Ggplot2 v3

Graphing with R & ggplot2
Week 1: Introduction to R

Chicago Data Visualization Group
Workshop
October 8, 2012

Agenda
• Introductions
• Survey: http://j.mp/ggplot2-2012.
• Overview
• Workshop

Data Objects: Factors 2

Workshop Overview
Introduction to R (Week 1)
We will familiarized ourselves with the R environment with a gentle introduction to the basic functions.
After installing R, we will import and inspect data sets while becoming familiar with R terminology. By
the end of the class, we will conduct basic descriptions and plots of the data.
Introduction to ggplot2 (Week 2)
We will begin to use the ggplot2 package to create basic, but handsome, univariate, bivariate, and time-
series graphs. We will introduce the functions and terminology used in ggplot2. We will also explain the
fundamentals of proper data visualization techniques and how it relates to the ggplot2 defaults.
Grammar of Graphics (Week 3)
We will continue to show more advanced features of ggplot2, including how it relates to Leland
Wilkinson's Grammar of Graphics. We will show how to plot more than 2 variables in a single graph
using colors, shapes, and sizes. We will also discuss how human ability to perceive different shapes and
colors should drive the choices we make in data visualization.
Plots for Publications (Week 4)
After learning how to make plots, we will learn how to customize graphs with custom colors, labels, and
themes. We will emphasize how to create a customized look to be included in publications, including
addings labels in diagrams to help readers.


Installing R
1. Go to http://cran.r-project.org/
2. Download the installation for your OS.
3. Follow instructions for installation.
4. Start R from menu.


The R Console
• R can execute scripts or respond interactively at the console.
> 5+4 # addition
[1] 9
> 2^10 # exponentiation
[1] 1024
> a=2; b=10 # two commands on same line
> a^b
[1] 1024
>ls() # list of existing objects
> x = rnorm(100); y = rnorm(100) # generate random distributions
> length(x) # get the length of object
[1] 100
> mean(x) # calculate the mean
[1] 0.07163738
> sd(x) # calculate the std.dev
[1] 1.086229
> plot(x, y) # create a simple plot


The R Working Directory
• R works in the context of a directory. This is usually the directory from
where R was started.
• Get and Set your working directory.
> getwd() # Determine your working directory
> setwd(“your directory") # set to your data directory location
> getwd() # check that you are in the correct directory
> dir() # list file names in the current directory


Getting Help
• At the console
> help.start() # general help page
> Help(functionname) # help on function
> ?(functionname)
> ??(search string) # find all references to search string
> Example(topic) # see an example of topic
> Demo() # see demos


Data Types & Data Objects
• Data objects
– Vector: a set of elements of the same type.
– Matrix: a set of elements in rows & columns of the same type.
– data frame: rows & columns of elements of different types.
– Lists & Arrays*

• Data types (aka mode) of data objects
– Numeric: 3.14 and 3,4,5,….
– Character: “abc”, “cat”, “dog”,…
– Logical: TRUE, FALSE, NA
– Complex and Raw*

* Out of scope for this presentation


Data Types: Numeric
• Decimal values are numeric in R.
> x = 10.5 # assign a decimal value
> x # print the value of x
[1] 10.5
> class(x) # print the class name of x
[1] "numeric“

• Integer values are saved as numeric.
> k = 1
> k # print the value of k
[1] 1
> class(k) # print the class name of k
[1] "numeric“

• The fact that k is not an integer can be confirmed with
the is.integer().
> is.integer(k) # is k an integer?
[1] FALSE


Data Types: Integer
• Create an integer with the as.integer().
> y = as.integer(3)
> y # print the value of y
[1] 3
> class(y) # print the class name of y
[1] "integer"
> is.integer(y) # is y an integer?
[1] TRUE

• Coerce a numeric value into an integer with the
same as.integer().
> as.integer(3.14) # coerce a numeric value
[1] 3

• Parse a string for decimal values in much the same way.
> as.integer("5.27") # coerce a decimal string
[1] 5


