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Your hands-on guide

2 Introduction

17 Painless data

6 Getting your
data into R
10 Easy ways to do
basic data analysis

26 Syntax quirks youll

want to know
33 Useful resources

by Sharon Machlis
edited by Johanna Ambrosio

R: a beginners guide

R is hot. Whether measured by more than
6,100 add-on packages, the 41,000+ members of LinkedIns R group or the 170+ R
Meetup groups currently in existence, there
is soaring.

as easy to run multiple data sets through

spreadsheet formulas to check results as it
is to put several data sets through a script,
he explains.
Indeed, the mantra of Make sure your
work is reproducible! is a common theme
among R enthusiasts.

Why R? Its free, open source, powerful and

highly extensible. You have a lot of prepackaged stuff thats already available, so
youre standing on the shoulders of giants,
Googles chief economist told The New York
Times back in 2009.

Who uses R?
Relatively high-profile users of R include:
Facebook: Used by some within the company for tasks such as analyzing user

Because its a programmable environment

that uses command-line scripting, you can
store a series of complex data-analysis
you were using a point-and-click interface,
notes Hadley Wickham, author of several
popular R packages and chief scientist with

Google: There are more than 500 R users

at Google, according to David Smith at
Revolution Analytics, doing tasks such as
making online advertising more effective.
National Weather Service: Flood forecasts.
Orbitz: Statistical analysis to suggest best
hotels to promote to its users.

for errors an issue that cropped up in the
news recently after an Excel coding error

Trulia: Statistical modeling.

Source: Revolution Analytics
Why not R? Well, R can appear daunting at
first. Thats often because R syntax is different from that of many other languages,
not necessarily because its any more difficult than others.

The error itself wasnt a surprise, blogs

Despite our best efforts we always will
make errors, he notes. The problem is that
we often use tools and practices that make
it difficult to find and correct our mistakes.

I have written software professionally in

perhaps a dozen programming languages,
and the hardest language for me to learn
has been R, writes consultant John D.
Cook in a Web post about R programming
for those coming from other languages.
The language is actually fairly simple, but
it is unconventional.

Sure, you can easily examine complex formulas on a spreadsheet. But its not nearly

R: a beginners guide

And so, this guide. Our aim here isnt R

mastery, but giving you a path to start
using R for basic data work: Extracting key
statistics out of a data set, exploring a data
set with basic graphics and reshaping data
to make it easier to analyze.

code editor allowing you to create a file

with multiple lines of R code or open an
existing file and then run the entire file
or portions of it.
Bottom left is the interactive console where
you can type in R statements one line
at a time. Any lines of code that are run
from the editor window also appear in the

Your first step

To begin using R, head to to
download and install R for your desktop or
laptop. It runs on Windows, OS X and a
wide variety of Unix platforms, but not yet
on Android or iOS.

The top right window shows your workspace, which includes a list of objects currently in memory. Theres also a history tab
with a list of your prior commands; whats
handy there is that you can select one,
some or all of those lines of code and oneclick to send them either to the console or
to whatever file is active in your code editor.

Installing R is actually all you need to get

started. However, Id suggest also installing
the free R integrated development environment (IDE) RStudio. Its got useful features
youd expect from a coding platform, such
as syntax highlighting and tab for suggested code auto-completion. I also like its
four-pane workspace, which better manages multiple R windows for typing commands, storing scripts, viewing command
histories, viewing visualizations and more.

The window at bottom right shows a plot

if youve created a data visualization with
your R code. Theres a history of previous
plots and an option to export a plot to an
image file or PDF. This window also shows
external packages (R extensions) that are
available on your system, files in your working directory and help files when called
from the console.

Learning the shortcuts

Wickham, the RStudio chief scientist, says
these are the three most important keyboard shortcuts in RStudio:
NN Tab is a generic auto-complete function.
If you start typing in the console or editor
and hit the tab key, RStudio will suggest
functions or file names; simply select the
one you want and hit either tab or enter
to accept it.
Although you dont need the free RStudio IDE to
get started, it makes working with R much easier.

NN Control + the up arrow (command +

up arrow on a Mac) is a similar autocomplete tool. Start typing and hit that
key combination, and it shows you a list
of every command youve typed starting

The top left window is where youll probably do most of your work. Thats the R

R: a beginners guide

with those keys. Select the one you want

and hit return. This works only in the
interactive console, not in the code editor

If you dont want to type the command,

in RStudio theres a Packages tab in the
lower right window; click that and youll
see a button to Install Packages. (Theres
also a menu command; the location varies
depending on your operating system.)

NN Control + enter (command + enter on

a Mac) takes the current line of code in
the editor, sends it to the console and
executes it. If you select multiple lines of
code in the editor and then hit ctrl/cmd +
enter, all of them will run.

To see which packages are already installed

on your system, type:
Or, in RStudio, go to the Packages tab in
the lower right window.

For more about RStudio features, including

a full list of keyboard shortcuts, head to the
online documentation.

To use a package in your work once its

installed, load it with:

Setting your working


If youd like to make sure your packages
stay up to date, you can run:

Change your working directory with the

setwd() function, such as:

and get the latest versions for all your
installed packages.

Note that the slashes always have to be forward slashes, even if youre on a Windows
system. For Windows, the command might
look something like:

If you no longer need or want a package on

your system, use the function:



If you are using RStudio, you can also use

the menu to change your working directory
under Session > Set Working Directory.

If you want to find out more about a function, you can type a question mark followed
by the function name one of the rare
times parentheses are not required in R,
like so:

Installing and using packages

Chances are if youre going to be doing,
well, pretty much anything in R, youre
going to want to take advantage of some of
the thousands of add-on packages available for R at CRAN, the Comprehensive R
Archive Network. The command for installing a package is:

This is a shortcut to the help function,
which does use parentheses:
Although Im not sure why youd want to
use this as opposed to the shorter ?functionName command.


R: a beginners guide

If you already know what a function does

and just want to see formats for using it
properly, you can type:
and youll get a list with examples of the
function being used, if theres one available.
The arguments (args) function:
just displays a list of a functions
If you want to search through Rs help
documentation for a specific term, you can
use: search term)
That also has a shortcut:
??(my search term)
No parentheses are needed if the search
term is a single word without spaces.

R: a beginners guide

Get your data into R

Once youve installed and configured R
to your liking, its time to start using it to
work with data. Yes, you can type your data
directly into Rs interactive console. But
for any kind of serious work, youre a lot
more likely to already have data in a file
somewhere, either locally or on the Web.
Here are several ways to get data into R for
further work.


Existing local data

R has a function dedicated to reading
comma-separated files. To import a local
CSV file named filename.txt and store the
data into one R variable named mydata, the
syntax would be:

Sample data

mydata <- read.csv(filename.txt)

If you just want to play with some test data

R comes with several data sets. Type:

(Aside: Whats that <- where you expect to

see an equals sign? Its the R assignment
operator. I said R syntax was a bit quirky.
More on this in the section on R syntax


And if youre wondering what kind of object

is created with this command, mydata is
an extremely handy data type called a data
frame basically a table of data. A data
frame is organized with rows and columns,
similar to a spreadsheet or database table.

into the R console and youll get a listing

of pre-loaded data sets. Not all of them are
try analysis and plotting commands. And
some online tutorials use these sample sets.

The read.csv function assumes that your

file has a header row, so row 1 is the name
of each column. If thats not the case, you
can add header=FALSE to the command:

One of the less esoteric data sets is mtcars,

data about various automobile models that
come from Motor Trends. (Im not sure
from what year the data are from, but given
that there are entries for the Valiant and
Duster 360, Im guessing theyre not very
recent; still, its a bit more compelling than
whether beavers have fevers.)

mydata <- read.csv(filename.txt,

not column headers (and assigns default
column header names you can change

Youll get a printout of the entire data set if

you type the name of the data set into the
console, like so:

more general read.table function. So if your
separator is a tab, for instance, this would

There are better ways of examining a data
set, which Ill get into later in this series.
Also, R does have a print() function for

R: a beginners guide

mydata <- read.table(filename.txt,

sep=\t, header=TRUE)
The command above also indicates theres
a header row in the file with header=TRUE.
If, say, your separator is a character such
as | you would change the separator part of
the command to sep=|
Categories or values? Because of Rs roots
as a statistical tool, when you import nonnumerical data, R may assume that character strings are statistical factors things
like poor, average and good or
success and failure.

UCLA statistics grad student Miles Chen

shows an RStudio point-and-click data import.

