Introduction to R
for Absolute Beginners: Part I
Melinda Fricke
Department of Linguistics
University of California, Berkeley
melindafricke@berkeley.edu
D-Lab Workshop Series, Spring 2013
Why this workshop?
"The questions that statistical analysis is designed to answer
can often be stated simply. This may encourage the layperson to
believe that the answers are similarly simple. Often, they are
not…
No-one should be embarrassed that they have difficulty
with analyses that involve ideas that professional statisticians
may take 7 or 8 years of professional training and experience to
master.”
(Maindonald and Braun, 2010. Data Analysis and Graphics Using R: An
Example-Based Approach.)
What we will cover today
• Getting around in R
– working directories, managing your workspace,
creating and removing objects (types of variable
assignment), inspecting objects, viewing
functions, getting help
• Types of data and basic manipulations
– data types, object types, reading and writing data,
modifying data and objects, basic functions
(What we will cover next time)
Downloading and installing external packages
Common statistical tests
correlation, simple linear regression, t-tests, ANOVA
Graphing
R makes really beautiful graphs, and is very flexible
What we will not cover (ever)
"The best any analysis can do is to highlight the information in
the data. No amount of statistical or computing technology can
be a substitute for good design of data collection, for
understanding the context in which data are to be interpreted,
or for skill in the use of statistical analysis methodology.
Statistical software systems are one of several components of
effective data analysis.”
(Maindonald and Braun, 2010)
Why use R?
R is an incredibly flexible, high-level programming language that will allow you to
conduct nearly any statistical analysis, and create any visualization you can think of.
This video was created by Ben Schmidt using the ggplot2 package.
http://sappingattention.blogspot.com/2012/10/data-narratives-and-structural.html
A simpler example…
Fricative and Vowel Spectra in Perseverative Context
High Frequency Centroid
fricative spectra at 20% fricative duration
adults
children
3750
adults, anticipatory
●
●
children, anticipatory
5720
amplitude (dB)
10
3725
vowel context
front
round
0
5700
3700
●
●
5680
−10
5660
3675
0
2000
4000
6000
8000
0
2000
4000
6000
5640
8000
Hz
frequency (Hz)
adults, perseverative
vowel spectra at 20 ms before fricative onset
adults
children
5720
20
amplitude (dB)
children, perseverative
3750
3725
10
vowel type
front
round
0
−10
−20
●
●
3700
4000
6000
8000
0
frequency (Hz)
2000
4000
6000
●
5660
−30
2000
●
5680
3675
0
5700
5640
8000
beg
end
beg
end
(These are from my own work on the production of “s” sounds.)
context
● front
round
But first, the basics…
Creating objects
Open R and type the following:
x=1
y <- 2
3 -> z
[enter]
[enter]
[enter]
x
y
z
[enter]
[enter]
[enter]
There are 3 ways to assign variables in R.
Creating objects
Now try this:
x+y+z
x + y + z -> q
q
What’s the difference between the first line and the
second?
Creating objects
These are vectors. A vector is just a bunch of values
that have been concatenated together, in a
sequence.
When we type “q”, R tells us that the first element
in the vector “q” is 6:
[1] 6
(It’s also the only element, but that’s okay.)
Creating objects
We can create vectors that are longer than 1
element by concatenating multiple elements
together:
x = c(7, 8, 8, 7, 4, 1)
x
length(x)
A little bit about “looping”
R is a “high level” programming language. This
means it takes care of a lot of things for us.
x*2
x+y
Most programming languages require you to write
loops, but R takes care of a lot of “looping” on its
own.
Pop quiz!
What will this code produce?
length(x + y)
A little bit about looping
What will this code produce?
length(x + y)
[1] 6
A little bit about looping
What will this code produce?
length(x + y)
[1] 6
The length of x is 6. (x + y) loops through the vector x, adding y to each
number, yielding 6 new values (and therefore a vector of length 6).
Remember: if you want to concatenate, use c():
length(c(x,y))
[1] 7
Pop quiz!
What will this code produce?
length(x + y)
[1] 6
length(length(x + y))
Pop quiz!
