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A Crash R Course on Statistical Graphics
Dr. Isabella R. Ghement
Ghement Statistical Consulting Company Ltd.
American Statistical Association Conference on Statistical Practice February 21, 2013, 1:00pm – 5:00pm
New Orleans, LA
1
Outline
1. Learning Goals
2. Overview of R
3. Things to Know about R
4. Good Practices
5. Getting Started with R
6. Data Import/Export
7. Graphical Systems in R
2
Outline - Continued
8. Basic R Graphics
9. Customizing Basic R Graphics
10. Advanced R Graphics
11. Customizing Advanced R Graphics
12. R Graphics Housekeeping
13. Summary
14. References
3
Learning Goals
4
Overarching Learning Goals
After attending this course, you will be able to:
Organize your work in R by creating and saving R scripts;
Import/export data using R;
Produce standard statistical plots using the R package graphics;
Produce advanced statistical plots using the R package lattice;
Customize basic and advanced statistical plots;
Save basic and advanced statistical plots in a variety of formats (e.g., jpeg, pdf).
5
Overview of R
Learning Goal: Understand what R is, what it can do for you and where to find R resources.
6
Overview of R R is an open-source software environment and
programming language for statistical computing and graphics.
R’s use is governed by the GNU general Public License.
R was created in the mid 90’s by Ross Ihaka and Robert Gentleman (also known as “R & R”) of the Statistics Department at the University of Auckland, New Zealand.
Some people claim that R was created by academics for academics. This may explain the steep learning curve some learners face when switching to R.
7
Overview of R R gets updated several times a year and each upgrade
includes new functionality. It’s good to keep up with the latest upgrades by installing the latest version of R. However, it is also important to keep all previously installed versions of R, as sometimes old R code will no longer work with recent versions of R.
You can check the website http://cran.stat.ucla.edu/ for R upgrades.
R is supported by all major operating systems: Windows, Mac, Linux and Unix.
R is developed at present by the R Development Core Team, a group of researchers with write access to the R source code.
8
Overview of R R has its own dedicated website:
http://www.r-project.org/
The R website provides access to a variety of
resources, including:
- R Mailing Lists (e.g., R-help)
- R Conferences (e.g., UseR!)
- CRAN (i.e., go to website for installing R)
- Search resources
- R Manuals
- R Books
- R Journal 9
Overview of R
To cite R in publications, use the following:
R Development Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
ISBN 3-900051-07-0, URL http://www.R-project.org/.
10
Overview of R
The original R is focused on function rather than form and its graphical user interface reflects this focus.
Efforts to improve R’s graphical user interface have led to enhanced versions of R such as: • RStudio (http://www.rstudio.com) • Revolution R (http://www.revolutionanalytics.com)
11
Overview of R R offers a powerful and versatile platform for:
Data Processing and Manipulation (e.g., packages plyr, reshape);
Statistical Graphics (e.g., packages graphics, grid, lattice, ggplot2); Statistical Analyses (see CRAN Task Views website for list of R packages dedicated to implementing specific statistical analyses, http://cran.r-project.org/web/views/) ; Statistical Programming (e.g., built-in R programming language as
well as ability to interface with C++, FORTRAN, Java and Python via packages such as Rcpp, Rfortran, rJava and RPy);
Statistical Reporting (e.g., excellent interface with Latex via Sweave and some interface with Word via packages such as R2HTML and rtf).
12
Overview of R To learn more about R, you can refer to introductory R books such as: “R in Action: Data Analysis and Graphics with R”, by
Robert I. Kabacoff (Manning Publications Co., 2011)
“R for Dummies”, by Andrie de Vries and Joris Meys (John Wiley & Sons, 2012)
“R Cookbook”, by Paul Teetor (O'Reilly, 2011) “R for Statistics”, by P.-A. Cornillon et al. (CRC Press, 2012) The first of these books is accompanied by an excellent website, Quick-R: http://www.statmethods.net/. 13
Overview of R R users familiar with other statistical software (i.e., Stata, SAS, SPSS) can also consult these books: “R for Stata Users”, by Robert A. Muenchen and
Joseph M. Hilbe (Springer, 2010); “R for SAS and SPSS Users”, by Robert A.
Muenchen (Springer, 2009). See http://www.r-project.org/doc/bib/R-books.html for additional R book references.
14
Things To Know About R
Learning Goal: Be aware of some of R’s unique features and quirks. 15
Things to know about R
R is case sensitive!
e.g.: anova is different from Anova
R uses the assignment operator <-
to assign names or create new data objects.
e.g.: m <- 1 + 2
R provides access to help files via the question mark.
e.g.: ?mean
16
Things to know about R
R uses the concatenate operator c to combine values or labels.
e.g.: var <- c(1, 2,3)
R uses quotation marks for character strings that can be interpreted as names or labels.
e.g.: col <- c("red", "blue")
data <- read.csv("datafile.csv")
17
Things to know about R
R uses different types of structures for storing data:
vectors
factors
matrices
arrays
data frames
lists
e.g.: Vector: Factor: Matrix: Array: Data Frame:
2
1
3
1
2
3
10
20
30
4
5
17
M
F
F
M
1 10
2 20
18 7
40 9
1
2
3
M
F
M
1.5
1.8
1.7 18
Things to know about R
• R uses the symbol NA to denote missing values (i.e., Not Available).
• In R, operations performed on variables which include missing values produce a missing value as a result.
e.g.:
1
NA
3
19
Things to know about R
R relies on functions for the automation of operations.
e.g.:
f <- function(x){
plot(x)
return(summary(x))
}
20
Things to know about R R uses packages to bundle up functions useful for
performing certain data processing tasks, producing certain types of graphs or performing specialized statistical analyses. R packages may also include data sets and help documentation.
Thousands of R packages are available on CRAN (Comprehensive R Archive Network) and can be installed in R with the command:
install.packages("package_name")
Once installed in R, packages need to be attached to the current R working session: require(package_name)
For a list of R package available on CRAN, see: http://cran.r-project.org/web/packages/
21
Things to know about R
For the purpose of creating graphs or implementing statistical analyses, R uses formulas such as:
y ~ x (y as a function of x) y ~ x | f (y as a function of x, conditional on f) y ~ x1*x2 (y as a function of x1, x2 and their interaction)
22
Things to know about R
R uses various types of brackets:
[ ]
[[ ]]
( )
{ }
It takes a while to get used to the meaning of
each of these brackets and know when and how
to use them.
23
Good Practices
Learning Goal: Adopt a basic set of good practices when working with R in order to keep your R work organized and ensure it is reproducible.
24
Be organized when working with R • Set your working directory at the very beginning of each R
session. This way, everything you save during that session is placed in your working directory.
• Type all of your R commands in script files, to ensure your work is reproducible. Script files are simply text files having the extension .R.
• Set desired options for controlling various aspects of the
session (e.g., maximum object size? maximum memory size?).
options(object.size=10e10) memory.size(max=TRUE)
Note: To access the help file for the options() function, type the following R command in the R Console window: ?options 25
Be organized when working with R • It pays off to be diligent about dating, versioning and
commenting all of your R script files.
• The pound symbol, #, is used to comment lines in an R script file.
e.g.:
• R script files bear the extension .R.