But your text columns may not be categories that you want to group and measure,
just names of companies or employees. If
you dont want your text data to be read in
as factors, add stringsAsFactor=FALSE to
read.table, like this:

If youve got just a small section of data

already in a table a spreadsheet, say, or a
Web HTML table you can control-C copy
those data to your Windows clipboard and
import them into R.

mydata <- read.table(filename.

txt, sep=\t, header=TRUE,

The command below handles clipboard

data with a header row thats separated by
tabs, and stores the data in a data frame

Copying data snippets

If youd prefer, R allows you to use a series

of menu clicks to load data instead of reading data from the command line as just
described. To do this, go to the Workspace
tab of RStudios upper-right window, find
the menu option to Import Dataset, then
choose a local text file or URL.

x <- read.table(file = clipboard,

sep=\t, header=TRUE)
You can read more about using the Windows clipboard in R at the R For Dummies
On a Mac, the pipe (pbpaste) function
will access data youve copied with command-c, so this will do the equivalent of the
previous Windows command:

As data are imported via menu clicks,

the R command that RStudio generated
from your menu clicks will appear in your
console. You may want to save that datareading command into a script file if youre
using this for significant analysis work, so
that others or you can reproduce that

x <- read.table(pipe(pbpaste),

Other formats

This 3-minute YouTube video, recorded by

UCLA statistics grad student Miles Chen,
shows an RStudio point-and-click data

There are R packages that will read files

from Excel, SPSS, SAS, Stata and various
relational databases. I dont bother with
the Excel package; it requires both Java

R: a beginners guide

and Perl, and in general Id rather export a

spreadsheet to CSV in hopes of not running
into Microsoft special-character problems. For more info on other formats, see
UCLAs How to input data into R which
discusses the foreign add-on package for
importing several other statistical software
file types.

Center data about mobile shopping are

available as a CSV file for download. You
can store the data in a variable called pew_
data like this:
pew_data <- read.csv(http://bit.
Its important to make sure the file youre
downloading is in an R-friendly format
first: in other words, that it has a maximum
of one header row, with each subsequent
row having the equivalent of one data
record. Even well-formed government data
might include lots of blank rows followed
by footnotes -- thats not what you want in
an R data table if you plan on running statistical analysis functions on the file.

If youd like to try to connect R with a database, there are several dedicated packages
such as RPostgreSQL, RMySQL, RMongo,
RSQLite and RODBC.
(You can see the entire list of available R
packages at the CRAN website.)

Remote data

Help with external data

read.csv() and read.table() work pretty

much the same to access files from the Web
as they do for local data.

R with a minimum of fuss.

Do you want Google Spreadsheets data in

R? You dont have to download the spreadsheet to your local system as you do with a
CSV. Instead, in your Google spreadsheet
properly formatted with just one row for
headers and then one row of data per line
select File > Publish to the Web. (This
will make the data public, although only to
someone who has or stumbles upon the
correct URL. Beware of this process, especially with sensitive data.)

For instance, the financial analysis package Quantmod, developed by quantitative

software analyst Jeffrey Ryan, makes it easy
to not only pull in and analyze stock prices
but graph them as well.
All you need are four short lines of code
to install the Quantmod package, load it,
retrieve a companys stock prices and then
chart them using the barChart function.

Select the sheet with your data and click

Start publishing. You should see a box
with the option to get a link to the published data. Change the format type from
Web page to CSV and copy the link. Now
you can read those data into R with a command such as:


mydata <- read.csv(http://bit.


Want to see just the last couple of weeks?

You can use a command like this:

The command structure is the same for any

file on the Web. For example, Pew Research

barChart(AAPL, subset=last 14 days)

R: a beginners guide

chartSeries(AAPL, subset=last 14

save the script file when youre finished. If

you havent been doing that, you can find a
history of all the commands youve typed
in the history tab in the top right window;
select the ones you want and click the to
source menu option to copy them into a
file in the script window for saving.

Or grab a particular date range like this:

Quantmod is a very powerful financial
analysis package, and you can read more
about it on the Quantmod website.

You can also save your entire workspace.

While youre in R, use the function:

There are many other packages with R

and rdatamarket for access to millions
of data sets at Quandl and Data Market,

That stores your workspace to a file named
.RData by default. This will ensure you
dont lose all your work in the event of a
power glitch or system reboot while youve
stepped away.
When you close R, it asks if you want to
save your workspace. If you say yes, the
next time you start R that workspace will
be loaded. That saved file will be named
.RData as well. If you have different projects
in different directories, each can have its
own .RData workspace file.

Looking for a specific type of data to pull

into R but dont know where to find it? You
can try searching Quandl and Datamarket,
where data can be downloaded in R format
even without needing to install the sitespecific packages mentioned above.

You can also save an individual R object for

later loading with the save function:

Removing unneeded data

save(variablename, file=filename.

If youre finished with variable x and want

to remove it from your workspace, use the
rm() remove function:

Reload it at any time with:



Saving your data

Once youve read in your data and set up
your objects just the way you want them,
you can save your work in several ways. Its
a good idea to store your commands in a
script file, so you can repeat your work if
How best to save your commands? You
can type them first into the RStudio script
editor (top left window) instead of directly
into the interactive console, so you can

R: a beginners guide

Easy ways to do basic

data analysis
So youve read your data into an R object.
Now what?

tail(mydata, 10)

Examine your data object

Tail can be useful when youve read in data

from an external source, helping to see if
anything got garbled (or there was some
footnote row at the end you didnt notice).

Before you start analyzing, you might

want to take a look at your data objects
structure and a few row entries. If its a
2-dimensional table of data stored in an R
data frame object with rows and columns
one of the more common structures youre
likely to encounter here are some ideas.
Many of these also work on 1-dimensional
vectors as well.

This will tell you the type of object you
have; in the case of a data frame, it will
also tell you how many rows (observations
of data in each column and the first few
entries in each column.

Many of the commands below assume that

your data are stored in a variable called
mydata (and not that mydata is somehow
part of these functions names).
If you type:
R will display mydatas column headers and
first 10 rows instead of 6? Thats:

Results of the str() function on the

sample data set PlantGrowth.
For a vector, str() tells you how many items
there are for 8 items, itll display as [1:8]
along with the type of item (number,
character, etc.) and the first few entries.

head(mydata, n=10)
Or just:
head(mydata, 10)

Various other data types return slightly different results.

Note: If your object is just a 1-dimensional

first 6 items in the vector.

If you want to see just the column names in

the data frame called mydata, you can use
the command:

To see the last few rows of your data, use

the tail() function:




R: a beginners guide

Likewise, if youre interested in the row

names in essence, all the values in the
first column of your data frame use:

load the psych package. Install it with this



You need to run this install only once on a

system. Then load it with:

Pull basic stats from your

data frame

You need to run the library command each
time you start a new R session if you want
to use the psych package.

Because R is a statistical programming

platform, its got some pretty elegant ways
to extract statistical summaries from data.
To extract a few basic stats from a data
frame, use the summary() function:

Now try the command:

and youll get several more statistics
from the data including standard deviation, mad (mean absolute deviation),
skew (measuring whether or not the data
distribution is symmetrical) and kurtosis
(whether the data have a sharp or flatter
peak near its mean).


R has the statistical functions youd expect,

including mean(), median(), min(), max(),
sd() [standard deviation], var() [variance] and range()which you can run on a
1-dimensional vector of numbers. (Several
of these functions such as mean() and
median() will not work on a 2-dimensional data frame).

Results of the summary function on

a data set called diamonds, which is
included in the ggplot2 add-on package.

Oddly, the mode() function returns information about data type instead of the
statistical mode; theres an add-on package,
modeest, that adds a mfv() function (most
frequent value) to find the statistical mode.

That returns some basic calculations for

each column. If the column has numbers,
youll see the minimum and maximum values along with median, mean, 1st quartile
and 3rd quartile. If its got factors such as
fair, good, very good and excellent, youll
get the number of each factor listed in the

R also contains a load of more sophisticated functions that let you do analyses
with one or two commands: probability distributions, correlations, significance tests,
regressions, ANOVA (analysis of variance
between groups) and more.

The summary() function also returns stats

for a 1-dimensional vector.

As just one example, running the correlation function cor() on a dataframe such as:

If youd like even more statistical summaries from a single command, install and


R: a beginners guide

trim argument for mean() that lets you

exclude some outliers.

will give you a matrix of correlations for
each column of numerical data compared
with every other column of numerical data.

Not all R functions need a robust data

set to be useful for statistical work. For
example, how many ways can you select a
committee of 4 people from a group of 15?
You can pull out your calculator and find 15!
divided by 4! times 11! ... or you can use the
R choose() function:

Results of the correlation function on

the sample data set of U.S.arrests.

Or, perhaps you want to see all of the possible pair combinations of a group of 5
people, not simply count them. You can
create a vector with the peoples names and
store it in a variable called mypeople:

Note: Be aware that you can run into problems when trying to run some functions
on data where there are missing values. In
some cases, Rs default is to return NA even
if just a single value is missing. For example, while the summary() function returns
column statistics excluding missing values
(and also tells you how many NAs are in
the data), the mean() function will return
NA if even only one value is missing in a

mypeople <- c(Bob, Joanne,

Sally, Tim, Neal)
In the example above, c() is the combine
Then run the combn() function, which
takes two arguments your entire set first
and then the number you want to have in
each group:

In most cases, adding the argument:

to NA-sensitive functions will tell that function to remove any NAs when performing
calculations, such as:

combn(mypeople, 2)

mean(myvector, na.rm=TRUE)
If youve got data with some missing values,
read a functions help file by typing a question mark followed by the name of the function, such as:

Use the combine function to see all possible combinations from a group.

The function description should say

whether the na.rm argument is needed to
exclude missing values.

