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.
nomisr is available on CRAN:
You can install the development version nomisr from github with:
nomisrnomisr 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. For full details on all available functions and demonstrations of their use, please see the package vignette.
The example below gets 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)
#> Observations: 17
#> Variables: 14
#> $ agencyid <chr> "NOMIS", "NOMIS", "NOMIS"...
#> $ id <chr> "NM_1_1", "NM_4_1", "NM_8...
#> $ uri <chr> "Nm-1d1", "Nm-4d1", "Nm-8...
#> $ version <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1...
#> $ annotations.annotation <list> [<c("Current (being acti...
#> $ components.attribute <list> [<c("Mandatory", "Condit...
#> $ components.dimension <list> [<c("CL_1_1_GEOGRAPHY", ...
#> $ components.primarymeasure.conceptref <chr> "OBS_VALUE", "OBS_VALUE",...
#> $ components.timedimension.codelist <chr> "CL_1_1_TIME", "CL_4_1_TI...
#> $ components.timedimension.conceptref <chr> "TIME", "TIME", "TIME", "...
#> $ description.value <chr> "Records the number of pe...
#> $ description.lang <chr> "en", "en", NA, "en", "en...
#> $ name.value <chr> "Jobseeker's Allowance wi...
#> $ name.lang <chr> "en", "en", "en", "en", "...
jobseekers_measures <- nomis_get_metadata("NM_1_1", "measures")
tibble::glimpse(jobseekers_measures)
#> Observations: 4
#> Variables: 2
#> $ description <chr> "claimants", "workforce", "active", "residence"
#> $ value <int> 20100, 20201, 20202, 20203
jobseekers_geography <- nomis_get_metadata("NM_1_1", "geography", "TYPE")
tail(jobseekers_geography)
#> # A tibble: 6 x 2
#> description value
#> <chr> <chr>
#> 1 government office regions tec / lec based TYPE490
#> 2 government office regions (former inc. Merseyside) TYPE491
#> 3 standard statistical regions TYPE492
#> 4 pre-1996 local authority districts TYPE496
#> 5 pre-1996 counties / scottish regions TYPE498
#> 6 countries TYPE499
jobseekers_sex <- nomis_get_metadata("NM_1_1", "sex", "TYPE")
tibble::glimpse(jobseekers_sex)
#> Observations: 3
#> Variables: 2
#> $ description <chr> "Male", "Female", "Total"
#> $ value <int> 5, 6, 7
z <- nomis_get_data(id = "NM_1_1", time = "latest", geography = "TYPE499",
measures=c(20100, 20201), sex=5)
tibble::glimpse(z)
#> Observations: 70
#> Variables: 34
#> $ DATE <chr> "2018-04", "2018-04", "2018-04", "2018-04"...
#> $ DATE_NAME <chr> "April 2018", "April 2018", "April 2018", ...
#> $ DATE_CODE <chr> "2018-04", "2018-04", "2018-04", "2018-04"...
#> $ DATE_TYPE <chr> "date", "date", "date", "date", "date", "d...
#> $ DATE_TYPECODE <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0...
#> $ DATE_SORTORDER <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0...
#> $ GEOGRAPHY <chr> "2092957697", "2092957697", "2092957697", ...
#> $ GEOGRAPHY_NAME <chr> "United Kingdom", "United Kingdom", "Unite...
#> $ GEOGRAPHY_CODE <chr> "K02000001", "K02000001", "K02000001", "K0...
#> $ GEOGRAPHY_TYPE <chr> "countries", "countries", "countries", "co...
#> $ GEOGRAPHY_TYPECODE <chr> "499", "499", "499", "499", "499", "499", ...
#> $ GEOGRAPHY_SORTORDER <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0...
#> $ SEX <chr> "5", "5", "5", "5", "5", "5", "5", "5", "5...
#> $ SEX_NAME <chr> "Male", "Male", "Male", "Male", "Male", "M...
#> $ SEX_CODE <chr> "5", "5", "5", "5", "5", "5", "5", "5", "5...
#> $ SEX_TYPE <chr> "sex", "sex", "sex", "sex", "sex", "sex", ...
#> $ SEX_TYPECODE <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0...
#> $ SEX_SORTORDER <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0...
#> $ ITEM <chr> "1", "1", "2", "2", "3", "3", "4", "4", "9...
#> $ ITEM_NAME <chr> "Total claimants", "Total claimants", "Stu...
#> $ ITEM_CODE <chr> "1", "1", "2", "2", "3", "3", "4", "4", "9...
#> $ ITEM_TYPE <chr> "item", "item", "item", "item", "item", "i...
#> $ ITEM_TYPECODE <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0...
#> $ ITEM_SORTORDER <chr> "0", "0", "1", "1", "2", "2", "3", "3", "4...
#> $ MEASURES <chr> "20100", "20201", "20100", "20201", "20100...
#> $ MEASURES_NAME <chr> "Persons claiming JSA", "Workplace-based e...
#> $ OBS_VALUE <chr> "283937", "1.5", NA, NA, NA, NA, NA, NA, N...
#> $ OBS_STATUS <chr> "A", "A", "Q", "Q", "Q", "Q", "Q", "Q", "Q...
#> $ OBS_STATUS_NAME <chr> "Normal Value", "Normal Value", "These fig...
#> $ OBS_CONF <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F...
#> $ OBS_CONF_NAME <chr> "Free (free for publication)", "Free (free...
#> $ URN <chr> "Nm-1d1d32292e0d2092957697d5d1d20100", "Nm...
#> $ RECORD_OFFSET <chr> "0", "1", "2", "3", "4", "5", "6", "7", "8...
#> $ RECORD_COUNT <chr> "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
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.
Get citation information for nomisr in R with citation(package = 'nomisr')
Odell E (2018). nomisr: Access Nomis UK labour market data on with R. doi: 10.5281/zenodo.1246845 (URL: http://doi.org/10.5281/zenodo.1246845), R package version 0.2.0, <URL: https://github.com/ropensci/nomisr>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {{nomisr}: Access Nomis UK Labour Market Data With R},
author = {Evan Odell},
year = {2018},
note = {R package version 0.2.0},
doi = {10.5281/zenodo.1157908},
url = {https://github.com/ropensci/nomisr},
}License: MIT
