Tutorial
Getting Started with the R Programming Language
Authors: Leah A. Wasser  Adapted from Software Carpentry
Last Updated: Apr 8, 2021
R is a versatile, open source programming language that was specifically designed for data analysis. R is extremely useful for data management, statistics and analyzing data.
This tutorial should be seem more as a reference on the basics of R and not a tutorial for learning to use R. Here we define many of the basics, however, this can get overwhelming if you are brand new to R.
Learning Objectives
After completing this tutorial, you will be able to:
 Use basic R syntax
 Explain the concepts of objects and assignment
 Explain the concepts of vector and data types
 Describe why you would or would not use factors
 Use basic few functions
Things You’ll Need To Complete This Tutorial
You will need the most current version of R and, preferably, RStudio
loaded
on your computer to complete this tutorial.
Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets.
An overview of setting the working directory in R can be found here.
R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.
The Very Basics of R
R is a versatile, open source programming language that was specifically designed for data analysis. R is extremely useful for data management, statistics and analyzing data.
R is:
 Open source software under a GNU General Public License (GPL).
 A good alternative to commercial analysis tools. R has over 5,000 user contributed packages (as of 2014) and is widely used both in academia and industry.
 Available on all platforms.
 Not just for statistics, but also general purpose programming.
 Supported by a large and growing community of peers.
Introduction to R
You can use R alone or with a user interace like RStudio to write your code. Some people prefer RStudio as it provides a graphic interface where you can see what objects have been created and you can also set variables like your working directory, using menu options.
Learn more about RStudio with their online learning materials.
We want to use R to create code and a workflow is more reproducible. We can document everything that we do. Our end goal is not just to "do stuff" but to do it in a way that anyone can easily and exactly replicate our workflow and results  this includes ourselves in 3 months when the paper reviews come back!
Code & Comments in R
Everything you type into an R script is code, unless you demark it otherwise.
Anything to the right of a #
is ignored by R. Use these comments within the
code to describe what it is that you code is doing. Comment liberally in your R
scripts. This will help you when you return to it and will also help others
understand your scripts and analyses.
# this is a comment. It allows text that is ignored by the program.
# for clean, easy to read comments, use a space between the # and text.
# there is a line of code below this comment
a < 1 + 2
Basic Operations in R
Let's take a few moments to play with R. You can get output from R simply by typing in math
# basic math
3 + 5
## [1] 8
12 / 7
## [1] 1.714286
or by typing words, with the command writeLines()
. Words that you want to be
recognized as text (as opposed to a field name or other text that signifies an
object) must be enclosed within quotes.
# have R write words
writeLines("Hello World")
## Hello World
We can assign our results to an object
and name the object. Objects names
cannot contain spaces.
# assigning values to objects
secondsPerHour < 60 * 60
hoursPerYear < 365 * 24
# object names can't contain spaces. Use a period, underscore, or camelCase to
# create longer names
temp_HARV < 90
par.OSBS < 180
We can then return the value of an object
we created.
secondsPerHour
## [1] 3600
hoursPerYear
## [1] 8760
Or create a new object
with existing ones.
secondsPerYear < secondsPerHour * hoursPerYear
secondsPerYear
## [1] 31536000
The result of the operation on the right hand side of <
is assigned to
an object with the name specified on the left hand side of <
. The result
could be any type of R object, including your own functions (see the
Build & Work With Functions in R tutorial).
Assignment Operator: Drop the Equals Sign
The assignment operator is <
. It assigns values on the right to objects
on
the left. It is similar to =
but there are some subtle differences. Learn to
use <
as it is good programming practice. Using =
in place of <
can lead
to issues down the line.
# this is preferred syntax
a < 1 + 2
# this is NOT preferred syntax
a = 1 + 2
List All Objects in the Environment
Some functions are the same as in other languages. These might be familiar from command line.

