In ropensci/stats19: Work with Open Road Traffic Casualty Data from Great Britain

This function generates the data object stats19_schema.

The function also generates stats19_variables (see the function's source code for details).

library(tidyverse)
devtools::load_all()

Load stats19 schema and save variable names

schema_url = "https://data.dft.gov.uk/road-accidents-safety-data/dft-road-casualty-statistics-road-safety-open-dataset-data-guide-2024.xlsx"
schema_f = basename(schema_url)
schema_saved = file.path(get_data_directory(), schema_f)
download.file(schema_url, destfile = schema_saved)
schema_dft = readxl::read_excel(schema_saved)
names(schema_dft)
# [1] "table"       "field name"  "code/format" "label"       "note"       
schema_dft
stats19_schema_dft = schema_dft %>%
  rename(variable = `field name`, code = `code/format`) 

# Pre-prepared dataset:
stats19_variables
stats19_variables_dft = stats19_schema_dft %>%
  group_by(table, variable) %>%
  summarise(
    note = first(note)
  )
stats19_variables_dft
stats19_variables_dft$type = "character"
stats19_variables_dft$type[
  grepl(pattern = "year", x = stats19_variables_dft$variable)
] = "numeric"
# Check variables:
stats19_variables_dft$variable
stats19_variables_dft |>
  filter(str_detect(variable, "age_of"))
stats19_variables_dft$type[
  grepl(pattern = "age_of", x = stats19_variables_dft$variable)
] = "numeric"
reg = "adjusted|engine|east|north|longitude|latitude"
stats19_variables_dft |>
  filter(str_detect(variable, reg))
stats19_variables_dft$type[
  grepl(pattern = reg, x = stats19_variables_dft$variable)
] = "numeric"

# Save the updated variables
stats_19_variables_old = read_csv("data-raw/stats19_variables.csv")
stats19_variables = stats19_variables_dft
waldo::compare(names(stats_19_variables_old), names(stats19_variables)) # Same names
waldo::compare(stats_19_variables_old, stats19_variables) # Different values
readr::write_csv(stats19_variables, "data-raw/stats19_variables.csv")

Save the schema

# Previous version of the dataset
stats19_schema
readr::write_csv(stats19_schema, "data-raw/stats19_schema.csv")
table(stats19_schema_dft$variable)
stats19_schema_joined = left_join(stats19_schema_dft, stats19_variables_dft %>% select(variable, type))

Update the schemas

stats19_variables_old = stats19_variables
stats19_schema_old = stats19::stats19_schema
stats19_schema = stats19_schema_joined
waldo::compare(names(stats19_schema_old), names(stats19_schema)) # Same names
waldo::compare(stats19_schema_old, stats19_schema) # Different values
summary(is.na(stats19_schema))
table(stats19_schema$type)
readr::write_csv(stats19_schema, "data-raw/stats19_schema.csv")
usethis::use_data(stats19_variables, overwrite = TRUE)
usethis::use_data(stats19_schema, overwrite = TRUE)