dummy_cols

0th

Percentile

Fast creation of dummy variables

dummy_cols() quickly creates 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.

Usage
dummy_cols(.data, select_columns = NULL, remove_first_dummy = FALSE,
  remove_most_frequent_dummy = FALSE, ignore_na = FALSE,
  split = NULL)
Arguments
.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.

Value

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.

See Also

dummy_rows For creating dummy rows

Other dummy functions: dummy_columns, dummy_rows

Aliases
  • dummy_cols
Examples
# NOT RUN {
crime <- data.frame(city = c("SF", "SF", "NYC"),
    year = c(1990, 2000, 1990),
    crime = 1:3)
dummy_cols(crime)
# Include year column
dummy_cols(crime, select_columns = c("city", "year"))
# Remove first dummy for each pair of dummy columns made
dummy_cols(crime, select_columns = c("city", "year"),
    remove_first_dummy = TRUE)
# }
Documentation reproduced from package fastDummies, version 1.5.0, License: MIT + file LICENSE

Community examples

akaEmma@gmail.com at Sep 1, 2018 fastDummies v0.1.2

##Using Centers for Disease Control and Prevention. National Immunization Surveys, 2016. Public-use data file and documentation. ##https://www.cdc.gov/vaccines/imz-managers/nis/datasets.html. August 2018. ##It has a LOT of categorical variables. ``` vaccine_data <- vaccine_data %>% select(-c(seqnumc, seqnumhh)) # Take out IDs for correlations head(vaccine_data) vaccine_data <- vaccine_data %>% dummy_cols() names(vaccine_data) # lots more variables ! and they are beautifully binary for the correlations I want to do. ```