You can use all_equal() with any data frame, and dplyr also provides
tbl_df methods for all.equal().
all_equal(target, current, ignore_col_order = TRUE, ignore_row_order = TRUE, convert = FALSE, ...) # S3 method for tbl_df all.equal(target, current, ignore_col_order = TRUE, ignore_row_order = TRUE, convert = FALSE, ...)
| 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 |
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.
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") df2 <- data.frame(x = factor("a")) all_equal(df1, df2)#> [1] TRUE# But you can request dplyr convert similar types all_equal(df1, df2, convert = TRUE)#> [1] TRUE