sharla_diff

sharla_diff <- function(df, expected_df) {
  data_as_expected <- dplyr::all_equal(expected_df, df)

  if (!isTRUE(data_as_expected)) {
    data_diffs <- janitor::compare_df_cols(expected_df, df)

    cols_mismatch <- dplyr::filter(data_diffs, is.na(expected_df) | is.na(df))

    extra_cols <- cols_mismatch %>%
      dplyr::filter(is.na(expected_df)) %>%
      dplyr::pull(column_name)

    missing_cols <- cols_mismatch %>%
      dplyr::filter(is.na(df)) %>%
      dplyr::pull(column_name)

    type_mismatch <- dplyr::filter(data_diffs, expected_df != df)

    if (length(extra_cols) > 0) {
      warning("`", deparse(substitute(df)), "`", " contains extra column(s): ",
        glue::glue_collapse(extra_cols, sep = ", "), ".",
        call. = FALSE
      )
    } 
    
    if (length(missing_cols) > 0) {
      stop("`", deparse(substitute(df)), "`", " is missing column(s): ", glue::glue_collapse(missing_cols, sep = ", "), ".",
        call. = FALSE
      )
    } 
    
    if (nrow(type_mismatch) > 0) {
      type_mismatch_errors <- type_mismatch %>%
        dplyr::mutate(error = glue::glue("{column_name} should be class {expected_df}, is {df}")) %>%
        dplyr::pull(error) %>%
        as.character() %>%
        paste0("\n")

      stop("`", deparse(substitute(df)), "`", " column types are not as expected. These columns have issues:\n", type_mismatch_errors,
        call. = FALSE
      )
    }
  }

  df
}