- •brief contents
- •contents
- •preface
- •acknowledgments
- •about this book
- •What’s new in the second edition
- •Who should read this book
- •Roadmap
- •Advice for data miners
- •Code examples
- •Code conventions
- •Author Online
- •About the author
- •about the cover illustration
- •1 Introduction to R
- •1.2 Obtaining and installing R
- •1.3 Working with R
- •1.3.1 Getting started
- •1.3.2 Getting help
- •1.3.3 The workspace
- •1.3.4 Input and output
- •1.4 Packages
- •1.4.1 What are packages?
- •1.4.2 Installing a package
- •1.4.3 Loading a package
- •1.4.4 Learning about a package
- •1.5 Batch processing
- •1.6 Using output as input: reusing results
- •1.7 Working with large datasets
- •1.8 Working through an example
- •1.9 Summary
- •2 Creating a dataset
- •2.1 Understanding datasets
- •2.2 Data structures
- •2.2.1 Vectors
- •2.2.2 Matrices
- •2.2.3 Arrays
- •2.2.4 Data frames
- •2.2.5 Factors
- •2.2.6 Lists
- •2.3 Data input
- •2.3.1 Entering data from the keyboard
- •2.3.2 Importing data from a delimited text file
- •2.3.3 Importing data from Excel
- •2.3.4 Importing data from XML
- •2.3.5 Importing data from the web
- •2.3.6 Importing data from SPSS
- •2.3.7 Importing data from SAS
- •2.3.8 Importing data from Stata
- •2.3.9 Importing data from NetCDF
- •2.3.10 Importing data from HDF5
- •2.3.11 Accessing database management systems (DBMSs)
- •2.3.12 Importing data via Stat/Transfer
- •2.4 Annotating datasets
- •2.4.1 Variable labels
- •2.4.2 Value labels
- •2.5 Useful functions for working with data objects
- •2.6 Summary
- •3 Getting started with graphs
- •3.1 Working with graphs
- •3.2 A simple example
- •3.3 Graphical parameters
- •3.3.1 Symbols and lines
- •3.3.2 Colors
- •3.3.3 Text characteristics
- •3.3.4 Graph and margin dimensions
- •3.4 Adding text, customized axes, and legends
- •3.4.1 Titles
- •3.4.2 Axes
- •3.4.3 Reference lines
- •3.4.4 Legend
- •3.4.5 Text annotations
- •3.4.6 Math annotations
- •3.5 Combining graphs
- •3.5.1 Creating a figure arrangement with fine control
- •3.6 Summary
- •4 Basic data management
- •4.1 A working example
- •4.2 Creating new variables
- •4.3 Recoding variables
- •4.4 Renaming variables
- •4.5 Missing values
- •4.5.1 Recoding values to missing
- •4.5.2 Excluding missing values from analyses
- •4.6 Date values
- •4.6.1 Converting dates to character variables
- •4.6.2 Going further
- •4.7 Type conversions
- •4.8 Sorting data
- •4.9 Merging datasets
- •4.9.1 Adding columns to a data frame
- •4.9.2 Adding rows to a data frame
- •4.10 Subsetting datasets
- •4.10.1 Selecting (keeping) variables
- •4.10.2 Excluding (dropping) variables
- •4.10.3 Selecting observations
- •4.10.4 The subset() function
- •4.10.5 Random samples
- •4.11 Using SQL statements to manipulate data frames
- •4.12 Summary
- •5 Advanced data management
- •5.2 Numerical and character functions
- •5.2.1 Mathematical functions
- •5.2.2 Statistical functions
- •5.2.3 Probability functions
- •5.2.4 Character functions
- •5.2.5 Other useful functions
- •5.2.6 Applying functions to matrices and data frames
- •5.3 A solution for the data-management challenge
- •5.4 Control flow
- •5.4.1 Repetition and looping
- •5.4.2 Conditional execution
- •5.5 User-written functions
- •5.6 Aggregation and reshaping
- •5.6.1 Transpose
- •5.6.2 Aggregating data
- •5.6.3 The reshape2 package
- •5.7 Summary
- •6 Basic graphs
- •6.1 Bar plots
- •6.1.1 Simple bar plots
- •6.1.2 Stacked and grouped bar plots
- •6.1.3 Mean bar plots
- •6.1.4 Tweaking bar plots
- •6.1.5 Spinograms
- •6.2 Pie charts
- •6.3 Histograms
- •6.4 Kernel density plots
- •6.5 Box plots
- •6.5.1 Using parallel box plots to compare groups
