Flexible equality comparison for data frames

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.

all_equal(
  target,
  current,
  ignore_col_order = TRUE,
  ignore_row_order = TRUE,
  convert = FALSE,
  ...
)

Arguments

target, current	Two data frames to compare.
ignore_col_order	Should order of columns be ignored?
ignore_row_order	Should order of rows be ignored?
convert	Should similar classes be converted? Currently this will convert factor to character and integer to double.
...	Ignored. Needed for compatibility with all.equal().

Value

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.

Examples

scramble <- function(x) x[sample(nrow(x)), sample(ncol(x))]

# By default, ordering of rows and columns ignored
all_equal(mtcars, scramble(mtcars))
#> [1] TRUE

# But those can be overriden if desired
all_equal(mtcars, scramble(mtcars), ignore_col_order = FALSE)
#> [1] "- Same column names, but different order"
all_equal(mtcars, scramble(mtcars), ignore_row_order = FALSE)
#> [1] "Same row values, but different order"

# 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)
#> [1] "- Different types for column `x`: character vs factor<4d52a>\n"
# But you can request dplyr convert similar types
all_equal(df1, df2, convert = TRUE)
#> [1] TRUE