Data Type: Logical
• A logical value is often created via comparison between
variables.
> x = 1; y = 2 # sample values
> z = x > y # is x larger than y?
> z # print the logical value
[1] FALSE
> class(z) # print the class name of z
[1] "logical"

• Standard logical operations: "&", "|", and "!".
> u = TRUE; v = FALSE
> u & v # u AND v
[1] FALSE
> u | v # u OR v
[1] TRUE
> !u # negation of u
[1] FALSE


Data Type: Character
• A character data type is used to represent string values in R.
> x = as.character(3.14)
> x # print the character string
[1] "3.14"
> class(x) # print the class name of x
[1] "character"
• Multiple character values can be concatenated with the paste()or
sprintf().
> name = "Joe"; amount = 100
> paste(name, "has", amount, "dollars") Both expressions produce
> sprintf("%s has %d dollars", name, amount) the same result
[1] "Joe has 100 dollars“
• To extract a substring, we apply the substr().
> substr("Mary has a little lamb.", start=3, stop=12)
[1] "ry has a l"
• Replace strings with the sub().
> sub("little", "big", "Mary has a little lamb.")
[1] "Mary has a big lamb."


Data Objects: Vectors
• A vector is a sequence of data elements of the same basic type.
• Here is a numeric vector created by the concatenation function c().
> c(2, 3, 5)
[1] 2 3 5
• Vectors can be combined via the function c.
> n = c(2, 3, 5)
> s = c("aa", "bb", "cc", "dd", "ee")
> c(n, s)
[1] "2" "3" "5" "aa" "bb" "cc" "dd" "ee“
– Notice the numeric values are coerced into character strings.


Vectors: Sequences & Patterns
• Sequences & patterns in vectors can be created with seq() & rep()
> seq(1,9,by=2)
[1] 1 3 5 7 9
> seq(8,20,length=6)
[1] 8.0 10.4 12.8 15.2 17.6 20.0
> rep(1:3,6)
[1] 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
> rep(1:3,rep(6,3))
[1] 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3
• Ranges can be created with the : operator
> 1:5
[1] 1 2 3 4 5


Vectors: Accessing elements
• Access vectors values with [i] operator.
> s = c("aa", "bb", "cc", "dd", "ee")
> s[3]
[1] "cc"
• Other methods of accessing vector values by index
> s[-3] # Negative index removes value.
[1] "aa" "bb" "dd" "ee"
> s[c(2, 3)] # Numeric index
[1] "bb" "cc"
> s[c(2, 3, 3)] # Duplicate indexes
[1] "bb" "cc" "cc"
> s[c(2, 1, 3)] # Out of order indexes
[1] "bb" "aa" "cc"
> s[2:4] # Range index
[1] "bb" "cc" "dd“
> s[c(FALSE, TRUE, FALSE, TRUE, FALSE)] # Logical index
[1] "bb" "dd"


Vectors: Arithmetic
• Arithmetic operations of vectors are performed member-by-member.
• For example, suppose we have two vectors a and b.
> a = c(1, 3, 5, 7); b = c(1, 2, 4, 8)
• Examples of arithmetic operations.
> 5 * a # Multiplication of a by 5.
[1] 5 15 25 35
> a + b # Addition of a & b vectors
[1] 2 5 9 15
> a * b # Multiplication of a & b
[1] 1 6 20 56
# Division and subtraction are also possible.
• If two vectors are of unequal length, the shorter one will be recycled in
order to match the longer vector.
> u = c(10, 20, 30); v = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
> u + v
[1] 11 22 33 14 25 36 17 28 39


Vectors: Subsetting and Summary
• Let's suppose we've collected some data from an experiment
> x=c(7.5,8.2,3.1,5.6,8.2,9.3,6.5,7.0,9.3,1.2,14.5,6.2)
• Some simple summary statistics of these data can be produced:
> mean(x)
[1] 7.216667
> summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.200 6.050 7.250 7.217 8.475 14.500
• Suppose we want summaries of two extracts of this data.
> summary(x[1:6])
3.100 6.075 7.850 6.983 8.200 9.300
> summary(x[7:12])
1.200 6.275 6.750 7.450 8.725 14.500


