The goal of dfeR is to help standardise R programming across the Department for Education (DfE), and facilitate sharing of business specific functions, making our code easier to read and write.
Credit to lauraselby for the logo featuring Frederick!
This package is open to all of DfE and anything we think could be useful to other programmers and analysts can be contributed.
Functionality for dfeR is expected to focus around the following:
- DfE specific formatting and helper functions
- Working with DfE databases
- Templates for analytical projects
- API wrappers commonly needed in DfE analysis (where they don’t have their own separate package)
- Geography lookup files and helper functions
Documentation for what has been included in the package so far is on our pkgdown site.
We also maintain the dfeshiny package, and expect any functions specific to R Shiny applications will live there.
For connecting to data in the explore education statistics, we are building the eesyapi package.
There is a giasr package, which has been developed for connecting to data in the get information about schools service.
While we have some DfE specific data in the dfeR package taken from the Open Geography Portal. If you’re looking at getting new data from the portal it is also worth looking at the boundr package, as this gives more functions for directly extracting data from there.
dfeR is available on CRAN and you can install directly from there:
install.packages("dfeR")You can install the development version from GitHub.
If you are using renv in your project (recommended):
renv::install("dfe-analytical-services/dfeR")Otherwise:
# install.packages("devtools")
devtools::install_github("dfe-analytical-services/dfeR")The need for setting proxy settings in order to be able to work with R and Git within the DfE estate has now ended. If you previously run the proxy script in previous versions of the dfeR package, then contact the Statistics Development Team to assist in cleaning out your system settings.
Ideas for dfeR should first be raised as a GitHub issue after which anyone is free to write the code and create a pull request for review.
For more details on contributing to dfeR, see our contributing guidelines.
Please note that the dfeR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Here are some example functions from within the package:
library(dfeR)
#> Note: The dataset `wd_pcon_lad_la_rgn_ctry` is deprecated and will be removed in the next major release. Use `geo_hierarchy` instead.
# Prettify large numbers
pretty_num(111111111, gbp = TRUE)
#> [1] "£111 million"
pretty_num(-11^8, dp = -1)
#> [1] "-210 million"
# Convert bytes to readable size
pretty_filesize(77777777)
#> [1] "77.78 MB"
# Calculate elapsed time and present prettily
start <- Sys.time()
end <- Sys.time() + 789890
pretty_time_taken(start, end)
#> [1] "219 hours 24 minutes 50 seconds"
# Round 5's up instead of bankers round used by round() in base R
round_five_up(2.5)
#> [1] 3
round(2.5) # base R
#> [1] 2
# Custom formatting for academic and financial years
format_ay(202425)
#> [1] "2024/25"
format_fy(202425)
#> [1] "2024-25"
format_ay_reverse("2024/25")
#> [1] "202425"
format_fy_reverse("2024-25")
#> [1] "202425"
# Get hierachical lookup file for common geographies
my_data <- dfeR::geo_hierarchy
head(my_data) # show first 5 rows in console
#> first_available_year_included most_recent_year_included ward_name
#> 1 2017 2021 Abbey
#> 2 2022 2023 Abbey
#> 3 2024 2025 Abbey
#> 4 2017 2017 Abbey
#> 5 2024 2025 Abbey
#> 6 2017 2023 Abbey
#> pcon_name lad_name la_name
#> 1 Barking Barking and Dagenham Barking and Dagenham
#> 2 Barking Barking and Dagenham Barking and Dagenham
#> 3 Barking Barking and Dagenham Barking and Dagenham
#> 4 Bath Bath and North East Somerset Bath and North East Somerset
#> 5 Belfast North Antrim and Newtownabbey Antrim and Newtownabbey
#> 6 Belfast North Antrim and Newtownabbey Antrim and Newtownabbey
#> cauth_name region_name country_name ward_code pcon_code