• Suggested naming conventions for R script files: Project_Results.R ProjectResults.R Project.Results.R
# This is a comment in an R script file. demo(graphics)
26
Getting Started with R
Learning Goal: Understand the R workflow and know how to interact with R via R script files.
27
R Workflow
Launch R and set up current session
Type R commands in an R script
Send R commands to the R Console for
execution
Create R output (e.g., numerical output, graphs,
processed data sets)
Save R output and
quit R
28
Launching R
• If you have an R icon on your desktop, double click on it to launch R.
• If you don’t have an R icon on your desktop, go to Start All Programs. Find the R application among the list of programs installed on your computer and select it in order to launch R.
Example of R Icon
29
Taking Stock of R’s Interface
Notes:
R has an R Console window, where we can either type commands directly or send commands stored in script files for execution. R also has a GUI menu, which allows us to change the working directory for the current R working session and open new R script files. GUI = Graphical User Interface
30
Setting Up the Current R Session • Set your working directory for the current R session via the R
Gui commands:
File Change dir... • Check your current working directory using the R command: getwd() • Set your options for the current R session by typing the
following in your R script: # set options for current session options(object.size=10e10) memory.size(max=TRUE) 31
Opening and Saving an R Script File • Open a new R script using the following commands from
the R Gui menu: File New Script. • Save the script using the R Gui commands File Save as... For now, you can call the script
Script.R. • Make it a good habit to keep saving the script file as you
continue to add R commands to it. Simply press the keyboard keys Ctrl + S whenever you are ready to save the script.
Note: Existing R script files can be accessed in R via the R GUI menu commands: File Open script...
32
Modifying an R Script File • Create a header for your script file, similar to the one
below.
################################### # A Crash R Course on Statistical Graphics # New Orleans, LA # February 21, 2013 ###################################
• Save the script file and carry on.
• As we progress through the course, please copy and paste R commands from the course slides into your R script file(s) and then send these commands to R for execution by selecting them and using the keyboard keys Ctrl + R.
33
R Graphics Demo • For now, type the following command
in your R script to access an R Graphics
Demo:
demo(graphics)
Press the Enter key to scroll through the various graphs available in this Demo.
• You can send the demo(graphics) command to R for execution by selecting it in the script file and pressing the keys Ctrl + R.
• You can also send commands to R for execution by copying them from the script file with Ctrl + C and pasting them into the R Console window with Ctrl + V).
34
Quitting R • To quit R at the end of a session , you can simply
type the following command in the R Console window:
quit()
• In general, you don’t need to save the working space attached to the current R session if you save all of the script files and numerical and graphical output they produce.
• For this course, we do not need to quit R yet.
35
Data Import Learning Goal: Be able to import comma delimited data files and text data files in R. 36
R Functions for Data Import R offers a variety of functions for importing data files. Two of these functions are shown below.
Note that read.csv() is a special version of read.table(). Both of these functions require the name of the import file to be specified (provided the file is located in the current R working directory):
dataset <- read.table("datafile.csv") dataset <- read.table("datafile.txt")
File Type File Extension R Function R Help
Comma Separated File
.csv read.csv() ?read.csv
Text File .txt read.table() ?read.table
37
read.csv() One of the easiest ways to import data into R is to save that data as a comma delimited file (.csv) and then use the function read.csv() to bring this file into R. dataset <- read.csv("datafile.csv", as.is = TRUE) name of csv file where option for preserving
import data are stored; character variables this file must be located in the R working directory
38
read.csv()
When calling read.csv(), it is important to use
the option as.is = TRUE.
This will prevent R from automatically
converting all of the character variables in the
data to factors.
As a result, dates will be particularly easy to
handle in R using the lubridate package.
39
read.csv()
The command read.csv() can also be used with the following arguments: dataset <- read.csv(file.choose(), as.is = TRUE) browse interactively for the import data file dataset <- read.csv("C://desktop//datafile.csv", as.is = TRUE) extract the import data file from a specific location on the computer
40
read.table() The function read.table() is used to import text data files (.txt) into R. In general, this function requires more arguments than read.csv(). dataset <- read.table("datafile.txt", sep="\t", header = TRUE, as.is = TRUE)
Notes: sep stands for Type of separator used to delimitate data columns, such as:
sep="\t" tab sep= " " white space sep= "," comma header indicates whether or not the file header should be retained
as.is indicates whether or not character variables should be preserved
41
read.table()
The command read.table() can also be used with the following arguments: dataset <- read.table(file.choose(), as.is = TRUE) browse interactively for the import data file dataset <- read.table("C://desktop//datafile.table", as.is = TRUE) extract the import data file from a specific location on the computer
42
Example of Data Import in R
air <- read.csv("Air Quality Baton Rouge 2011.csv", as.is=TRUE) str(air) require(lubridate) air$Date <- mdy(air$Date) str(air) air <- air[1:365, ] View(air)
Notes on lubridate package: The lubridate package includes the following functions for converting character variables storing dates into date variables: ymd() year month day mdy() month day year dmy() day month year Example of dates handled by these functions: ymd() "2012-10-31 " or "2012/10/31" mdy() "10-31-2011" or "10/31/2012" dmy() "31-10-2012" or "31/10/2012".
43
Exploring Data Imported in R R stores any data set imported via read.csv() or read.table() as a
data frame (i.e., a tabular data set whose columns correspond to
statistical variables and whose rows correspond to records).
R Commands for Exploring a Data Frame Description
View(dataset) View data frame
str(dataset) Explore structure of data frame
names(dataset) rownames(dataset)
Extract names of variables and records in data frame
nrow(dataset) ncol(dataset)
Extract number of rows and columns in data frame
summary(dataset) Summarize the data frame
attach(dataset) detach(dataset)
Attach/detach the data frame to the R working space 44
Exercise on Data Import
ozone <- read.table("ozone.txt", header=TRUE, as.is=TRUE) head(ozone) str(ozone) rownames(ozone) ozone$date <- rownames(ozone) require(lubridate) ozone$date <- ymd(ozone$date) head(ozone) attach(ozone)
Details on ozone.txt • Ozone and meteorological variables collected in Rennes (France) during the summer of 2001. • The variables available are: - maxO3 (maximum daily ozone) - T12 (temperature at midday) - wind (wind direction) - rain - Wx12 (projection of the wind speed vector on the east-west axis at midday)
Import the data file ozone.txt into R. For ease, the R commands for data import are given below. Explore the resulting data frame.
45
Data Export
Learning Goal: Be able to export data from R in the form of comma delimited or text files.
46
R Functions for Data Export
R offers a variety of functions for exporting data files, but
we will focus only on the two functions listed below.
File Type File Extension R Function R Help
Comma Separated File
.csv write.csv() ?write.csv
Text File .txt write.table() ?write.table
47
Data Export
R Command:
write.csv()
Generic Syntax:
write.csv(dataframe, "datafile.csv",
row.names=FALSE,
quote=FALSE)
Notes: When using write.csv(): • The argument dataframe can be any data frame available in your R working space; • The argument "datafile.csv" represents the name of the csv file storing the exported data frame; • The option row.names = FALSE prevents R from adding row names to the exported data file; • The option quote=FALSE prevents R from adding quotes around values of character variables.
48
Data Export
R Command:
write.table()
Generic Syntax:
write.table(dataframe, "datafile.txt",
sep= "\t",
row.names=FALSE,
quote=FALSE)
Notes: When using write.table():
• The argument dataframe can be any data frame available in your R working space; • The argument sep is used to specify the name of the column separator.
