Back to Basics: Refresh Your R Skills

Don’t we all know it? Summer ends and we’ve forgotten where we left off before the break. Has this happened with your R programming? No problem! In this session, we’ll refresh your R skills and give you a boost for the upcoming academic year. On 6th of October, we will cover the technical basics (basic syntax, how to run R scripts, and how to install and load packages), simple data processing (how to import and explore your data), and basic visualization (using base plots and ggplot2).

The technical basics

Before we start looking at how to use R to analyze your data, let’s have a look at the technical basics. We will refresh how to name objects, how to use comments, what a function is and how to find out which arguments it needs. After that, we will shortly look at data types and how to run RScripts and how to install and load packages.

Naming objects

You can use R similar to a calculator. It is possible to just type in numbers into your console and receive results immediately.

9 * 5
[1] 45

Everything that you create (and that exists) in R is an object. To be able to use them efficiently, we can name objects. For that, we use the assignment operator <-.

result <- 9 * 5

If we want to print the value of an object, you can run a line that just contains the object name.

result
[1] 45

Comments

Comments are a programmers friend. They help you to keep an overview of your code, understand what you did (and why) and keep your code readable. In R, you can use the # character to create a comment. You can do this either behind a line of code, above or below it.

# This is a comment
code <- 10 * 3 # I can also add a comment here
# This is also a comment

You can also use comments to exclude specific lines of code from being run. To do that, you “comment out” a line of code. In the following example, we commented the line out that assigns b the new value 20.

a <- 30
b <- 100
# b <- 20 

c <- a * b
c
[1] 3000

Functions and arguments

R offers various in-built functions, i.e., a block of code that runs when the function is used. To use a function, we use its name and provide it with the needed arguments, i.e., the input that the function needs to run. In the following example, we use the function sqrt to calculate the square root of 4.

sqrt(4)
[1] 2

If you want to know more about a function, you can use the ? to access the documentation.

?sqrt()

Vectors and data types

In R, vectors are a commonly used data type. Vectors consists of a series of values which can be connected using the c()-function.

my_vector <- c(1,2,3,4,5) # 1,2,3 are numeric data types
my_vector
[1] 1 2 3 4 5

We can also store characters in a vector. Characters are indicated with " ".

my_second_vector <- c("these","are","characters") # these words are seen as character data type
my_second_vector
[1] "these"      "are"        "characters"

Aside from numeric and character data types, R also allows to use logical data (TRUE vs. FALSE), integer, complex and raw.

You can use various function on vectors (and even subset them). Commonly used are the following:

length(my_vector) # returns the length of the vector
[1] 5
typeof(my_vector) # returns the type of the vector (e.g., character)
[1] "double"
str(my_vector) # returns an overview of the structure of the vector
 num [1:5] 1 2 3 4 5

How to run R scripts

You can run R code either through the command line or by using an R script. Usually, you run the code line by line. An example of an R script can be found in our Programming Café session on IDEs in which we present how RStudio can be used to run R scripts.

How to install and load packages

To install new packages, you can use the command install.packages("PACKAGE"). To import packages and use them in your code, use library(PACKAGE). The following example shows how to install and import ggplot2.

```{r}
install.packages("ggplot2")
library(ggplot2)
```

Simple data analyses

While we cannot cover data analyses in this short session, I want to shortly remind you how to import, explore, slightly manipulate and visualize your data.

How to import your data

You can import your data with the help of various packages. The most straight-forward way is to use one of the following functions:

# Read file in table format (each row is one line in the document)
data <- read.table(file = "material/example.csv", header = TRUE, sep = "\t")

# import file with sep = "\t"
data <- read.delim(file = "material/example.csv") 
# import .csv file
data <- read.csv(file = "material/example.csv")

How to explore your data

You can have a look at your data with various in-built functions. The following overview is taken from the Data Carpentry for SSH workshop and adapted to fit our example:

Size

dim(data) # - returns a vector with the number of rows as the first element, and the number of columns as the second element (the dimensions of the object)
[1] 44  4
nrow(data) # - returns the number of rows
[1] 44
ncol(data) # - returns the number of columns
[1] 4

Content

head(data) #- shows the first 6 rows
    name age  group temperature
1  Peter  11 junior          37
2   Alex  25  adult          41
3 Sandra  43  adult          35
4    Eva  55  adult          38
5   Adam  66 senior          39
6 Marijn   7 junior          41
tail(data) #- shows the last 6 rows
          name age  group temperature
39  Henny-Dani  59  adult          38
40 Jaimy-Henny  54  adult          40
41   Jamie-Ali  63  adult          40
42   Dominique  12 junior          37
43   Jaimy-Sam  12 junior          40
44 Robin-Jaimy  69 senior          37

Names

names(data) #- returns the column names (synonym of colnames() for data.frame objects)
[1] "name"        "age"         "group"       "temperature"

Summary

str(data) #- structure of the object and information about the class, length and content of each column
'data.frame':   44 obs. of  4 variables:
 $ name       : chr  "Peter" "Alex" "Sandra" "Eva" ...
 $ age        : int  11 25 43 55 66 7 78 63 69 63 ...
 $ group      : chr  "junior" "adult" "adult" "adult" ...
 $ temperature: int  37 41 35 38 39 41 38 35 37 41 ...
summary(data) #- summary statistics for each column
     name                age           group            temperature   
 Length:44          Min.   : 1.00   Length:44          Min.   :35.00  
 Class :character   1st Qu.:13.00   Class :character   1st Qu.:36.00  
 Mode  :character   Median :44.00   Mode  :character   Median :38.00  
                    Mean   :39.07                      Mean   :38.05  
                    3rd Qu.:63.00                      3rd Qu.:40.00  
                    Max.   :78.00                      Max.   :41.00

How to use characters as factors

As two categories of our data consists of characters, we might want to make one of them categorical. We can do that by casting them as factors using the as.factor()-function. We address the specific column using $.

data$group <- as.factor(data$group)

How to create a base plot

Creating a base plot in R is as easy as typing plot(data).

plot(data)

For our data, the plot is not really helpful, but you can also focus on one specific column instead using $ to indicate the column name.

plot(data$group)

plot() allows for multiple other arguments to be used. You can find more information on it here.

A handy package to create plots is ggplot2. You can find a manual on how to visualize your data with it here.

Further reading

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