#> 1 Not applicable London England E05000026 E14000540
#> 2 Not applicable London England E05014053 E14000540
#> 3 Not applicable London England E05014053 E14001073
#> 4 West of England South West England E05001935 E14000547
#> 5 Not applicable Northern Ireland Northern Ireland N08000101 N05000002
#> 6 Not applicable Northern Ireland Northern Ireland N08000101 N06000002
#> lad_code old_la_code new_la_code cauth_code region_code country_code
#> 1 E09000002 301 E09000002 z E12000007 E92000001
#> 2 E09000002 301 E09000002 z E12000007 E92000001
#> 3 E09000002 301 E09000002 z E12000007 E92000001
#> 4 E06000022 800 E06000022 E47000009 E12000009 E92000001
#> 5 N09000001 z N09000001 z N92000002 N92000002
#> 6 N09000001 z N09000001 z N92000002 N92000002
# Get all countries
dfeR::countries
#> country_code country_name
#> 1 E92000001 England
#> 2 K02000001 United Kingdom
#> 3 K03000001 Great Britain
#> 4 K04000001 England and Wales
#> 5 N92000002 Northern Ireland
#> 6 S92000003 Scotland
#> 7 W92000004 Wales
#> 8 z England, Wales and Northern Ireland
#> 9 z Outside of England and unknown
#> 10 z Outside of the United Kingdom and unknown
# Get all PCon names and codes for 2024
fetch_pcons(2024) |>
head() # show first 5 rows only
#> # A tibble: 6 × 2
#> pcon_code pcon_name
#> <chr> <chr>
#> 1 E14001073 Barking
#> 2 N05000002 Belfast North
#> 3 E14001149 Cambridge
#> 4 E14001193 Derby North
#> 5 E14001194 Derby South
#> 6 S14000073 Dumfries and Galloway
# Get All LADs in Scotland in 2017
fetch_lads(2017, "Scotland") |>
head() # show first 5 rows only
#> # A tibble: 6 × 2
#> lad_code lad_name
#> <chr> <chr>
#> 1 S12000006 Dumfries and Galloway
#> 2 S12000034 Aberdeenshire
#> 3 S12000017 Highland
#> 4 S12000044 North Lanarkshire
#> 5 S12000033 Aberdeen City
#> 6 S12000036 City of Edinburgh
# Get all LAs in Scotland and Northern Ireland in 2022
fetch_las(2022, c("Scotland", "Northern Ireland")) |>
head() # show first 5 rows only
#> # A tibble: 6 × 3
#> new_la_code la_name old_la_code
#> <chr> <chr> <chr>
#> 1 N09000001 Antrim and Newtownabbey z
#> 2 S12000006 Dumfries and Galloway z
#> 3 N09000010 Newry, Mourne and Down z
#> 4 S12000034 Aberdeenshire z
#> 5 N09000008 Mid and East Antrim z
#> 6 N09000004 Causeway Coast and Glens z
# Get all Welsh wards for 2021
fetch_wards(2021, "Wales") |>
head() # show first 5 rows only
#> # A tibble: 6 × 2
#> ward_code ward_name
#> <chr> <chr>
#> 1 W05000720 Aber Valley
#> 2 W05000284 Aber-craf
#> 3 W05000357 Aberaeron
#> 4 W05000655 Aberaman North
#> 5 W05001017 Aberaman South
#> 6 W05000551 Aberavon
# Get all combined mayoral authorities for 2025
fetch_mayoral(2025) |>
head() # show first 5 rows only
#> # A tibble: 6 × 2
#> cauth_code cauth_name
#> <chr> <chr>
#> 1 E47000009 West of England
#> 2 E47000008 Cambridgeshire and Peterborough
#> 3 E47000013 East Midlands
#> 4 E47000017 Greater Lincolnshire
#> 5 E47000007 West Midlands
#> 6 E47000001 Greater Manchester
# The following have no specific years available and return all values
fetch_regions()
#> region_code region_name
#> 1 E12000001 North East
#> 2 E12000002 North West
#> 3 E12000003 Yorkshire and The Humber
#> 4 E12000004 East Midlands
#> 5 E12000005 West Midlands
#> 6 E12000006 East of England
#> 7 E12000007 London
#> 8 E12000008 South East
#> 9 E12000009 South West
#> 10 E13000001 Inner London
#> 11 E13000002 Outer London
#> 12 z Outside of England and unknown
#> 13 z Outside of the United Kingdom and unknown
#> 14 z Outside of England
#> 15 z Outside of United Kingdom
#> 16 z Unknown
fetch_countries()
#> country_code country_name
#> 1 E92000001 England
#> 2 K02000001 United Kingdom
#> 3 K03000001 Great Britain
#> 4 K04000001 England and Wales
#> 5 N92000002 Northern Ireland
#> 6 S92000003 Scotland
#> 7 W92000004 Wales
#> 8 z England, Wales and Northern Ireland
#> 9 z Outside of England and unknown
#> 10 z Outside of the United Kingdom and unknownFor more details on all the functions available in this package, and examples of how to use them, please see our dfeR package reference documentation.