ls()
: to list objects in your current environment. 
rm()
: remove objects from your current environment.
Now try them in the console.
# assign value "5" to object "x"
x < 5
ls()
# remove x
rm(x)
# what is left?
ls()
# remove all objects
rm(list = ls())
ls()
Using rm(list=ls())
, you combine several functions to remove all objects.
If you typed x
on the console now you will get Error: object 'x' not found'
.
Data Types and Structures
To make the best of the R language, you'll need a strong understanding of the basic data types and data structures and how to operate on those. These are the objects you will manipulate on a daytoday basis in R. Dealing with object conversions is one of the most common sources of frustration for beginners.
First, everything in R is an object. But there are different types of objects. One of the basic differences in in the data structures which are different ways data are stored.
R has many different data structures. These include
 atomic vector
 list
 matrix
 data frame
 array
These data structures vary by the dimensionality of the data and if they can handle data elements of a simgle type (homogeneous) or multiple types (heterogeneous).
Dimensions  Homogenous  Heterogeneous 

1D  atomic vector  list 
2D  matrix  data frame 
none  array 
Vectors
A vector is the most common and basic data structure in R and is the workhorse of R. Technically, vectors can be one of two types:
 atomic vectors
 lists
although the term "vector" most commonly refers to the atomic types not to lists.
Atomic Vectors
R has 6 atomic vector types.
 character
 numeric (real or decimal)
 integer
 logical
 complex
 raw (not discussed in this tutorial)
By atomic, we mean the vector only holds data of a single type.

character:
"a"
,"swc"

numeric:
2
,15.5

integer:
2L
(theL
tells R to store this as an integer) 
logical:
TRUE
,FALSE

complex:
1+4i
(complex numbers with real and imaginary parts)
R provides many functions to examine features of vectors and other objects, for example

typeof()
 what is it? 
length()
 how long is it? What about two dimensional objects? 
attributes()
 does it have any metadata?
Let's look at some examples:
# assign word "april" to x"
x < "april"
# return the type of the object
class(x)
## [1] "character"
# does x have any attributes?
attributes(x)
## NULL
# assign all integers 1 to 10 as an atomic vector to the object y
y < 1:10
y
## [1] 1 2 3 4 5 6 7 8 9 10
class(y)
## [1] "integer"
# how many values does the vector y contain?
length(y)
## [1] 10
# coerce the integer vector y to a numeric vector
# store the result in the object z
z < as.numeric(y)
z
## [1] 1 2 3 4 5 6 7 8 9 10
class(z)
## [1] "numeric"
A vector is a collection of elements that are most commonly character
,
logical
, integer
or numeric
.
You can create an empty vector with vector()
. (By default the mode is
logical
. You can be more explicit as shown in the examples below.) It is more
common to use direct constructors such as character()
, numeric()
, etc.
x < vector()
# Create vector with a length and type
vector("character", length = 10)
## [1] "" "" "" "" "" "" "" "" "" ""
# create character vector with length of 5
character(5)
## [1] "" "" "" "" ""
# numeric vector length=5
numeric(5)
## [1] 0 0 0 0 0
# logical vector length=5
logical(5)
## [1] FALSE FALSE FALSE FALSE FALSE
# create a list or vector with combine `c()`
# this is the function used to create vectors and lists most of the time
x < c(1, 2, 3)
x
## [1] 1 2 3
length(x)
## [1] 3
class(x)
## [1] "numeric"
x
is a numeric vector. These are the most common kind. They are numeric
objects and are treated as double precision real numbers (they can store
decimal points). To explicitly create integers (no decimal points), add an
L
to each (or coerce to the integer type using as.integer()
.
# a numeric vector with integers (L)
x1 < c(1L, 2L, 3L)
x1
## [1] 1 2 3
class(x1)
## [1] "integer"
# or using as.integer()
x2 < as.integer(x)
class(x2)
## [1] "integer"
You can also have logical vectors.
# logical vector
y < c(TRUE, TRUE, FALSE, FALSE)
y
## [1] TRUE TRUE FALSE FALSE
class(y)
## [1] "logical"
Finally, you can have character vectors.
# character vector
z < c("Sarah", "Tracy", "Jon")
z
## [1] "Sarah" "Tracy" "Jon"
# what class is it?
class(z)
## [1] "character"
#how many elements does it contain?
length(z)
## [1] 3
# what is the structure?
str(z)
## chr [1:3] "Sarah" "Tracy" "Jon"
You can also add to a list or vector
# c function combines z and "Annette" into a single vector
# store result back to z
z < c(z, "Annette")
z
## [1] "Sarah" "Tracy" "Jon" "Annette"
More examples of how to create vectors
 x < c(0.5, 0.7)
 x < c(TRUE, FALSE)
 x < c("a", "b", "c", "d", "e")
 x < 9:100
 x < c(1 + (0 + 0i), 2 + (0 + 4i))
You can also create vectors as a sequence of numbers.
# simple series
1:10
## [1] 1 2 3 4 5 6 7 8 9 10
# use seq() 'sequence'
seq(10)
## [1] 1 2 3 4 5 6 7 8 9 10
# specify values for seq()
seq(from = 1, to = 10, by = 0.1)
## [1] 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5
## [17] 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1
## [33] 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7
## [49] 5.8 5.9 6.0 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.0 7.1 7.2 7.3
## [65] 7.4 7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9
## [81] 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 10.0
You can also get nonnumeric outputs.

