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#' Flexible equality comparison for data frames
#'
#' @description
#' \Sexpr[results=rd, stage=render]{lifecycle::badge("questioning")}
#' `all_equal()` allows you to compare data frames, optionally ignoring
#' row and column names. It is questioning as of dplyr 1.0.0, because it
#' seems to solve a problem that no longer seems that important.
#'
#' @param target,current Two data frames to compare.
#' @param ignore_col_order Should order of columns be ignored?
#' @param ignore_row_order Should order of rows be ignored?
#' @param convert Should similar classes be converted? Currently this will
#' convert factor to character and integer to double.
#' @param ... Ignored. Needed for compatibility with `all.equal()`.
#' @return `TRUE` if equal, otherwise a character vector describing
#' the reasons why they're not equal. Use [isTRUE()] if using the
#' result in an `if` expression.
#' @export
#' @keywords internal
#' @examples
#' scramble <- function(x) x[sample(nrow(x)), sample(ncol(x))]
#'
#' # By default, ordering of rows and columns ignored
#' all_equal(mtcars, scramble(mtcars))
#'
#' # But those can be overriden if desired
#' all_equal(mtcars, scramble(mtcars), ignore_col_order = FALSE)
#' all_equal(mtcars, scramble(mtcars), ignore_row_order = FALSE)
#'
#' # By default all_equal is sensitive to variable differences
#' df1 <- data.frame(x = "a", stringsAsFactors = FALSE)
#' df2 <- data.frame(x = factor("a"))
#' all_equal(df1, df2)
#' # But you can request dplyr convert similar types
#' all_equal(df1, df2, convert = TRUE)
all_equal <- function(target, current, ignore_col_order = TRUE,
ignore_row_order = TRUE, convert = FALSE, ...) {
equal_data_frame(target, current,
ignore_col_order = ignore_col_order,
ignore_row_order = ignore_row_order,
convert = convert
)
}
equal_data_frame <- function(x, y, ignore_col_order = TRUE, ignore_row_order = TRUE, convert = FALSE) {
compat <- is_compatible_data_frame(x, y, ignore_col_order = ignore_col_order, convert = convert)
if (!isTRUE(compat)) {
return(compat)
}
nrows_x <- nrow(x)
nrows_y <- nrow(y)
if (nrows_x != nrows_y) {
return("Different number of rows")
}
if (ncol(x) == 0L) {
return(TRUE)
}
# suppressMessages({
x <- as_tibble(x, .name_repair = "universal")
y <- as_tibble(y, .name_repair = "universal")
# })
x_split <- vec_split_id_order(x)
y_split <- vec_split_id_order(y[, names(x), drop = FALSE])
# keys must be identical
msg <- ""
if (any(wrong <- !vec_in(x_split$key, y_split$key))) {
rows <- sort(map_int(x_split$loc[which(wrong)], function(.x) .x[1L]))
msg <- paste0(msg, "- Rows in x but not in y: ", glue_collapse(rows, sep = ", "), "\n")
}
if (any(wrong <- !vec_in(y_split$key, x_split$key))) {
rows <- sort(map_int(y_split$loc[which(wrong)], function(.x) .x[1L]))
msg <- paste0(msg, "- Rows in y but not in x: ", glue_collapse(rows, sep = ", "), "\n")
}
if (msg != "") {
return(msg)
}
# keys are identical, check that rows occur the same number of times
if (any(wrong <- lengths(x_split$loc) != lengths(y_split$loc))) {
rows <- sort(map_int(x_split$loc[which(wrong)], function(.x) .x[1L]))
return(paste0("- Rows with difference occurences in x and y: ",
glue_collapse(rows, sep = ", "),
"\n"
))
}
# then if we care about row order, the id need to be identical
if (!ignore_row_order && !all(vec_equal(x_split$loc, y_split$loc))) {
return("Same row values, but different order")
}
TRUE
}
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