Minor documentation feedback

[Hadrien-lcrx]

DataFrame or omitted double quotes?

• print(mtcars) or print("mtcars")
• impact of check_output_expr()

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Vague definition

The functions in testwhat related to testing functions allow you to test whether students correctly called particular functions, with the correct arguments and whether these arguments are correct.

• The functions in testwhat related to testing functions allow you to test whether students correctly called particular functions, with the expected arguments and whether the values passed to these arguments are correct.

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Typo

Here, check_function() looks for the call of round() in both student and solution code, and then check_result() runs both (in their respective environments).

• Here, check_function() looks for the call of sum() in both student and solution code, and then check_result() runs both (in their respective environments).

Whenever there are is a multitude of ways to call a function

• Whenever there is a multitude of ways to call a function

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Remind / remember

Remind that functions like check_object() and check_function() often depend on both the student and solution environment.

• Remember that functions like check_object() and check_function() often depend on both the student and solution environment.

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check_or or check_or

check_or and check_or allow you to add logic to your SCT. Instead of running all tests and failing as soon as one of the tests fail, you can conditionally execute tests, and interpret the results separately.

• check_correct and check_or allow you to add logic to your SCT. Instead of running all tests and failing as soon as one of the tests fail, you can conditionally execute tests, and interpret the results separately.

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res or result

As an example, suppose you want the student to calculate the mean of a vector vec and store it in res. A possible solution could be:

# vec is predefined for you
vec <- c(1, 2, 3, 4, 5, 6)

Calculate result

result <- mean(vec)

* As an example, suppose you want the student to calculate the mean of a vector `vec` and store it in `result`. A possible solution could be:
```r
# vec is predefined for you
vec <- c(1, 2, 3, 4, 5, 6)

# Calculate result
result <- mean(vec)

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Remove “something”

If something result is not correct, you want to dig a little deeper and see if the student used the mean() function correctly.

• If result is not correct, you want to dig a little deeper and see if the student used the mean() function correctly.

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test_object() or check_object()

Because there is clearly something wrong - result is not correct - the ‘check’ sub-SCT, ex() %>% test_object("result") is executed again, but loudly this time.

• Because there is clearly something wrong - result is not correct - the ‘check’ sub-SCT, ex() %>% check_object("result") is executed again, but loudly this time.

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test_object or check_object?

For testwhat and DataCamp’s R Backend it communicates with, this can cause problems: there’s a solution enviroment and a student environment (so that testwhat can easily compare objects that student created with the object that they should’ve created, for example with test_object())

• For testwhat and DataCamp’s R Backend it communicates with, this can cause problems: there’s a solution environment and a student environment (so that testwhat can easily compare objects that student created with the object that they should’ve created, for example with check_object())

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Cc @filipsch

[filipsch]

@Hadrien-lcrx Thanks for this, very helpful!