Function for applying rule packs to data.

expose(.tbl, ..., .rule_sep = inside_punct("\\._\\."),
  .remove_obeyers = TRUE, .guess = TRUE)

Arguments

.tbl

Data frame of interest.

...

Rule packs. They can be in pure form or inside a list (at any depth).

.rule_sep

Regular expression used as separator between column and rule names in col packs and cell packs.

.remove_obeyers

Whether to remove elements which obey rules from report.

.guess

Whether to guess type of unsupported rule pack type (see Details).

Value

A .tbl with possibly added 'exposure' attribute containing the resulting exposure. If .tbl already contains 'exposure' attribute then the result is binded with it.

Details

expose() applies all supplied rule packs to data, creates an exposure object based on results and stores it to attribute 'exposure'. It is guaranteed that .tbl is not modified in any other way in order to use expose() inside a pipe.

It is a good idea to name all rule packs: explicitly in ... (if they are supplied not inside list) or during creation with respective rule pack function. In case of missing name it is imputed based on possibly existing exposure attribute in .tbl and supplied rule packs. Imputation is similar to one in rules() but applied to every pack type separately.

Default value for .rule_sep is the regular expression characters ._. surrounded by non alphanumeric characters. It is picked to be used smoothly with dplyr's scoped verbs and rules() instead of funs(). In most cases it shouldn't be changed but if needed it should align with .prefix in rules().

Guessing

To work properly in some edge cases one should specify pack types with appropriate function. However with .guess equals to TRUE expose will guess the pack type based on its output after applying to .tbl. It uses the following features:

  • Presence of non-logical columns: if present then the guess is group pack. Grouping columns are guessed as all non-logical. This works incorrectly if some grouping column is logical: it will be guessed as result of applying the rule. Note that on most occasions this edge case will produce error about grouping columns define non-unique levels.

  • Combination of whether number of rows equals 1 (n_rows_one) and presence of .rule_sep in all column names (all_contain_sep). Guesses are:

    • Data pack if n_rows_one == TRUE and all_contain_sep == FALSE.

    • Column pack if n_rows_one == TRUE and all_contain_sep == TRUE.

    • Row pack if n_rows_one == FALSE and all_contain_sep == FALSE. This works incorrectly if output has one row which is checked. In this case it will be guessed as data pack.

    • Cell pack if n_rows_one == FALSE and all_contain_sep == TRUE. This works incorrectly if output has one row in which cells are checked. In this case it will be guessed as column pack.

Examples

my_rule_pack <- . %>% dplyr::summarise(nrow_neg = nrow(.) < 0) my_data_packs <- data_packs(my_data_pack_1 = my_rule_pack) # These pipes give identical results mtcars %>% expose(my_data_packs) %>% get_report()
#> Tidy data validation report: #> # A tibble: 1 x 5 #> pack rule var id value #> <chr> <chr> <chr> <int> <lgl> #> 1 my_data_pack_1 nrow_neg .all 0 FALSE
mtcars %>% expose(my_data_pack_1 = my_rule_pack) %>% get_report()
#> Tidy data validation report: #> # A tibble: 1 x 5 #> pack rule var id value #> <chr> <chr> <chr> <int> <lgl> #> 1 my_data_pack_1 nrow_neg .all 0 FALSE
# This throws an error because no pack type is specified for my_rule_pack
# NOT RUN { mtcars %>% expose(my_data_pack_1 = my_rule_pack, .guess = FALSE) # }
# Edge cases against using 'guess = TRUE' for robust code group_rule_pack <- . %>% dplyr::mutate(vs_one = vs == 1) %>% dplyr::group_by(vs_one, am) %>% dplyr::summarise(n_low = n() > 10) group_rule_pack_dummy <- . %>% dplyr::mutate(vs_one = vs == 1) %>% dplyr::group_by(mpg, vs_one, wt) %>% dplyr::summarise(n_low = n() > 10) row_rule_pack <- . %>% dplyr::transmute(neg_row_sum = rowSums(.) < 0) cell_rule_pack <- . %>% dplyr::transmute_all(rules(neg_value = . < 0)) # Only column 'am' is guessed as grouping which defines non-unique levels.
# NOT RUN { mtcars %>% expose(group_rule_pack, .remove_obeyers = FALSE, .guess = TRUE) %>% get_report() # }
# Values in `var` should contain combination of three grouping columns but # column 'vs_one' is guessed as rule. No error is thrown because the guessed # grouping column define unique levels. mtcars %>% expose(group_rule_pack_dummy, .remove_obeyers = FALSE, .guess = TRUE) %>% get_report()
#> Tidy data validation report: #> # A tibble: 64 x 5 #> pack rule var id value #> <chr> <chr> <chr> <int> <lgl> #> 1 group_pack..1 vs_one 10.4.5.25 0 FALSE #> 2 group_pack..1 vs_one 10.4.5.424 0 FALSE #> 3 group_pack..1 vs_one 13.3.3.84 0 FALSE #> 4 group_pack..1 vs_one 14.3.3.57 0 FALSE #> 5 group_pack..1 vs_one 14.7.5.345 0 FALSE #> 6 group_pack..1 vs_one 15.3.57 0 FALSE #> 7 group_pack..1 vs_one 15.2.3.435 0 FALSE #> 8 group_pack..1 vs_one 15.2.3.78 0 FALSE #> 9 group_pack..1 vs_one 15.5.3.52 0 FALSE #> 10 group_pack..1 vs_one 15.8.3.17 0 FALSE #> # ... with 54 more rows
# Results should have in column 'id' value 1 and not 0. mtcars %>% dplyr::slice(1) %>% expose(row_rule_pack) %>% get_report()
#> Tidy data validation report: #> # A tibble: 1 x 5 #> pack rule var id value #> <chr> <chr> <chr> <int> <lgl> #> 1 data_pack..1 neg_row_sum .all 0 FALSE
mtcars %>% dplyr::slice(1) %>% expose(cell_rule_pack) %>% get_report()
#> Tidy data validation report: #> # A tibble: 11 x 5 #> pack rule var id value #> <chr> <chr> <chr> <int> <lgl> #> 1 col_pack..1 neg_value mpg 0 FALSE #> 2 col_pack..1 neg_value cyl 0 FALSE #> 3 col_pack..1 neg_value disp 0 FALSE #> 4 col_pack..1 neg_value hp 0 FALSE #> 5 col_pack..1 neg_value drat 0 FALSE #> 6 col_pack..1 neg_value wt 0 FALSE #> 7 col_pack..1 neg_value qsec 0 FALSE #> 8 col_pack..1 neg_value vs 0 FALSE #> 9 col_pack..1 neg_value am 0 FALSE #> 10 col_pack..1 neg_value gear 0 FALSE #> 11 col_pack..1 neg_value carb 0 FALSE