tblcheck

Overview

Introducing tblcheck

tblcheck works with gradethis to help instructors compare students’ exercise results with intended solutions in learnr tutorials. If you are new to grading learnr tutorials, we recommend that you get comfortable with gradethis before incorporating tblcheck into your tutorials.

tblcheck provides four levels of grading:

1. For fully automatic grading as a drop-in replacement for gradethis::grade_this(), use grade_this_table() or grade_this_vector().
2. To integrate tblcheck into existing grading code, you can use tbl_grade() or vec_grade() for specific automated checks.
3. You can directly call the specialized individual grading functions that power the higher-level functions.
4. Finally, you can check for table and vector problems to create your own custom feedback.

The most common use case for tblcheck is to provide automatic feedback for common problems in tables. Because the columns of data frames in R are vectors, tblcheck can also provide feedback for common problems in vectors with vec_grade().

These all-in-one grading functions check for a set of possible problems, or differences, between the exercise solution and the student’s table or vector. You can control which checks are applied with the arguments of these functions, or you can directly call individual grading functions. The table grading functions are prefixed with tbl_grade_ and the vector grading functions prefixed with vec_grade_.

For complete control over the feedback presented to users, each tbl_grade_ or vec_grade_ function is paired with a tbl_check_ or vec_check_ counterpart that finds problems and returns an object that you can use to create custom feedback.

Usage

To use tblcheck in a learnr tutorial, load tblcheck after learnr and gradethis in the setup chunk of your tutorial:

```{r setup}
library(learnr)
library(gradethis)
library(tblcheck)
```

Then, ensure your exercise has a -solution chunk and choose one of the following grading functions to grade your exercise:

• If the solution is a table, use grade_this_table()

  ```{r ex-check}
  grade_this_table()
  ```

  or use tbl_grade() in existing grading code.

  ```{r ex-check}
  grade_this({
    pass_if_equal()
    tbl_grade()
    pass()
  })
  ```

• If the solution is a column in a table, use grade_this_column()

  ```{r ex-check}
  grade_this_column("eruptions")
  ```

  or use tbl_grade_column() in existing grading code.

  ```{r ex-check}
  grade_this({
    pass_if_equal()
    tbl_grade_column()
    pass()
  })
  ```

• If the solution is a vector, use grade_this_vector()

  ```{r ex-check}
  grade_this_vector()
  ```

  or use vec_grade() in existing grading code.

  ```{r ex-check}
  grade_this({
    pass_if_equal()
    vec_grade()
    pass()
  })
  ```

In each of the above cases, the fully automated first version is functionally equivalent to the second version that uses gradethis::grade_this().

Grading tables

Automated table checking

grade_this_table() uses tbl_grade() to compare the result of the user’s input to the result of the -solution chunk, automatically returning targeted feedback to the user if any problems are discovered.

tbl_grade() checks that the user’s table

1. is the correct class,
2. has the correct column names,
3. has the correct number of rows and columns,
4. and that each column of the user’s table
   1. is the correct class and
   2. contains the correct values.

If any of these checks detect a problem in the submitted code, the student will see a single message with the first detected issue, based on the order described above.

Usage

To grade an exercise where the solution is a table, ensure you have a -solution chunk and call grade_this_table() in your -check chunk

```{r example-check}
grade_this_table()
```

or add tbl_grade() to the grading code in your -check chunk.

```{r example-check}
grade_this({
  pass_if_equal()
  tbl_grade()
  pass()
})
```

By default, tblcheck functions compare the gradethis objects .result and .solution, just like gradethis::pass_if_equal() and gradethis::fail_if_equal().

If you are using tbl_grade(), be sure to include a function like pass() or pass_if_equal() in your checking code to ensure students can get a passing grade!

tbl_grade() only returns feedback to the student if it discovers a problem; if the student gives the correct answer, it produces no output. This lets you quickly check for simple problems, following up with more detailed checking with other gradethis functions.

Finding problems

If the user’s submitted table differs from the correct table, grade_this_table() returns a failing grade and a message with an explanation for what went wrong. If there are multiple problems with a student’s submission, grade_this_table() tries to give the most actionable item first.