Exercises
1. Define (c) seq(4,10,by = 2)
(d) seq(3,30,length = 10)
> x = c(4,2,6)
(e) seq(6,-4,by = -2)
> y = c(1,0,-1)
Decide what the result will be of 3. Determine these patterns.
(a) rep(2,4)
the following: (b) rep(c(1,2),4)
(a) length(x)
(c) rep(c(1,2),c(4,4))
(b) sum(x)
(d) rep(1:4,4)
(c) sum(x^2)
(e) rep(1:4,rep(3,4))
(d) x+y
(e) x*y 4. Use the rep()to define the
(f) x-2
following vectors.
(g) x^2
(a) 6,6,6,6,6,6
(b) 5,8,5,8,5,8,5,8
2. Determine these sequences. (c) 5,5,5,5,8,8,8,8
(a) 7:11
(b) seq(2,9)


Exercises
5. If x =c(5,9,2,3,4,6,7,0,8,12,2,9) determine the following.
(a) x[2]
(b) x[2:4]
(c) x[c(2,3,6)]
(d) x[c(1:5,10:12)]
(e) x[-(10:12)]

Exercises taken from R – A Self Learn Tutorial from the National Center for Ecological Analysis and Synthesis


Data Objects: Matrices
• A matrix is a collection of data elements of the same type arranged in a
two-dimensional rectangular layout.
• Matrices can be created in R in a variety of ways. Perhaps the simplest is
to create the columns and then glue them together with the command
cbind.
> x=c(5,7,9)
> y=c(6,3,4)
> z=cbind(x,y)
> z
x y
[1,] 5 6
[2,] 7 3
[3,] 9 4
> dim(z) # Get the dimensions of the matrix
[1] 3 2
• Execute the expression rbind(x,y)and observe the result.


Matrices: Creating
• Using the matrix() to create matrices.
> matrix(c(2, 4, 3, 1, 5, 7),nrow=2,ncol=3,byrow = TRUE)
[,1] [,2] [,3]
[1,] 2 4 3
[2,] 1 5 7
• Removing, or changing, the values of nrow, ncol, & byrow will affect
the shape of the matrix. Experiment with this to see how the shape
changes.
• The dim() can also be used to create a matrix from a vector.
> a = c(5,10,15,20,25,30)
> dim(a) = c(2,3) # Reshape “a” by assigning it dimensions
> a
[,1] [,2] [,3]
[1,] 5 15 25
[2,] 10 20 30
• Transpose matrices with the t().

Matrices: Accessing Elements
• An element from our matrix “a” can be accessed by with [].
> a[2,3] # access the element in the second row, third column
[1] 30
> a[2,] # access the entire second row
[1] 10 20 30
> a[,3] # access the entire third column
[1] 25 30
> a[ ,c(1,3)] # access the first and third column
[,1] [,2]
[1,] 5 25
[2,] 10 30
• Rows and columns can be named.
> dimnames(a) = list(c("row 1","row 2"),c("col 1","col 2","col 3"))
> a[ ,c("col 1","col 3")]
col 1 col 3
row 1 5 25
row 2 10 30


Matrices: Arithmetic
> ## The Matrices > ## Matrix Math
> z =matrix(c(5,7,9,6,3,4),nrow=3,byrow=T) > y + z
> z # a 3 x 2 matrix [,1] [,2]
[,1] [,2] [1,] 6 10
[1,] 5 7 [2,] 9 15
[2,] 9 6 [3,] 8 3
[3,] 3 4 > y * z
> y=matrix(c(1,3,0,9,5,-1),nrow=3,byrow=T) [,1] [,2]
> y # a 3 x 2 matrix [1,] 5 21
[,1] [,2] [2,] 0 54
[1,] 1 3 [3,] 15 -4
[2,] 0 9 > y%*%x
[3,] 5 -1 [,1] [,2]
> x=matrix(c(3,4,-2,6),nrow=2,byrow=T) [1,] -3 22
> x # a 2 x 2 [2,] -18 54
[,1] [,2] [3,] 17 14
[1,] 3 4 • Link to matrix
[2,] -2 6 multiplication tutorial.