Inf
is infinity. You can have either positive or negative infinity. 
NaN
means Not a Number. It's an undefined value.
Try it out in the console.
# infinity return
1/0
## [1] Inf
# non numeric return
0/0
## [1] NaN
Indexing
Vectors have positions, these positions are ordered and can be called using
object[index]
# index
z[2]
## [1] "Tracy"
# to call multiple items (a subset of our data), we can put a vector of which
# items we want in the brackets
group1 < c(1, 4)
z[group1]
## [1] "Sarah" "Annette"
# this is especially useful with a sequence vector
z[1:3]
## [1] "Sarah" "Tracy" "Jon"
Objects can have attributes. Attribues are part of the object. These include:
 names: the field or variable name within the object
 dimnames:
 dim:
 class:
 attributes: this contain metadata
You can also glean other attributelike information such as length()
(works on vectors and lists) or number of characters nchar()
(for
character strings).
# length of an object
length(1:10)
## [1] 10
length(x)
## [1] 3
# number of characters in a text string
nchar("NEON Data Skills")
## [1] 16
Heterogeneous Data  Mixing Types?
When you mix types, R will create a resulting vector that is the least common denominator. The coercion will move towards the one that's easiest to coerce to.
Guess what the following do:
 m < c(1.7, "a")
 n < c(TRUE, 2)
 o < c("a", TRUE)
Were you correct?
n < c(1.7, "a")
n
## [1] "1.7" "a"
o < c(TRUE, 2)
o
## [1] 1 2
p < c("a", TRUE)
p
## [1] "a" "TRUE"
This is called implicit coercion. You can also coerce vectors explicitly using
the as.<class_name>
.
# making values numeric
as.numeric("1")
## [1] 1
# make values charactor
as.character(1)
## [1] "1"
# make values
as.factor(c("male", "female"))
## [1] male female
## Levels: female male
Matrix
In R, matrices are an extension of the numeric or character vectors. They are not a separate type of object but simply an atomic vector with dimensions; the number of rows and columns.
# create an empty matrix that is 2x2
m < matrix(nrow = 2, ncol = 2)
m
## [,1] [,2]
## [1,] NA NA
## [2,] NA NA
# what are the dimensions of m
dim(m)
## [1] 2 2
Matrices in R are by default filled columnwise. You can also use the byrow
argument to specify how the matrix is filled.
# create a matrix. Notice R fills them by columns by default
m2 < matrix(1:6, nrow = 2, ncol = 3)
m2
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
# set the byrow argument to TRUE to fill by rows
m2_row < matrix(c(1:6), nrow = 2, ncol = 3, byrow = TRUE)
m2_row
## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
dim()
takes a vector and transform into a matrix with 2 rows and 5 columns.
Another way to shape your matrix is to bind columns cbind()
or rows rbind()
.
# create vector with 1:10
m3 < 1:10
m3
## [1] 1 2 3 4 5 6 7 8 9 10
class(m3)
## [1] "integer"
# set the dimensions so it becomes a matrix
dim(m3) < c(2, 5)
m3
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 3 5 7 9
## [2,] 2 4 6 8 10
class(m3)
## [1] "matrix" "array"
# create matrix from two vectors
x < 1:3
y < 10:12
# cbind will bind the two by column
cbind(x, y)
## x y
## [1,] 1 10
## [2,] 2 11
## [3,] 3 12
# rbind will bind the two by row
rbind(x, y)
## [,1] [,2] [,3]
## x 1 2 3
## y 10 11 12
Matrix Indexing
We can call elements of a matrix with square brackets just like a vector, except now we must specify a row and a column.
z < matrix(c("a", "b", "c", "d", "e", "f"), nrow = 3, ncol = 2)
z
## [,1] [,2]
## [1,] "a" "d"
## [2,] "b" "e"
## [3,] "c" "f"
# call element in the third row, second column
z[3, 2]
## [1] "f"
# leaving the row blank will return contents of the whole column
# note: the column's contents are displayed as a vector (horizontally)
z[, 2]
## [1] "d" "e" "f"
class(z[, 2])
## [1] "character"
# return the contents of the second row
z[2, ]
## [1] "b" "e"
List
In R, lists act as containers. Unlike atomic vectors, the contents of a list are not restricted to a single mode and can encompass any mixture of data types. Lists are sometimes called generic vectors, because the elements of a list can by of any type of R object, even lists containing further lists. This property makes them fundamentally different from atomic vectors.
A list is different from an atomic vector because each element can be a different type  it can contain heterogeneous data types.
Create lists using list()
or coerce other objects using as.list()
. An empty
list of the required length can be created using vector()
x < list(1, "a", TRUE, 1 + (0 + 4i))
x
## [[1]]
## [1] 1
##
## [[2]]
## [1] "a"
##
## [[3]]
## [1] TRUE
##
## [[4]]
## [1] 1+4i
class(x)
## [1] "list"
x < vector("list", length = 5) ## empty list
length(x)
## [1] 5
#call the 1st element of list x
x[[1]]
## NULL
x < 1:10
x < as.list(x)
Questions:
 What is the class of
x[1]
?  What about
x[[1]]
?
Try it out.
We can also give the elements of our list names, then call those elements with
