Sunder the data, splitting it into 'pass' and 'fail' pieces
Validation of the data is one thing but, sometimes, you want to use the best part of the input dataset for something else. The get_sundered_data()
function works with an agent object that has intel (i.e., post interrogate()) and gets either the 'pass' data piece (rows with no failing test units across all row-based validation functions), or, the 'fail' data piece (rows with at least one failing test unit across the same series of validations). As a final option, we can have emit all the data with a new column (called .pb_combined) which labels each row as passing or failing across validation steps. These labels are "pass" and "fail" by default but their values can be easily customized.
get_sundered_data( agent, type = c("pass", "fail", "combined"), pass_fail = c("pass", "fail"), id_cols = NULL )
agent: The pointblank agent object
obj:<ptblank_agent> // required
A pointblank agent object that is commonly created through the use of the create_agent() function. It should have had interrogate() called on it, such that the validation steps were actually carried out.
type: The desired piece of data resulting from the splitting. Options for returning a single table are "pass" (the default) and "fail". Each of these options return a single table with, in the "pass" case, only the rows that passed across all validation steps (i.e., had no failing test units in any part of a row for any validation step), or, the complementary set of rows in the "fail" case. Providing NULL returns both of the split data tables in a list (with the names of "pass" and "fail"). The option "combined" applies a categorical (pass/fail) label (settable in the pass_fail argument) in a new .pb_combined flag column. For this case the ordering of rows is fully retained from the input table.
pass_fail: A vector for encoding the flag column with 'pass' and 'fail' values when type = "combined". The default is c("pass", "fail") but other options could be c(TRUE, FALSE), c(1, 0), or c(1L, 0L).
id_cols: An optional specification of one or more identifying columns. When taken together, we can count on this single column or grouping of columns to distinguish rows. If the table undergoing validation is not a data frame or tibble, then columns need to be specified for id_cols.
A list of table objects if type is NULL, or, a single table if a type is given.
There are some caveats to sundering. The validation steps considered for this splitting has to be of the row-based variety (e.g., the col_vals_*()
functions or conjointly(), but not rows_distinct()). Furthermore, validation steps that experienced evaluation issues during interrogation are not considered, and, validation steps where active = FALSE will be disregarded. The collection of validation steps that fulfill the above requirements for sundering are termed in-consideration validation steps.
If using any preconditions for validation steps, we must ensure that all in-consideration validation steps use the same specified preconditions
function. Put another way, we cannot split the target table using a collection of in-consideration validation steps that use different forms of the input table.
Create a series of two validation steps focused on testing row values for part of the small_table object. Then, use interrogate() to put the validation plan into action.
agent <-
create_agent(
tbl = small_table %>%
dplyr::select(a:f),
label = "`get_sundered_data()`"
) %>%
col_vals_gt(columns = d, value = 1000) %>%
col_vals_between(
columns = c,
left = vars(a), right = vars(d),
na_pass = TRUE
) %>%
interrogate()
Get the sundered data piece that contains only rows that passed both validation steps (the default piece). This yields 5 of 13 total rows.
agent %>% get_sundered_data()
## # A tibble: 5 × 6
## a b c d e f
## <int> <chr> <dbl> <dbl> <lgl> <chr>
## 1 2 1-bcd-345 3 3423. TRUE high
## 2 3 5-egh-163 8 10000. TRUE low
## 3 2 5-jdo-903 NA 3892. FALSE mid
## 4 4 2-dhe-923 4 3291. TRUE mid
## 5 1 3-dka-303 NA 2230. TRUE high
Get the complementary data piece: all of those rows that failed either of the two validation steps. This yields 8 of 13 total rows.
agent %>% get_sundered_data(type = "fail")
## # A tibble: 8 × 6
## a b c d e f
## <int> <chr> <dbl> <dbl> <lgl> <chr>
## 1 6 8-kdg-938 3 2343. TRUE high
## 2 8 3-ldm-038 7 284. TRUE low
## 3 7 1-knw-093 3 843. TRUE high
## 4 4 5-boe-639 2 1036. FALSE low
## 5 3 5-bce-642 9 838. FALSE high
## 6 3 5-bce-642 9 838. FALSE high
## 7 4 2-dmx-010 7 834. TRUE low
## 8 2 7-dmx-010 8 108. FALSE low
We can get all of the input data returned with a flag column (called
.pb_combined). This is done by using type = "combined" and that rightmost
column will contain "pass" and "fail" values.
