## How it works
```
In the editor on the right you should type R code to solve the exercises. When you hit the ‘Submit Answer’ button, every line of code is interpreted and executed by R and you get a message whether or not your code was correct. The output of your R code is shown in the console in the lower right corner.
R makes use of the `#` sign to add comments, so that you and others can understand what the R code is about. Just like Twitter! Comments are not run as R code, so they will not influence your result. For example, _Calculate 3 + 4_ in the editor on the right is a comment.
You can also execute R commands straight in the console. This is a good way to experiment with R code, as your submission is not checked for correctness.
`@instructions`
- In the editor on the right there is already some sample code. Can you see which lines are actual R code and which are comments?
- Add a line of code that calculates the sum of 6 and 12, and hit the ‘Submit Answer’ button.
`@hint`
Just add a line of R code that calculates the sum of 6 and 12, just like the example in the sample code!
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Calculate 3 + 4
3 + 4
# Calculate 6 + 12
```
`@solution`
```{r}
# Calculate 3 + 4
3 + 4
# Calculate 6 + 12
6 + 12
```
`@sct`
```{r}
ex() %>% check_output_expr(“18”, missing_msg = “Make sure to add `6 + 12` on a new line. Do not start the line with a `#`, otherwise your R code is not executed!”)
success_msg(“Awesome! See how the console shows the result of the R code you submitted? Now that you’re familiar with the interface, let’s get down to R business!”)
```
— -
## Arithmetic with R
```yaml
type: NormalExercise
key: 720745eda5
xp: 100
skills: 1
```
In its most basic form, R can be used as a simple calculator. Consider the following arithmetic operators:
- Addition: `+`
- Subtraction: `-`
- Multiplication: `*`
- Division: `/`
- Exponentiation: `^`
- Modulo: `%%`
The last two might need some explaining:
- The `^` operator raises the number to its left to the power of the number to its right: for example `3²` is 9.
- The modulo returns the remainder of the division of the number to the left by the number on its right, for example 5 modulo 3 or `5 %% 3` is 2.
With this knowledge, follow the instructions to complete the exercise.
`@instructions`
- Type `2⁵` in the editor to calculate 2 to the power 5.
- Type `28 %% 6` to calculate 28 modulo 6.
- Submit the answer and have a look at the R output in the console.
- Note how the `#` symbol is used to add comments on the R code.
`@hint`
Another example of the modulo operator: `9 %% 2` equals `1`.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# An addition
5 + 5
# A subtraction
5–5
# A multiplication
3 * 5
# A division
(5 + 5) / 2
# Exponentiation
# Modulo
```
`@solution`
```{r}
# An addition
5 + 5
# A subtraction
5–5
# A multiplication
3 * 5
# A division
(5 + 5) / 2
# Exponentiation
2 ^ 5
# Modulo
28 %% 6
```
`@sct`
```{r}
msg = “Do not remove the other arithmetic examples!”
ex() %>% check_output_expr(“2⁵”, missing_msg = “The exponentiation example is not correct. Write `2 ^ 5` on a new line.”)
ex() %>% check_output_expr(“28 %% 6”, missing_msg = “There seems to be an issue with the modulo example. Write `28 %% 6` on a new line.”)
success_msg(“Great! Head over to the next exercise.”)
```
— -
## Variable assignment
```yaml
type: NormalExercise
key: 5f200ffd43
xp: 100
skills: 1
```
A basic concept in (statistical) programming is called a **variable**.
A variable allows you to store a value (e.g. 4) or an object (e.g. a function description) in R. You can then later use this variable’s name to easily access the value or the object that is stored within this variable.
You can assign a value 4 to a variable `my_var` with the command
```
my_var <- 4
```
`@instructions`
Over to you: complete the code in the editor such that it assigns the value 42 to the variable `x` in the editor. Submit the answer. Notice that when you ask R to print `x`, the value 42 appears.
`@hint`
Look at how the value 4 was assigned to `my_variable` in the exercise’s assignment. Do the exact same thing in the editor, but now assign 42 to the variable `x`.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Assign the value 42 to x
x <-
# Print out the value of the variable x
x
```
`@solution`
```{r}
# Assign the value 42 to x
x <- 42
# Print out the value of the variable x
x
```
`@sct`
```{r}
ex() %>% check_object(“x”, undefined_msg = “Make sure to define a variable `x`.”) %>% check_equal(incorrect_msg = “Make sure that you assign the correct value to `x`.”)
success_msg(“Good job! Have you noticed that R does not print the value of a variable to the console when you did the assignment? `x <- 42` did not generate any output, because R assumes that you will be needing this variable in the future. Otherwise you wouldn’t have stored the value in a variable in the first place, right? Proceed to the next exercise!”)
```
— -
## Variable assignment (2)
```yaml
type: NormalExercise
key: c5944b90eb
xp: 100
skills: 1
```
Suppose you have a fruit basket with five apples. As a data analyst in training, you want to store the number of apples in a variable with the name `my_apples`.
`@instructions`
- Type the following code in the editor: `my_apples <- 5`. This will assign the value 5 to `my_apples`.
