Practical 2

This practical will guide you through creating, manipulating, and subsetting data frames, importing/exporting data, and practicing functions in R.

Installing packages

This practical will require you to install a few new packages:

• haven
• writexl
• palmerpenguins

Remember, avoid putting install.packages statement in your scripts (since this would mean the are re-installed every time the script is run. Instead, install them once, and load with library each time you need them.

install.packages(c("haven", "writexl", "palmerpenguins"))

Data frames

1. Combine the three vectors we created in Practical 1 into a new data frame. Remember to store the result as a new object.

x <- 1:5
y <- c("a", "b", "c", "d", "e")
z <- rnorm(5)

Solution

my_data <- data.frame(x, y, z)

2. Count the number of rows and columns (e.g., nrow, ncol) and get the column names (names) of this data frame.

Solution

# Assuming your data frame above was stored in an object called 'practical':
nrow(my_data)

[1] 5

ncol(my_data)

[1] 3

names(my_data)

[1] "x" "y" "z"

Built-in datasets

R has a number of built-in datasets that can be loaded with the data function. For example:

data(mtcars)
head(mtcars)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Note

Note that, once you’ve loaded the dataset with data, you can see it in the “Environment” pane in RStudio.

Many packages also come with datasets. For example, if we load the palmerpenguins package, we can then load the penguins dataset:

library(palmerpenguins)

data(penguins)
head(penguins)

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
6 Adelie  Torgersen           39.3          20.6               190        3650
# ℹ 2 more variables: sex <fct>, year <int>

If you’re having problems installing palmerpenguins, you can download the dataset in Stata format here.

Importing data

To practice importing data, let’s first export the starwars dataset in a variety of formats by running the code below:

library(tidyverse)
library(haven)
library(writexl)

write_csv(penguins, file = "penguins.csv")
write_xlsx(penguins, path = "penguins.xlsx")
write_dta(penguins, path = "penguins.dta")
write_sav(penguins, path = "penguins.sav")

1. Import penguins.csv using read_csv from the readr package.

Solution

penguins_csv <- read_csv("penguins.csv")

Note

• What does this output mean?
• What does the col_types argument do?

2. Import penguins.xlsx using read_xlsx from the readxl package.

Solution

library(readxl)

penguins_xlsx  <- read_xlsx("penguins.xlsx")

Tip

If you want to use a function without loading the package, you can type :: to refer to a specific function. For example:

readxl::read_xlsx("penguins.xlsx")

You might want to do this when you only need a single function once (e.g., importing).

3. Import penguins.dta (a Stata dataset) and penguins.sav (an SPSS dataset) using appropriate functions from the haven package.

Subsetting

1. Define a vector containing letters A to F.

Solution

a_to_f <- c("A", "B", "C", "D", "E", "F")

Note

You could also create this vector using the built-in LETTERS vector and subsetting:

a_to_f <- LETTERS[1:5]

2. Use subsetting to select:

1. The first element (i.e., A)
2. The third element (i.e., C)
3. All except the last element (i.e., A, B, C, D)

Solution

We can select elements by position:

# First element
a_to_f[1]

[1] "A"

# Third element
a_to_f[3]

[1] "C"

To select ‘all except the last’ element, we need to use negation:

a_to_f[-5]

[1] "A" "B" "C" "D"

Note that this would only work if the vector has exactly five elements. We could instead define the position using length (i.e., the number of elements in the vector):

a_to_f[-length(a_to_f)]

[1] "A" "B" "C" "D"

This would work for vectors of any length, for example:

week_days <- c("Monday", "Tuesday", "Wednesday")

# This works:
week_days[-length(week_days)]

[1] "Monday"  "Tuesday"

# Whereas our previous solution would fail:
week_days[-5]

[1] "Monday"    "Tuesday"   "Wednesday"

3. Define a list containing a character vector, a logical value, and another list:

mylist <- list(a = c("Monday",
                     "Tuesday",
                     "Wednesday"),
               b = TRUE,
               c = list(rnorm(5),
                        21.3,
                        "firstname"))

4. Use subsetting to select:

1. The first element
2. The element named b
3. Tuesday
4. 21.3

Solution

# i.  The first element;

mylist[[1]]

[1] "Monday"    "Tuesday"   "Wednesday"

# ii. The element named `b`;

mylist$b

[1] TRUE

mylist[["b"]]

[1] TRUE

mylist["b"]

$b
[1] TRUE

mylist[names(mylist) == "b"]

$b
[1] TRUE

# iii. `Tuesday`

mylist[["a"]][2]

[1] "Tuesday"

mylist[[1]][2]

[1] "Tuesday"

# iv. `21.3`

mylist$c[[2]]

[1] 21.3

mylist[[3]][[2]]

[1] 21.3