agent %>% get_sundered_data(type = "combined")
## # A tibble: 13 × 7
## a b c d e f .pb_combined
## <int> <chr> <dbl> <dbl> <lgl> <chr> <chr>
## 1 2 1-bcd-345 3 3423. TRUE high pass
## 2 3 5-egh-163 8 10000. TRUE low pass
## 3 6 8-kdg-938 3 2343. TRUE high fail
## 4 2 5-jdo-903 NA 3892. FALSE mid pass
## 5 8 3-ldm-038 7 284. TRUE low fail
## 6 4 2-dhe-923 4 3291. TRUE mid pass
## 7 7 1-knw-093 3 843. TRUE high fail
## 8 4 5-boe-639 2 1036. FALSE low fail
## 9 3 5-bce-642 9 838. FALSE high fail
## 10 3 5-bce-642 9 838. FALSE high fail
## 11 4 2-dmx-010 7 834. TRUE low fail
## 12 2 7-dmx-010 8 108. FALSE low fail
## 13 1 3-dka-303 NA 2230. TRUE high pass
We can change the "pass" or "fail" text values to another type of coding
with the pass_fail argument. One possibility is TRUE/FALSE.
agent %>%
get_sundered_data(
type = "combined",
pass_fail = c(TRUE, FALSE)
)
## # A tibble: 13 × 7
## a b c d e f .pb_combined
## <int> <chr> <dbl> <dbl> <lgl> <chr> <lgl>
## 1 2 1-bcd-345 3 3423. TRUE high TRUE
## 2 3 5-egh-163 8 10000. TRUE low TRUE
## 3 6 8-kdg-938 3 2343. TRUE high FALSE
## 4 2 5-jdo-903 NA 3892. FALSE mid TRUE
## 5 8 3-ldm-038 7 284. TRUE low FALSE
## 6 4 2-dhe-923 4 3291. TRUE mid TRUE
## 7 7 1-knw-093 3 843. TRUE high FALSE
## 8 4 5-boe-639 2 1036. FALSE low FALSE
## 9 3 5-bce-642 9 838. FALSE high FALSE
## 10 3 5-bce-642 9 838. FALSE high FALSE
## 11 4 2-dmx-010 7 834. TRUE low FALSE
## 12 2 7-dmx-010 8 108. FALSE low FALSE
## 13 1 3-dka-303 NA 2230. TRUE high TRUE
...and using 0 and 1 might be worthwhile in some situations.
agent %>%
get_sundered_data(
type = "combined",
pass_fail = 0:1
)
## # A tibble: 13 × 7
## a b c d e f .pb_combined
## <int> <chr> <dbl> <dbl> <lgl> <chr> <int>
## 1 2 1-bcd-345 3 3423. TRUE high 0
## 2 3 5-egh-163 8 10000. TRUE low 0
## 3 6 8-kdg-938 3 2343. TRUE high 1
## 4 2 5-jdo-903 NA 3892. FALSE mid 0
## 5 8 3-ldm-038 7 284. TRUE low 1
## 6 4 2-dhe-923 4 3291. TRUE mid 0
## 7 7 1-knw-093 3 843. TRUE high 1
## 8 4 5-boe-639 2 1036. FALSE low 1
## 9 3 5-bce-642 9 838. FALSE high 1
## 10 3 5-bce-642 9 838. FALSE high 1
## 11 4 2-dmx-010 7 834. TRUE low 1
## 12 2 7-dmx-010 8 108. FALSE low 1
## 13 1 3-dka-303 NA 2230. TRUE high 0
8-3
Other Post-interrogation: all_passed(), get_agent_x_list(), get_data_extracts(), write_testthat_file()
Useful links