• The argument "datafile.txt" represents the name of the text file storing the exported data frame; • The option row.names = FALSE prevents R from adding row names to the exported data file; • The option quote=FALSE prevents R from adding quotes around values of character variables. 49
Example of Data Export in R
# Export comma separated file
write.csv(air, "airexport.csv",
row.names=FALSE,
quote=FALSE)
# Export text file
write.table(air, "airexport.txt",
sep= "\t",
row.names=FALSE,
quote=FALSE)
50
Exercise on Data Export
Export the data frame ozone in your working space as a
comma delimited file (.csv). For your convenience, the
R command for data export is given below.
# Export as a comma separated file
write.csv(ozone, "ozone.csv",
row.names=TRUE,
quote=FALSE)
51
Graphical Systems in R
Learning Goal: Know about the 4 graphical systems available in R and how to access references and help for each system. 52
Graphical Systems in R
Grid Graphics
Grammar of Graphics
Trellis Graphics
Base Graphics Least Sophisticated
Most Sophisticated
53
Graphical Systems in R Graphical System R Package Book Reference
Base Graphics graphics “Graphics for Statistics and Data Analysis with R”, by Kevin J. Keen (CRC Press, 2010)
Trellis Graphics lattice “Lattice: Multivariate Data Visualization with R”, by Deepayan Sarkar (Springer, 2008)
Grammar of Graphics ggplot2 “ggplot2: Elegant Graphics for Data Analysis”, by Hadley Wickham (Springer-Verlag, 2009)
Grid Graphics grid “R Graphics”, 2nd Edition, by Paul Murrell (Chapman & Hall/CRC, 2006)
Note: The graphics packages comes with the default installation of R. The other packages need to be installed in R one time only and then required for each R session.
install.packages(c("lattice", "ggplot2", "grid"))
require("lattice") require("ggplot2") require("grid") 54
Example of Graph Produced in R
graphics package
January February March April May June July August September October November December
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
2011 BATON ROUGE/CAPITOL
Month
Daily
Max O
zone (
ppm
)
55
Example of Graph Produced in R
2011 BATON ROUGE/CAPITOL
Month
Daily
Max O
zone (
ppm
)
0.00
0.05
0.10
January February March April May June July August September October November December
lattice package
56
Example of Graph Produced in R
0.00
0.05
0.10
January February March April May June July August September October November December
Month
Daily
Max O
zone (
ppm
)
2011 BATON ROUGE/CAPITOL
ggplot2 package
57
Getting Help on R Graphical Systems To access the R help files associated with each of the three graphical systems, type the following commands in the R Console window: help(package="graphics") help(package="lattice") help(package="ggplot2") help(package="grid")
58
Getting Help on R Graphical Systems
To access the R help files associated with specific functions within a particular graphical system package, use commands similar to the ones below: function name package name
| | help(barplot, package="graphics") help(bwplot, package="lattice") help(qplot, package="ggplot2") help(arrowsGrob, package="grid")
59
Basic R Graphics Learning Goal: Learn how to produce basic graphs using the R graphics package.
60
Basic R Graphics
R offers a collection of functions for producing
standard graphics that are useful when
conducting exploratory data analysis.
These functions are available via the graphics
package, which is pre-installed in R.
61
Basic R Graphics
Graph Type R Command
Histogram hist(x)
Density Plot plot(density(x))
Boxplot boxplot(x)
Cumulative Distribution Plot
plot.ecdf(x)
x = quantitative variable
62
Basic R Graphics
Graph Type R Command
Bar chart barplot(table(f))
Dot chart dotchart(table(f))
Pie Chart pie(table(f))
f = qualitative variable (i.e., factor)
63
Basic R Graphs Graph Type R Command
Scatter plot plot(y ~ x)
Time series plot plot(y ~ date)
Coplot coplot(y ~ x|z)
Line Plot matplot(x, cbind(y,z))
Pairs plot pairs(cbind(y,x,z))
Side-by-side boxplots boxplot(y ~ f)
Side-by-side bar charts barplot(table(f1, f2))
x, y, z = quantitative variables f, f1, f2 = qualitative variables (i.e., factors) date = time variable
64
Histogram
Air Quality in Baton Rouge in 2011
Histogram
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
Fre
quency
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
020
40
60
80
?hist
65
R Code for Histogram
air <- read.csv("Air Quality Baton Rouge 2011.csv", as.is=TRUE) View(air) str(air) require(lubridate) air$Date <- mdy(air$Date) str(air) air <- air[1:365, ] names(air) attach(air)
66
R Code for Histogram
hist(Ozone,
xlab="Daily Max Ozone (ppm)",
main="Histogram",
col="lightblue",
sub="2011 BATON ROUGE/CAPITOL",
col.sub="red")
67
Density Plot
0.00 0.05 0.10 0.15
05
10
15
20
25
Kernel Density Plot
N = 365 Bandwidth = 0.004334
Density
?density
68
R Code for Density Plot
plot(density(Ozone),
main="Kernel Density Plot")
69
Boxplot
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Boxplot
2011 BATON ROUGE/CAPITOL
Daily
Max O
zone (
ppm
)
?boxplot
70
R Code for Boxplot
boxplot(Ozone,
ylab="Daily Max Ozone (ppm)",
main="Boxplot",
col="lightblue",
sub="2011 BATON ROUGE/CAPITOL",
col.sub="red")
71
Side-by-Side Boxplots
January February March April May June July August September October November December
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Side-by-Side Boxplots
2011 BATON ROUGE/CAPITOL
Month
Daily
Max O
zone (
ppm
)
?boxplot
72
R Code for Side-by-Side Boxplots
Month <- months(Date) Month <- factor(Month, levels=unique(Month)) boxplot(Ozone ~ Month, xlab="Month", ylab="Daily Max Ozone (ppm)", main="Side-by-Side Boxplots", col="lightblue", sub="2011 BATON ROUGE/CAPITOL", col.sub="red")
73
Empirical CDF
?plot.ecdf
plot.ecdf(Ozone, xlab="Daily Max Ozone (ppm) ", main="Empirical Cumulative Distribution Function")
0.00 0.05 0.10 0.15
0.0
0.2
0.4
0.6
0.8
1.0
Empirical Cumulative Distribution Function
Daily Max Ozone (ppm)
Fn(x
)
74
Barchart
United States
Decade
Perc
ent
Change in H
ispanic
Popula
tion f
rom
Pre
vio
us D
ecade
010
20
30
40
50
60
70
53%
58%
43%
Decade US
1990 53%
2000 58%
2010 43%
Percent Change in Hispanic Population from Previous Decade
?barplot
75
Barchart Decade <- c(1990, 2000, 2010)
Pct.Change.Hispanic.US <- c(53, 58, 43)
b <- barplot(Pct.Change.Hispanic.US,
col=c("#599ad3"),
xlab="Decade",