- •6.5.2 Violin plots
- •6.6 Dot plots
- •6.7 Summary
- •7 Basic statistics
- •7.1 Descriptive statistics
- •7.1.1 A menagerie of methods
- •7.1.2 Even more methods
- •7.1.3 Descriptive statistics by group
- •7.1.4 Additional methods by group
- •7.1.5 Visualizing results
- •7.2 Frequency and contingency tables
- •7.2.1 Generating frequency tables
- •7.2.2 Tests of independence
- •7.2.3 Measures of association
- •7.2.4 Visualizing results
- •7.3 Correlations
- •7.3.1 Types of correlations
- •7.3.2 Testing correlations for significance
- •7.3.3 Visualizing correlations
- •7.4 T-tests
- •7.4.3 When there are more than two groups
- •7.5 Nonparametric tests of group differences
- •7.5.1 Comparing two groups
- •7.5.2 Comparing more than two groups
- •7.6 Visualizing group differences
- •7.7 Summary
- •8 Regression
- •8.1 The many faces of regression
- •8.1.1 Scenarios for using OLS regression
- •8.1.2 What you need to know
- •8.2 OLS regression
- •8.2.1 Fitting regression models with lm()
- •8.2.2 Simple linear regression
- •8.2.3 Polynomial regression
- •8.2.4 Multiple linear regression
- •8.2.5 Multiple linear regression with interactions
- •8.3 Regression diagnostics
- •8.3.1 A typical approach
- •8.3.2 An enhanced approach
- •8.3.3 Global validation of linear model assumption
- •8.3.4 Multicollinearity
- •8.4 Unusual observations
- •8.4.1 Outliers
- •8.4.3 Influential observations
- •8.5 Corrective measures
- •8.5.1 Deleting observations
- •8.5.2 Transforming variables
- •8.5.3 Adding or deleting variables
- •8.5.4 Trying a different approach
- •8.6 Selecting the “best” regression model
- •8.6.1 Comparing models
- •8.6.2 Variable selection
- •8.7 Taking the analysis further
- •8.7.1 Cross-validation
- •8.7.2 Relative importance
- •8.8 Summary
- •9 Analysis of variance
- •9.1 A crash course on terminology
- •9.2 Fitting ANOVA models
- •9.2.1 The aov() function
- •9.2.2 The order of formula terms
- •9.3.1 Multiple comparisons
- •9.3.2 Assessing test assumptions
- •9.4 One-way ANCOVA
- •9.4.1 Assessing test assumptions
- •9.4.2 Visualizing the results
- •9.6 Repeated measures ANOVA
- •9.7 Multivariate analysis of variance (MANOVA)
- •9.7.1 Assessing test assumptions
- •9.7.2 Robust MANOVA
- •9.8 ANOVA as regression
- •9.9 Summary
- •10 Power analysis
- •10.1 A quick review of hypothesis testing
- •10.2 Implementing power analysis with the pwr package
- •10.2.1 t-tests
- •10.2.2 ANOVA
- •10.2.3 Correlations
- •10.2.4 Linear models
- •10.2.5 Tests of proportions
- •10.2.7 Choosing an appropriate effect size in novel situations
- •10.3 Creating power analysis plots
- •10.4 Other packages
- •10.5 Summary
- •11 Intermediate graphs
- •11.1 Scatter plots
- •11.1.3 3D scatter plots
- •11.1.4 Spinning 3D scatter plots
- •11.1.5 Bubble plots
- •11.2 Line charts
- •11.3 Corrgrams
- •11.4 Mosaic plots
- •11.5 Summary
- •12 Resampling statistics and bootstrapping
- •12.1 Permutation tests
- •12.2 Permutation tests with the coin package
- •12.2.2 Independence in contingency tables
- •12.2.3 Independence between numeric variables
- •12.2.5 Going further
- •12.3 Permutation tests with the lmPerm package
- •12.3.1 Simple and polynomial regression
- •12.3.2 Multiple regression
- •12.4 Additional comments on permutation tests
- •12.5 Bootstrapping
- •12.6 Bootstrapping with the boot package
- •12.6.1 Bootstrapping a single statistic
- •12.6.2 Bootstrapping several statistics
- •12.7 Summary
- •13 Generalized linear models
- •13.1 Generalized linear models and the glm() function
- •13.1.1 The glm() function
- •13.1.2 Supporting functions
- •13.1.3 Model fit and regression diagnostics