We’ll demonstrate how this works for a simple exercise that asks students to create the following table using tibble().

food	vegetable	color
lettuce	TRUE	green
tomato	FALSE	red

In the R Markdown source of the learnr tutorial, we use an exercise chunk labelled food, with a food-solution chunk with the expected solution and a food-check chunk with the exercise checking code using gradethis and tblcheck.

```{r food, exercise=TRUE}

```

```{r food-solution}
tibble(
  food = c("lettuce", "tomato"),
  vegetable = c(TRUE, FALSE),
  color = c("green", "red")
)
```

```{r food-check}
grade_this_table()
```

We’ll use this example throughout the sections that follow to demonstrate how tblcheck will respond to various types of errors that students may make. Keep in mind this is a contrived example designed for this vignette. In real-world usage, students are likely to only encounter one or two of the problems grade_this_table() is designed to find.

Checking class

First, grade_this_table() ensures that the class of the student’s submission matches the class of the expected solution. Here, the student attempts to store the data in the table as a list rather than by using tibble().

list(
  food = "lettuce",
  fruit = "TRUE",
  color = "green"
)
#> $food
#> [1] "lettuce"
#> 
#> $fruit
#> [1] "TRUE"
#> 
#> $color
#> [1] "green"

Your table should be a tibble (class tbl_df), but it is a list (class list).

Based on this advice, the student revises their solution to use tibble() instead of list().

Checking column names

Next, the code checks that the student used the correct column names, and they haven’t missed any columns or included any unexpected columns. Here, grade_this_table() notices that the student has an unexpected column named fruit.

tibble(
  food = "lettuce",
  fruit = "TRUE",
  color = "green"
)
#> # A tibble: 1 × 3
#>   food    fruit color
#>   <chr>   <chr> <chr>
#> 1 lettuce TRUE  green

Your table should have a column named vegetable. Your table should not have a column named fruit.

Based on this advice, the student revises their solution to name the second column vegetable instead of fruit.

Checking length

Next, grade_this_table() checks that the student has submitted the correct number of rows, and in this case notices that the student has only included one row.

tibble(
  food = "lettuce",
  vegetable = "TRUE",
  color = "green"
)
#> # A tibble: 1 × 3
#>   food    vegetable color
#>   <chr>   <chr>     <chr>
#> 1 lettuce TRUE      green

Your table should have 2 rows, but it has 1 row.

Based on this advice, the student realizes they’ve only entered the first row of the table. They go back to the example table and add the second row to their submission.

Checking column classes

Next, grade_this_table() checks that each individual column contains the correct type of data. Here, the student has stored the values of the vegetable column as a string, but we were expecting them to be logical values.

tibble(
  food = c("lettuce", "tomato"),
  vegetable = c("TRUE", "TRUE"),
  color = c("green", "red")
)
#> # A tibble: 2 × 3
#>   food    vegetable color
#>   <chr>   <chr>     <chr>
#> 1 lettuce TRUE      green
#> 2 tomato  TRUE      red

Your vegetable column should be a vector of TRUE/FALSE values (class logical), but it is a vector of text (class character).

Based on this advice, the student removes the " around the values in the vegetable column to use R’s logical TRUE.

Checking column values

Finally, grade_this_table() gives a hint as to what the values in each column should look like. Here, the student made a mistake during their transcription of the vegetable column.

tibble(
  food = c("lettuce", "tomato"),
  vegetable = c(TRUE, TRUE),
  color = c("green", "red")
)
#> # A tibble: 2 × 3
#>   food    vegetable color
#>   <chr>   <lgl>     <chr>
#> 1 lettuce TRUE      green
#> 2 tomato  TRUE      red

The first 2 values of your vegetable column should be TRUE and FALSE, not TRUE and TRUE.

Based on this advice, the student revises their submission, changing the second value of the vegetable column from TRUE to FALSE.

tibble(
  food = c("lettuce", "tomato"),
  vegetable = c(TRUE, FALSE),
  color = c("green", "red")
)
#> # A tibble: 2 × 3
#>   food    vegetable color
#>   <chr>   <lgl>     <chr>
#> 1 lettuce TRUE      green
#> 2 tomato  FALSE     red

That’s a first-class answer! Correct!

