Quickly create dummy (binary) columns from character and factor type columns in the inputted data (and numeric columns if specified.) This function is useful for statistical analysis when you want binary columns rather than character columns.
dummy_cols( .data, select_columns = NULL, remove_first_dummy = FALSE, remove_most_frequent_dummy = FALSE, ignore_na = FALSE, split = NULL, remove_selected_columns = FALSE )
| .data | An object with the data set you want to make dummy columns from. |
|---|---|
| select_columns | Vector of column names that you want to create dummy variables from. If NULL (default), uses all character and factor columns. |
| remove_first_dummy | Removes the first dummy of every variable such that only n-1 dummies remain. This avoids multicollinearity issues in models. |
| remove_most_frequent_dummy | Removes the most frequently observed category such that only n-1 dummies remain. If there is a tie for most frequent, will remove the first (by alphabetical order) category that is tied for most frequent. |
| ignore_na | If TRUE, ignores any NA values in the column. If FALSE (default), then it will make a dummy column for value_NA and give a 1 in any row which has a NA value. |
| split | A string to split a column when multiple categories are in the cell. For example, if a variable is Pets and the rows are "cat", "dog", and "turtle", each of these pets would become its own dummy column. If one row is "cat, dog", then a split value of "," this row would have a value of 1 for both the cat and dog dummy columns. |
| remove_selected_columns | If TRUE (not default), removes the columns used to generate the dummy columns. |
A data.frame (or tibble or data.table, depending on input data type) with same number of rows as inputted data and original columns plus the newly created dummy columns.
dummy_rows For creating dummy rows
Other dummy functions:
dummy_columns(),
dummy_rows()
crime <- data.frame(city = c("SF", "SF", "NYC"), year = c(1990, 2000, 1990), crime = 1:3) dummy_cols(crime)#> city year crime city_NYC city_SF #> 1 SF 1990 1 0 1 #> 2 SF 2000 2 0 1 #> 3 NYC 1990 3 1 0#> city year crime city_NYC city_SF year_1990 year_2000 #> 1 SF 1990 1 0 1 1 0 #> 2 SF 2000 2 0 1 0 1 #> 3 NYC 1990 3 1 0 1 0# Remove first dummy for each pair of dummy columns made dummy_cols(crime, select_columns = c("city", "year"), remove_first_dummy = TRUE)#> city year crime city_SF year_2000 #> 1 SF 1990 1 1 0 #> 2 SF 2000 2 1 1 #> 3 NYC 1990 3 0 0