Lesson 1: R Introduction, Working with Datasets

1 Introduction

Welcome to this introduction to R. You will learn how to enter data and to perform some basic operations. R is a programming language that is used to work with data. It can do lots of things, including basic arithmetic.

First, let’s try using R. In the code chunk right below this, try typing in a basic math problem, like 35*10. Click the green run code button and see what happens.

The rest of this tutorial will teach you how to work with data in R. It is interactive, so you’ll be able to write your own code in code chunks like the one above throughout the tutorial. See the home page for more details on using the tutorials and for troubleshooting tips!

There are also hints and answers for most code chunks. Try to do each problem on your own, but feel free to use the hints and answers if you get stuck and to check your work!

2 Working with data

R is a program for dealing with data. In Biology courses, most of the time your data will be in the form of a table with columns and rows.

We will start by working with a very simple set of data: data for a single variable. Later on, you will learn how to import your own data table into R in order to work with it.

For example, suppose you have measured the diameters of a sample of 8 bacterial colonies. The data values (in mm) are: 4, 3.5, 6.1, 2.2, 4.7, 3, 5.2, and 4.6.

2.1 Assigning values to a variable name

In R, you ‘assign’ these values to a variable name using an ‘arrow’ formed by the ‘less than’ symbol and the dash, like this: <-.

On the left side of the arrow, put the name of the variable you wish to create. The name should be short, descriptive, and must not contain any spaces.

On the right of the arrow, type c(your data values here). The c stands for ‘concatenate’ or ‘combine’ all the values into a single vector of values. In the parentheses, type your data values, separated by commas.

This command creates a variable called ‘diam’ that contains our example data values:

diam <- c(4, 3.5, 6.1, 2.2, 4.7, 3, 5.2, 4.6)

Now you try creating the diam variable using these same data. In the code chunk below, type in a command to create diam.

If you want to see what a variable contains, you can just type its name. R is case-sensitive, so be careful about whether you type in upper or lower case.

Try typing diam and see what you get.

2.2 Importing data

Next, we’ll learn how to import and use large amounts of data. Specifically, we’ll use R to view our data as a dataframe. A dataframe is just the R version of a data table.

We’ll start with a .csv file. We can then use R to convert the .csv file into a dataframe, which will be much easier to work with.

The command we’ll use is: myDataFrame <- read.csv("myCSVFile.csv").

  • On the left side of the arrow, you’ll create a name for your dataframe and write that instead of myDataFrame.

  • On the right side of the arrow, in the quotation marks, you’ll write the name of your CSV file.

We have pre-loaded a CSV file into this tutorial. It is called “physiology_data.csv”. Let’s try converting the CSV file into a dataframe in the code chunk below. We’ll name the dataframe data.

The name of your dataframe should not be in quotes, but the name of the .csv file should be in quotes.

__________ <- read.csv("physiology_data.csv")

data <- read.csv("physiology_data.csv")

2.3 Our data

Now, let’s check if you did it right. In R, you can type the name of a dataframe to see it displayed. Try typing data into the code chunk below.

You can see that this dataframe contains 4 variables and 15 rows. (Also, each row is numbered sequentially; these numbers are not considered a variable.)

These data are from a physiological study. Each individual’s respiratory quotient (RQ) was measured, either after exercise (1), or during rest (2). Their heart rates in beats per minute were also measured. NA stands for ‘not available,’ meaning that the data for these combinations of individual and variable were lost or not recorded.

2.4 Using data tables

2.4.1 Dataframe set up:

  • In an R dataframe, each column represents a different variable.

  • Each observational unit is represented by a separate row in the table.

  • The first row of the table contains the names of the different variables. In R, variable names cannot contain any spaces.

2.4.2 Specifying Variables

You might wish to see just one of the variables in this data file. The way to specify an individual variable is to use the name of the data file, followed by the dollar sign, $, followed by the name of the variable with no spaces.

The syntax is: yourDataName$variableName

What would you type to see a list of the heart_rate data values in this file?

data$heart_rate

2.5 Data Structure

In R, each variable is encoded as either numeric (a number), integer (a whole number), a factor (a category, expressed either as a word or a number), or character (a string of letters). There are other less common variable properties as well. A variable’s property is important because some operations can only be carried out on variables with certain properties.

A data file’s ‘structure’ refers to the properties of its variables. To find out the properties of all the variables in a data set, you would use the command str(yourDataName), using the name of your data file in place of the placeholder here.

Write a command that will allow you to see the structure of data.

str(data)

2.5.1 Changing a variable’s property

You can see that the variable data$Condition is currently encoded as an integer. But it is actually a category variable.

You can change a variable’s property with the assignment arrow, in the following way: yourDataName$variableName <- as.variableType(yourDataName$variableName).

For example, to change the property of the Condition variable to a factor variable, you can run: data$Condition <- as.factor(data$Condition).

Try changing the variable named Individual to a factor variable. You will notice that a little prompt box shows up. You can save yourself some typing by choosing the rest of the expression from among the choices in the prompt box.

Run the structure command again to see if Individual has been changed to a factor variable.

Remember to use the dollar sign notation, and that R is case-sensitive.

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

3 R Packages

R has a set of commands that it understands automatically. But, there are other commands that you can teach R. These “extra” commands live in packages that you can install and then use when you code with R. Essentially, a package contains a set of commands (and sometimes data) someone created that you can use in R, and in order to use the package, you have to install it.

Throughout this tutorial, we’ve been using something called the tidyverse package to run commands. It contains helpful commands for viewing and rearranging data as well as graphing data (which we’ll get to in future tutorials).

tidyverse was pre-installed for you, but if you ever have to install a package in the future, run the command:

install.packages("tidyverse")

After installing the package, you can load it using the command:

library(tidyverse)

In this tutorial, you won’t need to worry about installing or loading packages, because they are pre-loaded for you. But when you start working on your own data outside of these tutorials, you will sometimes need to tell R to load certain packages.

4 Congratulations!

You have finished the first R tutorial. You have learned how to create a variable in R, import data and create a dataframe, and how to examine and change the properties of different variables.