Grading vectors

Automated vector checking

Many of the table-grading tests that apply to the columns of tables can also be applied to vectors — after all, data frame columns in R are vectors.

When your exercise uses vectors rather than tables, grade_this_vector() and vec_grade() allows you to apply the same tests that are normally applied to the columns of a table to a vector. They check that the user’s vector

1. is the correct class
2. is the correct length
3. has the correct factor levels (if the vector is a factor)
4. contains the correct values
5. has the correct names (if the vector has names)

Like grade_this_table(), if a problem is detected by any of these checks, the student will see a single message with the first detected problem, based on the order described above.

Usage

To grade an exercise where the solution is a vector, ensure you have a -solution chunk and call grade_this_vector() in your -check chunk

```{r vector-check}
grade_this_vector()
```

or add vec_grade() to the grading code in your -check chunk, e.g.

```{r vector-check}
grade_this({
  pass_if_equal()
  vec_grade()
  pass()
})
```

Just like tbl_grade() and other tblcheck functions, vec_grade() automatically compares the user’s .result to the .solution when used in gradethis::grade_this().

While grade_this_vector() always returns a passing or failing grade, note that vec_grade() only returns feedback when a problem is detected. Be sure to include gradethis::pass() or gradethis::pass_if_equal() when using vec_grade() to ensure that students can get a passing grade.

Finding problems

If the user’s submitted vector differs from the correct vector, grade_this_vector() returns a failing grade and a message with an explanation for what went wrong. If there are multiple problems with a student’s submission, grade_this_vector() tries to give the most actionable item first.

Suppose an exercise asks a student to create a factor of the sandwich toppings — lettuce, tomato, avocado.

factor(c("lettuce", "tomato", "avocado"))
#> [1] lettuce tomato  avocado
#> Levels: avocado lettuce tomato

In the R Markdown source of the learnr tutorial, we use an exercise chunk labelled toppings, with a toppings-solution chunk with the expected solution and a toppings-check chunk with the exercise checking code using gradethis and tblcheck.

```{r toppings, exercise=TRUE}

```

```{r toppings-solution}
factor(c("lettuce", "tomato", "avocado"))
```

```{r toppings-check}
grade_this_vector()
```

For example, if the student submits a vector of the wrong class, that will be the first message returned by grade_this_vector().

c("lettuce", "tomato", "avocado")
#> [1] "lettuce" "tomato"  "avocado"

Your result should be a vector of factors (class factor), but it is a vector of text (class character).

If the student submits a factor with the wrong factor levels, grade_this_vector() will warn the student about their mistake.

factor(c("lettuce", "tomato", "avocado"), c("lettuce", "tomato", "avocado"))
#> [1] lettuce tomato  avocado
#> Levels: lettuce tomato avocado

Your result’s levels were not in the expected order. The first 3 levels of your result should be avocado, lettuce, and tomato, but they were lettuce, tomato, and avocado.

Custom Grading

There are a number of ways to control which mistakes are detected and how the feedback is given to the students.

• The first is to enable or disable specific checks using the check_* arguments of grade_this_table() and grade_this_vector() (or their counterparts, tbl_grade() and vec_grade()).

• Both grade_this_table() and grade_this_vector() include pre_check and post_check options that allow you to add additional tests and logic to the grading code.

• You may also choose to call specific grading functions associated with the checks underlying tbl_grade() and vec_grade().

• Or you can check rather than grade for specific problems to obtain a problem object, i.e. a description of the problem found by tblcheck. You can then use the problem object to construct a feedback message using gradethis::fail().

Skipping tests

Every test performed by grade_this_table() and grade_this_vector() (or tbl_grade() and vec_grade()) can be enabled or disabled with an argument. The argument names are prefixed with check_ — such as check_class or check_groups — and each take a TRUE or FALSE value.

For example, suppose a student answering our food exercise used a data.frame when the exercise expects a tibble.

data.frame(
  food = c("lettuce", "tomato"),
  vegetable = c(TRUE, FALSE),
  color = c("green", "red"),
  stringsAsFactors = FALSE
)
#>      food vegetable color
#> 1 lettuce      TRUE green
#> 2  tomato     FALSE   red

Your table should be a tibble (class tbl_df), but it is a data frame (class data.frame).