- •13.2 Logistic regression
- •13.2.1 Interpreting the model parameters
- •13.2.2 Assessing the impact of predictors on the probability of an outcome
- •13.2.3 Overdispersion
- •13.2.4 Extensions
- •13.3 Poisson regression
- •13.3.1 Interpreting the model parameters
- •13.3.2 Overdispersion
- •13.3.3 Extensions
- •13.4 Summary
- •14 Principal components and factor analysis
- •14.1 Principal components and factor analysis in R
- •14.2 Principal components
- •14.2.1 Selecting the number of components to extract
- •14.2.2 Extracting principal components
- •14.2.3 Rotating principal components
- •14.2.4 Obtaining principal components scores
- •14.3 Exploratory factor analysis
- •14.3.1 Deciding how many common factors to extract
- •14.3.2 Extracting common factors
- •14.3.3 Rotating factors
- •14.3.4 Factor scores
- •14.4 Other latent variable models
- •14.5 Summary
- •15 Time series
- •15.1 Creating a time-series object in R
- •15.2 Smoothing and seasonal decomposition
- •15.2.1 Smoothing with simple moving averages
- •15.2.2 Seasonal decomposition
- •15.3 Exponential forecasting models
- •15.3.1 Simple exponential smoothing
- •15.3.3 The ets() function and automated forecasting
- •15.4 ARIMA forecasting models
- •15.4.1 Prerequisite concepts
- •15.4.2 ARMA and ARIMA models
- •15.4.3 Automated ARIMA forecasting
- •15.5 Going further
- •15.6 Summary
- •16 Cluster analysis
- •16.1 Common steps in cluster analysis
- •16.2 Calculating distances
- •16.3 Hierarchical cluster analysis
- •16.4 Partitioning cluster analysis
- •16.4.2 Partitioning around medoids
- •16.5 Avoiding nonexistent clusters
- •16.6 Summary
- •17 Classification
- •17.1 Preparing the data
- •17.2 Logistic regression
- •17.3 Decision trees
- •17.3.1 Classical decision trees
- •17.3.2 Conditional inference trees
- •17.4 Random forests
- •17.5 Support vector machines
- •17.5.1 Tuning an SVM
- •17.6 Choosing a best predictive solution
- •17.7 Using the rattle package for data mining
- •17.8 Summary
- •18 Advanced methods for missing data
- •18.1 Steps in dealing with missing data
- •18.2 Identifying missing values
- •18.3 Exploring missing-values patterns
- •18.3.1 Tabulating missing values
- •18.3.2 Exploring missing data visually
- •18.3.3 Using correlations to explore missing values
- •18.4 Understanding the sources and impact of missing data
- •18.5 Rational approaches for dealing with incomplete data
- •18.6 Complete-case analysis (listwise deletion)
- •18.7 Multiple imputation
- •18.8 Other approaches to missing data
- •18.8.1 Pairwise deletion
- •18.8.2 Simple (nonstochastic) imputation
- •18.9 Summary
- •19 Advanced graphics with ggplot2
- •19.1 The four graphics systems in R
- •19.2 An introduction to the ggplot2 package
- •19.3 Specifying the plot type with geoms
- •19.4 Grouping
- •19.5 Faceting
- •19.6 Adding smoothed lines
- •19.7 Modifying the appearance of ggplot2 graphs
- •19.7.1 Axes
- •19.7.2 Legends
- •19.7.3 Scales
- •19.7.4 Themes
- •19.7.5 Multiple graphs per page
- •19.8 Saving graphs
- •19.9 Summary
- •20 Advanced programming
- •20.1 A review of the language
- •20.1.1 Data types
- •20.1.2 Control structures
- •20.1.3 Creating functions
- •20.2 Working with environments
- •20.3 Object-oriented programming
- •20.3.1 Generic functions
- •20.3.2 Limitations of the S3 model
- •20.4 Writing efficient code
- •20.5 Debugging
- •20.5.1 Common sources of errors
- •20.5.2 Debugging tools
- •20.5.3 Session options that support debugging
- •20.6 Going further
- •20.7 Summary
- •21 Creating a package
- •21.1 Nonparametric analysis and the npar package
- •21.1.1 Comparing groups with the npar package
- •21.2 Developing the package
