# Function
help("sqrt")

# Short cut
?sqrt

… or use the bottom-right help panel in R Studio

Take a look at the mean() function:
What arguments could be specified?

help("mean")
?mean

The mean function takes three arguments:

• x : A vector with values
• trim : A fraction (e.g. 0.3) of values that are trimmed from each each end of the vector x
• na.rm : If TRUE (na.rm = TRUE), missing values (NAs) are removed before calculating the mean

Objects (also called variables) have an object name and contain data.
The data are assigned to an object with the <- or = operator.

x <- 10

You can see the value(s) of an object with the print() function, or by just typing the object name:

print(x)
x

Objects can be used for operators and arguments in functions:

x <- 16
y <- 13

x * y
sqrt(x)

You can write the return values of a function into a new object:

z <- sqrt(x)
z

And you can combine these:

exp(z) + sqrt(y)

Assign the values 40 and 24 to the variables a and b. Calculate the square root of the sum of a and b.

a <- 40
b <- 24
sqrt(a + b)

## [1] 8

Data are organized in structures:

• Atomic vectors: A single value
• Vectors: A chain of values
• Factors: Values with assigned labels
• List: A series elements, each one containing one or more (atomic) vectors
• Data Frames: A list with one vector for each element and all vectors of the same length
• Matrix: A two dimensional table with values of the same data type.
• Array: Like a matrix but with more dimensions.

You can build a vector of any data type:

firstname <- c("Dustin", "Mike", "Will")
curly <- c(TRUE, FALSE, FALSE)
age <- c(9, 11, 10)

But do not mix data types in a vector. You will get an error or they are internally changed:

age <- c("quite young", 10, 12, "very old")
age

## [1] "quite young" "10"          "12"          "very old"

Create a vector (named friends comprising four names of your friends.

friends <- c("Matthias", "Markus", "Thomas", "Christian")

A missing value is represented with NA (Not Available).

age <- c(9, NA, 11)
name <- c("Tick", "Trick", NA)
age

## [1]  9 NA 11

name

## [1] "Tick"  "Trick" NA

Create a vector with the values 2, 5, 7, 4, 7, 2, 6. Calculate the mean of these values. (Note: Use the mean() function to calculate the mean).

x <- c(2, 5, 7, 4, 7, 2, 6)
mean(x)

## [1] 4.714286

Create a vector with the values 2, NA, 7, 4, NA, 2, 6. Calculate the mean of these values.
(Note: Read through ?mean if you encounter problems.)

x <- c(2, NA, 7, 4, NA, 2, 6)
mean(x, na.rm = TRUE)

## [1] 4.2

Take the vector
names <- c("Sheldon", "Leonard", "Penny", "Amy")
and reorder it to get the following result:
[1] "Sheldon" "Amy" "Sheldon" "Amy" "Leonard" "Penny"

x <- c(1, 4, 1, 4, 2, 3)
new_order <- names[x]
new_order

## [1] "Sheldon" "Amy"     "Sheldon" "Amy"     "Leonard" "Penny"

Build a factor for gender with the labels male, female, non-binary. Include a vector for six fictitious gender values.

gender <- factor(
  c(1, 3, 2, 1, 2, 1), 
  levels = 1:3, 
  labels = c("male", "female", "non-binary")
)
gender

## [1] male       non-binary female     male       female     male      
## Levels: male female non-binary

Variables within a data frame are extracted with double square brackets.

study[["sen"]]

## [1] 0 1 0 1 0 1

study[["IQ"]]

## [1] 90 85 90 87 99 89

Note: an alternative approach is to use the $ sign: study$sen. But we will not use this approach for now.

Specific cases are selected within square brackets: object_name[rows, columns].

study[5, ]

study[c(2, 6), ]

study[c(2, 6), "IQ"]

## [1] 85 89

study[c(2, 6), c("sen", "IQ")]

You could also use numbers to address the columns:

study[, 2]

## [1] "M" "M" "F" "M" "F" "F"

study[c(2, 6), c(1, 3)]

Please create a new data frame (study2) comprising the gender and age variables for the cases 1, 3, and 5 of the study data frame.

study2 <- study[c(1, 3, 5), c("gender", "age")]
study2