ylab="Percent Change in Hispanic Population from Previous Decade",
ylim=c(0,70),
main="United States")
abline(h=0)
text(b, Pct.Change.Hispanic.US + 2,
paste(Pct.Change.Hispanic.US,"%",sep=""))
76
Side-by-Side Barcharts
1990 2000 2010
Decade
Perc
ent
Change in H
ispanic
Popula
tion f
rom
Pre
vio
us D
ecade
010
20
30
40
50
60
70
53%
58%
43%
6%9%
57%
United States
New Orleans Metro
Decade US New Orleans Metro
1990 53% 6%
2000 58% 9%
2010 43% 57%
Percent Change in Hispanic Population from Previous Decade
?barplot
77
R Code for Side-by-Side Barcharts
Decade <- c(1990, 2000, 2010) Pct.Change.Hispanic.US <- c(53, 58, 43) Pct.Change.Hispanic.NewOrleansMetro <- c(6,9,57) Pct.Change.Hispanic <- data.frame(Pct.Change.Hispanic.US, Pct.Change.Hispanic.NewOrleansMetro) Pct.Change.Hispanic <- data.matrix(Pct.Change.Hispanic) rownames(Pct.Change.Hispanic) <- Decade Pct.Change.Hispanic
78
R Code for Side-by-Side Barcharts b <- barplot(t(Pct.Change.Hispanic), beside=TRUE, col=c("#599ad3", "#79c36a"), xlab="Decade", ylab="Percent Change in Hispanic Population from Previous Decade", ylim=c(0,70)) abline(h=0) text(b[1,], Pct.Change.Hispanic.US + 2, paste(Pct.Change.Hispanic.US,"%",sep="")) text(b[2,], Pct.Change.Hispanic.NewOrleansMetro + 2, paste(Pct.Change.Hispanic.NewOrleansMetro,"%",sep="")) legend("topleft", c("United States","New Orleans Metro"), fill=c("#599ad3", "#79c36a"), bty="n") 79
Stacked Barcharts
Proportion
1980
1990
2000
2010
0.0 0.2 0.4 0.6 0.8 1.0
0.08 0.22 0.60 0.09
0.08 0.20 0.61 0.11
0.07 0.20 0.62 0.11
0.07 0.17 0.64 0.12
Under 5 years 5 to 17 years 18 to 64 years 65 years and over
Decade Under 5 years
5 to 17 years
18 to 64 years
65 years and older
1980 105,801 285,440 774,773 116,291
1990 97,768 256,363 771,383 138,877
2000 90,471 261,362 815,010 149,667
2010 77,154 195,664 752,855 142,091
Age Distribution in New Orleans Metro (Expressed as Counts)
80
R Code for Stacked Barcharts
library(lattice) library(plyr) aged <- matrix(c(105801, 285440, 774773, 116291, 97768, 256363, 771383, 138877, 90471, 261362, 815010, 149667, 77154, 195664, 752855, 142091), nrow=4, ncol=4, byrow=TRUE) aged colnames(aged) <- c("Under 5 years", "5 to 17 years", "18 to 64 years", "65 years and over") rownames(aged) <- c("1980","1990","2000","2010") aged
81
R Code for Stacked Barcharts
colors <- c(rgb(166,27,30,maxColorValue = 255),
rgb(192,80,77,maxColorValue = 255),
rgb(24,65,83,maxColorValue = 255),
rgb(130,184,208,maxColorValue = 255))
colorset <- simpleTheme(col=colors, border="white")
82
R Code for Stacked Barcharts
sb <- barchart(prop.table(aged, margin=1), xlab="Proportion", par.settings=colorset, panel=function(...) { panel.barchart(...) tmp <- list(...) tmp <- data.frame(x=tmp$x, y=tmp$y) # calculate positions of text labels df <- ddply(tmp, .(y), function(x) { data.frame(x, pos=cumsum(x$x)-x$x/2) }) panel.text(x=df$pos, y=df$y, label=sprintf("%.02f", df$x), cex=0.7) }, auto.key=list(columns=4, space="bottom", cex=0.8, size=1.4, adj=1, between=0.2, between.colums=0.1)) plot(sb) 83
Comparative Pie Charts
Mexican
Puerto Rican
Cuban
Other
Mexican
Puerto Rican
CubanOther
Mexican
Puerto Rican
Cuban
Other
Parish Mexican Puerto Rican Cuban Other
Jefferson 10,194 2,682 3,840 36,986
Orleans 4,298 948 1,285 11,520
St. Tammany 3,593 933 816 5,628
Share of Hispanic Population by Nationality in Three New Orleans Parishes in 2010 (Expressed as a Count)
?pie 84
R Code for Comparative Piecharts
PopulationShare <- c(10194, 2682, 3840, 36986, 4298, 948, 1285, 11520, 3593, 933, 816, 5628) PopulationShare <- matrix(PopulationShare, nrow=3, ncol=4, byrow=TRUE) PopulationShare rownames(PopulationShare) <- c("Jefferson","Orleans","St. Tammany") colnames(PopulationShare) <- c("Mexican","Puerto Rican","Cuban","Other") PopulationShare
85
R Code for Comparative Piecharts
layout(matrix(c(1,2,3),1,3, byrow=TRUE)) cols <- c("#599ad3", "#9e66ab", "#79c36a", "#f9a65a") pie(PopulationShare["Jefferson",], init=90, clockwise=T, col=cols,
radius=1.2) pie(PopulationShare["Orleans",],init=90, clockwise=T, col=cols,
radius=1.2) pie(PopulationShare["St. Tammany",], init=90, clockwise=T, col=cols,
radius=1.2)
86
Line Charts
Year
Popula
tion L
ivin
g in P
overt
y
1979 1989 1999 2009
050000
100000
150000
62,114
105,687110,179
104,349
143,793
152,42
130,896
82,469
Orleans Parish
Rest of the New Orleans Metro
?matplot
?matpoints
?matlines
87
R Code for Line Charts Year <- c(1979, 1989, 1999, 2009) OrleansParish <- c(62114, 105687, 110179, 104349) RestNewOrleansMetro <- c(143793, 152042, 130896, 82469) matplot(Year, cbind(OrleansParish, RestNewOrleansMetro), type="l", ylab="Population Living in Poverty", ylim=c(0,160000), lty=1, lwd=2, col=c("darkgreen","orange"), axes=FALSE ) axis(1, at=c(1979, 1989, 1999, 2009), labels=c(1979, 1989, 1999, 2009)) axis(2,at=pretty(0:160000))
88
R Code for Line Charts
segments(Year[1],OrleansParish[1]-10000, Year[1], OrleansParish[1])
segments(Year[2],OrleansParish[2]-10000, Year[2], OrleansParish[2])
segments(Year[3],OrleansParish[3]-10000, Year[3], OrleansParish[3])
segments(Year[4],OrleansParish[4]+10000, Year[4], OrleansParish[4])
text(Year[1], OrleansParish[1]-15000, paste(62,114,sep=","),col="darkgreen")
text(Year[2], OrleansParish[2]-15000, paste(105,687,sep=","),col="darkgreen")
text(Year[3], OrleansParish[3]-15000, paste(110,179,sep=","),col="darkgreen")
text(Year[4], OrleansParish[4]+15000,paste(104,349,sep=","),col="darkgreen")
89
R Code for Line Charts
segments(Year[1],RestNewOrleansMetro[1]-10000, Year[1], RestNewOrleansMetro[1]) segments(Year[2],RestNewOrleansMetro[2]-10000, Year[2], RestNewOrleansMetro[2]) segments(Year[3],RestNewOrleansMetro[3]+10000, Year[3],
RestNewOrleansMetro[3]) segments(Year[4],RestNewOrleansMetro[4]-10000, Year[4], RestNewOrleansMetro[4]) text(Year[1], RestNewOrleansMetro[1]-15000, paste(143,793,sep=","), col="orange") text(Year[2], RestNewOrleansMetro[2]-15000, paste(152,042,sep=","), col="orange") text(Year[3], RestNewOrleansMetro[3]+15000, paste(130,896,sep=","), col="orange") text(Year[4], RestNewOrleansMetro[4]-15000, paste(82,469,sep=","), col="orange")
90
R Code for Line Charts
legend("bottomright", c("Orleans Parish","Rest of the New Orleans Metro"), col=c("darkgreen","orange"), lty=1, lwd=2, bty="n" ) box()
91
Time Series Plot
Used for plotting the values of a quantitative
variable Y versus a time variable T.
