Basic features - exercise sheet

Block 1: creating a project, working with objects

1.1 In RStudio, create a project called RIntro (note that R is case-sensitive).

1.2 Create the following objects. What are the values after each statement?

mass <- 47.5            # mass?
age  <- 122             # age?
mass <- mass * 2.0      # mass?
age  <- age - 20        # age?
mass_index <- mass/age  # mass_index?

1.3 Take a moment to try out different mathematical expressions until you get the hang of it.

You can use the objects you already created as initial values or as intermediates. You can also try adding annotations to your code and removing (as well as re-creating) your objects. Remember the guidelines we covered earlier for naming objects!

Block 2: functions and vectors

2.1 Round the number 3.49339 to three decimal points.

round(3.49339, digits = 3)

2.2 Create a numeric vector called “weights” using the numbers: 23, 55, 97, 12.

weights <- c(23, 55, 97, 12)

2.3 Create a character vector called “names” using these animal names: cat, tortoise, alpaca.

names <- c("cat", "tortoise", "alpaca")

2.4 We’ve seen that atomic vectors can be of type character, numeric (or double), integer, and logical. What happens if we try to mix these types in a single vector?

R implicitly converts them to all be the same type.

2.5 What will happen in each of these examples? (hint: use class() to check the data type of your objects):

num_char <- c(1, 2, 3, "a")
num_logical <- c(1, 2, 3, TRUE)
char_logical <- c("a", "b", "c", TRUE)
tricky <- c(1, 2, 3, "4")

Vectors can be of only one data type. R tries to convert (coerce) the content of this vector to find a “common denominator” that doesn’t lose any information.

2.6. You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

logical → numeric → character ← logical

Block 3: continuing with vectors

3.1 Using this vector of heights in inches, create a new vector, heights_no_na, with the NAs removed.

heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)

heights_no_na <- heights[!is.na(heights)] 
# or
heights_no_na <- na.omit(heights)
# or
heights_no_na <- heights[complete.cases(heights)]

3.2 Use the function median() to calculate the median of the heights vector.

median(heights, na.rm = TRUE)

3.3 Use R to figure out how many people in the set are taller than 67 inches.

To make things easier to remember, here is the list of comparisons and operators we discussed earlier (this one is useful to memorize):

## different comparisons:
# less than `<`
# greater than `>`
# less or equal `<=`
# greater or equal `>=`
# equal to `==`
# not equal to `!=`

## logical operators:
# and `&`
# or `|`
# not `!` 

heights_above_67 <- heights_no_na[heights_no_na > 67]
length(heights_above_67)

Block 4: finding help

4.1 Look at the help for the c() function. What kind of vector do you expect you will create if you evaluate the following?

c(1, 2, 3)
c('d', 'e', 'f')
c(1, 2, 'f')

?c

The c() function creates a vector, in which all elements are the same type. In the first case, the elements are numeric, in the second, they are characters, and in the third they are characters: the numeric values are “coerced” to be characters.

4.2 Try to figure out how to use a completely new function based only on its documentation. Look at the help for the function sample() and find out how to use sample() to produce

• a Finnish lottery ticket: 7 randomly drawn numbers out of 40
• a vector of 0s and 1s, representing 10 coin tosses

Tip: look at the section Examples in the end.

sample(1:40, 7)
# sample(40, 7)

sample(c(0, 1), 10, replace = TRUE) # sampling with replacement
# sample(0:1, 10, replace = TRUE)
# sample(2, 10, replace = TRUE)