2: Working with data

Adding functionality

Your R installation comes pre-packed with lots of functionality. Just by opening R you are able to perform computations, create graphics (you’ll see this later on), use functions, and much more.

However, sometimes you may wish to use additional functionality that does not come directly with the installed software. R is a free and open-source programming language with a vast user network. This has led in turn to a myriad of user-contributed additional functionality, each bundled in different packages (also called libraries).

These packages are stored in the Comprehensive R Archive Network (CRAN) and can be installed.

You only need to install a package once, to bring it from the web to your local computer.

Then, to actually use the package in every R session, you simply need to load the package (and not install it every single time).

You install a package by going to the Files & Plots panel, click “Packages” in the tabs, then select Install, type the package names and click Install.

Remember, you only need to do this once.

As we said, package are pre-shipped with your R installation. Examples are the base, stats, and graphics packages. Each of those can be considered as a “folder” providing different functionality. You will see the use of those functions later on in the course.

Actually, when you install R it comes with many more packages. Some are automatically loaded when you open R, making their functionality immediately available to you, while others are left in the background unopened. In the picture below, the loaded packages are shown in orange, and the ones not loaded are in gray.

For each new R session, in order to use the functionality from packages that are not automatically loaded, or that you have installed later, you must load the package with the function library(package_name) at the top of the R script.

Prelude

In today’s lesson you will need the tidyverse package. We will use this package to read data files into R. Try loading it:

library(tidyverse)

Did you get an error?

If yes, you don’t have the package installed. Install the package following the instructions above. Then try again running library(tidyverse).

If you don’t get an error, amazing! It should look like this:

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2          ✔ readr     2.1.4     
✔ forcats   1.0.0          ✔ stringr   1.5.0     
✔ ggplot2   3.5.0.9000     ✔ tibble    3.2.1     
✔ lubridate 1.9.2          ✔ tidyr     1.3.0     
✔ purrr     1.0.1          
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Important. It is common practice to put all code loading packages at the top of the R script.

There are some messages returned, but none of those are errors. In fact, in R errors start with the word “Error”. If you don’t read the word “Error” in the message, it’s not an error.

Open RStudio, create a new R script called lesson2.R and save it into your r-bootcamp folder.

In the previous examples we only entered data manually. However, this isn’t very efficient if you are working with lots of data and it increases the chance of typing errors. This lesson will show you how to read into R data stored in files.

Before going ahead, please download the following data files and save them into the subfolder “data” of r-bootcamp

Data files

A data file is any file storing data. The following types of data files are commonly used:

  • .csv (Comma Separated Values)
  • .tsv or .txt (Tab Separated Values)
  • .xlsx (Excel data file)

How does each look?

Directories

Before importing the data into R we need to tell R in which folder we are working. This is because R will look for the data files in that folder only. If the files are stored somewhere else, R will not find them.

This step is called setting the working directory. You can set the working directory, i.e. tell R in which folder you are working, by going to the RStudio menu -> Session -> Set working directory -> Choose directory -> Navigate to r-bootcamp

Alternatively, you can go to the Files and Plots panel, click the Files tab, navigate to the r-bootcamp folder, click More -> Set as working directory.

Within the r-bootcamp folder, you should have a subfolder “data” with the data files saved inside.

Reading data into R

To read a CSV file into R we use the following function:

unpop <- read_csv('data/UNpop.csv')
Rows: 8 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (2): year, world_pop

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
unpop
# A tibble: 8 × 2
   year world_pop
  <dbl>     <dbl>
1  1950   2536431
2  1960   3034950
3  1970   3700437
4  1980   4458003
5  1990   5327231
6  2000   6143494
7  2010   6956824
8  2020   7794799

To read a file in which the values are separated by tabs, we use the read_tsv() function:

unpop_txt <- read_tsv('data/UNpop.txt')
Rows: 8 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
dbl (2): year, world_pop

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
unpop_txt
# A tibble: 8 × 2
   year world_pop
  <dbl>     <dbl>
1  1950   2536431
2  1960   3034950
3  1970   3700437
4  1980   4458003
5  1990   5327231
6  2000   6143494
7  2010   6956824
8  2020   7794799

To read Excel files, you need to load a package called readxl. Edit the top of your R script to have also the library(reaxl) command. The top of your R script should look like this:

Now, read the excel file into R:

unpop_xlsx <- read_excel('data/UNpop.xlsx')
unpop_xlsx
# A tibble: 8 × 2
   year world_pop
  <dbl>     <dbl>
1  1950   2536431
2  1960   3034950
3  1970   3700437
4  1980   4458003
5  1990   5327231
6  2000   6143494
7  2010   6956824
8  2020   7794799

As you can see, they all produce the same data in R. Since they’re all the same, let’s look at the first one.

unpop
# A tibble: 8 × 2
   year world_pop
  <dbl>     <dbl>
1  1950   2536431
2  1960   3034950
3  1970   3700437
4  1980   4458003
5  1990   5327231
6  2000   6143494
7  2010   6956824
8  2020   7794799

The data table is stored in an object of class tibble. If you inspect the class it looks quite complicated but it has “tbl” among the values.

class(unpop)
[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

How many rows and columns are there?

nrow(unpop)
[1] 8
ncol(unpop)
[1] 2
dim(unpop)
[1] 8 2

The data store the world population estimates for the last 8 decades. Hence, we have 2 variables (year and world_pop), and 8 rows (one for each decade).