What will this code produce?
length(x + y)
[1] 6
length(length(x + y))
[1] 1
A little bit about looping
What will this code produce?
x = c(7, 8, 8, 7, 4, 1)
y = c(1, 2)
x+y
A little bit about looping
What will this code produce?
x = c(7, 8, 8, 7, 4, 1)
y = c(1, 2)
x+y
[1] 8 10 9 9 5 3
A little bit about looping
What will this code produce?
x = c(7, 8, 8, 7, 4, 1)
y = c(1, 2)
x+y
[1] 8 10 9 9 5 3
“Loop through x and y simultaneously, adding 2 elements
together.”
For operations using 2 vectors of different lengths, the shorter
one will be “recycled”. (Look at y + x.)
Data types
class(x)
class(‘x’)
Data types
class(x)
[1] “numeric”
class(‘x’)
[1] “character”
Data types
class(x)
[1] “numeric”
class(‘x’)
[1] “character”
There are different types of data in R. Putting
quotes around something indicates you want R to
treat it literally - as a character, not a variable.
e.g. class(‘1’)
Data types
as.character(x) -> k
k+1
Data types
as.character(x) -> k
k+1
Your first error message!
as.numeric(k) -> k
k+1
Data types
as.factor(k)
Data types
as.factor(k)
A factor is R-speak for a categorical variable: a type of data that
can have one of several fixed levels. By default, factor levels are
ordered alphabetically.
levels(k)
Data types
as.factor(k)
A factor is R-speak for a categorical variable: a type of data that
can have one of several fixed levels. By default, factor levels are
ordered alphabetically.
levels(k)
Oops! Why doesn’t this work?
Data types
as.factor(k) -> k
A factor is R-speak for a categorical variable: a type of data that
can have one of several fixed levels. By default, factor levels are
ordered alphabetically.
levels(k)
[1] “1” “4” “7” “8”
Data types
c(10, 11, 10, 8, 6, 12, 11, 8, 10, 6, 10, 11) -> p
Using what we’ve learned so far…
c(10, 11, 10, 8, 6, 12, 11, 8, 10, 6, 10, 11) -> p
How many items are in the vector ‘p’?
How many unique values are in ‘p’?
Using what we’ve learned so far…
c(10, 11, 10, 8, 6, 12, 11, 8, 10, 6, 10, 11) -> p
How many items are in the vector ‘p’?
length(p)
[1] 12
How many unique values are in ‘p’?
Using what we’ve learned so far…
c(10, 11, 10, 8, 6, 12, 11, 8, 10, 6, 10, 11) -> p
How many items are in the vector ‘p’?
length(p)
[1] 12
How many unique values are in ‘p’?
length(levels(as.factor(p)))
Your first R weirdness
as.factor(p) -> p
as.numeric(p)
What happened??
Your first R weirdness
as.factor(p) -> p
as.numeric(p)
[1] 3 4 3 2 1 5 4 2 3 1 3 4
( 10 11 10 8 6 12 11 8 10 6 10 11)
When you try to change a factor directly into
numeric mode, the factors are replaced by their
“order”. How could we avoid this?
Your first R weirdness
as.numeric(as.character(p))
Your first R weirdness
as.numeric(as.character(p))
What if we want to change the order of the
levels?
Your first R weirdness
as.numeric(as.character(p))
What if we want to change the order of the
levels?
factor(p, levels=c(‘6’, ‘8’, ‘10’, ‘12’, ‘11’)) -> p
levels(p)
Ordered factors
Factors may make more sense if we give our categories names
other than numbers.
