# Example of correcting data issues: correcting column names
# Changes here in the following PR (N.B. Mercurial repo)
# https://bitbucket.org/annajayne/tdor_data/pull-requests/4/correct-source-ref-column-names/diff
library("dplyr")
library("purrr")
library("readr")
# Get Data ----
# Data sourced from https://bitbucket.org/annajayne/tdor_data/downloads/, unzipped
files <- list.files("./Data",
"*.csv$",
recursive = TRUE,
full.names = TRUE)
# Read in raw data
tdor_raw <- files %>%
map(~ suppressWarnings(read_csv(.)))
# Get all column names
nm <- tdor_raw %>%
map(names)
# ID files with column name "Source Ref"
id <- numeric(0)
for (i in seq_along(nm))
if (any(nm[[i]] %in% "Source Ref")) id <- c(id, i)
# Correct to "Source ref"
for (i in id){
read_csv(files[i]) %>%
rename(`Source ref` = `Source Ref`) %>%
write_csv(files[i], na = "")
}
# Repeat (separate commit) for TGEU ref to Source ref
id <- numeric(0)
for (i in seq_along(nm))
if (any(nm[[i]] %in% c("TGEU ref"))) id <- c(id, i)
## Here it was important to read and write all columns as character to avoid
## making unwanted changes
col_types <- cols_only(
Name = col_character(),
Age = col_character(),
Photo = col_character(),
`Photo source` = col_character(),
Date = col_character(),
`TGEU ref` = col_character(),
Location = col_character(),
`State/Province` = col_character(),
Country = col_character(),
Latitude = col_character(),
Longitude = col_character(),
`Cause of death` = col_character(),
Description = col_character(),
Tweet = col_character(),
Permalink = col_character()
)
for (i in id){
read_csv(files[i], col_types = col_types) %>%
rename(`Source ref` = `TGEU ref`) %>%
write_csv(files[i], na = "")
}
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