- Type: `my_apples` below the second comment. This will print out the value of `my_apples`.
- Submit your answer, and look at the output: you see that the number 5 is printed. So R now links the variable `my_apples` to the value 5.
`@hint`
Remember that if you want to assign a number or an object to a variable in R, you can make use of the assignment operator `<-`. Alternatively, you can use `=`, but `<-` is widely preferred in the R community.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Assign the value 5 to the variable my_apples
# Print out the value of the variable my_apples
```
`@solution`
```{r}
# Assign the value 5 to the variable my_apples
my_apples <- 5
# Print out the value of the variable my_apples
my_apples
```
`@sct`
```{r}
ex() %>% check_object(“my_apples”, undefined_msg = “Please make sure to define a variable `my_apples`.”) %>% check_equal(incorrect_msg = “Make sure that you assign the correct value to `my_apples`.”)
ex() %>% check_output_expr(“my_apples”, missing_msg = “Have you explicitly told R to print out the `my_apples` variable to the console?”)
success_msg(“Great! Continue to the next exercise!”)
```
— -
## Variable assignment (3)
```yaml
type: NormalExercise
key: 1c1bd25045
xp: 100
skills: 1
```
Every tasty fruit basket needs oranges, so you decide to add six oranges. As a data analyst, your reflex is to immediately create the variable `my_oranges` and assign the value 6 to it. Next, you want to calculate how many pieces of fruit you have in total. Since you have given meaningful names to these values, you can now code this in a clear way:
```
my_apples + my_oranges
```
`@instructions`
- Assign to `my_oranges` the value 6.
- Add the variables `my_apples` and `my_oranges` and have R simply print the result.
- Assign the result of adding `my_apples` and `my_oranges` to a new variable `my_fruit`.
`@hint`
`my_fruit` is just the sum of `my_apples` and `my_oranges`. You can use the `+` operator to sum the two and `<-` to assign that value to the variable `my_fruit`.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Assign a value to the variables my_apples and my_oranges
my_apples <- 5
# Add these two variables together
# Create the variable my_fruit
```
`@solution`
```{r}
# Assign a value to the variables my_apples and my_oranges
my_apples <- 5
my_oranges <- 6
# Add these two variables together
my_apples + my_oranges
# Create the variable my_fruit
my_fruit <- my_apples + my_oranges
```
`@sct`
```{r}
msg <- “Have you used `my_fruit <- my_apples + my_oranges` to create the `my_fruit` variable?”
ex() %>% check_object(“my_apples”) %>% check_equal(incorrect_msg = “Keep the line that assigns 5 to `my_apples`.”)
ex() %>% check_object(“my_oranges”) %>% check_equal(incorrect_msg = “Keep the line that assigns 6 to `my_oranges`.”)
ex() %>% check_output_expr(“my_apples + my_oranges”,missing_msg = “Make sure to print out the result of adding `my_apples` and `my_oranges`. The code example in the description already gives away the answer to this instruction!”)
ex() %>% check_object(“my_fruit”, undefined_msg = msg) %>% check_equal(incorrect_msg = msg)
success_msg(“Nice one! The great advantage of doing calculations with variables is reusability. If you just change `my_apples` to equal 12 instead of 5 and rerun the script, `my_fruit` will automatically update as well. Continue to the next exercise.”)
```
— -
## Apples and oranges
```yaml
type: NormalExercise
key: 915fcc7c99
xp: 100
skills: 1
```
Common knowledge tells you not to add apples and oranges. But hey, that is what you just did, no :-)? The `my_apples` and `my_oranges` variables both contained a number in the previous exercise. The `+` operator works with numeric variables in R. If you really tried to add “apples” and “oranges”, and assigned a text value to the variable `my_oranges` (see the editor), you would be trying to assign the addition of a numeric and a character variable to the variable `my_fruit`. This is not possible.
`@instructions`
- Submit the answer and read the error message. Make sure to understand why this did not work.
- Adjust the code so that R knows you have 6 oranges and thus a fruit basket with 11 pieces of fruit.
`@hint`
You have to assign the numeric value `6` to the `my_oranges` variable instead of the character value `”six”`. Note how the quotation marks are used to indicate that `”six”` is a character.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Assign a value to the variable my_apples
my_apples <- 5
# Fix the assignment of my_oranges
my_oranges <- “six”
# Create the variable my_fruit and print it out
my_fruit <- my_apples + my_oranges
my_fruit
```
`@solution`
```{r}
# Assign a value to the variable my_apples
my_apples <- 5
# Fix the assignment of my_oranges
my_oranges <- 6
# Create the variable my_fruit and print it out
my_fruit <- my_apples + my_oranges
my_fruit
```
`@sct`
```{r}
ex() %>% check_error(incorrect_msg = “You can do this by setting the `my_oranges` variable to a numeric value, not a string!”)
ex() %>% check_object(“my_apples”) %>% check_equal(incorrect_msg = “Make sure that `my_apples` still contains `5`.”)