e.g.: Y = Ozone
T = Date
plot(Y ~ T) plot(Y ~ T, type= "l") plot(Y ~ T, type= "h")
Jan 01 Jan 06 Jan 11 Jan 16 Jan 21 Jan 26 Jan 31
0.0
20.0
30.0
40.0
50.0
6
T
Y
Jan 01 Jan 06 Jan 11 Jan 16 Jan 21 Jan 26 Jan 31
0.0
20.0
30.0
40.0
50.0
6
T
Y
Jan 01 Jan 06 Jan 11 Jan 16 Jan 21 Jan 26 Jan 31
0.0
20.0
30.0
40.0
50.0
6
T
Y
?plot
92
Time Series Plot (v.1)
Jan Mar May Jul Sep Nov Jan
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Time Series Plot
2011 BATON ROUGE/CAPITOL
Date
Daily
Max O
zone (
ppm
)
plot(Ozone ~ Date, ylab="Daily Max Ozone (ppm)", main="Time Series Plot", sub="2011 BATON ROUGE/CAPITOL", col.sub="red")
93
Time Series Plot (v.2)
Jan Mar May Jul Sep Nov Jan
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Time Series Plot
2011 BATON ROUGE/CAPITOL
Date
Daily
Max O
zone (
ppm
)
plot(Ozone ~ Date, type="l", ylab="Daily Max Ozone (ppm)", main="Time Series Plot", sub="2011 BATON ROUGE/CAPITOL", col.sub="red")
94
Time Series Plot (v.3)
Jan Mar May Jul Sep Nov Jan
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Time Series Plot
2011 BATON ROUGE/CAPITOL
Date
Daily
Max
Ozo
ne (
ppm
)
plot(Ozone ~ Date, type="h", ylab="Daily Max Ozone (ppm)", main="Time Series Plot", sub="2011 BATON ROUGE/CAPITOL", col.sub="red")
95
Time Series Plot (v.4)
Jan Mar May Jul Sep Nov Jan
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Time Series Plot
2011 BATON ROUGE/CAPITOL
Date
Daily
Max O
zone (
ppm
)
0.10
bad <- ifelse(Ozone > 0.10, "red", "darkgrey") plot(Ozone ~ Date, type="h", ylab="Daily Max Ozone (ppm)", col=bad, main="Time Series Plot", sub="2011 BATON ROUGE/CAPITOL", col.sub="blue") abline(h=0.10, lty=2, col="red") text(locator(1),"0.10")
96
Scatterplot (v.1)
30 40 50 60 70 80 90 100
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Scatterplot
Temperature (°F)
Daily
Max O
zone (
ppm
)
plot(Ozone ~ Temp, xlab="Temperature (°F)", ylab="Daily Max Ozone (ppm)", main="Scatterplot")
97
Scatterplot (v.2)
30 40 50 60 70 80 90 100
0.0
00.0
20.0
40.0
60.0
80.1
00.1
20.1
4
Scatterplot
Temperature (°F)
Daily
Max O
zone (
ppm
)
require(car) scatterplot(Ozone ~ Temp, xlab="Temperature (°F)", ylab="Daily Max Ozone (ppm)", smooth=FALSE, reg.line=FALSE, main="Scatterplot")
help(scatterplot, package="car")
98
Coplot
0.0
00.0
40.0
80.1
2
30 40 50 60 70 80 90 100
30 40 50 60 70 80 90 100 30 40 50 60 70 80 90 100
0.0
00.0
40.0
80.1
2
Temp
Ozone
60 70 80 90 100
Given : RelativeHumidity
coplot(Ozone ~ Temp | RelativeHumidity, panel = panel.smooth)
?coplot
99
Pairs Plot
Ozone
30 40 50 60 70 80 90
0.0
00.0
40.0
80.1
2
30
40
50
60
70
80
90
Temp
0.00 0.04 0.08 0.12 0 5 10 15
05
10
15
WindSpeed
pairs(cbind(Ozone, Temp, WindSpeed))
100
Exercise on Basic R Graphics For this exercise, refer to the ozone data frame
available in your R working space and follow the
instructions below to create a variety of basic R
graphs using variables from this data frame.
1. Create a histogram of maxO3.
2. Create a density plot of maxO3.
3. Create a boxplot of maxO3.
4. Create a cumulative distribution plot of maxO3.
101
Exercise on Basic R Graphics
5. Create a scatter plot of maxO3 versus T12.
6. Create a time series plot of maxO3.
7. Create side-by-side boxplots of maxO3 for the
four wind directions stored in the wind
variable).
8. Create a bar chart for the variable rain.
9. Create a bar chart for rain according to wind.
102
Customizing Basic R Graphics
Learning Goal: Learn how to customize basic graphs using the R graphics package.
103
Customizing Basic R Graphics
Adding a main title: e.g.: hist(Ozone,
main="BATON ROUGE")
Adding a subtitle: e.g.: hist(Ozone,
sub="Year 2011")
BATON ROUGE
Ozone
Fre
quency
0.00 0.04 0.08 0.12
040
80
Histogram of Ozone
Year 2011
Ozone
Fre
quency
0.00 0.04 0.08 0.12
040
80
104
Customizing Basic R Graphics
Adding x-axis and y- axis labels:
e.g.: hist(Ozone,
xlab="Ozone",
ylab="Frequency")
Histogram of Ozone
Ozone
Fre
quency
0.00 0.04 0.08 0.12
040
80
105
Customizing Basic R Graphics
Adding a legend:
hist(Ozone, freq=FALSE, ylim=c(0,30))
lines(density(Ozone))
legend("topright", "Density Curve", lty=1,bty="n")
Histogram of Ozone
Ozone
Density
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
05
10
15
20
25
30
Density Curve
106
Customizing Basic R Graphics
Adding text annotation:
hist(Ozone)
text(locator(1), "BATON ROUGE")
text(0.12, 20, "Year 2011")
Histogram of Ozone
Ozone
Fre
quency
0.00 0.04 0.08 0.120
20
40
60
80
BATON ROUGE
Year 2011
Notes:
When using the text() function:
• locator(1) places text wherever we click on the current graph;
• text(x,y, "some text") places text at graph location defined by (x,y) coordinates.