- •21.2.1 Computing the statistics
- •21.2.2 Printing the results
- •21.2.3 Summarizing the results
- •21.2.4 Plotting the results
- •21.2.5 Adding sample data to the package
- •21.3 Creating the package documentation
- •21.4 Building the package
- •21.5 Going further
- •21.6 Summary
- •22 Creating dynamic reports
- •22.1 A template approach to reports
- •22.2 Creating dynamic reports with R and Markdown
- •22.3 Creating dynamic reports with R and LaTeX
- •22.4 Creating dynamic reports with R and Open Document
- •22.5 Creating dynamic reports with R and Microsoft Word
- •22.6 Summary
- •afterword Into the rabbit hole
- •appendix A Graphical user interfaces
- •appendix B Customizing the startup environment
- •appendix C Exporting data from R
- •Delimited text file
- •Excel spreadsheet
- •Statistical applications
- •appendix D Matrix algebra in R
- •appendix E Packages used in this book
- •appendix F Working with large datasets
- •F.1 Efficient programming
- •F.2 Storing data outside of RAM
- •F.3 Analytic packages for out-of-memory data
- •F.4 Comprehensive solutions for working with enormous datasets
- •appendix G Updating an R installation
- •G.1 Automated installation (Windows only)
- •G.2 Manual installation (Windows and Mac OS X)
- •G.3 Updating an R installation (Linux)
- •references
- •index
- •Symbols
- •Numerics
- •23.1 The lattice package
- •23.2 Conditioning variables
- •23.3 Panel functions
- •23.4 Grouping variables
- •23.5 Graphic parameters
- •23.6 Customizing plot strips
- •23.7 Page arrangement
- •23.8 Going further
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CHAPTER 22 Creating dynamic reports |
22.3 Creating dynamic reports with R and LaTeX
LaTeX is a document-preparation system for high-quality typesetting that’s freely available for Windows, Mac, and Linux platforms. LaTeX allows you to create beautiful, complex, multipart documents, and it can convert from one type of document (such as an article) to another type of document (such as a report) by changing just a few lines of code. It’s extraordinarily powerful software and, as such, has a significant learning curve.
If you’re unfamiliar with LaTeX, you may want to read “The Not So Short Introduction to LaTeX 2e” by Tobias Oetiker et al. (http://mng.bz/45vP) or LaTeX Tutorials: A Primer by the Indian TEX Users Group (http://mng.bz/2c0O) before continuing. The language is definitely worth learning, but it will take some time and patience to master. Once you’re familiar with LaTeX, creating a dynamic report is a straightforward process.
The knitr package allows you to embed R code within the LaTeX document using techniques that are analogous to the ones used previously for creating web pages. If you installed rmarkdown or are using RStudio, you already have knitr. If not, install it now (install.packages("knitr")). Additionally, you’ll need a LaTeX compiler; see section 22.2 for details.
In this section, you’ll create a report describing patients’ reactions to various drugs, using data from the multcomp package. If you didn’t install it in chapter 9, be sure to run install.packages("multcomp") before continuing.
To generate a report using R and LaTeX, you first create a text file (typically with the filename extension .Rnw) containing the report text, LaTeX markup code, and R code chunks. An example is given in listing 22.2. Each R code chunk starts with the delimiter <<options>>= and ends with the delimiter @. The code chunk options are listed in table 22.3. Inline R code is included using the \Sexpr{R code} syntax. When the R code is evaluated, the number or string is inserted at that point in the text.