Try this:
mycolors = c(‘blue’, ‘yellow’, ‘green’, ‘purple’, ‘red’)
class(mycolors)
factor(mycolors, levels=c(‘red’, ‘yellow’, ‘green’, ‘blue’, ‘purple’)) -> mycolors
class(mycolors)
levels(mycolors)
Taking stock
Objects and operations
values
e.g. ‘1’
vectors
c(‘1’, ‘4’, ‘a’, ‘word’)
functions
as.factor(x)
variable assignment
=, ->, <Data types (‘classes’)
numeric
8
character
‘8’, ‘x’, ‘female’
factor
‘8’, ‘x’, ‘female’
 the difference between strings of characters and
factors is that factors have one of a set of fixed values
e.g. ‘male’ vs. ‘female’
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
edit(p)
ls
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
edit(p)
ls
lists the objects currently in your workspace
also useful: rm()
(remove)
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
edit(p)
ls
lists the objects currently in your workspace
creates a table of counts
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
edit(p)
ls
lists the objects currently in your workspace
creates a table of counts
lists all existing unique values
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
edit(p)
ls
lists the objects currently in your workspace
creates a table of counts
lists all existing unique values
sorts values from lowest to highest
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
edit(p)
ls
lists the objects currently in your workspace
creates a table of counts
lists all existing unique values
sorts values from lowest to highest
mean of the values
median (middle) of the values
standard deviation
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
lists the objects currently in your workspace
creates a table of counts
lists all existing unique values
sorts values from lowest to highest
mean of the values
median (middle) of the values
standard deviation
edit(p)
lets you interact directly with the data!
“edit(p) -> p” to save your changes
ls
Some more useful functions
Type these commands in to see what they do:
ls()
table(p)
unique(p)
sort(p)
mean(p)
median(p)
sd(p)
lists the objects currently in your workspace
creates a table of counts
lists all existing unique values
sorts values from lowest to highest
mean of the values
median (middle) of the values
standard deviation
edit(p)
lets you interact directly with the data!
ls
displays the internal workings of the function
Getting help with functions
?sort
help(sort)
search current packages for a function
(these two are equivalent)
??sort
search all packages for a word
Getting help with functions
?sort
help(sort)
search current packages for a function
(these two are equivalent)
??sort
search all packages for a word
sort(p, decreasing = T)
Data frames
A really handy data structure!
ind
1
2
3
4
5
dept
ling
ling
anth
hist
econ
year
1
4
2
5
2
prog
R
Excel
Excel
Stata
SPSS
Data frames are organized by rows and columns.
Each column can contain a different type of data.
Let’s try to create this data frame in R…
Data frames
ind
1
2
3
4
5
1)
dept
ling
ling
anth
hist
econ
year
1
4
2
5
2
prog
R
Excel
Excel
Stata
SPSS
Create a vector for each column. Name the vector with the column
header, e.g.:
c(‘ling’, ‘ling’, ‘anth’, ‘hist’, ‘econ’) -> dept
2) Combine the vectors into a data frame:
data.frame(ind, dept, year, prog) -> gradstats
3) Type ‘gradstats’ to display your whole data frame
think about which ones
should be factors!
Data frames
Did your data get entered properly?
Check the data type for each column, and
think about what it should be.
e.g. class(gradstats$dept)
Data frames
Did your data get entered properly?
Check the data type for each column, and
think about what it should be.
Factors: ind, dept, prog
Numeric: year (probably…)
Data frames
Now try out these functions:
head(gradstats, n = 3)
tail(gradstats, n = 2)
names(gradstats)
summary(gradstats)
dim(gradstats)
length(gradstats)
length(gradstats$ind)
table(gradstats$dept)
table(gradstats)
Data frames
Now try out these functions:
head(gradstats, n = 3)
tail(gradstats, n = 2)
names(gradstats)
summary(gradstats)
dim(gradstats)
length(gradstats)
length(gradstats$ind)
table(gradstats$dept)
table(gradstats)
displays the first n rows of the data frame
last n rows
gives the name of each column
summarizes the whole data frame
gives the dimensions, n rows x n columns
Data frames
Now try out these functions:
head(gradstats, n = 3)
tail(gradstats, n = 2)
names(gradstats)
summary(gradstats)
dim(gradstats)
displays the first n rows of the data frame
last n rows
gives the name of each column
summarizes the whole data frame
gives the dimensions, n rows x n columns
length(gradstats)
length(gradstats$ind)
length of a data frame = # of columns
length of a vector = # of values
table(gradstats$dept)
table(gradstats)
Data frames
Now try out these functions:
head(gradstats, n = 3)
tail(gradstats, n = 2)
names(gradstats)
summary(gradstats)
dim(gradstats)
displays the first n rows of the data frame
last n rows
gives the name of each column
summarizes the whole data frame
gives the dimensions, n rows x n columns
length(gradstats)
length(gradstats$ind)
length of a data frame = # of columns
length of a vector = # of values
table(gradstats$dept)
table(gradstats)
table of counts for a single vector (column)
table of counts for all vectors (crossed)
Data frames
Look at the help file for table().