107
Customizing Basic R Graphics
Adding colors:
hist(Ozone, col="violet")
Histogram of Ozone
Ozone
Fre
quency
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
020
40
60
80
Note: To see the list of 600+ colors available in R, type the following command in the R Console window: colors() See also the help files for functions such as rainbow(), heat.colors(), terrain.colors() and palette(). 108
Customizing Basic R Graphics
Adding graphical symbols:
30 40 50 60 70 80 90
0.0
00.0
40.0
80.1
2
pch=1
Temp
Ozone
30 40 50 60 70 80 90
0.0
00.0
40.0
80.1
2
pch=19
Temp
Ozone
par(mfrow=c(1,2)) plot(Ozone ~ Temp, pch=1, main="pch=1") plot(Ozone ~ Temp, pch=19, main="pch=19")
Note: To see the graphical symbols available in R, use the command: example(pch) 109
Customizing Basic R Graphics
Controlling the size of graphical symbols: par(mfrow=c(1,3))
plot(Ozone ~ Temp, cex=1, main="cex=1")
plot(Ozone ~ Temp, cex=0.5, main="cex=0.5")
plot(Ozone ~ Temp, cex=2, main= "cex=2")
30 40 50 60 70 80 90 100
0.0
00.0
40.0
80.1
2
cex=1
Temp
Ozone
30 40 50 60 70 80 90 100
0.0
00.0
40.0
80.1
2
cex=0.5
Temp
Ozone
30 40 50 60 70 80 90 1000.0
00.0
40.0
80.1
2
cex=2
Temp
Ozone
Options for cex: cex = 1 (default size) cex = 0.5 (half default) cex = 2 (twice default)
110
Customizing Basic R Graphics
Adding lines:
hist(Ozone, freq=FALSE, ylim=c(0,30))
lines(density(Ozone))
Controlling the type and width of lines:
hist(Ozone, freq=FALSE, ylim=c(0,30))
lines(density(Ozone), lty=2, lwd=2)
Histogram of Ozone
Ozone
Density
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
05
10
15
20
25
30
Options for line type: lty=1 (solid) lty=5 (longdash) lty=2 (dashed) lty=6 (twodash) lty=3 (dotted) lty=4 (dotdash)
Options for line width: lwd = 1 (default) lwd = 0.5 (half default) lwd= 2 (twice default)
111
Exercise on Customizing Basic R Graphics
Create a scatter plot of maxO3 versus T12 using the variables in the ozone data frame. Enhance this scatter plot by adding the following elements to it: - main title and subtitle - x-axis and y-axis labels - some text annotation - some color - a particular type of graphical symbol (e.g., pch=8)
112
Advanced R Graphics
Learning Goal: Learn how to produce advanced graphs using the R lattice package.
113
Advanced R Graphics Recall that advanced R graphics can be produced
using any of the following R packages:
• grid (not covered in this course)
• trellis (covered in this course)
• ggplot2 (not covered in this course)
Note: The lattice package can replicate most of the basic graphics. However, the lattice package is particularly helpful for visualizing data conditional on the values of one or more variables.
114
Lattice Functions
lattice function graphics function Description
histogram() hist() Histogram
densityplot() plot(density()) Density Plot
bwplot() boxplot() Boxplot
stripplot() stripchart() Strip Plot
xyplot() plot() Scatter Plot
dotplot() dotchart() Dot Plot
barchart() barplot() Bar Chart
splom() pairs() Pairwise Scatterplot
cloud() persp() 3-D Scatterplot
115
Lattice Formulas
The functions in the lattice package rely on a
formula framework. For instance:
histogram(~ Y)
histogram(~ Y|F)
histogram(~Y|F1*F2)
xyplot(Y ~ X)
xyplot(Y ~ X |F)
xyplot(Y ~ X|F1*F2)
Symbol interpretation
~ as a function of | conditional on * crossed with
116
Histogram 2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
Count
0
50
100
0.00 0.05 0.10 0.15
require(lattice) histogram(~Ozone, xlab="Daily Max Ozone (ppm)", type="count", main="2011 BATON ROUGE/CAPITOL")
Note: For the histogram function, we can also use type= "density" to obtain a density histogram or type="percent" to obtain a percent of total histogram.
117
Conditional Histograms
histogram(~Ozone | Month, xlab="Daily Max Ozone (ppm)", type="count", main="2011 BATON ROUGE/CAPITOL")
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
Count
0
5
10
15
20
0.00 0.05 0.10 0.15
January February
0.00 0.05 0.10 0.15
March April
May June July
0
5
10
15
20
August0
5
10
15
20
September
0.00 0.05 0.10 0.15
October November
0.00 0.05 0.10 0.15
December
118
Density Plot densityplot(~Ozone, xlab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL")
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
Density
0
5
10
15
20
25
0.00 0.05 0.10 0.15
119
Conditional Density Plots
densityplot(~Ozone | Month, xlab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL", as.table=TRUE)
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
Density
0
10
20
30
40
50
January
0.00 0.05 0.10 0.15
February March
0.00 0.05 0.10 0.15
April
May June July
0
10
20
30
40
50
August
0
10
20
30
40
50
0.00 0.05 0.10 0.15
September October
0.00 0.05 0.10 0.15
November December
120
Boxplot
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
0.00 0.05 0.10
bwplot(~Ozone, xlab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL")
121
Side-by-Side Boxplots
Month
Daily
Max O
zone (
ppm
)
0.00
0.05
0.10
January February March April May June July August September October November December
bwplot(Ozone ~ Month, xlab="Month", ylab="Daily Max Ozone (ppm)")
122
Conditional Boxplots
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
0.00 0.05 0.10
January February
0.00 0.05 0.10
March April
0.00 0.05 0.10
May June
July
0.00 0.05 0.10
August September
0.00 0.05 0.10
October November
0.00 0.05 0.10
December
bwplot(~Ozone | Month, xlab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL")
123
Strip Plot
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
0.00 0.05 0.10
stripplot(~Ozone, xlab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL")
124
Side-by-Side Strip Plots 2011 BATON ROUGE/CAPITOL
Month
Daily M
ax O
zone (
ppm
)
0.00
0.05
0.10
January February March April May June July August September October November December
2011 BATON ROUGE/CAPITOL
Month
Daily
Max O
zone (
ppm
)
0.00
0.05
0.10
January February March April May June July August September October November December
stripplot(Ozone ~ Month, xlab="Month", ylab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL", jitter=FALSE)
stripplot(Ozone ~ Month, xlab="Month", ylab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL", jitter=TRUE)
125
Conditional Strip Plots
2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
January
0.00 0.05 0.10
February March
0.00 0.05 0.10
April
May June July August
0.00 0.05 0.10
September October
0.00 0.05 0.10
November December
stripplot(~ Ozone | Month, xlab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL", as.table=TRUE)
126
Time Series Plot
2011 BATON ROUGE/CAPITOL
Date
Daily
Max O
zone (
ppm
)
0.00
0.05
0.10
Jan Apr Jul Oct Jan
xyplot(Ozone ~ Date, type="l", ylab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL")
127
Scatterplot
2011 BATON ROUGE/CAPITOL
Temperature (°F)
Daily
Max O
zone (
ppm
)
0.00
0.05
0.10
40 60 80 100
xyplot(Ozone ~ Temp, xlab="Temperature (°F)", ylab="Daily Max Ozone (ppm)", main="2011 BATON ROUGE/CAPITOL")
128
Conditional Scatterplots
2011 BATON ROUGE/CAPITOL
Temperature (°F)
Daily
Max O
zone (
ppm
) 0.00
0.05
0.10
January
40 60 80 100
February March
40 60 80 100
April
May June July
0.00
0.05
0.10
August
0.00
0.05
0.10
40 60 80 100
September October
40 60 80 100
November December
xyplot(Ozone ~ Temp | Month, xlab="Temperature (°F)", ylab="Daily Max Ozone (ppm)", as.table=TRUE, main="2011 BATON ROUGE/CAPITOL")
129
Dot Plot (v.1)
require(plyr) air$Month <- Month View(air) OzoneMonthlySummary <- ddply(air, "Month", summarise, Median = median(Ozone), Q1 = quantile(Ozone, prob=0.25), Q3 = quantile(Ozone, prob=0.75)) OzoneMonthlySummary
Median Value of Daily Max Ozone on a Given Month (ppm)
January
February
March
April
May
June
July
August
September
October
November
December
0.03 0.04 0.05 0.06
130
Dot Plot (v.1)
dotplot(Month ~ Median, data=OzoneMonthlySummary,
aspect=1.0,
xlab="Median Value of Daily Max Ozone on a Given Month (ppm)",
scales=list(cex=1.0),
panel = function (x, y) {
panel.abline(h = as.numeric(y), col = "gray", lty = 2)
panel.xyplot(x, as.numeric(y), col = "blue", pch = 16)
}
)
131
Dot Plot (v.2)
Median Value of Daily Max Ozone on a Given Month (ppm)
January
February
March
April
May
June
July
August
September
October
November
December
0.02 0.04 0.06 0.08
Note: Reported ranges represent inter-quartile ranges.
