get_sundered_data: Sunder the data, splitting it into 'pass' and 'fail' pieces
In pointblank: Data Validation and Organization of Metadata for Local and Remote Tables
View source: R/get_sundered_data.R
get_sundered_data R Documentation
Sunder the data, splitting it into 'pass' and 'fail' pieces
Description
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.
Usage
get_sundered_data(
agent,
type = c("pass", "fail", "combined"),
pass_fail = c("pass", "fail"),
id_cols = NULL
)
Arguments
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.
Details
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.
Value
A list of table objects if type is NULL, or, a single table if a
type is given.
Examples
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
Function ID
8-3
See Also
Other Post-interrogation:
all_passed(),
get_agent_x_list(),
get_data_extracts(),
write_testthat_file()
pointblank documentation built on Oct. 30, 2024, 9:29 a.m.
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View source: R/get_sundered_data.R
| get_sundered_data | R Documentation |
Sunder the data, splitting it into 'pass' and 'fail' pieces
Description
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.
Usage
get_sundered_data(
agent,
type = c("pass", "fail", "combined"),
pass_fail = c("pass", "fail"),
id_cols = NULL
)
Arguments
agent |
The pointblank agent object
A pointblank agent object that is commonly created through the use of
the |
type |
The desired piece of data resulting from the splitting. Options
for returning a single table are |
pass_fail |
A vector for encoding the flag column with 'pass' and 'fail'
values when |
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 |
Details
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.
Value
A list of table objects if type is NULL, or, a single table if a
type is given.
Examples
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
Function ID
8-3
See Also
Other Post-interrogation:
all_passed(),
get_agent_x_list(),
get_data_extracts(),
write_testthat_file()
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