Try to figure out how to make a contingency
table for departments x stat programs.
Data frames
Look at the help file for table().
Try to figure out how to make a contingency
table for departments x stat programs.
table(gradstats$dept, gradstats$prog)
Reading in data
Download the data file located at
http://linguistics.berkeley.edu/~mfricke/R_Work
shop_files/salary.txt.
This file contains data on professors’ salaries.
(S. Weisberg (1985). Applied Linear Regression, Second Edition. New
York: John Wiley and Sons. Page 194. Downloaded from
http://data.princeton.edu/wws509/datasets/#salary on January 31st,
2013.)
Reading in data: working directory
Your working directory is where R looks for (and saves) files.
Check to see what it is by typing:
getwd()
You can change it to the directory where you saved the data file with:
setwd()
setwd(“/Users/melindafricke/Desktop”)
But there’s an easier way:
On a Mac: command + d, then select your directory.
In Windows: go to “File”, then “Change dir…”, and select your directory.
Reading in data
Open the help file for
read.table()
See if you can read in the data file we just
downloaded and start inspecting it…
Reading in data
read.table(“salary.txt”, header=T) -> salary
read.table() has several options, to deal with differently formatted files.
file
header
sep
quote
dec
row.names
nrows
skip
the filename, in quotes (must be in working dir)
does the first row contain column names?
how are the fields separated? (e.g. ‘\t’, ‘,’)
what character was used for quoting? (‘ ‘ ‘)
what character is used as a decimal point?
does one column contain row names?
(if not, R will number the rows)
how many rows to read in (default is all of them)
how many rows to skip before reading data
Using what we know already…
How many rows does this data set contain? columns?
What is the average salary (sl) for these professors?
How many professors are male vs. female (sx)?
For each rank (rk), how many professors have a doctorate vs. masters (dg)?
Using what we know already…
How many rows does this data set contain? columns?
dim(salary)
[1] 52 6
What is the average salary (sl) for these professors?
How many professors are male vs. female (sx)?
For each rank (rk), how many professors have a doctorate vs. masters (dg)?
Using what we know already…
How many rows does this data set contain? columns?
dim(salary)
[1] 52 6
What is the average salary (sl) for these professors?
mean(salary$sl)
[1] 23797.65
()
How many professors are male vs. female (sx)?
For each rank (rk), how many professors have a doctorate vs. masters (dg)?
Using what we know already…
How many rows does this data set contain? columns?
dim(salary)
[1] 52 6
What is the average salary (sl) for these professors?
mean(salary$sl)
[1] 23797.65
How many professors are male vs. female (sx)?
table(salary$sx)
female
male
14
38
For each rank (rk), how many professors have a doctorate vs. masters (dg)?
Using what we know already…
How many rows does this data set contain? columns?
dim(salary)
[1] 52 6
What is the average salary (sl) for these professors?
mean(salary$sl)
[1] 23797.65
How many professors are male vs. female (sx)?
table(salary$sx)
female
male
14
38
For each rank (rk), how many professors have a doctorate vs. masters (dg)?
table(salary$rk, salary$dg)
doctorate
masters
assistant
14
4
associate
5
9
full
15
5
Manipulating data frames
Subscripting is a way to reference columns and rows in a data frame.
The basic syntax is:
salary[1,1]
name of dataframe
row #(s)
column #(s)
comma!
N.B. You always need to include the comma when you use
subscripting on a dataframe.
Manipulating data frames
You can combine this syntax with other conventions we’ve learned (and a few
we haven’t!).
Try these:
salary[c(1,4), 1]
salary[c(1,4), c(1,6)]
salary[c(1:4), c(1,6)]
salary[c(10:15), ]
what does the colon do?
what if you leave the column # out?
salary[salary$sx==“female”,]
salary[salary$sl>30000,]
“display all the rows where sx is female”
“display all the rows where sl is > 30,000”
Using what you know now…
What is the mean salary for a female professor? a male?
