View source: R/write_testthat_file.R
write_testthat_file | R Documentation |
With a pointblank agent, we can write a testthat test file and opt
to place it in the testthat/tests
if it is available in the project path
(we can specify an alternate path as well). This works by transforming the
validation steps to a series of expect_*()
calls inside individual
testthat::test_that()
statements.
A major requirement for using write_testthat_file()
on an agent is the
presence of an expression that can retrieve the target table. Typically, we
might supply a table-prep formula, which is a formula that can be invoked to
obtain the target table (e.g., tbl = ~ pointblank::small_table
). This
user-supplied statement will be used by write_testthat_file()
to generate a
table-loading statement at the top of the new testthat test file so that
the target table is available for each of the testthat::test_that()
statements that follow. If an agent was not created using a table-prep
formula set for the tbl
, it can be modified via the set_tbl()
function.
Thresholds will be obtained from those applied for the stop
state. This can
be set up for a pointblank agent by passing an action_levels
object
to the actions
argument of create_agent()
or the same argument of any
included validation function. If stop
thresholds are not available, then a
threshold value of 1
will be used for each generated expect_*()
statement
in the resulting testthat test file.
There is no requirement that the agent first undergo interrogation with
interrogate()
. However, it may be useful as a dry run to interactively
perform an interrogation on the target data before generating the
testthat test file.
write_testthat_file(
agent,
name = NULL,
path = NULL,
overwrite = FALSE,
skips = NULL,
quiet = FALSE
)
agent |
The pointblank agent object
A pointblank agent object that is commonly created through the use of
the |
name |
Name for generated testthat file
An optional name for for the testhat test file. This should be a name
without extension and without the leading |
path |
File path
A path can be specified here if there shouldn't be an attempt to place the
file in |
overwrite |
Overwrite a previous file of the same name
Should a testthat file of the same name be overwritten? |
skips |
Test skipping
This is an optional vector of test-skipping keywords modeled after the
testthat |
quiet |
Inform (or not) upon file writing
Should the function not inform when the file is written? |
Tests for inactive validation steps will be skipped with a clear message
indicating that the reason for skipping was due to the test not being active.
Any inactive validation steps can be forced into an active state by using the
activate_steps()
on an agent (the opposite is possible with the
deactivate_steps()
function).
The testthat package comes with a series of skip_on_*()
functions which
conveniently cause the test file to be skipped entirely if certain conditions
are met. We can quickly set any number of these at the top of the
testthat test file by supplying keywords as a vector to the skips
option of write_testthat_file()
. For instance, setting
skips = c("cran", "windows)
will add the testthat skip_on_cran()
and
skip_on_os("windows")
statements, meaning that the generated test file
won't run on a CRAN system or if the system OS is Windows.
Here is an example of testthat test file output ("test-small_table.R"
):
# Generated by pointblank tbl <- small_table test_that("column `date_time` exists", { expect_col_exists( tbl, columns = date_time, threshold = 1 ) }) test_that("values in `c` should be <= `5`", { expect_col_vals_lte( tbl, columns = c, value = 5, threshold = 0.25 ) })
This was generated by the following set of R statements:
library(pointblank) agent <- create_agent( tbl = ~ small_table, actions = action_levels(stop_at = 0.25) ) %>% col_exists(date_time) %>% col_vals_lte(c, value = 5) write_testthat_file( agent = agent, name = "small_table", path = "." )
Invisibly returns TRUE
if the testthat file has been written.
Let's walk through a data quality analysis of an extremely small table. It's
actually called small_table
and we can find it as a dataset in this
package.
small_table #> # A tibble: 13 x 8 #> date_time date a b c d e f #> <dttm> <date> <int> <chr> <dbl> <dbl> <lgl> <chr> #> 1 2016-01-04 11:00:00 2016-01-04 2 1-bcd-345 3 3423. TRUE high #> 2 2016-01-04 00:32:00 2016-01-04 3 5-egh-163 8 10000. TRUE low #> 3 2016-01-05 13:32:00 2016-01-05 6 8-kdg-938 3 2343. TRUE high #> 4 2016-01-06 17:23:00 2016-01-06 2 5-jdo-903 NA 3892. FALSE mid #> 5 2016-01-09 12:36:00 2016-01-09 8 3-ldm-038 7 284. TRUE low #> 6 2016-01-11 06:15:00 2016-01-11 4 2-dhe-923 4 3291. TRUE mid #> 7 2016-01-15 18:46:00 2016-01-15 7 1-knw-093 3 843. TRUE high #> 8 2016-01-17 11:27:00 2016-01-17 4 5-boe-639 2 1036. FALSE low #> 9 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE high #> 10 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE high #> 11 2016-01-26 20:07:00 2016-01-26 4 2-dmx-010 7 834. TRUE low #> 12 2016-01-28 02:51:00 2016-01-28 2 7-dmx-010 8 108. FALSE low #> 13 2016-01-30 11:23:00 2016-01-30 1 3-dka-303 NA 2230. TRUE high
Creating an action_levels
object is a common workflow step when creating a
pointblank agent. We designate failure thresholds to the warn
, stop
, and
notify
states using action_levels()
.
al <- action_levels( warn_at = 0.10, stop_at = 0.25, notify_at = 0.35 )
A pointblank agent
object is now created and the al
object is provided to
the agent. The static thresholds provided by the al
object make reports a
bit more useful after interrogation.
agent <- create_agent( tbl = ~ small_table, label = "An example.", actions = al ) %>% col_exists(c(date, date_time)) %>% col_vals_regex( b, regex = "[0-9]-[a-z]{3}-[0-9]{3}" ) %>% col_vals_gt(d, value = 100) %>% col_vals_lte(c, value = 5) %>% interrogate()
This agent and all of the checks can be transformed into a testthat file with
write_testthat_file()
. The stop
thresholds will be ported over to the
expect_*()
functions in the new file.
write_testthat_file( agent = agent, name = "small_table", path = "." )
The above code will generate a file with the name "test-small_table.R"
. The
path was specified with "."
so the file will be placed in the working
directory. If you'd like to easily add this new file to the tests/testthat
directory then path = NULL
(the default) will makes this possible (this is
useful during package development).
What's in the new file? This:
# Generated by pointblank tbl <- small_table test_that("column `date` exists", { expect_col_exists( tbl, columns = date, threshold = 1 ) }) test_that("column `date_time` exists", { expect_col_exists( tbl, columns = date_time, threshold = 1 ) }) test_that("values in `b` should match the regular expression: `[0-9]-[a-z]{3}-[0-9]{3}`", { expect_col_vals_regex( tbl, columns = b, regex = "[0-9]-[a-z]{3}-[0-9]{3}", threshold = 0.25 ) }) test_that("values in `d` should be > `100`", { expect_col_vals_gt( tbl, columns = d, value = 100, threshold = 0.25 ) }) test_that("values in `c` should be <= `5`", { expect_col_vals_lte( tbl, columns = c, value = 5, threshold = 0.25 ) })
An agent on disk as a YAML file can be made into a testthat file. The
"agent-small_table.yml"
file is available in the pointblank package
and the path can be obtained with system.file()
.
yml_file <- system.file( "yaml", "agent-small_table.yml", package = "pointblank" )
Writing the testthat file into the working directory is much the same as
before but we're reading the agent from disk this time. It's a read and write
combo, really. Again, we are choosing to write the file to the working
directory by using path = "."
.
write_testthat_file( agent = yaml_read_agent(yml_file), name = "from_agent_yaml", path = "." )
8-5
Other Post-interrogation:
all_passed()
,
get_agent_x_list()
,
get_data_extracts()
,
get_sundered_data()
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