Probably most experienced R users would

combine these two steps into one like this:

Checking a functions help files even for

simple functions can also uncover additional useful options, such as an optional

combn(c(Bob, Joanne, Sally,

Tim, Neal),2)


R: a beginners guide

But separating the two can be more readable for beginners.

That will give you a 1-dimensional vector of

numbers like this:
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4
22.8 19.2 17.8

Get slices or subsets of your


[12] 16.4 17.3 15.2 10.4 10.4 14.7 32.4 30.4

33.9 21.5 15.5

Maybe you dont need correlations for

every column in your data frame and you
just want to work with a couple of columns,
not 15. Perhaps you want to see data that
meets a certain condition, such as within 3
standard deviations. R lets you slice your
data sets in various ways, depending on the
data type.

[23] 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7

15.0 21.4
The numbers in brackets are not part of
your data, by the way. They indicate what
item number each line is starting with. If
youve only got one line of data, youll just
see [1]. If theres more than one line of data
and only the first 11 entries can fit on the
first line, your second line will start with
[12], and so on.

To select just certain columns from a data

frame, you can either refer to the columns
by name or by their location (i.e., column 1,
2, 3, etc.).

Sometimes a vector of numbers is exactly

what you want if, for example, you want
to quickly plot mtcars$mpg and dont need
item labels, or youre looking for statistical
info such as variance and mean.

For example, the mtcars sample data frame

has these column names: mpg, cyl, disp, hp,
drat, wt, qsec, vs, am, gear and carb.
Cant remember the names of all the columns in your data frame? If you just want
to see the column names and nothing else,
instead of functions such as str(mtcars)
and head(mtcars) you can type:

Chances are, though, youll want to subset your data by more than one column
at a time. Thats when youll want to use
bracket notation, what I think of as rowscomma-columns. Basically, you take the
name of your data frame and follow it by
[rows,columns]. The rows you want come
first, followed by a comma, followed by the
columns you want. So, if you want all rows
but just columns 2 through 4 of mtcars,
you can use:

Thats handy if you want to store the names
in a variable, perhaps called mtcars.colnames (or anything else youd like to call
mtcars.colnames <- names(mtcars)


But back to the task at hand. To access

only the data in the mpg column in mtcars,
you can use Rs dollar sign notation:

Do you see that comma before the 2:4?

Thats leaving a blank space where the
which rows do you want? portion of the
bracket notation goes, and it means Im
not asking for any subset, so return all.
Although its not always required, its not a
bad practice to get into the habit of using
a comma in bracket notation so that you

More broadly, then, the format for accessing a column by name would be:


R: a beginners guide

remember whether you were slicing by columns or rows.


To turn that into a listing of the data you
want, use that logical test condition and
row-comma-column bracket notation.
Remember that this time you want to select
rows by condition, not columns. This:

If you want multiple columns that arent

contiguous, such as columns 2 AND 4 but
not 3, you can use the notation:


A couple of syntax notes here:

tells R to get all rows from mtcars where

mpg > 20, and then to return all the

R indexes from 1, not 0. So your first column is at [1] and not [0].
R is case sensitive everywhere. mtcars$mpg
is not the same as mtcars$MPG.

If you dont want to see all the column data

for the selected rows but are just interested
in displaying, say, mpg and horsepower
for cars with an mpg greater than 20, you
could use the notation:

mtcars[,-1] will not get you the last column

of a data frame, the way negative indexing
works in many other languages. Instead,
negative indexing in R means exclude that
item. So, mtcars[,-1] will return every column except the first one.

using column locations, or:

To create a vector of items that are not contiguous, you need to use the combine function c(). Typing mtcars[,(2,4)] without the
c will not work. You need that c in there:

using the column names.
Why do you need to specify mtcars$mpg
in the row spot but mpg in the column
spot? Just another R syntax quirk is the
best answer I can give you.

What if want to select your data by data
characteristic, such as all cars with mpg >
20, and not column or row location? If you
use the column name notation and add a
condition like:

If youre finding that your selection statement is starting to get unwieldy, you can
put your row and column selections into
variables first, such as:


mpg20 <- mtcars$mpg > 20

you dont end up with a list of all rows

where mpg is greater than 20. Instead, you
get a vector showing whether each row
meets the condition, such as:

cols <- c(mpg, hp)

Then you can select the rows and columns
with those variables:



mtcars[mpg20, cols]
making for a more compact select statement but more lines of code.



Getting tired of including the name of the

data set multiple times per command? If
youre using only one data set and you




R: a beginners guide

are not making any changes to the data

that need to be saved, you can attach and
detach a copy of the data set temporarily.

What if you wanted to find the row with the

highest mpg?
subset(mtcars, mpg==max(mpg))

The attach() function works like this:


If you just wanted to see the mpg information for the highest mpg:

So, instead of having to type:

subset(mtcars, mpg==max(mpg), mpg)

mpg20 <- mtcars$mpg > 20

If you just want to use subset to extract

some columns and display all rows, you can
either leave the row conditional spot blank
with a comma, similar to bracket notation:

You can leave out the data set reference and

type this instead:
mpg20 <- mpg > 20

subset(mtcars, , c(mpg, hp))

After using attach() remember to use the

detach function when youre finished:

Or, indicate your second argument is for

columns with select= like this:


subset(mtcars, select=c(mpg, hp))

Some R users advise avoiding attach()

because it can be easy to forget to detach().
If you dont detach() the copy, your variables could end up referencing the wrong
data set.

Update: The dplyr package, released in early

2014, is aimed at making manipulation of
data frames faster and more rational, with
similar syntax for a variety of tasks. To
select certain rows based on specific logical
criteria, youd use the filter() function with
the syntax filter(dataframename, logical expression). As with subset(), column
names stand alone after the data frame
name, so mpg>20 and not mtcars$mpg >

Alternative to bracket
Bracket syntax is pretty common in R code,
but its not your only option. If you dislike
that format, you might prefer the subset()
function instead, which works with vectors
and matrices as well as data frames. The
format is:

filter(mtcars, mpg>20)
To choose only certain columns, you use
the select() function with syntax such as
select(dataframename, columnName1,
columnName2). No quotation marks are
needed with the column names:

subset(your data object, logical

condition for the rows you want to
return, select statement for the columns you want to return)

select(mtcars, mpg, hp)

So, in the mtcars example, to find all rows

where mpg is greater than 20 and return
only those rows with their mpg and hp data,
the subset() statement would look like:

You can also combine filter and subset with

the dplyr %>% chaining operation
that allows you to string together multiple
commands on a data frame. The chaining
syntax in general is:

subset(mtcars, mpg>20, c(mpg,


dataframename %>%

R: a beginners guide

for first function) %>%

secondfunction(argument for second
function) %>% thirdfunction(argument
for third function)
So viewing just mpg and hp for rows where
mpg is greater than 20:
mtcars %>% filter(mpg > 20) %>%
select(mpg, hp)

Rs table function returns a count

of each factor in your data.

No need to keep repeating the data frame

name. To order those results from highest
to lowest mpg, add the arrange() function
to the chain with desc(columnName) for
descending order:

If you are interested in learning more about

statistical functions in R and how to slice
and dice your data, there are a number of
free academic downloads with many more
details. These include Learning statistics
with R by Daniel Navarro at the University of Adelaide in Australia (500+ page
PDF download, may take a little while).
And although not free, books such as
The R Cookbook and R in a Nutshell have
a lot of good examples and well-written

mtcars %>% filter(mpg > 20)

%>% select(mpg, hp) %>%
You can find out more about dplyr in the
dplyr packages introduction vignette.

Counting factors
To tally up counts by factor, try the table
command. For the diamonds data set, to
see how many diamonds of each category
of cut are in the data, you can use:
This will return how many diamonds of
each factor fair, good, very good, premium and ideal exist in the data. Want to
see a cross-tab by cut and color?
table(diamonds$cut, diamonds$color)


R: a beginners guide

Painless data visualization

One of the most appealing things about R
is its ability to create data visualizations
with just a couple of lines of code.

If you find having the y-axis labels rotated

90 degrees annoying (as I do), you can
position them for easier reading with the
las=1 argument:

For example, it takes just one line of code

and a short one at that to plot two
variables in a scatterplot. Lets use as an
example the mtcars data set installed with
R by default. To plot the engine displacement column disp on the x axis and mpg on

plot(mtcars$disp, mtcars$mpg,
xlab=Engine displacement,
ylab=mpg, main=MPG vs engine displacement, las=1)

plot(mtcars$disp, mtcars$mpg)

Adding a main headline and

axes labels to an R plot.
Default scatterplot in R.

Whats las and why is it 1? las refers to label

style, and its got four options. 0 is the
default, with text always parallel to its axis.
For much more on plot parameters, run the
help command on par like so:

You really cant get much easier than that.

Of course thats a pretty no-frills graphic. If
youd like to label your x and y axes, use the
parameters xlab and ylab. To add a main
headline, such as Page views by time of
day, use the parameter main:


plot(mtcars$disp, mtcars$mpg,
xlab=Engine displacement,
ylab=mpg, main=MPG compared with
engine displacement)

numerous add-on packages to expand Rs
visualization capabilities. Some packages
are for specific disciplines such as biosta17

R: a beginners guide

tistics or finance; others add general visualization features.

