Creates the start of a 'column key' for harmonizing data. A column key includes a column for the file names to be harmonized into a single data object as well as a column for the column names in those files. Finally, it includes a column indicating the tidied name that corresponds with each raw column name. Harmonization can accept this key object and use it to rename all raw column names–in a reproducible way–to standardize across datasets. Currently supports raw files of the following formats: CSV, TXT, XLS, and XLSX
Usage
begin_key(
raw_folder = NULL,
data_format = c("csv", "txt", "xls", "xlsx"),
guess_tidy = FALSE
)Arguments
- raw_folder
(character) folder / folder path containing data files to include in key
- data_format
(character) file extensions to identify within the
raw_folder. Default behavior is to search for all supported file types.- guess_tidy
(logical) whether to attempt to "guess" what the tidy name equivalent should be for each raw column name. This is accomplished via coercion to lowercase and removal of special character/repeated characters. If
FALSE(the default) the "tidy_name" column is returned empty
Examples
# Generate two simple tables
## Dataframe 1
df1 <- data.frame("xx" = c(1:3),
"unwanted" = c("not", "needed", "column"),
"yy" = letters[1:3])
## Dataframe 2
df2 <- data.frame("LETTERS" = letters[4:7],
"NUMBERS" = c(4:7),
"BONUS" = c("plantae", "animalia", "fungi", "protista"))
# Generate a local folder for exporting
temp_folder <- tempdir()
# Export both files to that folder
utils::write.csv(x = df1, file = file.path(temp_folder, "df1.csv"), row.names = FALSE)
utils::write.csv(x = df2, file = file.path(temp_folder, "df2.csv"), row.names = FALSE)
# Generate a column key with "guesses" at tidy column names
ltertools::begin_key(raw_folder = temp_folder, data_format = "csv", guess_tidy = TRUE)
#> source raw_name tidy_name
#> 1 df1.csv xx xx
#> 2 df1.csv unwanted unwanted
#> 3 df1.csv yy yy
#> 4 df2.csv LETTERS letters
#> 5 df2.csv NUMBERS numbers
#> 6 df2.csv BONUS bonus