Exercises


Data Objects: Data Frames
• A data frame is the most common data object in R and is used for storing
data tables. It is a list of vectors of equal length.
> n = c(2,3,5); s = c("aa","bb","cc"); b = c(TRUE,FALSE,TRUE)
> data.frame(n,s,b) # output the data frame
n s b
1 2 aa TRUE
2 3 bb FALSE
3 5 cc TRUE
• Data frames can contain columns of data from different types.
• Import data using read.table() and read.csv(). Both return data
frames.


Data Frames: Importing data
• Get the following data file and save to working directory.
– http://www.ats.ucla.edu/stat/R/notes/hs0.csv
• Import with read.table()
> schdat = read.table("hs0.csv", header=T, sep=",")
> names(schdat)
[1] "gender" "id" "race" "ses" "schtyp" "prgtype" "read"
"write" "math" "science" "socst"
> head(schdat)
gender id race ses schtyp prgtype read write math science socst
1 0 70 4 1 1 general 57 52 41 47 57
2 1 121 4 2 1 vocati 68 59 53 63 61
3 0 86 4 3 1 general 44 33 54 58 31
4 0 141 4 3 1 vocati 63 44 47 53 56
5 0 172 4 2 1 academic 47 52 57 53 61
6 0 113 4 2 1 academic 44 52 51 63 61


Data Frames: Subsetting
• Retrieving a column vector using [[]] or $ or [,]
– These return the same vector: schdat[[3]];schdat$race;schdat[,”race”]
> str(schdat$race) #get the structure of schdat$race
int [1:200] 4 4 4 4 4 4 3 1 4 3 ...
• Retrieving a data frame column slice using []
– The return the same data frame: schdat[3];schdat[“Race”]
> str(schdat["race"]) #get the structure of schdat[“race”]
'data.frame': 200 obs. of 1 variable:
$ race: int 4 4 4 4 4 4 3 1 4 3 ...
• Retrieving a data frame row slice using []
– The return the same data frame: schdat[3];schdat[“Race”]
> schdat[5,]
gender id race ses schtyp prgtype read write math science socst
5 0 172 4 2 1 academic 47 52 57 53 61
– Run str(schdat[5,]) to see the structure of this data frame.


Data Frames: Subsetting
• We can use the subset()to slice both columns and rows. Let’s extract
only the read, write, math, science scores for the “academic” schools.
> schdat.academic = subset(schdat, prgtype=="academic",
+ select=c("read","write","math","science"))
> head(schdat.academic)
read write math science
5 47 52 57 53
6 44 52 51 63
8 34 46 45 39
10 57 55 52 50
12 57 65 51 63
13 73 60 71 61


Data Frames: Exploring data
• Let’s subset the read, write, math, and science scores for analysis
> read.sci = schdat[ , c("read","write","math","science")]
> summary(read.sci) # get a 5 number summary
read write math science
Min. :28.00 Min. :31.00 Min. :33.00 Min. :26.00
1st Qu.:44.00 1st Qu.:45.75 1st Qu.:45.00 1st Qu.:44.00
Median :50.00 Median :54.00 Median :52.00 Median :53.00
Mean :52.23 Mean :52.77 Mean :52.65 Mean :51.66
3rd Qu.:60.00 3rd Qu.:60.00 3rd Qu.:59.00 3rd Qu.:58.00
Max. :76.00 Max. :67.00 Max. :75.00 Max. :74.00
NA's :5


Data Frames: Further Analysis
• Let’s look at additional statistics in the school data set
> attach(schdat) # allow access to data by variable name only.
> options(digits=2) # set significant digits
> m = tapply(write,prgtype,mean) # tapply() calculates for every row
> v = tapply(write,prgtype,var)
> med = tapply(write,prgtype,median)
> n = tapply(write,prgtype,length)
> sd = tapply(write,prgtype,sd)
> cbind(mean=m,var=v,std.dev=sd,median=med,n=n)
mean var std.dev median n
academic 56 63 7.9 59 105
general 51 88 9.4 54 45
vocati 47 87 9.3 46 50
> options(digits=7)


