fctdrop

fctdrop is a function provided by the forcats package, part of the tidyverse collection of R packages. It is designed to modify factor objects by removing levels that are not present in the data or that the user explicitly requests. The function helps keep factor levels tidy, improving downstream analysis and visualizations that depend on factor ordering.

The typical usage of fctdrop is to pass a factor variable as the first argument. The function examines the levels of the factor and drops any that have zero counts. Users may also supply a vector of levels to remove, overriding the default behavior of dropping only unused levels. The function returns a new factor object with a reduced set of levels, preserving the original order of the remaining ones unless otherwise specified.

Parameters of fctdrop include the factor to modify, an optional character vector of levels to drop, and options such as whether to keep the order of levels or to produce a warning when attempting to drop a level that does not exist. The function integrates seamlessly with dplyr verbs, enabling its use inside mutate or summarise calls. When combined with other forcats utilities like fct_reorder or fct_inorder, fctdrop contributes to a robust chain of data wrangling steps.

A basic example shows a data frame with an factor column containing levels A, B, C and D, but only A and B appear in the rows. Applying fctdrop on this column results in a factor that only contains A and B, making subsequent plot calls and statistical models more efficient. The function handles edge cases such as factors with missing values or factors that are already only used, providing informative messages without altering data unexpectedly.

fctdrop is often paired with related functions such as fct_drop_all, fct_cap, or fct_revalue to manipulate factor levels fully. It is documented in the forcats vignettes and available on CRAN. The function’s concise API and integration into the tidyverse ecosystem make it a staple for R users who need deterministic control over factor levels in their data pipelines.