ex() %>% check_object(“my_oranges”) %>% check_equal(incorrect_msg = “Make sure that `my_oranges` is equal to `6`.”)
ex() %>% check_object(“my_fruit”) %>% check_equal(incorrect_msg = “The value of `my_fruit` is not correct. It should be 11, the sum of `my_apples` and `my_oranges`.”)
ex() %>% check_object(“my_fruit”)%>% check_or(
check_code(.,”my_fruit\\s*<-\\s*my_apples\\s*\\+\\s*my_oranges”, missing_msg = “Did you create `my_fruit` as the sum of variables `my_apples` and `my_oranges`?”, append=F),
check_code(.,”my_fruit\\s*<-\\s*my_oranges\\s*\\+\\s*my_apples”, missing_msg = “Did you create `my_fruit` as the sum of variables `my_apples` and `my_oranges`?”, append=F)
)
ex() %>% check_output_expr(“my_fruit”, missing_msg = “Don’t remove the line that prints out `my_fruit`.”)
success_msg(“Awesome, keep up the good work! Continue to the next exercise.”)
```
— -
## Basic data types in R
```yaml
type: NormalExercise
key: 0f23107394
xp: 100
skills: 1
```
R works with numerous data types. Some of the most basic types to get started are:
- Decimal values like `4.5` are called **numerics**.
- Whole numbers like `4` are called **integers**. Integers are also numerics.
- Boolean values (`TRUE` or `FALSE`) are called **logical**.
- Text (or string) values are called **characters**.
Note how the quotation marks in the editor indicate that `”some text”` is a string.
`@instructions`
Change the value of the:
- `my_numeric` variable to `42`.
- `my_character` variable to `”universe”`. Note that the quotation marks indicate that `”universe”` is a character.
- `my_logical` variable to `FALSE`.
Note that R is case sensitive!
`@hint`
Replace the values in the editor with the values that are provided in the exercise. For example:
`my_numeric <- 42` assigns the value 42 to the variable `my_numeric`.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Change my_numeric to be 42
my_numeric <- 42.5
# Change my_character to be “universe”
my_character <- “some text”
# Change my_logical to be FALSE
my_logical <- TRUE
```
`@solution`
```{r}
# Change my_numeric to be 42
my_numeric <- 42
# Change my_character to be “universe”
my_character <- “universe”
# Change my_logical to be FALSE
my_logical <- FALSE
```
`@sct`
```{r}
ex() %>% check_object(“my_numeric”) %>% check_equal(incorrect_msg = “Have you correctly changed the declaration of `my_numeric` so it contains the value 42?”)
ex() %>% check_object(“my_character”) %>% check_equal(incorrect_msg = “Have you correctly changed `my_character` to `\”universe\”`? Don’t forget the quotes!”)
ex() %>% check_object(“my_logical”) %>% check_equal(incorrect_msg = “Have you correctly changed `my_logical` to `FALSE`? All letters of `FALSE` should be capitalized!”)
success_msg(“Great work! Continue to the next exercise.”)
```
— -
## What’s that data type?
```yaml
type: NormalExercise
key: 99b549229d
xp: 100
skills: 1
```
Do you remember that when you added `5 + “six”`, you got an error due to a mismatch in data types? You can avoid such embarrassing situations by checking the data type of a variable beforehand. You can do this with the `class()` function, as the code in the editor shows.
`@instructions`
Complete the code in the editor and also print out the classes of `my_character` and `my_logical`.
`@hint`
The code that prints the data type of `my_numeric` is already included; do a similar things for `my_character` and `my_logical`.
`@pre_exercise_code`
```{r}
# no pec
```
`@sample_code`
```{r}
# Declare variables of different types
my_numeric <- 42
my_character <- “universe”
my_logical <- FALSE
# Check class of my_numeric
class(my_numeric)
# Check class of my_character
# Check class of my_logical
```
`@solution`
```{r}
# Declare variables of different types:
my_numeric <- 42
my_character <- “universe”
my_logical <- FALSE
# Check class of my_numeric
class(my_numeric)
# Check class of my_character
class(my_character)
# Check class of my_logical
class(my_logical)
```
`@sct`
```{r}
msg <- “Do not change the declaration of the variables!”
ex() %>% check_object(“my_numeric”, undefined_msg = msg) %>% check_equal(incorrect_msg = msg)
ex() %>% check_object(“my_character”, undefined_msg = msg) %>% check_equal(incorrect_msg = msg)
ex() %>% check_object(“my_logical”, undefined_msg = msg) %>% check_equal(incorrect_msg = msg)
patt <- “Have you included `class(%1$s)` to print out the data type of `%1$s`?”
ex() %>% check_output_expr(“class(my_numeric)”,missing_msg = “Do not remove the code that prints out the type of `my_numeric`.”)
ex() %>% check_output_expr(“class(my_character)”,missing_msg = sprintf(patt, “my_character”))
ex() %>% check_output_expr(“class(my_logical)”,missing_msg = sprintf(patt, “my_logical”))
success_msg(“Congratulations! This was the last exercise for this chapter. Head over to the next chapter to get immersed in the world of vectors!”)
