Make sure you have the latest version of testthat
installed.
if (!require(pak)) {
install.packages("pak",
repos = "http://cran.us.r-project.org"
)
}
pak::pkg_install("r-lib/testthat", upgrade = TRUE, ask = FALSE)
library(testthat)Bundling
Ideally, pickler’s functions are developed independently
and then wrapped in a bundle() before passing to the
testthat BDD functions. Consider the feature()
and scenario() below for the split_cols()
function:
bundle(
feature(
title = "Split a single column into multiple columns using a pattern",
as_a = "user of split_cols()",
i_want = "to specify a column and a pattern",
so_that = "I can quickly generate multiple columns."
),
background(
title = "Input dataframe with text data",
given = "a dataframe [data] with text columns",
and = list(
"a column prefix [cols_]",
"a specified column [col]"))
)Bundling makes it easier to combine features and backgrounds into a single call:
Feature: Split a single column into multiple columns using a pattern
As a user of split_cols()
I want to specify a column and a pattern
So that I can quickly generate multiple columns.
Background: Input dataframe with text data
Given a dataframe [data] with text columns
And a column prefix [cols_]
And a specified column [col]
bundle() also allows us to pass a feature()
and background() to describe(), then test a
particular scenario() with it():
describe(
# bundle feature and background
bundle(
feature(
title = "Split a single column into multiple columns using a pattern",
as_a = "user of split_cols()",
i_want = "to specify a column and a pattern",
so_that = "I can quickly generate multiple columns."
),
background(
title = "Input dataframe with text data",
given = "a dataframe [data] with text columns",
and = list(
"a column prefix [cols_]",
"a specified column [col]"))
), code = {
it(
# test scenario 1
scenario(
title = "Split column with a default pattern",
given = "a dataframe [data] with a specified column [col]",
when = "I split the [col] column using the default [[^[:alnum:]]+]",
then = "the [col] column should be split into multiple columns"
), code = {
expect_true(TRUE)
})
it(
# test scenario 2
scenario(
title = "Split column with a default pattern",
given = "a dataframe [data] with a specified column [col]",
when = "I split the [col] column using the default [[^[:alnum:]]+]",
then = "the new columns should be named with the default prefix [cols_]"
), code = {
expect_true(TRUE)
})
})
#> Test passed
#> Test passed