To get the names of the recorded variables you could either use names() or colnames():

names(unpop)
[1] "year"      "world_pop"
colnames(unpop)
[1] "year"      "world_pop"

The summary() function produces quick summaries for the variables in the data table. However, this is just for a quick exploration, it is not appropriate copying and pasting such output for a journal publication, essay, or report. That would require better styling.

summary(unpop)
      year        world_pop      
 Min.   :1950   Min.   :2536431  
 1st Qu.:1968   1st Qu.:3534065  
 Median :1985   Median :4892617  
 Mean   :1985   Mean   :4994021  
 3rd Qu.:2002   3rd Qu.:6346826  
 Max.   :2020   Max.   :7794799

We can create a publication quality summary table by doing for example

stats <- unpop %>%
  summarise(Mean = mean(world_pop), 
            SD = sd(world_pop),
            Min = min(world_pop),
            Max = max(world_pop))
stats
# A tibble: 1 × 4
      Mean       SD     Min     Max
     <dbl>    <dbl>   <dbl>   <dbl>
1 4994021. 1888599. 2536431 7794799

The function %>% is called pipe and it simply means “take what’s on the left, then do …”

For example these are equivalent:

round(exp(sqrt(1:10)), 2)
 [1]  2.72  4.11  5.65  7.39  9.36 11.58 14.09 16.92 20.09 23.62
1:10 %>%
  sqrt() %>%
  exp() %>%
  round(2)
 [1]  2.72  4.11  5.65  7.39  9.36 11.58 14.09 16.92 20.09 23.62

To format the tables for journals or reports, you can use one of the following options.

The kable() function from the knitr package. If you don’t have the package installed, make sure you run install.packages("knitr") before!

Mean SD Min Max
4994021 1888599 2536431 7794799

The gt() function from the gt package. If you don’t have the package installed, make sure you run install.packages("gt") before!

library(gt)

gt(stats)
Mean SD Min Max
4994021 1888599 2536431 7794799

Indexing data tables

We can extract particular parts of a tibble (i.e. data table) as follows

unpop[1:3, ]
# A tibble: 3 × 2
   year world_pop
  <dbl>     <dbl>
1  1950   2536431
2  1960   3034950
3  1970   3700437
unpop[c(2, 5, 6), ]
# A tibble: 3 × 2
   year world_pop
  <dbl>     <dbl>
1  1960   3034950
2  1990   5327231
3  2000   6143494
unpop[2, ]
# A tibble: 1 × 2
   year world_pop
  <dbl>     <dbl>
1  1960   3034950
unpop[, 2]
# A tibble: 8 × 1
  world_pop
      <dbl>
1   2536431
2   3034950
3   3700437
4   4458003
5   5327231
6   6143494
7   6956824
8   7794799
unpop$world_pop
[1] 2536431 3034950 3700437 4458003 5327231 6143494 6956824 7794799
unpop$world_pop[1:3]
[1] 2536431 3034950 3700437
unpop[1:3, 2]
# A tibble: 3 × 1
  world_pop
      <dbl>
1   2536431
2   3034950
3   3700437
unpop[1:3, 'world_pop']
# A tibble: 3 × 1
  world_pop
      <dbl>
1   2536431
2   3034950
3   3700437

Extract the first 3 years from the data.

The following returns a tibble

unpop[1:3, 'year']
# A tibble: 3 × 1
   year
  <dbl>
1  1950
2  1960
3  1970

While this way returns a vector

unpop$year[1:3]
[1] 1950 1960 1970

To create a new variable, you simply use the $ symbol with a new name

unpop$world_pop_millions <- unpop$world_pop /  1000

The tidyverse way

There is a simpler way to work with data. The tidyverse package, which you can load with

Has the following functions:

  • slice() to keep only some rows

  • filter() to only keep the rows matching some condition

  • select() to only keep specific columns

  • mutate() to create new columns via a computation

Display all data:

unpop
# A tibble: 8 × 3
   year world_pop world_pop_millions
  <dbl>     <dbl>              <dbl>
1  1950   2536431              2536.
2  1960   3034950              3035.
3  1970   3700437              3700.
4  1980   4458003              4458.
5  1990   5327231              5327.
6  2000   6143494              6143.
7  2010   6956824              6957.
8  2020   7794799              7795.