If you don’t care about the class of the table, you can add check_class = FALSE to grade_this_table(). This will skip checking the table’s class, but still run all other tests.

```{r food-check}
grade_this_table(check_class = FALSE)
```

Super job! Correct!

Since the only problem with the student’s submission was the class of the table, grade_this_table() doesn’t directly return any feedback.

Additional Checks

Both grade_this_table() and grade_this_vector() provide two additional arguments, pre_check and post_check, that allow you to add additional checks or modify the .result or .solution.

For both functions, the gradethis::grade_this() flow is roughly equivalent to the following code sketch:

grade_this({
  # ... pre_check ...
  
  # if requested
  pass_if_equal()
  
  # grade the table or vector
  tbl_grade()
  
  # ... post_check ...
  
  pass()
})

Two examples of reasons why you might want to use these arguments are to limit the table grading checks to specific columns only, or to include additional checks after tbl_grade() or vec_grade().

Suppose we extend our food example into an exercise labelled food-percentage that adds a count column to our foods table and asks students to add a new column, pct with the percentage of our total food is represented by each food.

```{r food-percentage-setup}
library(dplyr)

foods <- tibble(
  food = c("lettuce", "tomato"),
  vegetable = c(TRUE, FALSE),
  color = c("green", "red"),
  count = c(5, 3)
)
```

```{r food-percentage, exercise=TRUE}

```

```{r food-percentage-solution}
.solution <-
foods %>%
  mutate(pct = count / sum(count))
```

We expect the final solution to look like this

but a student might decide to store the total food in a temporary total column.

.result <- foods %>% 
  mutate(
    total = sum(count),
    pct = count / total
  )

Knowing that we don’t mind the additional column, we can use the pre_check argument to limit .result to the columns that also appear in .solution.

```r
grade_this_table(pre_check = {
  tbl_grade_is_table(.result)
  .result <- .result[intersect(names(.result), names(.solution))]
})
```

Awesome! Correct!

Specific grading functions

tbl_grade() and vec_grade() calls a number of grading functions internally. You can call these functions directly to perform more specific grading, either in the pre_check or post_check arguments of grade_this_table() or grade_this_vector(), or in standard gradethis::grade_this() grading code.

Function	Grades
tbl_grade_class() vec_grade_class()	the class of an object
tbl_grade_column()	applies the tests in vec_grade() to a single column of a table
tbl_grade_dimensions() vec_grade_dimensions()	the length and dimensions of an object
tbl_grade_groups()	the groups of a table
tbl_grade_names() vec_grade_names()	the names of an object
vec_grade_levels()	the levels of a factor
vec_grade_values()	the values of a vector

Suppose we modified our food example, telling students that we have 3 tomatoes and 5 heads of lettuce. We’d like the students to create a fourth column count containing the number of each food item in our possession. For this example, we’ll use the lower-level functions in conjunction with gradethis::grade_this().

```{r food-count-setup}
library(dplyr)

foods <- tibble(
  food = c("lettuce", "tomato"),
  vegetable = c(TRUE, FALSE),
  color = c("green", "red")
)
```


```{r food-count, exercise=TRUE}

```

```{r food-count-solution}
foods %>%
  mutate(count = c(5, 3))
```

In our grading code, we may choose to grade only the count column of foods using tbl_grade_column(), ignoring the other columns since they were provided by our setup code.

```{r food-count-check}
grade_this({
  tbl_grade_column("count")
  pass_if_equal()
  fail()
})
```

A student who quickly scanned the exercise prompt might reverse the expected order of the values in the count column.

foods %>%
  mutate(count = c(3, 5))
#> # A tibble: 2 × 4
#>   food    vegetable color count
#>   <chr>   <lgl>     <chr> <dbl>
#> 1 lettuce TRUE      green     3
#> 2 tomato  FALSE     red       5

The first 2 values of your count column should be 5 and 3, not 3 and 5.

Custom mistake handling

Sometimes, we want to handle specific circumstance in a special way. Every tbl_grade_ and vec_grade_ function includes a tbl_check_ or vec_check_ counterpart that returns the detected problem rather than converting the problem into feedback for the user (a grade in gradethis terms).