Data Frames: Graphing
• Exploring data through graphs. Execute the following and examine the
output.
> hist(write)
> library(lattice) # load trellis graphics
> histogram(~write, schdat, type="count") # trellis graphs
> histogram(~write | gender, schdat, type="count") # histogram
> hist(write, breaks=15) # change the number of bins to 15
> boxplot(write) # boxplot function in the graphics package
> bwplot(ses~ write| gender, schdat) # boxplot by gender
> barplot(table(ses, gender), legend=c("low", "medium", "high"))
> barplot(table(ses, gender), beside=T, legend=c("low", "medium",
+ "high"), ylim=c(0, 50))


Data Frames: Frequency & Correlation
• Calculating Frequency Tables
> table(ses) # One way
> tab1=table(gender, ses) # Two way crosstab
> prop.table(tab1,1) # row proportions
> prop.table(tab1,2) # column proportions
> rowSums(tab1) # row frequencies
> colSums(tab1) # column frequencies
• Correlations & scatter plot
> # correlation of a pair of variables
> cor(write, math)
> cor(write, science)
> cor(write, science, use="complete.obs")
> # correlation matrix
> cor(read.sci, use="complete.obs")
> plot(math, write) # scatter plot matrix
> plot(read.sci)


Data Objects: Factors
• Factors are numeric or character vectors that have an associated set of
levels—a finite set of values the categorical variable can have.
• Let’s create a numeric vector of school types. 0 = private, 1 = public.
> sch.type = sample(0:1, 6, replace=T)
> sch.type
[1] 0 1 0 1 0 0
> is.factor(sch.type)
[1] FALSE
> factor(sch.type) # return factor of sch.type
[1] 0 1 0 1 0 0
Levels: 0 1
> sch.type.f = factor(sch.type,labels=c("private","public"))
> sch.type.f
[1] private public private public private private
Levels: private public


• Let’s create a character vector of socioeconomic status.
> ses = c("low","high","high","middle","low","low")
> ses
[1] "low" "high" "high" "middle" "low" "low"
> is.factor(ses)
[1] FALSE
> factor(ses)
[1] low high high middle low low
Levels: high low middle
> ses.f = factor(ses, levels=c("low","middle","high"))
> ses.f
[1] low high high middle low low
Levels: low middle high
• The levels()will also tell us the levels of a factor.
> levels(ses.f)
[1] "low" "middle" "high"


• Comparing a table without factors to a table with factors.
> table(ses,sch.type) # without factors
sch.type
ses 0 1
high 1 1
low 3 0
middle 0 1

> table(ses.f,sch.type.f) # with factors
sch.type.f
ses.f private public
low 3 0
middle 0 1
high 1 1


Exercises
1. Use the data set mtcars included in R.
Sort the data set by weight, heaviest first.
Which car gets the best mileage (largest mpg)?
Which gets the worst?
The cars in rows c(1:3, 8:14, 18:21, 26:28, 30:32) were imported into the United States. Compare the
variable mpg for imported and domestic cars using a boxplot. Is there a difference?
Make a scatterplot of weight, wt, versus miles per gallon, mpg. Label the points according to the number
of cylinders, cyl. Describe any trends.
2. The data set cfb (http://wiener.math.csi.cuny.edu/UsingR/Data/cfb.R) contains
consumer finance data for 1,000 consumers.
Create a data frame consisting of just those consumers with positive INCOME and negative NETWORTH.
What is its size?
3. Use the data set ewr (http://wiener.math.csi.cuny.edu/UsingR/Data/ewr.R). We
extract just the values for the times with df=ewr [,3:10]. The mean of each column
is found by using mean (df).
How would you find the mean of each row? Why might this be interesting?
Exercises from Using R for Introductory Statistics by John Verzani.


Resources
The following resources were used for the workshop materials.
They are also excellent R references for your continued learning.
UCLA’s Resources for R
R-Bloggers
R-Tutor
Quick R
Using R for Introductory Statistics by John Verzani.


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