Create a new column with the population in millions

unpop %>%
  mutate(world_pop_millions = world_pop / 1000)
# A tibble: 8 × 3
   year world_pop world_pop_millions
  <dbl>     <dbl>              <dbl>
1  1950   2536431              2536.
2  1960   3034950              3035.
3  1970   3700437              3700.
4  1980   4458003              4458.
5  1990   5327231              5327.
6  2000   6143494              6143.
7  2010   6956824              6957.
8  2020   7794799              7795.

Check if the column is still in the original data:

unpop
# A tibble: 8 × 3
   year world_pop world_pop_millions
  <dbl>     <dbl>              <dbl>
1  1950   2536431              2536.
2  1960   3034950              3035.
3  1970   3700437              3700.
4  1980   4458003              4458.
5  1990   5327231              5327.
6  2000   6143494              6143.
7  2010   6956824              6957.
8  2020   7794799              7795.

It is not there! Why? Because we didn’t overwrite the unpop variable when we created the new column:

unpop <- unpop %>%
  mutate(world_pop_millions = world_pop / 1000)

Keep first 3 rows only:

unpop %>%
  slice(1:3)
# A tibble: 3 × 3
   year world_pop world_pop_millions
  <dbl>     <dbl>              <dbl>
1  1950   2536431              2536.
2  1960   3034950              3035.
3  1970   3700437              3700.

Keep only the world_pop column:

unpop %>%
  select(world_pop)
# A tibble: 8 × 1
  world_pop
      <dbl>
1   2536431
2   3034950
3   3700437
4   4458003
5   5327231
6   6143494
7   6956824
8   7794799

Keep only the world_pop column and the first 3 rows only:

unpop %>%
  select(world_pop) %>%
  slice(1:3)
# A tibble: 3 × 1
  world_pop
      <dbl>
1   2536431
2   3034950
3   3700437

Let’s compute the average population over that time period:

avg_pop <- mean(unpop$world_pop)
avg_pop
[1] 4994021

Keep only the rows with a world population larger than the mean:

unpop %>%
  filter(world_pop > avg_pop)
# A tibble: 4 × 3
   year world_pop world_pop_millions
  <dbl>     <dbl>              <dbl>
1  1990   5327231              5327.
2  2000   6143494              6143.
3  2010   6956824              6957.
4  2020   7794799              7795.

In all the code above, the symbol %>% is called pipe and can be inserted with Control + Shift + M on Windows or Command + Shift + M on macOS.

Its role is telling R to continue the computation. Basically it allows you to write nested expressions

bop(scoop(hop(foo_foo, through = forest), up = field_mice), on = head)

using a more human-readable form1:

foo_foo %>%
  hop(through = forest) %>%
  scoop(up = field_mouse) %>%
  bop(on = head)

The idea is that if you have some function f(), you can do

df %>%
  f(y)

Which has the same meaning as:

f(df, y)

So whatever you carry forward with the pipe gets passed as the first argument of the next function.

If you want to be specific, you can use a . inside:

10 %>%
  seq(., to = 20, by = 1)
 [1] 10 11 12 13 14 15 16 17 18 19 20
10 %>%
  seq(from = 1, to = ., by = 1)
 [1]  1  2  3  4  5  6  7  8  9 10

Tibbles

A data table is an object which stores data. Up to now, you have only read data tables in R from files stored on your PC.

You can also create it directly yourself by typing the values into a function called tibble(), also part of the tidyverse package.

In lesson 1, we stored the world population and the years in separate numeric vectors:

year <- seq(1950, 2020, by = 10)
world_pop <- c(2536431, 3034950, 3700437, 4458003, 
               5327231, 6143494, 6956824, 7794799)

But the first value in world_pop is linked to the first year, and so on. So it’s better to keep the values linked together and arranged into a data table.

In a tibble we write the column names, an equal sign, and the values

tibble(
  column_name = values,
  another_column_name = other_values
)
tbl <- tibble(
  year = seq(1950, 2020, by = 10),
  world_pop = c(2536431, 3034950, 3700437, 4458003, 5327231, 6143494, 6956824, 7794799)
)
tbl
# A tibble: 8 × 2
   year world_pop
  <dbl>     <dbl>
1  1950   2536431
2  1960   3034950
3  1970   3700437
4  1980   4458003
5  1990   5327231
6  2000   6143494
7  2010   6956824
8  2020   7794799

Missing values

We do not have the estimates for the current year yet, and also we do not have the estimates for 1930 and 1940. Let’s add rows corresponding to those years in the data table, but for the world population estimate value we will provide NA = not available.