If we replace tbl_grade_column() with tbl_check_column(), we can store and inspect the problem detected by the column checking function. We’ll experiment in our local R console before writing our final exercise checking code.

solution <- foods %>% mutate(count = c(5, 3))
user <- foods %>% mutate(count = c(3, 5))

problem <- tbl_check_column("count", object = user, expected = solution)

problem
#> <tblcheck problem>
#> The first 2 values of your `count` column should be `5` and `3`, not `3` and `5`.
#> $ type    : chr "values"
#> $ expected: num [1:2] 5 3
#> $ actual  : num [1:2] 3 5
#> $ location: chr "column"
#> $ column  : chr "count"

Every problem object contains at least three items:

1. The problem type describes the issue discovered by the checking function. The help pages for every check function contain a section named Problems where the problem types detected by the check function are enumerated.

   • Your Turn: What types of problems are discovered by tbl_check_column()? Use the help pages to find out.

2. actual contains the value returned by the user’s code and inspected by the check function.

3. expected contains the value returned by the solution code and inspected by the check function.

Problems also include additional information depending on the problem type. In the case of a values problem detected by tbl_check_column(), the problem object also includes the column name.

tblcheck includes a helper function, is_problem() that you can use to detect and differentiate between different problem types.

is_problem(problem)
#> [1] TRUE

We can use the type argument of is_problem() to differentiate between the problem types detected by tbl_check_column().

is_problem(problem, type = "length")
#> [1] FALSE
is_problem(problem, type = "values")
#> [1] TRUE

In this exercise, we know in advance that our wording is likely to trip up students, so we may want to create feedback specifically for the case where a student has reversed the food counts. We can use is_problem() together with all.equal() to isolate this specific case.

if (is_problem(problem, "values") && all.equal(problem$actual, c(3, 5))) {
  feedback <- paste(
    "Make sure that the values in `count` are ordered",
    "to match their respective `food`.",
    "Remember, we have **3** tomatoes and **5** heads of lettuce."
  )
  gradethis::fail(feedback)
}
#> <gradethis_graded: [Incorrect]
#>   Make sure that the values in `count` are ordered to match
#>   their respective `food`. Remember, we have **3** tomatoes
#>   and **5** heads of lettuce.
#> >

For problems not handled by your custom grading code, you can pass the problem to tbl_grade() to create a grade with the default feedback provided by tblcheck’s grade functions. If there are no problems, tblcheck_grade(problem) won’t return anything.

Here’s the default feedback tbl_grade_column() would have returned without our custom grading code.

tblcheck_grade(problem)
#> <gradethis_graded: [Incorrect]
#>   The first 2 values of your `count` column should be `5` and
#>   `3`, not `3` and `5`.
#> >

Tip: You can also use if statements to ignore differences that you don’t care about in your grading code.

Putting everything together into our grading food-count-check chunk, our grading code for this exercise would look like this:

```{r food-count-check}
grade_this({
  problem <- tbl_check_column("count")
  
  if (is_problem(problem, "values") && all.equal(problem$actual, c(3, 5))) {
    feedback <- paste(
      "Make sure that the values in `count` are ordered",
      "to match their respective `food`.",
      "Remember, we have **3** tomatoes and **5** heads of lettuce."
    )
    fail(feedback)
  }
  
  tblcheck_grade(problem)
  pass("Great job!")
})
```

And the student who reversed the count column values

foods %>%
  mutate(count = c(3, 5))
#> # A tibble: 2 × 4
#>   food    vegetable color count
#>   <chr>   <lgl>     <chr> <dbl>
#> 1 lettuce TRUE      green     3
#> 2 tomato  FALSE     red       5

would receive our custom feedback.

Make sure that the values in count are ordered to match their respective food. Remember, we have 3 tomatoes and 5 heads of lettuce.

By following our specific advice, the student revises their code to correctly create the count column.

foods %>%
  mutate(count = c(5, 3))
#> # A tibble: 2 × 4
#>   food    vegetable color count
#>   <chr>   <lgl>     <chr> <dbl>
#> 1 lettuce TRUE      green     5
#> 2 tomato  FALSE     red       3

Great job!