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#' Generate a data grid.
#'
#' To visualise a model, it is very useful to be able to generate an
#' evenly spaced grid of points from the data. `data_grid` helps you
#' do this by wrapping around [tidyr::expand()].
#'
#' @param data A data frame
#' @param ... Variables passed on to [tidyr::expand()]
#' @param .model A model. If supplied, any predictors needed for the model
#' not present in `...` will be filled in with "\link{typical}" values.
#' @export
#' @seealso [seq_range()] for generating ranges from continuous
#' variables.
#' @examples
#' data_grid(mtcars, vs, am)
#'
#' # For continuous variables, seq_range is useful
#' data_grid(mtcars, mpg = mpg)
#' data_grid(mtcars, mpg = seq_range(mpg, 10))
#'
#' # If you supply a model, missing predictors will be filled in with
#' # typical values
#' mod <- lm(mpg ~ wt + cyl + vs, data = mtcars)
#' data_grid(mtcars, .model = mod)
#' data_grid(mtcars, cyl = seq_range(cyl, 9), .model = mod)
data_grid <- function(data, ..., .model = NULL) {
if (missing(...) && missing(.model)) {
abort("Must supply at least one of `...` and `.model`")
}
if (missing(...)) {
expanded <- NULL
} else {
expanded <- tidyr::expand(data, ...)
}
if (is.null(.model)) {
return(expanded)
}
# Generate grid of typical values
needed <- setdiff(predictor_vars(.model), names(expanded))
if (length(needed) > 0) {
typical_vals <- lapply(data[needed], typical)
typical_df <- tidyr::crossing(!!!typical_vals)
} else {
typical_df <- NULL
}
tidyr::crossing(expanded, typical_df)
}
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