Add Local Authority 2022 version of IMD 2019 for England and Wales

data-raw/imd2019_england_ltla22.R

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+library(tidyverse)
+library(geographr)
+library(sf)
+
+# Load package
+devtools::load_all(".")
+
+# ---- Check LTLA codes ----
+# Confirming that LTLA 2021 and LTLA 2022 codes are the same
+# dplyr::symdiff(
+#   geographr::lookup_ltla_ltla$ltla21_code,
+#   geographr::lookup_ltla22_ltla23$ltla22_code
+# )
+
+# ---- Load English IMD with scores ----
+query_url <-
+  query_urls |>
+  filter(data_set == "imd_lsoa_england") |>
+  pull(query_url)
+
+eimd_raw <-
+  read_csv(query_url)
+
+# ---- Aggregate IMD into Local Authority Districts (2022 codes) ----
+eimd <-
+  eimd_raw |>
+  select(
+    lsoa11_code = `LSOA code (2011)`,
+
+    IMD_score = `Index of Multiple Deprivation (IMD) Score`,
+    IMD_rank = `Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived)`,
+    IMD_decile = `Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Income_score = `Income Score (rate)`,
+    Income_rank = `Income Rank (where 1 is most deprived)`,
+    Income_decile = `Income Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Employment_score = `Employment Score (rate)`,
+    Employment_rank = `Employment Rank (where 1 is most deprived)`,
+    Employment_decile = `Employment Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Education_score = `Education, Skills and Training Score`,
+    Education_rank = `Education, Skills and Training Rank (where 1 is most deprived)`,
+    Education_decile = `Education, Skills and Training Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Health_score = `Health Deprivation and Disability Score`,
+    Health_rank = `Health Deprivation and Disability Rank (where 1 is most deprived)`,
+    Health_decile = `Health Deprivation and Disability Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Crime_score = `Crime Score`,
+    Crime_rank = `Crime Rank (where 1 is most deprived)`,
+    Crime_decile = `Crime Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Housing_and_Access_score = `Barriers to Housing and Services Score`,
+    Housing_and_Access_rank = `Barriers to Housing and Services Rank (where 1 is most deprived)`,
+    Housing_and_Access_decile = `Barriers to Housing and Services Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    Environment_score = `Living Environment Score`,
+    Environment_rank = `Living Environment Rank (where 1 is most deprived)`,
+    Environment_decile = `Living Environment Decile (where 1 is most deprived 10% of LSOAs)`,
+
+    population = `Total population: mid 2015 (excluding prisoners)`
+  ) |>
+
+  left_join(
+    geographr::lookup_lsoa11_ltla21 |>
+      select(lsoa11_code, ltla22_code = ltla21_code)
+  )
+
+# Aggregate into ltla22s
+eimd_ltla22 <-
+  eimd |> aggregate_scores(IMD_score, IMD_rank, IMD_decile, ltla22_code, population)
+
+eimd_ltla22_income   <- eimd |> aggregate_scores(Income_score, Income_rank, Income_decile, ltla22_code, population)
+eimd_ltla22_employ   <- eimd |> aggregate_scores(Employment_score, Employment_rank, Employment_decile, ltla22_code, population)
+eimd_ltla22_edu      <- eimd |> aggregate_scores(Education_score, Education_rank, Education_decile, ltla22_code, population)
+eimd_ltla22_health   <- eimd |> aggregate_scores(Health_score, Health_rank, Health_decile, ltla22_code, population)
+eimd_ltla22_crime    <- eimd |> aggregate_scores(Crime_score, Crime_rank, Crime_decile, ltla22_code, population)
+eimd_ltla22_barriers <- eimd |> aggregate_scores(Housing_and_Access_score, Housing_and_Access_rank, Housing_and_Access_decile, ltla22_code, population)
+eimd_ltla22_env      <- eimd |> aggregate_scores(Environment_score, Environment_rank, Environment_decile, ltla22_code, population)
+
+eimd_ltla22_income   <- eimd_ltla22_income   |> dplyr::rename(Income_Proportion = Proportion, Income_Extent = Extent, Income_Score = Score)
+eimd_ltla22_employ   <- eimd_ltla22_employ   |> dplyr::rename(Employment_Proportion = Proportion, Employment_Extent = Extent, Employment_Score = Score)
+eimd_ltla22_edu      <- eimd_ltla22_edu      |> dplyr::rename(Education_Proportion = Proportion, Education_Extent = Extent, Education_Score = Score)
+eimd_ltla22_health   <- eimd_ltla22_health   |> dplyr::rename(Health_Proportion = Proportion, Health_Extent = Extent, Health_Score = Score)
+eimd_ltla22_crime    <- eimd_ltla22_crime    |> dplyr::rename(Crime_Proportion = Proportion, Crime_Extent = Extent, Crime_Score = Score)
+eimd_ltla22_barriers <- eimd_ltla22_barriers |> dplyr::rename(Housing_and_Access_Proportion = Proportion, Housing_and_Access_Extent = Extent, Housing_and_Access_Score = Score)
+eimd_ltla22_env      <- eimd_ltla22_env      |> dplyr::rename(Environment_Proportion = Proportion, Environment_Extent = Extent, Environment_Score = Score)
+
+eimd_ltla22 <-
+  eimd_ltla22  |>
+  dplyr::left_join(eimd_ltla22_income,   by = "ltla22_code") |>
+  dplyr::left_join(eimd_ltla22_employ,   by = "ltla22_code") |>
+  dplyr::left_join(eimd_ltla22_edu,      by = "ltla22_code") |>
+  dplyr::left_join(eimd_ltla22_health,   by = "ltla22_code") |>
+  dplyr::left_join(eimd_ltla22_crime,    by = "ltla22_code") |>
+  dplyr::left_join(eimd_ltla22_barriers, by = "ltla22_code") |>
+  dplyr::left_join(eimd_ltla22_env,      by = "ltla22_code")
+
+# Rename
+imd2019_england_ltla22 <- eimd_ltla22
+
+# Save output to data/ folder
+usethis::use_data(imd2019_england_ltla22, overwrite = TRUE)
+readr::write_csv(imd2019_england_ltla22, "data-raw/imd2019_england_ltla22.csv")