Why use an add-on package if you dont
need something discipline-specific? If
youre doing more complex dataviz, or want
options. Another reason: The organization
and syntax of an add-on package might
appeal to you more than do the R defaults.

Using ggplot2
In particular, the ggplot2 package is quite
learn its Grammar of Graphics approach.

A scatterplot from ggplot2

using the qplot() function.

But once youve got that down, you have a


The qplot default starts the y axis at a value

that makes sense to R. However, you might
want your y axis to start at 0 so you can
better see whether changes are truly meaningful (starting a graphs y axis at your first
value instead of 0 can sometimes exaggerate changes).

If ggplot2 isnt installed on your system yet,

install it with the command:
You only need to do this once.

Use the ylim argument to manually set your

lower and upper y axis limits:

To use its functions, load the ggplot2 package into your current R session you only
need to do this once per R session with
the library() function:


Bonus intermediate tip: Sometimes on a
scatterplot you may not be sure if a point
represents just one observation or multiple
ones, especially if youve got data points
that repeat such as in this example that
ggplot2 creator Hadley Wickham generated
with the command:

Onto some ggplot2 examples.
ggplot2 has a quick plot function called
qplot() that is similar to Rs basic plot()
function but adds some options. The basic
quick plot code:
qplot(disp, mpg, data=mtcars)
generates a scatterplot.


R: a beginners guide

the data set youre plotting. But whats with

aes() and geom_point()?
aes stands for aesthetics what are
considered visual properties of the graph.
Those are things like position in space,
color and shape.
geom is the graphing geometry youre
using, such as lines, bars or the shapes of
your points.
Now if line and bar also seem like
aesthetic properties to you, similar to
shape, well, you can either accept thats
how it works or do some deep reading into
the fundamentals behind the Grammar of
Graphics. (Personally, I just take Wickhams word for it.)
Want a line graph instead? Simply swap
out geom_point() and replace it with geom_
line() , as in this example that plots temperature vs pressure in Rs sample pressure
data set:

Some scatterplots such as this dont show

the full picture because one point actually represents more than one entry in your data.
qplot(cty, hwy, data=mpg)

ggplot(pressure, aes(x=temperature,
y=pressure)) + geom_line()

The jitter geom parameter introduces just

a little randomness in the point placement
so you can better see multiple points:
qplot(cty, hwy, data=mpg,
As you might have guessed, if theres a
quick plot function in ggplot2 theres also
a more robust, full-featured plotting function. Thats called ggplot() yes, while the

Creating a line graph with ggplot2.

It may be a little confusing here since both
the data set and one of its columns are
called the same thing: pressure. That first
pressure represents the name of the data
frame; the second, y=pressure, represents
the column named pressure.

The code structure for a basic graph with

ggplot() is a bit more complicated than in
The first argument in the ggplot() function,
mtcars, is fairly easy to understand thats

we could add, such as color, axes and more.

R: a beginners guide

You can also use the ylim argument with

ggplot to change where the y axis starts. If
mydata is the name of your data frame, xcol
is the name of the column you want on the
x axis and ycol is the name of the column
Bar chart with Rs bar plot() function.


ylim=0) + geom_line()

To label the bars on the x axis, use the


Perhaps youd like both lines and points on

that temperature vs. pressure graph?

barplot(BOD$demand, main=Graph of
demand, names.arg = BOD$Time)

ggplot(pressure, aes(x=temperature,
y=pressure)) + geom_line() +

Sometimes youd like to graph the counts of

a particular variable but youve got just raw
data, not a table of frequencies. Rs table()
function is a quick way to generate counts
for each factor in your data.

The point here (pun sort of intended) is

that you can start off with a simple graphic
and then add all sorts of customizations:
Set the size, shape and color of the points,
plot multiple lines with different colors,
add labels and a ton more. See Bar and line
graphs (ggplot2) for a few examples, or
the The R Graphics Cookbook by Winston
Chang for many more.

The R Graphics Cookbook uses an example

8-cylinder vehicles in the mtcars data
set. Cylinders are listed in the cyl column,
which you can access in R using mtcars$cyl.
Heres code to get the count of how many
entries there are by cylinder with the

Bar graphs
To make a bar graph from the sample
BOD data frame included with R, the basic
R function is barplot(). So, to plot the
demand column from the BOD data set on
a bar graph, you can use the command:

cylcount <- table(mtcars$cyl)

Add main=Graph of demand if you want
a main headline on your graph:
barplot(BOD$demand, main=Graph of

Creating a bar plot.

That creates a table called cylcount


R: a beginners guide


11 7 14
Now you can create a bar graph of the cylinder count:

Histograms work pretty much the same,


ggplot2s qplot() quick plotting function
can also create bar graphs:

hist(mydata$columnName, breaks = n)
where columnName is the name of your
column in a mydata dataframe that you
want to visualize, and n is the number of
bins you want.


The ggplot2 commands are:

qplot(columnName, data=mydata,
For quick plots and, for the more robust
ggplot(mydata, aes(x=columnName)) +
While the ggplot() function is somewhat
less intuitive, once you wrap your head
around its general principles, you can do
other types of graphics in a similar way.

What happens to your bar chart when you

dont instruct R not to plot continuous variables.
set that could run from 4 through 8, so it
shows blank entries for 5 and 7.

Additional graphics options

There are many more graphics types in R
than these few Ive mentioned. Boxplots,
a statistical staple showing minimum and
maximum, first and third quartiles and
median, have their own function called,
intuitively, boxplot(). If you want to see a
boxplot of the mpg column in the mtcars
data frame its as simple as:

To treat cylinders as distinct groups that

is, youve got a group with 4 cylinders, a
group with 6 and a group with 8, not the
possibility of entries anywhere between 4
and 8 you want cylinders to be treated as
a statistical factor:


To create a bar graph with the more robust

ggplot() function, you can use syntax such

To see side-by-side boxplots in a single

plot, such as the x, y and z measurements


R: a beginners guide

boxplot(diamonds$x, diamonds$y,

So, if you want five colors from the rainbow

palette, use:

Creating a heat map in R is more complex

but not ridiculously so. Theres an easy-tofollow tutorial on Flowing Data.


resources section to find out more.


Using color
Looking at nothing but black and white
graphics can get tiresome after a while. Of
course, there are numerous ways of using
color in R.

Using three colors in the R rainbow palette.

Colors in R have both names and numbers

as well as the usual RGB hex code, HSV
(hue, saturation and value) specs and others. And when I say names, I dont mean
just the usual red, green, blue, black
and white. R has 657 named colors. The
colors() or colours() function R does not
discriminate against either American or
British English gives you a list of all of
them. If you want to see what they look like,
not just their text names, you can get a full,
multi-page PDF chart with color numbers,
and color swatches for each.

Now that youve got a list of colors, how do

you get them in your graphic? Heres one
way. Say youre drawing a 3-bar barchart
using ggplot() and want to use 3 colors
from the rainbow palette. You can create a
3-color vector like:
mycolors <- rainbow(3)
Or for the heat.colors pallette:
mycolors <- heat.colors(3)
Now instead of using the geom_bar()
function without any arguments, add
fill=mycolors to geombar() like this:

specific color palette such as rainbow or

You dont need to put your list of colors

in a separate variable, by the way; you can
merge it all in a single line of code such as:




But it may be easier to separate the colors

out if you want to create your own list of
colors instead of using one of the defaults.


R: a beginners guide

The basic R plotting functions can also

accept a vector of colors, such as:

entry in testscores is greater than or equal

to 80, add blue to the testcolors vector; otherwise add red to the testcolors

You can use a single color if you want all

Now that youve got the list of colors properly assigned to your list of scores, just add
the testcolors vector as your desired color

barplot(BOD$demand, col=royalblue3)
Chances are, youll want to use color to
show certain characteristics of your data,
as opposed to simply assigning random
colors in a graphic. That goes a bit beyond
beginning R, but to give one example, say
youve got a vector of test scores:

barplot(testscores, col=testcolors)
Note that the name of a color must be in
quotation marks, but a variable name that
holds a list of colors should not be within
quote marks.
Add a graph headline:



barplot(testscores, col=testcolors,
main=Test scores)

You can do a simple barplot of those scores

like this:

And have the y axis go from 0 to 100:


barplot(testscores, col=testcolors,
main=Test scores, ylim=c(0,100))

And you can make all the bars blue like this:

Then use las-1 to style the axis label to

be horizontal and not turned 90 degrees

barplot(testscores, col=blue)
But what if you want the scores 80 and
above to be blue and the lower scores to be
red? To do this, create a vector of colors
of the same length and in the same order
as your data, adding a color to the vector
based on the data. In other words, since
the first test score is 96, the first color in
your color vector should be blue; since the
second score is 71, the second color in your
color vector should be red; and so on.

barplot(testscores, col=testcolors,
main=Test scores, ylim=c(0,100),
And youve got a color-coded bar graph.

Of course, you dont want to create that

color vector manually! Heres a statement
that will do so:
testcolors <- ifelse(testscores >=
80, blue, red)
A color-coded bar graph.