What will this syntax tell us?
length(salary[salary$yd>20, ]$sl)
Using what you know now…
What is the mean salary for a female professor? a male?
mean(salary[salary$sx==“female”,]$sl)
[1] 21357.14
mean(salary[salary$sx==“male”,]$sl)
[1] 24696.79
What will this syntax tell us?
length(salary[salary$yd>20, ]$sl)
Using what you know now…
What is the mean salary for a female professor? a male?
mean(salary[salary$sx==“female”,]$sl)
[1] 21357.14
mean(salary[salary$sx==“male”,]$sl)
[1] 24696.79
What will this syntax tell us?
length(salary[salary$yd>20, ]$sl)
[1] 19
Using what you know now…
What is the mean salary for a female professor? a male?
mean(salary[salary$sx==“female”,]$sl)
[1] 21357.14
mean(salary[salary$sx==“male”,]$sl)
[1] 24696.79
What will this syntax tell us?
length(salary[salary$yd>20, ]$sl)
[1] 19
The number of professors that got their degree over 20 years ago.
One more cool function
aggregate() is really nice for creating data summaries. Try this:
aggregate(salary$sl, list(salary$sx, salary$rk), mean)
One more cool function
aggregate() is really nice for creating data summaries. Try this:
aggregate(salary$sl, list(salary$sx, salary$rk), mean)
“aggregate salaries, contingent on both sex and rank, and take the mean”
Group.1
female
male
female
male
female
male
Group.2
assistant
assistant
associate
associate
full
full
x
17580.00
17919.60
21570.00
23443.58
28805.00
29872.44
But… 
Aggregating
What’s the average salary for people with doctorates vs. masters?
On average, how many years ago did assistant vs. associate vs. full professors
get their degrees?
What’s the standard deviation for male vs. female salaries?
Aggregating
What’s the average salary for people with doctorates vs. masters?
aggregate(salary$sl, list(salary$dg), mean)
doctorate 23500.35
masters
24359.22
On average, how many years ago did assistant vs. associate vs. full professors
get their degrees?
What’s the standard deviation for male vs. female salaries?
Aggregating
What’s the average salary for people with doctorates vs. masters?
aggregate(salary$sl, list(salary$dg), mean)
doctorate 23500.35
masters
24359.22
On average, how many years ago did assistant vs. associate vs. full professors
get their degrees?
aggregate(salary$yd, list(salary$rk), mean)
assistant
6.33
associate
18.93
full
22.95
What’s the standard deviation for male vs. female salaries?
Aggregating
What’s the average salary for people with doctorates vs. masters?
aggregate(salary$sl, list(salary$dg), mean)
doctorate
23500.35
masters
24359.22
On average, how many years ago did assistant vs. associate vs. full professors get
their degrees?
aggregate(salary$yd, list(salary$rk), mean)
assistant
6.33
associate
18.93
full
22.95
What’s the standard deviation for male vs. female salaries?
aggregate(salary$sl, list(salary$sx), sd)
female
6151.873
male
5646.409
Writing data
Let’s say you’ve produced some data you want to share with someone else, or
have easy access to later.
aggregate(salary$sl, list(salary$sx, salary$rk), mean) -> mfsalaries
names(mfsalaries)
names(mfsalaries) = c(“sex”, “rank”, “salary”)
write.table(mfsalaries, “MFSalaries.txt”, sep=“\t”, row.names=F)
object
filename
separator?
include row names?
Saving your workspace
Look at all the objects we’ve created today!
ls()
If you want to save these, make sure you save your workspace
before you exit. (“Workspace…”, “Save workspace file…”)
R will create a file (in your working directory) that you can load
for use later, which includes all of these objects.
Saving your workspace
Look at all the objects we’ve created today!
ls()
If you want to save these, make sure you save your workspace
before you exit. (“Workspace…”, “Save workspace file…”)
R will create a file (in your working directory) that you can load
for use later, which includes all of these objects.
 If you want to save the text of your session, go to “File”,
“Save”.
Next up
Downloading and installing external packages
More sophisticated analyses
correlation, simple linear regression, t-tests, ANOVA
Graphing
R makes really beautiful graphs, and is very flexible
 Which of these topics are highest priority to you?
Thank you!
melindafricke@berkeley.edu