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:
install.packages("nomisr")You can install the development version nomisr from github with:
# install.packages("devtools")
devtools::install_github("ropensci/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. 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: 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 x 3
#> id label.en description.en
#> <chr> <chr> <chr>
#> 1 TYPE490 government office regions tec~ government office regions tec / ~
#> 2 TYPE491 government office regions (fo~ government office regions (forme~
#> 3 TYPE492 standard statistical regions standard statistical regions
#> 4 TYPE496 pre-1996 local authority dist~ pre-1996 local authority distric~
#> 5 TYPE498 pre-1996 counties / scottish ~ pre-1996 counties / scottish reg~
#> 6 TYPE499 countries countries
jobseekers_sex <- nomis_get_metadata("NM_1_1", "sex", "TYPE")
tibble::glimpse(jobseekers_sex)
#> Observations: 3
#> Variables: 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)
tibble::glimpse(z)
#> Observations: 70
#> Variables: 34
#> $ DATE <chr> "2018-06", "2018-06", "2018-06", "2018-06"...
#> $ DATE_NAME <chr> "June 2018", "June 2018", "June 2018", "Ju...
#> $ DATE_CODE <chr> "2018-06", "2018-06", "2018-06", "2018-06"...
#> $ 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 <dbl> 265106.0, 1.4, NA, NA, NA, NA, NA, NA, NA,...
#> $ 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-1d1d32294e0d2092957697d5d1d20100", "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, (2018). nomisr: Access ‘Nomis’ UK Labour Market Data. Journal of Open Source Software, 3(27), 859, https://doi.org/10.21105/joss.00859.
A BibTeX entry for LaTeX users is
@article{odell2018,
title = {Nomisr: {{Access Nomis UK Labour Market Data}}},
volume = {3},
issn = {2475-9066},
url = {https://github.com/ropensci/nomisr},
doi = {10.21105/joss.00859},
number = {27},
journaltitle = {Journal of Open Source Software},
urldate = {2018-08-01},
note = {R package version 0.4.0}
date = {2018-07-28},
pages = {859},
author = {Odell, Evan}
}
License: MIT