If youve got any programming experience,

and runs the conditional statement: If this

By the way, if you wanted the scores sorted

from highest to lowest, you could have set
your original testscores variable to:

R: a beginners guide


decreasing = TRUE)

Why stat = identity? Thats needed here


The sort() function defaults to ascending


ggplot2s qplot() also has easy ways to

color bars by a factor, such as number of
cylinders, and then automatically generate a legend. Heres an example of graph

If that code above is starting to seem

unwieldy to you as a beginner, break it

qplot(factor(cyl), data=mtcars,
geom=bar, fill=factor(cyl))



But, as I said, were getting somewhat

beyond a beginners overview of R when
coloring by factor. For a few more examples
tutorial Producing Simple Graphs with R.
For more on graphing with color, check
a lot of examples, such as this page for bar

testscores_sorted <- sort(testscores,

decreasing = TRUE)
If you had scores in a data frame called
results with one column of student names
called students and another column of
scores called testscores, you could use the
ggplot2 packages ggplot() function as well:
ggplot(results, aes(x=students,
y=testscores)) + geom_
bar(fill=testcolors, stat =

Exporting your graphics

You can save your R graphics to a file for
use outside the R environment. RStudio
has an export option in the plots tab of the
bottom right window.

Coloring bars by factor.


R: a beginners guide

mat myplot.jpg saves as a JPEG, myplot.

png saves as a PNG and so on.
One final note: If youre working in RStudio and would like to see a larger version
of your plot, click the Zoom button and a
larger window with the current graphic will

Exporting your graphics from RStudio.

If you are using plain R in Windows, you
can also right-click the graphics window to
save the file.
To save a plot with R commands and not
point-and-click, first create a container for
your image using a function such as jpeg(),
to have a file name as one argument and
optionally width and height, such as:
jpeg(myplot.jpg, width=350,
Generate the plot with whatever graphics
functions it requires, such as:
And then issue the command:
That will save your graphic to its container.
If you are using ggplot2, you can also
use the function ggsave(), which defaults
to saving the last plot you created using
ggplot2 at the size you displayed it. Based
on the filename you pass to the ggsave()
function, it will save in the appropriate for-


R: a beginners guide

Syntax quirks youll

want to know
I mentioned at the outset that R syntax
is a bit quirky, especially if your frame of
reference is, well, pretty much any other
programming language. Here are some
unusual traits of the language you may find
useful to understand as you embark on your
journey to learn R.

know the ins and outs of Rs 5 yes, count

em, 5 assignment options, check out the
R manuals Assignment Operators page.)
One more note about variables: R is a casesensitive language. So, variable x is not the
same as X. That applies to pretty much
everything in R; for example, the function
subset() is not the same as Subset().

Assigning values to variables

c is for combine (or

concatenate, and sometimes

In pretty much every other programming

language I know, the equals sign assigns a
certain value to a variable. You know, x = 3
means that x now holds the value of 3.
Not in R. At least, not necessarily.

like this:

In R, the primary assignment operator is

<- as in:


x <- 3


But not:


x = 3

Or maybe:

To add to the potential confusion, the

equals sign actually can be used as an
assignment operator in R but not all the
time. When can you use it and when can
you not?

In R, though, theres an extra piece: To put
multiple values into a single variable, you
need the c() function, such as:

The best way for a beginner to deal with

this is to use the preferred assignment
operator <- and forget that equals is
ever allowed. Hey, if its good enough for
Googles R style guide they advise not
using equals to assign values to variables
its good enough for me.


If you forget that c, youll get an error.
When youre starting out in R, youll probably see errors relating to leaving out that
And now that Ive stressed the importance
you that theres a case when you can leave

(If this isnt a good enough explanation for

you, however, and you really really want to


R: a beginners guide

it out if youre referring to consecutive

values in a range with a colon between
minimum and maximum, like this:

have for, while and repeat loops, youll

more likely see operations applied to a data
collection using apply() functions or by
using the plyr() add-on package functions.

my_vector <- (1:10)

But first, some basics.

I bring up this exception because Ive run

into that style quite a bit in R tutorials and
texts, and it can be confusing to see the c
required for some multiple values but not
others. Note that it wont hurt anything
to use the c with a colon-separated range,
though, even if its not required, such as:

If youve got a vector of numbers such as:

my_vector <- c(7,9,23,5)
and, say, you want to multiply each by 0.01
to turn them into percentages, how would
you do that? You dont need a for, foreach
or while loop. Instead, you can create a new
vector called my_pct_vectors like this:

my_vector <- c(1:10)

One more very important point about the
c() function: It assumes that everything in
your vector is of the same data type that

my_pct_vector <- my_vector * 0.01

Performing a mathematical operation on
a vector variable will automatically loop
through each item in the vector.
Typically in data analysis, though, you
want to apply functions to subsets of data:
Finding the mean salary by job title or the
standard deviation of property values by
community. The apply() function group
and plyr add-on package are designed for

my_vector <- c(1, 4, hello, TRUE)

You will not have a vector with two integer
to convert them all into all the same object
type, in this case all character objects. So
my_vector will contain 1, 4, hello and
TRUE. In other words, c() is also for convert or coerce.

There are more than half a dozen functions

in the apply family, depending on what type
of data object is being acted upon and what
sort of data object is returned. These functions can sometimes be frustratingly difficult to get working exactly as you intended,
especially for newcomers to R, says a blog
post at Revolution Analytics, which focuses
on enterprise-class R.

To create a collection with multiple object

types, you need a list, not a vector. You
such as:
My_list <- list(1,4,hello, TRUE)
Now youve got a variable that holds the
number 1, the number 4, the character
object hello and the logical object TRUE.

Plain old apply() runs a function on either

every row or every column of a 2-dimensional matrix where all columns are the
same data type. For a 2-D matrix, you also
need to tell the function whether youre
applying by rows or by columns: Add the
argument 1 to apply by row or 2 to apply by
column. For example:

Loopless loops
Iterating through a collection of data with
loops like for and while is a cornerstone of many programming languages.
Thats not the R way, though. While R does

R: a beginners guide

then, yes, youve got to know the ins and

outs of data types. But my assumption is
that youre here to try generating quick
plots and stats before diving in to create
complex code.

apply(my_matrix, 1, median)
returns the median of every row in my_
matrix and
apply(my_matrix, 2, median)
calculates the median of every column.

So, to start off with the basics, heres what

Id suggest you keep in mind for now: R
has multiple data types. Some of them
are especially important when doing basic
data work. And some functions that are
quite useful for doing your basic data work
require your data to be in a particular type
and structure.

Other functions in the apply() family such

as lapply() or tapply() deal with different
has a nice brief introduction to apply in R
in a blog post if youd like to find out more
and see some examples. (In case youre
wondering, bioinformatics involves issues
around storing, retrieving and organizing
biological data, not just analyzing it.)

More specifically, R has the Is it an integer or character or true/false? data type,

the basic building blocks. R has several of
these including integer, numeric, character and logical. Missing values are represented by NaN (if a mathematical function
wont work properly) or NA (missing or

Many R users who dislike the the apply

functions dont turn to for-loops, but
instead install the plyr package created by
Hadley Wickham. He uses what he calls
the split-apply-combine model of dealing
with data: Split up a collection of data the
way you want to operate on it, apply whatever function you want to each of your data
group(s) and then combine them all back
together again.

As mentioned in the prior section, you can

have a vector with multiple elements of the
same type, such as:
1, 5, 7

The plyr package is probably a step beyond

this basic beginners guide; but if youd like
to find out more about plyr, you can head to
Wickhams plyr website. Theres also a useful slide presentation on plyr in PDF format
from Cosma Shalizi, an associate professor
of statistics at Carnegie Mellon University,
and Vincent Vu. Another PDF presentation
on plyr is from an introduction to R workshop at Iowa State University.

Bill, Bob, Sue

A single number or character string is also
a vector a vector of 1. When you access
the value of a variable thats got just one
value, such as 73 or Learn more about R at, youll also see this in
your console before the value:

R data types in brief (very


Thats telling you that your screen printout is starting at vector item number one.
If youve got a vector with lots of values
so the printout runs across multiple lines,
each line will start with a number in brack-

Should you learn about all of Rs data types

and how they behave right off the bat, as
a beginner? If your goal is to be an R ninja

R: a beginners guide

ets, telling you which vector item number

that particular line is starting with. (See the
screen shot, below.)

R also has special vector and list types

that are of special interest when analyzing
data, such as matrices and data frames. A
matrix has rows and columns; you can find
a matrix dimension with dim() such as
A matrix needs to have all the same data
type in every column, such as numbers
Data frames are like matrices except one
column can have a different data type from
another column, and each column must
have a name. If youve got data in a format
that might work well as a database table (or
well-formed spreadsheet table), it will also
probably work well as an R data frame.

If youve got a vector with lots of values so the printout runs across multiple
lines, each line will start with a number in

In a data frame, you can think of each row

as similar to a database record and each
column like a database field. There are lots
of useful functions you can apply to data
frames, some of which Ive gone over in
earlier sections, such as summary() and the
psych packages describe().