extra <- tibble(year = c(1930, 1940, 2021), 
                world_pop = c(NA, NA, NA))
extra
# A tibble: 3 × 2
   year world_pop
  <dbl> <lgl>    
1  1930 NA       
2  1940 NA       
3  2021 NA       
unpop <- bind_rows(unpop, extra)
unpop
# A tibble: 11 × 3
    year world_pop world_pop_millions
   <dbl>     <dbl>              <dbl>
 1  1950   2536431              2536.
 2  1960   3034950              3035.
 3  1970   3700437              3700.
 4  1980   4458003              4458.
 5  1990   5327231              5327.
 6  2000   6143494              6143.
 7  2010   6956824              6957.
 8  2020   7794799              7795.
 9  1930        NA                NA 
10  1940        NA                NA 
11  2021        NA                NA

Let’s arrange the years in ascending order

unpop <- arrange(unpop, year)
unpop
# A tibble: 11 × 3
    year world_pop world_pop_millions
   <dbl>     <dbl>              <dbl>
 1  1930        NA                NA 
 2  1940        NA                NA 
 3  1950   2536431              2536.
 4  1960   3034950              3035.
 5  1970   3700437              3700.
 6  1980   4458003              4458.
 7  1990   5327231              5327.
 8  2000   6143494              6143.
 9  2010   6956824              6957.
10  2020   7794799              7795.
11  2021        NA                NA

Let’s try computing the summaries again:

unpop %>%
  summarise(Mean = mean(world_pop), 
            SD = sd(world_pop),
            Min = min(world_pop),
            Max = max(world_pop)) %>%
  gt()
Mean SD Min Max
NA NA NA NA

It’s not working anymore! Why is that?

R cannot sum a number with something that isn’t available.

2 + NA
[1] NA

Hence, there are 2 possible solutions:

  1. tell the R functions to ignore the NA values by specifying the argument na.rm = TRUE
  2. subset the data to only keep the rows without NAs
unpop %>%
  summarise(Mean = mean(world_pop, na.rm = TRUE), 
            SD = sd(world_pop, na.rm = TRUE),
            Min = min(world_pop, na.rm = TRUE),
            Max = max(world_pop, na.rm = TRUE)) %>%
  gt()
Mean SD Min Max
4994021 1888599 2536431 7794799 
unpop_clean <- na.omit(unpop)
unpop_clean
# A tibble: 8 × 3
   year world_pop world_pop_millions
  <dbl>     <dbl>              <dbl>
1  1950   2536431              2536.
2  1960   3034950              3035.
3  1970   3700437              3700.
4  1980   4458003              4458.
5  1990   5327231              5327.
6  2000   6143494              6143.
7  2010   6956824              6957.
8  2020   7794799              7795.
unpop_clean %>%
  summarise(Mean = mean(world_pop), 
            SD = sd(world_pop),
            Min = min(world_pop),
            Max = max(world_pop)) %>%
  gt()
Mean SD Min Max
4994021 1888599 2536431 7794799

Graphics

You may also want to display your data visually. We do that using the ggplot package, which is automatically loaded when you load the tidyverse package.

The idea of a ggplot is to build a plot layer by layer. You start by specifying the axes, then add the geometries you want shown (points, lines, columns/bars), and then add labels, and theme options.

Source: Michela Cameletti 
# Specify axes only
ggplot(data = unpop_clean, aes(x = year, y = world_pop))

# Add columns
ggplot(data = unpop_clean, aes(x = year, y = world_pop)) +
  geom_col()

# Change labels
ggplot(data = unpop_clean, aes(x = year, y = world_pop / 1000)) +
  geom_col() + 
  labs(x = 'Year', y = 'World population estimate (in millions)')

# Change theme
ggplot(data = unpop_clean, aes(x = year, y = world_pop / 1000)) +
  geom_col() + 
  labs(x = 'Year', y = 'World population estimate (in millions)') +
  theme_classic()

Alternatively, you could plot the population for each decade as a dot and join the dots with lines:

ggplot(data = unpop_clean, aes(x = year, y = world_pop / 1000)) +
  geom_point(size = 3) + 
  geom_line() +
  labs(x = 'Year', y = 'World population estimate (in millions)') +
  theme_classic()

Note the key components of a ggplot:

  • data = is where we provide the name of the data table

  • aes = where we provide the aesthetics. These are things which we map from the data to the graph. For instance, the x-axis, or if we wanted to colour the columns/bars according to some aspect of the data.

  • Then we add (using +) some geometry. These are the shapes (for instance, points or lines), which will be put in the correct place according to what we specified in aes().

  • + geom_... Adds different shapes (e.g., points) to the plot.

You can find great documentation on ggplot2 at https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/.

Back to top