the $
operator.
# note 'iris' is an example data frame included with R
# the head() function simply calls the first 6 rows of the data frame
xlist < list(a = "Karthik Ram", b = 1:10, data = head(iris))
xlist
## $a
## [1] "Karthik Ram"
##
## $b
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $data
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
# see names of our list elements
names(xlist)
## [1] "a" "b" "data"
# call individual elements by name
xlist$a
## [1] "Karthik Ram"
xlist$b
## [1] 1 2 3 4 5 6 7 8 9 10
xlist$data
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
 What is the length of this object? What about its structure?
 Lists can be extremely useful inside functions. You can “staple” together lots of different kinds of results into a single object that a function can return.
 A list does not print to the console like a vector. Instead, each element of the list starts on a new line.
 Elements are indexed by double brackets. Single brackets will still return a(nother) list.
Factors
Factors are special vectors that represent categorical data. Factors can be
ordered or unordered and are important for modelling functions such as lm()
and glm()
and also in plot()
methods. Once created, factors can only contain
a predefined set values, known as levels.
Factors are stored as integers that have labels associated the unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood. You need to be careful when treating them like strings. Some string methods will coerce factors to strings, while others will throw an error.
 Sometimes factors can be left unordered. Example: male, female.
 Other times you might want factors to be ordered (or ranked). Example: low,
medium, high.  Underlying it's represented by numbers 1, 2, 3.
 They are better than using simple integer labels because factors are what are called self describing. male and female is more descriptive than 1s and 2s. Helpful when there is no additional metadata.
Which is male? 1 or 2? You wouldn't be able to tell with just integer data. Factors have this information built in.
Factors can be created with factor()
. Input is often a character vector.
x < factor(c("yes", "no", "no", "yes", "yes"))
x
## [1] yes no no yes yes
## Levels: no yes
table(x)
will return a frequency table counting the number of elements in
each level.
If you need to convert a factor to a character vector, simply use
as.character(x)
## [1] "yes" "no" "no" "yes" "yes"
To see the integer version of the factor levels, use as.numeric
as.numeric(x)
## [1] 2 1 1 2 2
To convert a factor to a numeric vector, go via a character. Compare
fac < factor(c(1, 5, 5, 10, 2, 2, 2))
levels(fac) ## returns just the four levels present in our factor
## [1] "1" "2" "5" "10"
as.numeric(fac) ## wrong! returns the assigned integer for each level
## [1] 1 3 3 4 2 2 2
## integer corresponds to the position of that number in levels(f)
as.character(fac) ## returns a character string of each number
## [1] "1" "5" "5" "10" "2" "2" "2"
as.numeric(as.character(fac)) ## coerce the character strings to numbers
## [1] 1 5 5 10 2 2 2
In modeling functions, it is important to know what the 'baseline' level is. This
is the first factor, but by default the ordering is determined by alphanumerical
order of elements. You can change this by speciying the levels
(another option
is to use the function relevel()
).
# the default result (because N comes before Y alphabetically)
x < factor(c("yes", "no", "yes"))
x
## [1] yes no yes
## Levels: no yes
# now let's try again, this time specifying the order of our levels
x < factor(c("yes", "no", "yes"), levels = c("yes", "no"))
x
## [1] yes no yes
## Levels: yes no
Data Frames
A data frame is a very important data type in R. It's pretty much the de facto data structure for most tabular data and what we use for statistics.
 A data frame is a special type of list where every element of the list has same length.
 Data frames can have additional attributes such as
rownames()
, which can be useful for annotating data, likesubject_id
orsample_id
. But most of the time they are not used.
Some additional information on data frames:
 Usually created by
read.csv()
andread.table()
.  Can convert to matrix with
data.matrix()
(preferred) oras.matrix()
 Coercion will be forced and not always what you expect.
 Can also create with
data.frame()
function.  Find the number of rows and columns with
nrow(dat)
andncol(dat)
, respectively.  Rownames are usually 1, 2, ..., n.
Manually Create Data Frames
You can manually create a data frame using data.frame
.
# create a dataframe
dat < data.frame(id = letters[1:10], x = 1:10, y = 11:20)
dat
## id x y
## 1 a 1 11
## 2 b 2 12
## 3 c 3 13
## 4 d 4 14
## 5 e 5 15
## 6 f 6 16
## 7 g 7 17
## 8 h 8 18
## 9 i 9 19
## 10 j 10 20
Useful Data Frame Functions