If you want to mix numbers and strings

or numbers and TRUE/FALSE types, you
need a list. (If you dont create a list, you
may be unpleasantly surprised that your
variable containing (3, 8, small) was
turned into a vector of characters (3, 8,
small) ).

And speaking of quirks: There are several

ways to find an objects underlying data
type, but not all of them return the same
return data. frame on a data frame object,
but mode() returns the more generic list.

And by the way, R assumes that 3 is the

same class as 3.0 numeric (i.e., with
a decimal point). If you want the integer
3, you need to signify it as 3L or with the
as.integer() function. In a situation where
this matters to you, you can check what
type of number youve got by using the
class() function:

If youd like to learn more details about

data types in R, you can watch this video
Bloomberg School of Public Health:

as.character(), as.list() and


R: a beginners guide

provide a list of objects in your current R

Want to see your current working directory? pwd just throws an error; what you
want is getwd().
rm(my_variable) will delete a variable from
your current session.
R does include a Unix-like grep() function.
For more on using grep in R, see this brief
writeup on Regular Expressions with The R
Language at

of Public Health, explains data types in R.

Terminating your R

One more useful concept to wrap up this

section hang in there, were almost
done: factors. These represent categories
in your data. So, if youve got a data frame
with employees, their department and
their salaries, salaries would be numerical
data and employees would be characters
(strings in many other languages); but
youd likely want department to be a factor
in other words, a category you may want
to group or model your data by. Factors
can be unordered, such as department, or
ordered, such as poor, fair, good and

R doesnt need semicolons to end a line of

code (although its possible to put multiple
commands on a single line separated by
semicolons, you dont see that very often).
Instead, R uses line breaks (i.e., new line
characters) to determine when an expression has ended.
What if you want one expression to go
across multiple lines? The R interpreter
tries to guess if you mean for it to continue
to the next line: If you obviously havent
finished a command on one line, it will
assume you want to continue instead of
throwing an error. Open some parentheses without closing them, use an open
quote without a closing one or end a line
with an operator like + or - and R will wait
to execute your command until it comes
across the expected closing character and
the command otherwise looks finished.

R command line differs from

the Unix shell
In fact, some R command-line actions
behave as youd expect if you come from a
Unix environment, but others dont.
does in Unix keep hitting it to see prior

Syntax cheating: Run SQL

queries in R
If youve got SQL experience and R syntax
starts giving you a headache especially
when youre trying to figure out how to


but not of files as in Unix. Rather, it will


R: a beginners guide

get a subset of data with proper R syntax

you might start longing for the ability to
run a quick SQL SELECT command query
your data set.
You can.
The add-on package sqldf lets you run
SQL queries on an R data frame (there are
separate packages allowing you to connect
R with a local database). Install and load
sqldf, and then you can issue commands
such as:
Invoking Rs data editing window with the edit() function.

sqldf(select * from mtcars where mpg

> 20 order by mpg desc)

This can be useful if youve got a data set

with a lot of columns that are wrapping in
the small command-line window. However,
since theres no way to save your work as
you go along changes are saved only
when you close the editing window and
theres no command-history record of what
youve done, the edit window probably isnt
your best choice for editing data in a project where its important to repeat/reproduce your work.

This will find all rows in the mtcars sample

data frame that have an mpg greater than
20, ordered from highest to lowest mpg.
Most R experts will discourage newbies
from cheating this way: Falling back
on SQL makes it less likely youll power
through learning R syntax. However, its
there for you in a pinch or as a useful
way to double-check whether youre getting back the expected results from an R

In RStudio you can also examine a data

object (although not edit it) by clicking on
it in the workspace tab in the upper right

Examine and edit data with a

And speaking of cheating, if you dont want
to use the command line to examine and
edit your data, R has a couple of options.
The edit() function brings up an editor
where you can look at and edit an R object,
such as

Saving and exporting your

In addition to saving your entire R workspace with the save.image() function and
various ways to save plots to image files,
you can save individual objects for use in
other software. For example, if youve got a
data frame just so and would like to share it
with colleagues as a tab- or comma-delimited file, say for importing into a spreadsheet, you can use the command:



R: a beginners guide

write.table(myData, testfile.txt,
This will export all the data from an R
object called myData to a tab-separated file
called testfile.txt in the current working
directory. Changing sep=\t to sep=c will
generated a comma-separated file and so


R: a beginners guide

60+ R resources to
improve your data skills
R data structures to running regressions
and conducting factor analyses. The beginners section may be a bit tough to follow
if you havent had any exposure to R, but
it offers a good foundation in data types,
imports and reshaping once youve had a
aggregating, restructuring and subsetting
data, as well as a lot of applied statistics.
Note that if your interest in graphics is
learning ggplot2, theres relatively little

This list was originally published as part of

the Computerworld Beginners Guide to R
but has since been expanded to also include
resources for advanced beginner and intermediate users.
These websites, videos, blogs, social media/
communities, software and books/ebooks
can help you do more with R.; my favorites
are listed in bold.

Books and e-books

R Cookbook. Like the rest of the OReilly
Cookbook series, this one offers how-to
recipes for doing lots of different tasks,
has the added bonus of being well written.
worth adding to your reference library. By
Paul Teetor, a quantitative developer working in the financial sector.

The Art of R Programming. For those who

want to move beyond using R in an ad hoc
way ... to develop[ing] software in R. This
is best if youre already at least moderately
proficient in another programming language. Its a good resource for systematically learning fundamentals such as types
of objects, control statements (unlike many
R purists, the author doesnt actively discourage for loops), variable scope, classes
and debugging in fact, theres nearly as
large a chapter on debugging as there is on
graphics. With some robust examples of
solving real-world statistical problems in R.
By Norman Matloff.

R Graphics Cookbook. If you want to do

beyond-the-basics graphics in R, this is a
useful resource both for its graphics recipes
and brief introduction to ggplot2. While
this goes way beyond the graphics capabilities that I need in R, Id recommend this if
youre looking to move beyond advancedbeginner plotting. By Winston Chang, a
software engineer at RStudio.

R in a Nutshell. A reasonably readable

guide to R that teaches the languages
fundamentals syntax, functions, data
structures and so on as well as how-to
statistical and graphics tasks. Useful if you

R in Action: Data analysis and graphics

with R. This book aims at all levels of users,
with sections for beginning, intermediate
and advanced R ranging from Exploring

R: a beginners guide

want to start writing robust R programs, as

it includes sections on functions, objectoriented programming and high-performance R. By Joseph Adler, a senior data
scientist at LinkedIn.

be a downloadable PDF, but now the only

versions are for OS X or iOS.
R for Everyone. Author Jared P. Lander
promises to go over 20% of the functionality needed to accomplish 80% of the
work. And in fact, topics that are actually
covered, are covered pretty well; but be
warned that some items appearing in the
table of contents can be a little thin. This
is still a well-organized reference, though,
reshaping data, working with basic stats
and more.

Visualize This. Note; Most of this book is

not about R, but there are several examples
of visualizing data with R. And theres so
much other interesting info here about how
to tell stories with data that its worth a
read. By Nathan Yau, who runs the popular
Flowing Data blog and whose doctoral dissertation was on personal data collection
and how we can use visualization to learn
about ourselves.
R For Dummies. I havent had a chance to
read this one, but its garnered some good
reviews on If youre familiar
with the Dummies series and have found
them helpful in the past, you might want to
check this one out. You can get a taste of
the authors style in the Programming in R
section of, which has more
than a 100 short sections such as How to
construct vectors in R and How to use the
apply family of functions in R. By Joris
Meys and Andrie de Vries.

Statistical Analysis With R: Beginners

Guide. This book has you pretend youre
If you find that idea hokey, move along to
see another resource; if not, youll get a
beginner-level introduction to various tasks
in R, including tasks you dont always see
in an intro text, such as multiple linear
regressions and forecasting. Note: My early
e-version had a considerable amount of bad
spaces in my Kindle app, but it was still
certainly readable and usable.

Introduction to Data Science. Its highly

readable, packed with useful examples and
free what more could you want? This
e-book isnt technically an R book, but it
uses R for all of its examples as it teaches
concepts of data analysis. If youre familiar
with that topic you may find some of the
explanations rather basic, but theres still a
lot of R code for things like analyzing tweet
rates (including a helpful section on how to
get Twitter OAuth authorization working in
R), simple map mashups and basic linear
regression. Although Stanton calls this an
electronic textbook, Introduction to Data
Science has a conversational style thats
pleasantly non-textbook like. There used to

Reproducible Research with R and RStudio.

Although categorized as a bioinformatics textbook (and priced that way even
the Kindle edition is more than $50), this
is more general advice on steps to make
sure you can document and present your
work. This includes numerous sections on
creating report documents using the knitr
package, LaTeX and Markdown tasks
not often covered in-depth in general R
books. The author has posted source code
for generating the book on GitHub, though,
if you want to create an electronic version
of it yourself.


