTF-8.
tibble::glimpse(z)
#> Rows: 70
#> Columns: 34
#> $ DATE <chr> "2021-12", "2021-12", "2021-12", "2021-12", "2021-…
#> $ DATE_NAME <chr> "December 2021", "December 2021", "December 2021",…
#> $ DATE_CODE <chr> "2021-12", "2021-12", "2021-12", "2021-12", "2021-…
#> $ DATE_TYPE <chr> "date", "date", "date", "date", "date", "date", "d…
#> $ DATE_TYPECODE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ DATE_SORTORDER <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ GEOGRAPHY <dbl> 2092957697, 2092957697, 2092957697, 2092957697, 20…
#> $ GEOGRAPHY_NAME <chr> "United Kingdom", "United Kingdom", "United Kingdo…
#> $ GEOGRAPHY_CODE <chr> "K02000001", "K02000001", "K02000001", "K02000001"…
#> $ GEOGRAPHY_TYPE <chr> "countries", "countries", "countries", "countries"…
#> $ GEOGRAPHY_TYPECODE <dbl> 499, 499, 499, 499, 499, 499, 499, 499, 499, 499, …
#> $ GEOGRAPHY_SORTORDER <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,…
#> $ SEX <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
#> $ SEX_NAME <chr> "Male", "Male", "Male", "Male", "Male", "Male", "M…
#> $ SEX_CODE <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
#> $ SEX_TYPE <chr> "sex", "sex", "sex", "sex", "sex", "sex", "sex", "…
#> $ SEX_TYPECODE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ SEX_SORTORDER <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ ITEM <dbl> 1, 1, 2, 2, 3, 3, 4, 4, 9, 9, 1, 1, 2, 2, 3, 3, 4,…
#> $ ITEM_NAME <chr> "Total claimants", "Total claimants", "Students on…
#> $ ITEM_CODE <dbl> 1, 1, 2, 2, 3, 3, 4, 4, 9, 9, 1, 1, 2, 2, 3, 3, 4,…
#> $ ITEM_TYPE <chr> "item", "item", "item", "item", "item", "item", "i…
#> $ ITEM_TYPECODE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ ITEM_SORTORDER <dbl> 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 0, 0, 1, 1, 2, 2, 3,…
#> $ MEASURES <dbl> 20100, 20201, 20100, 20201, 20100, 20201, 20100, 2…
#> $ MEASURES_NAME <chr> "Persons claiming JSA", "Workplace-based estimates…
#> $ OBS_VALUE <dbl> 73931.0, 0.3, NA, NA, NA, NA, NA, NA, NA, NA, 6791…
#> $ OBS_STATUS <chr> "A", "A", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "Q", …
#> $ OBS_STATUS_NAME <chr> "Normal Value", "Normal Value", "These figures are…
#> $ OBS_CONF <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…
#> $ OBS_CONF_NAME <chr> "Free (free for publication)", "Free (free for pub…
#> $ URN <chr> "Nm-1d1d32348e0d2092957697d5d1d20100", "Nm-1d1d323…
#> $ RECORD_OFFSET <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, …
#> $ RECORD_COUNT <dbl> 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70…There is a lot of data available through Nomis, and there are some limits to the amount of data that can be retrieved within a certain period of time, although those are not published. For more details, see the full API documentation from Nomis. Full package documentation is available at docs.evanodell.com/nomisr.
Meta
Bug reports, suggestions, and code contributions are all welcome. Please see CONTRIBUTING.md for details.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Please note that this project is not affiliated with the Office for National Statistics or the University of Durham (who run Nomis on behalf of the Office for National Statistics).
Please use the reference below when citing nomisr, or use citation(package = 'nomisr').
Odell, (2018). nomisr: Access ‘Nomis’ UK Labour Market Data. Journal of Open Source Software, 3(27), 859, doi: 10.21105/joss.00859.
A BibTeX entry for LaTeX users is
@Article{odell2018,
title = {{nomisr}: Access Nomis UK Labour Market Data With R},
volume = {3},
doi = {10.21105/joss.00859},
number = {27},
journal = {The Journal of Open Source Software},
year = {2018},
month = {July},
pages = {859},
author = {Evan Odell},
}License: MIT