head()
 shown first 6 rows 
tail()
 show last 6 rows 
dim()
 returns the dimensions 
nrow()
 number of rows 
ncol()
 number of columns 
str()
 structure of each column 
names()
 shows thenames
attribute for a data frame, which gives the column names.
See that it is actually a special type of list:
list()
## list()
is.list(iris)
## [1] TRUE
class(iris)
## [1] "data.frame"
Instead of a list of single items, a data frame is a list of vectors!
# see the class of a single variable column within iris: "Sepal.Length"
class(iris$Sepal.Length)
## [1] "numeric"
A recap of the different data types
Dimensions  Homogenous  Heterogeneous 

1D  atomic vector  list 
2D  matrix  data frame 
none  array 
Functions
A function is an R object that takes inputs to perform a task. Functions take in information and may return desired outputs.
output < name_of_function(inputs)
# create a list of 1 to 10
x < 1:10
# the sum of all x
y < sum(x)
y
## [1] 55
Help
All functions come with a help screen. It is critical that you learn to read the
help screens since they provide important information on what the function does,
how it works, and usually sample examples at the very bottom. You can use
help(function)
or more simply ??function
# call up a help search
help.start()
# help (documentation) for a package
??ggplot2
# help for a function
??sum()
You can't ever learn all of R as it is ever changing with new packages and new tools, but once you have the basics and know how to find help to do the things that you want to do, you'll be able to use R in your science.
Sample Data
R comes with sample datasets. You will often find these as the date sets in
documentation files or responses to inquires on public forums like StackOverflow.
To see all available sample datasets you can type in data()
to the console.
Packages in R
R comes with a set of functions or commands that perform particular sets of
calculations. For example, in the equation 1+2
, R knows that the "+" means to
add the two numbers, 1 and 2 together. However, you can expand the capability of
R by installing packages that contain suites of functions and compiled code that
you can also use in your code.