R: a beginners guide

Exploring Everyday Things with R and

Ruby. This book oddly goes from a couple
of basic introductory chapters to some
starting to code, much of the book may
be tough to follow at the outset. However,
the intro to R is one of the better ones Ive
Plus experienced programmers can see how
author Sau Sheong Chang splits up tasks
between a general language like Ruby and
the statistics-focused R.

then Frequencies and crosstabs to get

an explainer of the table() function. This
ranges from basics (including useful howtos for customizing R startup) through
beyond-beginner statistics (matrix algebra,
despite its age (2004) and the fact that
a link to whats supposed to be the latest
version no longer works. By Tom Short, an
engineer at the Electric Power Research

Online references

A short list of R the most useful commands.

Commands grouped by function such as
input, moving around and statistics
and transformations. This offers minimal
explanations, but theres also a link to a
psychology professor at Northwestern

4 data wrangling tasks in R for advanced

beginners. This follow-up to our Beginners
Guide outlines how to do several specific
and reshape data. With sample code and
Data manipulation tricks: Even better in
R. From working with dates to reshaping
data to if-then-else statements, see how to
perform common data munging tasks. You
can also download these R tips & tricks as
a PDF (free Insider registration required).

R Graph Catalog. Lots of graph and other

plot examples, easily searchable and each
with downloadable code. All are made with
Joanna Zhao and Jennifer Bryan.

Cookbook for R. Not to be confused with

the R Cookbook book mentioned above,
this website by software engineer Winston
Chang (author of the R Graphics Cookbook) offers how-tos for tasks such as data
input and output, statistical analysis and
creating graphs. Its got a similar format
to an OReilly Cookbook; and while not
as complete, can be helpful for answering
some How do I do that? questions.


information, from starting with default
plots to customizing title, axes, legends;
creating multi-panel plots and more. By
Zev Ross.
Frequently Asked Questions about R. Some
basics about reading, writing, sorting and
shaping data as well as a lineup of how to
do various statistical operations and a few
specialized graphics such as spaghetti plots.

Quick-R. This site has a fair amount of

samples and brief explanations grouped
by major category and then specific items.
For example, youd head to Stats and

R: a beginners guide

From UCLAs Institute for Digital Research

and Education.

or twenty thousandth day on earth with R.

Id strongly recommend giving this a look if
textbook-style instruction leaves you cold.

R Reference Card for Data Mining. Includes

Data Mining published by Elsevier in 2012.
By Yanchang Zhao.
Spatial Cheat Sheet. For those doing GIS
and spatial analysis work, this list offers
Barry Stephen Rowlingson at Lancaster
University in the U.K.

Sample Twotorial video.

Online tools

Google Developers Intro to R. This series

of 21 short YouTube videos includes some
missing in R intros: working with and
writing your own functions. This YouTube
playlist offers a good programmers introduction to the language just note that if
youre looking to learn more about visualizations with R, thats not one of the topics

Web interface for ggplot2. This online

tool by UCLA Ph.D. candidate Jeroen
Ooms creates an interactive front end for
ggplot2, allowing users to input tasks they
want to do and get a plot plus R code in
return. Useful for those who want to learn
more about using ggplot2 for graphics
without having to read through lengthy
Ten Things You Can Do in R That You
Wouldve Done in Microsoft Excel. From
the R for Dummies Web site, these code
samples aim to help Excel users feel more
comfortable with R.

Twotorials. Youll either enjoy these snappy
2-minute twotorial videos or find them, oh,
corny or over the top. I think theyre both
informative and fun, a welcome antidote to
the typically dry how-tos you often find in
statistical programming. Analyst Anthony
Damico takes on R in 2-minute chunks,
from how to create a variable with R to
how to plot residuals from a regression in
R; he also tackles an occasional problem
such as how to calculate your ten, fifteen,

This video in the Google Developers R series introduces functions in R.

Up and Running with R. This
video class covers the basics of topics


R: a beginners guide

statistics. The curriculum is limited, but

presenter Barton Poulson tries to explain
what hes doing and why, not simply run
commands. He also has a more in-depth
6-hour class, R Statistics Essential Training. is a subscription service
that starts at $25/month, but several of the
videos are available free for you to view and
see if you like the instruction style, and
theres a 7-day free trial available.

Johns Hopkins, posted his lecture videos


Coursera: Computing for Data Analysis.

Courseras free online classes are timesensitive: Youve got to enroll while theyre
taking place or youre out of luck. However,
if theres no session starting soon, instructor Roger Peng, associate professor of
biostatistics at Johns Hopkins University,
posted his lectures on YouTube; Revolution
Analytics then collected them on a handy
single page. While I found some of these
a bit difficult to follow at times, they are
packed with information, and you may find
them useful.

Intro video for Coursera Data

Analysis online class
Coursera: Statistics One. If you dont mind
going through a full, 12-week stats course
along with learning R, Princeton University senior lecturer Andrew Conways
class includes an introduction to R. All
the examples and assignments will involve
writing code in R and interpreting R output, says the course description. You can
check the Coursera link to see if and when
future sessions are scheduled.
Introduction to Data Science with R. At
$160 this OReilly training course is somewhat pricey considering how many free and
lower-cost video classes there are out there.
However, if youre looking for a step-bystep intro to R, this is a useful course, starting with language and ggplot2 visualization
basics through modeling. Its taught by
as well as explaining a few of the languages
quirks. If cost is an issue and youre not in
a hurry, sign up for OReillys deal emails
and you may eventually find a 50% off sale.

Intro video for the Coursera Computing for Data Analysis course
Coursera: Data Analysis. This was more
of an applied statistics class that uses R as
opposed to one that teaches R; but if youve
got the R basics down and want to see it in
action, this might be a good choice. There
are no upcoming scheduled sessions for
this at Coursera, but instructor Jeff Leek
an assistant professor of biostatistics at

Data Analysis and Visualization Using R.

visualization basics, some statistical tests

R: a beginners guide

and exploratory data analysis including data.table. Videos by Princeton Ph.D.

Princeton Broadcast Center.

are still many sections that focus specifically on R.

R Tutorial. A reasonably robust beginning
guide that includes sections on data types,
probability and plots as well as sections
focused on statistical topics such as linear
Clarkson University.

Other online introductions

and tutorials
Try R This beginner-level interactive online
course will probably seem somewhat basic
for anyone who has experience in another
programming language. However, even if
the focus on pirates and plunder doesnt
appeal to you, it may be a good way to get
some practice and get more comfortable
using R syntax. This site is probably best

known in the R community for author Bob
Muenchens tracking of Rs popularity vs.
other statistical software. However, in the
Examples section, hes got some R tutorials
such as basic graphics and graphics with
ggplots. Hes also posted code for tasks

An Introduction to R. Lets not forget the

R Project site itself, which has numerous
resources on the language including this
intro. The style here is somewhat dry, but
youll know youre getting accurate, up-todate information from the R Core Team.

Aggregating and restructuring data. This

excerpt from R in Action goes over one of
the most important subjects in using R:
reshaping your data so its in the format
needed for analysis and then grouping
and summarizing that data by factors. In
addition to touching on base-R functions
with the reshape package. By Robert I.

How to Visualize and Compare Distributions. This short and highly readable
Flowing Data tutorial goes over traditional
visualizations such as histograms and box
plots. With downloadable code.
Handling and Processing Strings in
R. This PDF download covers many
things youre want to do with text, from
string lengths and formatting to search
and replace with regular expressions to
basic text analysis. By statistician Gaston

Getting started with charts in R. From the

popular FlowingData visualization website run by Nathan Yau, this tutorial offers
examples of basic plotting in R. Includes
downloadable source code. (While many
FlowingData tutorials now require a paid
membership to the site, as of May 2013 this
one did not.)

Learning statistics with R: A tutorial for

psychology students and other beginners
by Daniel Navarro at the University of
Adelaide (PDF). 500+ pages that go from
Why do we learn statistics and Statistics in every day life to linear regression
and ANOVA (ANalysis Of VAriance). If you
dont need/want a primer in statistics, there

Using R for your basic statistical Needs

LISA Short Course. Aimed at those who
already know stats but want to learn R, this
is a file of R code with comments, making it
easy to run (and alter) the code yourself.

R: a beginners guide

The programming is easy to follow, but if

you havent brushed up on your stats lately,
be advised that comments such as

ggplot2_tutorial.R. This online page at, prepared for the Santa Barbara
R User Group, includes a lot of commented
R code and graph examples for creating
data visualizations with ggplot2.

Suppose wed like to produce a reduced

set of independent variables. We could use
the function # step() to perform stepwise
model selection based on AIC which is
p = number of model parameters (beta

More and Fancier Graphics. This one-page

let you interact with R plots, locator() and
customization. By William B. King, Coastal
Carolina University.

may be tough to follow. By Nels Johnson at

Virginia Techs Laboratory for Interdisciplinary Statistical Analysis.

ggplot2 Guide. This ggplot2 explainer skips

the simpler qplot option and goes straight
to the more powerful but complicated
ggplot command, starting with basics of a
simple plot and going through geoms (type
of plot), faceting (plotting by subsets),
statistics and more. By data analyst George
Bull at Sharp Statistics.