132
Dot Plot (v.2)
dotplot(Month ~ Median, data = OzoneMonthlySummary,
aspect = 1,
xlim = c(0, 0.10),
xlab = "Median Value of Daily Max Ozone on a Given Month (ppm)",
panel = function (x, y) {
panel.xyplot(x, y, pch = 16, col = "red")
panel.segments(OzoneMonthlySummary$Q1, as.numeric(y),
OzoneMonthlySummary$Q3, as.numeric(y),
lty = 1, col = "black")
}
)
133
Bar Chart (v.1)
2011 BATON ROUGE/CAPITOL
Month
Media
n V
alu
e o
f D
aily
Max O
zone o
n a
Giv
en M
onth
(ppm
)
0.03
0.04
0.05
0.06
January February March April May June July August September October November December
barchart(Median ~ Month, data = OzoneMonthlySummary, xlab="Month", ylab="Median Value of Daily Max Ozone on a Given Month (ppm)", main="2011 BATON ROUGE/CAPITOL")
134
Bar Chart (v.2)
2011 BATON ROUGE/CAPITOL
Month
Media
n V
alu
e o
f D
aily
Max O
zone o
n a
Giv
en M
onth
(ppm
)
0.03
0.04
0.05
0.06
January February March April May June July August September October November December
0.036
0.041
0.044 0.0445 0.045
0.0565
0.043
0.068
0.054
0.052
0.0375
0.028
barchart(Median ~ Month, data = OzoneMonthlySummary, xlab="Month", ylab="Median Value of Daily Max Ozone on a Given Month (ppm)", main="2011 BATON ROUGE/CAPITOL", panel=function(x, y, ...) { panel.barchart(x, y, ...) ltext(x=x, y=y+0.001, labels=y) } )
135
Bar Chart (v.3)
2011 BATON ROUGE/CAPITOL
Month
Media
n V
alu
e o
f D
aily
Max O
zone o
n a
Giv
en M
onth
(ppm
)
January
February
March
April
May
June
July
August
September
October
November
December
0.02 0.04 0.06
0.036
0.041
0.044
0.0445
0.045
0.0565
0.043
0.068
0.054
0.052
0.0375
0.028
barchart(Month ~ Median, data = OzoneMonthlySummary, xlab="Month", xlim=c(0,0.08), ylab="Median Value of Daily Max Ozone on a Given Month (ppm)", main="2011 BATON ROUGE/CAPITOL", panel=function(x, y, ...) { panel.barchart(x, y, ...) ltext(x=x+0.003, y=y, labels=x) } )
136
Splom Plots
Scatter Plot Matrix
Ozone0.08
0.10
0.12
0.140.080.100.120.14
0.00
0.02
0.04
0.06
0.000.020.040.06
Temp70
80
90
100 70 80 90 100
30
40
50
60
30 40 50 60
RelativeHumidity80
90
10080 90 100
60
70
80
60 70 80
splom( ~ cbind(Ozone, Temp, RelativeHumidity))
137
Cloud Plot
TempRelativeHumidity
Ozone
cloud(Ozone ~ Temp*RelativeHumidity)
138
Conditional Cloud Plot
TempRelHum
Ozone
January
TempRelHum
Ozone
February
TempRelHum
Ozone
March
TempRelHum
Ozone
April
TempRelHum
Ozone
May
TempRelHum
Ozone
June
TempRelHum
Ozone
July
TempRelHum
Ozone
August
TempRelHum
Ozone
September
TempRelHum
Ozone
October
TempRelHum
Ozone
November
TempRelHum
Ozone
December
RelHum <- RelativeHumidity cloud(Ozone ~ Temp*RelHum | Month, as.table=TRUE, panel.aspect=0.8)
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Enhancing Lattice Graphs
Learning Goal: Learn how to enhance advanced graphs using the R lattice package.
