IMD

The IMD package provides easy access to the Indices of Multiple Deprivation for the UK’s nations at various geographical levels:

• England: Lower Layer Super Output Area (LSOA), Middle Layer Super Output Area (MSOA), Local Authority, Integrated Care Board, and Local Resilience Forum (includes new data for 2025)
• Wales: LSOA, MSOA, Ward, and Local Authority
• Scotland: Data Zone, Intermediate Zone, and Council Area
• Northern Ireland: Super Output Area and Local Government District

The package also provides data files for the Community Needs Index for each UK nation, as well as household-level multiple deprivation figures for England, Wales and Northern Ireland (from the 2011 and 2021 Censuses).

Installation

Install the released version of IMD from CRAN:

install.packages("IMD")

Alternatively, you can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("humaniverse/IMD")

Usage

The IMD package provides several datasets for the IMD in each UK nation. The format for the names of the data files follows this format:

imd[year]_[nation]_[geography code]

Composite 2020 UK Index of Multiple Deprivation

We have also included the composite 2020 UK Index of Multiple Deprivation produced by mySociety, allowing comparison of deprivation across UK nations. For more information about how and when to use this version of the IMD, see their README.

Rather than providing a copy of this data, the IMD package loads the data hosted on mySociety’s GitHub. To load these files, run the following code:

imd_uk <- load_composite_imd(nation = "E")

This function defaults to loading the England-focused composite index; change the nation parameter to the first letter of one of the devolved UK nations to load the other versions.

Example

How many Council Areas (Local Authorities) in Scotland where at least one-third of the population lives in areas classified as among the most deprived?

library(IMD)

imd2020_scotland_ltla24 |> 
  dplyr::filter(Extent >= 0.33) |> 
  dplyr::count()
#> # A tibble: 1 × 1
#>       n
#>   <int>
#> 1     5

Accessing the data for non-R users

You can download the data from R-Universe via https://humaniverse.r-universe.dev/datasets

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.

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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.