Producing Simple Graphs with R. Although

6+ years old now, this gives a few more
details and examples for several of the visualization concepts touched on in our beginners guide. By Frank McCown at Harding
Short courses. Materials from various
courses taught by Hadley Wickham, chief
scientist at RStudio and author of several
popular R packages including ggplot2.
Features slides and code for topics beyond
beginning R, such as R development master

Using R. In addition to covering basics,

there are useful sections on data manipulation an important topic not easily
covered for beginners as well as getting statistical summaries and generating
basic graphics with base R, the Lattice
package and ggplot2. Short explainers are
interspersed with demo code, making this
useful as both a tutorial and reference site.
By analytics consultant Alastair Sanderson,
University of Birmingham in the U.K.

Quick introduction to ggplot2. Very nice,

readable and as promised quick introduction to the ggplot2 add-on graphic
package in R, incuding lots of sample plots
and code. By Google engineer Edwin Chen.
ggplot2 workshop presentation. This
robust, single-but-very-long-page tutorial
offers a detailed yet readable introduction
to the ggplot2 graphing package. What sets
this apart is its attention to its theoretical
underpinnings while also offering useful,
concrete examples. From a presentation at
then a PhD candidate at the University of

The Undergraduate Guide to R. This is a

highly readable, painless introduction to R
that starts with installation and the command environment and goes through data
types, input and output, writing your own
functions and programming tips. Viewable
as a Google Doc or downloadable as a PDF,
plus accompanying files. By Trevor Martin,
then at Princeton University, funded in part
by an NIH grant.


R: a beginners guide

How to turn CSV data into interactive visualizations with R and rCharts. 9page slideshow gives step-by-step instructions on
various options for generating interactive
graphics. The charts and graphs use jQuery
libraries as the underlying technology but
only a couple of line of R code are needed.
By Sharon Machlis, Computerworld.

13 resources for time series analysis. A

video and 12 slide presentations by Rob
J. Hyndman, author of Forecasting time
series using R. Also has links to exercises
and answers to the exercises.
knitr in a knutshell. knitR is designed to
easily create reports and other documents
that can combine text, R code and the
results of R code in short, a way to share
your R analyses with others. This minimal
tutorial by Karl Broman goes over subjects
such as creating Markdown documents
and adding graphics and tables, along with
links to resources for more info.

Higher Order Functions in R. If youre at

you may start bumping up against the limits of Rs apply family. This post goes over
6 extremely useful base R functions with
readable explanations and helpful examples.
By John Mules White, soon-to-be scientist
at Facebook.

More free downloads and

websites from academia:

Introduction to Linear Regression Using R

and Quandl While this does indeed promote Quandl as your data source, that data
is free, and for those interested in using R
for regressions, youll find several detailed
walk-throughs from data import through
statistical analysis.

Introducing R. Slide presentation from the

UCLA Institute for Digital Research and
Education, with downloadable data and
Introducing R. Although titled for beginners and including sections on getting
started and reading data, this also shows
how to use R for various types of linear
models. By German Rodriguez at Princeton
Universitys Office of Population Research.

Introduction to dplyr. The dplyr package

to rationalize such functions by using a
common syntax. In this short introductory
vignette, youll learn about five basic data
manipulation filter(), arrange(), select(),
mutate() and summarise() including
examples, as well as how to chain them
together for more streamlined, readable

R: A self-learn tutorial. Intro PDF from

National Center for Ecological Analysis and
Synthesis at UC Santa Barbara. While a bit
dry, it goes over a lot of fundamentals and
includes exercises.
Statistics with R Computing and Graphics.
getting data into R and statistical modeling
then at the University of Oxford.

Applied Time Series Analysis. Text-based

online class from Penn State to learn and
apply statistical methods for the analysis
of data that have been observed over time.
Access to the articles is free, although there
is no community or instructor participation.


R: a beginners guide

Blogs & blog posts

Little Book of R for Time Series. This is

extremely useful if you want to use R for
analyzing data collected over time, and also
has some introductory sections for general
R use even if youre not doing time series.
By Avril Coghlan at the Wellcome Trust
Sanger Instituie, Cambridge, U.K.

R-bloggers. This site aggregates posts

and tutorials from more than 250 R blogs.
While both skill level and quality can vary,
this is a great place to find interesting posts
about R especially if you look at the top
articles of the week box on the home page.

Introduction to ggplot2. 11-page PDF with

some ggplot basics, by N. Matloff at UC

Revolutions. Theres plenty here of interest to all levels of R users. Although author
Revolution Analytics is in the business of
selling enterprise-class R platforms, the
blog is not focused exclusively on their

Pretty much every social media platform has an R group. Id particularly
Statistics and R on Google+. Community
members are knowledgeable and helpful,
and various conversation threads engage
both newbies and experts.

Post: 10 R packages I wish I knew about

earlier. Not sure all of these would be in
my top 10, but unless youve spent a fair
amount of time exploring packages, youll
likely find at least a couple of interesting
and useful R add-ons.

Twitter #rstats hashtag. Level of discourse

here ranges from beginner to extremely
advanced, with a lot of useful R resources
and commentary getting posted.

Post: R programming for those coming

from other languages. If youre an experienced programmer trying to learn R, youll
probably find some useful tips here.


Post: A brief introduction to apply in R. If

you want to learn how the apply() function
family works, this is a good primer.

questions. If youve got a specific coding
challenge, its definitely worth searching
here to see if someone else has already
asked about something similar.

Translating between R and SQL. If youre

more experienced (and comfortable) with
SQL than R, it can be frustrating and
confusing at times to figure out how to do
basic data tasks such as subsetting your
data. Statistics consultant Patrick Burns
shows how to do common data slicing in

groups not connected with
Revolution Analytics has an R User Group

Graphs & Charts in base R, ggplot2 and

rCharts. There are lots of sample charts
with code here, showing how to do similar
visualization tasks with basic R, the ggplot2


R: a beginners guide

add-on package and rCharts for interactive

HTML visualizations.

R site search returns results just from

R functions, package vignettes (documentation that helps explain how a function works) and task views (focusing on
a particular field such as social science or

When to use Excel, when to use R? For

spreadsheet users starting to learn R, this
is a useful question to consider. Michael
Milton, author of Head First Data Analysis
(which discusses both Excel and R), offers
practical (and short) advice on when to use

Googles R Style Guide. Want to write neat
code with a consistent style? Youll probably want a style guide; and Google has
helpfully posted their internal R style for
all to use. If that one doesnt work for you,
Hadley Wickham has a fairly abbreviated R
style guide based on Googles but with a
few tweaks.

A First Step Towards R From Spreadsheets.

Some advice on both when and how to start
moving from Excel to R, with a link to a
follow-up post, From spreadsheet thinking
to R thinking.
Using dates and times in R. This post from
a presentation by Bonnie Dixon at the
Davis R Users group goes over some of the
different options for performing date/time
calculations and other manipulations.

RStudio documentation. If youre using

RStudio, its worth taking a look at parts
of the documentation at some point so you
can take advantage of all it has to offer.
History of R Financial Time Series Plotting.
Although, as the name implies, this focuses
on financial time-series graphics, its also
samples along with graphics. By Timely
Portfolio on GitHub.

Scraping Pro-Football Data and Interactive

Charts using rCharts, ggplot2, and shiny.
see a sample of how to scrape data off a
website, clean and restructure the data and
then visualize it in several ways, including
interactive Web graphics all with downloadable code. By Vivek Patil, an associate
professor at Gonzaga University.

Grouping & Summarizing Data in R. There

are so many ways to do these tasks in R
that it can be a little overwhelming even for
those beyond the beginner stage to decide
which to use when. This downloadable
Slideshare presentation by analyst Jeffrey
Breen from the Greater Boston useR Group
is a useful overview.

Searching for R on a general search
English words include the letter r. Some
search possibilities:

R Instructor. This app is primarily a welldesigned, very thorough index to R, offering snippets on how to import, summarize
and plot data, as well as an introductory
section. An I want to... section gives

RSeek is a Web search engine that just

returns results from certain R-focused


R: a beginners guide

short how-tos on a variety of tasks such

as changing data classes or column/row
names, ordering or subsetting data and
more. Similar information is available free
online; the value-add is if you want the
info organized in an attractive mobile app.
Extras include instructional videos and a
statistical tests section explaining when
to use various tests as well as R code for
each. For iOS and Android, about $5.

examples, Show Me Shiny offers a gallery

of apps with links to code.
See more info on version 2.0.

Comprehensive R Archive Network
(CRAN). The most important of all: home
of the R Project for Statistical Computing,
detailed documentation and a number of
links to more resources.
RStudio. You can download the free RStudio IDE as well as RStudios Shiny project
aimed at turning R analyses into interactive
Web applications.
Revolution Analytics. In addition to its
commercial Revolution R Enterprise, you
can request a download of their free Revolution R Community (youll need to provide
an email address). Both are designed to
improve R performance and reliability.
Tibco. This software company recently
released a free Tibco Enterprise Runtime
for R Developers Edition to go along with
its commercial Tibco Enterprise Runtime
for R engine aimed at helping to integrate R
analysis into other enterprise platforms.
Shiny for interactive Web apps. This opensource project from RStudio is aimed at
creating interactive Web applications from
R analysis and graphics. Theres a Shiny
tutorial at the RStudio site; to see more