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Enhancing Lattice Graphs
Lattice graphs can be enhanced by:
• Adding basic features (e.g., titles, x-axis and
y-axis labels, colors)
• Using panel functions
• Using additional lattice graphics via the latticeExtra package
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Basic Enhancement of Lattice Graphs
histogram( ~ Ozone,
xlab = "Daily Max Ozone (ppm)",
main= "2011 BATON ROUGE/CAPITOL",
col= "lightblue") 2011 BATON ROUGE/CAPITOL
Daily Max Ozone (ppm)
Perc
ent
of
Tota
l
0
10
20
30
0.00 0.05 0.10 0.15
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Panel Enhancements of Lattice Graphs
histogram( ~ Ozone ,
xlab = "Ozone", type = "density", panel = function(x, ...) { panel.histogram(x, ...); panel.mathdensity(dmath = dnorm,
col = "black", args = list(mean=mean(x),sd=sd(x))) }
)
Ozone
Density
0
5
10
15
20
25
0.00 0.05 0.10 0.15
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Panel Enhancements of Lattice Graphs
mypanel <- function(x,y){ panel.xyplot(x,y) panel.lmline(x,y, col="red", lty=2) panel.loess(x,y, col="blue") } xyplot(Ozone ~ Date, panel=mypanel)
Date
Ozone
0.00
0.05
0.10
Jan Apr Jul Oct Jan
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The latticeExtra Package The latticeExtra package extends the lattice package and has its own dedicated website: http://latticeextra.r-forge.r-project.org/ The latticeExtra package can be installed in R with the command: install.packages("latticeExtra") Once installed in R, the latticeExtra package can be attached to the current R session with the command: require(latticeExtra)
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latticeExtra: marginal.plot()
require(latticeExtra) air$Month <- Month air1 <- subset(air, Month=="January" | Month=="February" | Month =="March") air2 <- air1[ ,c("Month","Ozone","Temp","RelativeHumidity","SolarRadiation")] air2$Month <- factor(air2$Month, levels=c("January","February","March")) marginal.plot(air2[ ,-1]) marginal.plot(air2[,-1], groups=air2$Month, auto.key=list(lines=TRUE))
0.020.04
0.06
Ozone
40 60 80
Temp
60 80100
RelativeHumidity
0.5 1.0
SolarRadiation
JanuaryFebruaryMarch
General Syntax: marginal.plot( ~ Y) marginal.plot( ~ Y, groups = G, auto.key=list(lines=TRUE))
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latticeExtra: xyplot()
xyplot(Ozone~Date, xlab="Year 2011", ylab="Daily Max Ozone (ppm)", main="BATON ROUGE/CAPITOL", panel = function(...) { panel.xyplot(...) panel.smoother(..., span = 0.9) } )
General Syntax: xyplot( Y~ X, xlab="x-axis label", ylab="y-axis label", main="Main Title", panel = function(...) { panel.xyplot(...) panel.smoother(..., span = 0.5) } )
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latticeExtra: ecdfplot()
require(latticeExtra)
ecdfplot(~Ozone,
xlab="Daily Max Ozone (ppm)",
main="Empirical Cumulative Distribution Plot")
ecdfplot(~Ozone | Month,
xlab="Daily Max Ozone (ppm)",
main="Empirical Cumulative Distribution Plot")
Empirical Cumulative Distribution Plot
Daily Max Ozone (ppm)
Em
piric
al C
DF
0.0
0.2
0.4
0.6
0.8
1.0
0.00 0.05 0.10
Empirical Cumulative Distribution Plot
Daily Max Ozone (ppm)
Em
piric
al C
DF
0.00.20.40.60.81.0
0.000.05 0.10
January February
0.00 0.05 0.10
March April
May June July
0.00.20.40.60.81.0
August0.00.20.40.60.81.0
September
0.00 0.05 0.10
October November
0.00 0.05 0.10
December
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Exercise on Lattice Graphics For this exercise, please refer to the variables in the ozone data frame. Before starting the exercise, create a month variable using the R commands provided below.
month <- months(date) month <- factor(month, levels=unique(month)) Remember to attach the lattice package to your current R session with the command: require(lattice)
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Exercise on Lattice Graphics Use the functions in the lattice package to
create the following graphs.
1) Create a histogram of maxO3.
2) Create conditional histograms of maxO3 for each month.
3) Create a density plot of maxO3.
4) Create conditional density plots of maxO3 for each month.
5) Create a boxplot of maxO3.
6) Create side-by-side boxplots of maxO3 for each month.
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Exercise on Lattice Graphics 7) Create a scatter plot of maxO3 vs. T12 and enhance it by adding a title and axes labels. 8) Create conditional scatter plots of maxO3 vs. T12 given month. 9) Create a time series plot of maxO3. 10) Create a separate time series plot of maxO3 for each month. 11) Create a splom plot of the variables maxO3, T12 and Wx12. 12) Create a cloud plot visualizing the dependency of maxO3 on T12 and Wx12.
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R Graphics Housekeeping
Learning Goal: Learn how to save the graphs you produce in R.
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Exporting R Graphics
R Graphics can be exported in a variety of formats:
• metafile (.wmf)
• postscript (.ps)
• pdf (.pdf)
• png (.png)
• bmp (.bmp)
• TIFF (.tiff)
• JPEG (.jpeg)
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Exporting R Graphics
Graphics can be exported from R in one of three
ways:
1. Using the R Gui Menu: File Save as.
2. Using Ctrl + C or Ctrl + W to copy the graph from the R Graphics window and Ctrl + V to insert the graph in a Word, Excel or Power Point document.
3. Using the R command line.
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Exporting R Graphics To save graphs from the R command line, use any of the following R functions: win.metafile() postscript() pdf() png() bmp() tiff() jpeg() Each of these functions will re-direct graphical output from the R graphics window to a file of the corresponding type (e.g., pdf). Using dev.off() after constructing the graph is required.
Accessing Help Files for R Graphics Export Functions: ?win.metafile ?postscript ?pdf ?png ?bmp ?tiff ?jpeg
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R Graphics Housekeeping
To create a windows metafile from the R
command line, use:
win.metafile("graph.wmf")
hist(rnorm(100))
dev.off()
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R Graphics Housekeeping
To create a postscript file from the R
command line, use:
postscript("graph.ps")
hist(rnorm(100))
dev.off()
Postscript files can be viewed with GPL Ghostscript
(http://www.cs.wisc.edu/~ghost/).
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R Graphics Housekeeping
To create a pdf file from the R command line,
use:
pdf("graph.pdf")
hist(rnorm(100))
dev.off()
158
R Graphics Housekeeping
To create a png file from the R command line,
use:
pdf("graph.png")
hist(rnorm(100))
dev.off()
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R Graphics Housekeeping
To create a bmp file from the R command line,
use:
bmp("graph.bmp")
hist(rnorm(100))
dev.off()
160
R Graphics Housekeeping
To create a tiff file from the R command line,
use:
tiff("graph.tiff")
hist(rnorm(100))
dev.off()
161
R Graphics Housekeeping
To create a jpeg file from the R command line,
use:
jpeg("graph.jpeg")
hist(rnorm(100))
dev.off()
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Exercise on R Graphics Housekeeping
With reference to the ozone data frame, create a histogram of the variable maxO3 using either the hist() function in the graphics package or the histogram() function in the lattice package. 1) Save this histogram as a pdf file using the R Gui
menu commands File Save as.
2) Save this histogram as a pdf file using the command line approach facilitated by the pdf() function.
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Summary
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Summary R provides 4 different graphical systems for producing
elegant, publication-quality graphics: graphics, grid, lattice, ggplot2.
In this course, we explored in more detail some of the functionality available in the graphics and lattice packages. The lattice package relies heavily on the grid package.
Once you get comfortable with the graphics and lattice packages, you can start exploring the ggplot2 package.
The ggplot2 package purports to combine the best
features of the graphics and lattice packages,
but has a completely different, more abstract syntax.
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References on Graphics in R
166
References on Graphics in R
Books:
• “Graphics for Statistics and Data Analysis with R”, by Kevin J. Keen (CRC Press, 2010)
• “Lattice: Multivariate Data Visualization with R”, by Deepayan Sarkar (Springer, 2008)
• “ggplot2: Elegant Graphics for Data Analysis”, by Hadley Wickham (Springer-Verlag, 2009)
• “R Graphics”, 2nd Edition, by Paul Murrell (Chapman & Hall/CRC, 2006)
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References on Graphics in R
Websites:
Website Address Website Description
http://www.r-project.org R Project
http://www.statmethods.net Quick-R
http://www.r-bloggers.com R Bloggers
http://lmdvr.r-forge.r-project.org Lattice Website
http://ggplot2.org Ggplot2 Website
http://www.stat.auckland.ac.nz/~paul/grid/grid.html Grid Website
http://gallery.r-enthusiasts.com R Graph Gallery
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Thank you
Thank you very much for attending this course. If you have any questions related to the content of this course, please contact Dr. Isabella Ghement at the following address: Dr. Isabella R. Ghement Ghement Statistical Consulting Company Ltd. 301-7031 Blundell Road Richmond, B.C. Canada, V6Y 1J5 Tel: 604-767-1250 Fax: 604-270-3922 E-Mail: [email protected]
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