The file is then processed by the knit() function:
library(knitr)
knit("drugs.Rnw")
During this step, the R code chunks are processed and, depending on the options, replaced with LaTeX-formatted R code and output. By default, knit("drugs.Rnw") inputs the file drugs.Rnw and outputs the file drugs.tex. The .tex file is then run through a LaTeX compiler, creating a PDF, PostScript, or DVI file.
As a simpler alternative, you can use the knit2pdf() helper function in the knitr package:
library(knitr)
knit2pdf("drugs.Rnw")
The function generates the .tex file and converts it to a finished PDF document named drugs.pdf. The resulting PDF document is displayed in figure 22.3.
Creating dynamic reports with R and LaTeX |
523 |
Listing 22.2 drugs.Rnw: a sample LaTeX template with embedded R code
\documentclass[11pt]{article} \title{Sample Report} \author{Robert I. Kabacoff, Ph.D.} \usepackage{float}
\usepackage[top=.5in, bottom=.5in, left=1in, right=1in]{geometry} \begin{document}
\maketitle
<<echo=FALSE, results='hide', message=FALSE>>= library(multcomp)
library(xtable) df <- cholesterol
@
\section{Results}
Cholesterol reduction was assessed in a study that randomized \Sexpr{nrow(df)} patients
to one of \Sexpr{length(unique(df$trt))} treatments. Summary statistics are provided in
Table \ref{table:descriptives}.
<<echo=FALSE, results='asis'>>=
descTable |
<- data.frame("Treatment" = sort(unique(df$trt)), |
||
"N" |
= as.vector(table(df$trt)), |
||
"Mean" |
= |
tapply(df$response, |
list(df$trt), mean, na.rm=TRUE), |
"SD" |
= |
tapply(df$response, |
list(df$trt), sd, na.rm=TRUE) |
)
print(xtable(descTable, caption = "Descriptive statistics for each treatment group", label = "table:descriptives"), caption.placement = "top", include.rownames = FALSE)
@
The analysis of variance is provided in Table \ref{table:anova}.
<<echo=FALSE, results='asis'>>=
fit <- aov(response ~ trt, data=df) print(xtable(fit, caption = "Analysis of variance",
label = "table:anova"), caption.placement = "top")
@
\noindent and group differences are plotted in Figure \ref{figure:tukey}.
\begin{figure}[H]\label{figure:tukey}
\begin{center}
<<echo=FALSE, fig.width=4, fig.height=3>>= par(mar=c(3,3,1,3))
boxplot(response ~ trt, data=df, col="lightgrey", xlab="Treatment", ylab="Response")
@
\caption{Distribution of response times by treatment.} \end{center}
\end{figure}
\end{document}
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CHAPTER 22 Creating dynamic reports |
Sample Report
Robert I. Kabacoff, Ph.D.
March 23, 2015
1 Results
Cholesterol reduction was assessed in a study that randomized 50 patients to one of 5 treatments. Summary statistics are provided in Table 1.
Table 1: Descriptive statistics for each treatment group
Treatment |
N |
Mean |
SD |
1time |
10 |
5.78 |
2.88 |
2times |
10 |
9.22 |
3.48 |
4times |
10 |
12.37 |
2.92 |
drugD |
10 |
15.36 |
3.45 |
drugE |
10 |
20.95 |
3.35 |
The analysis of variance is provided in Table 2.
Table 2: Analysis of variance
|
Df |
Sum Sq |
Mean Sq |
F value |
Pr( > F) |
trt |
4 |
1351.37 |
337.84 |
32.43 |
0.0000 |
Residuals |
45 |
468.75 |
10.42 |
|
|
and group differences are plotted in Figure 1.
5 10 15 20 25
1time |
4times |
drugE |
Figure 1: Distribution of response times by treatment.
1
Figure 22.3 The text file drugs.Rnw is processed through the knit2pdf() function, resulting in a typeset PDF document (drugs.pdf).
Creating dynamic reports with R and Open Document |
525 |
The knitr package is documented at http://yihui.name/knitr and in Yihui Xie’s book Dynamic Documents with R and knitr (Chapman and Hall/CRC, 2013). To learn more about LaTeX, check out the tutorials mentioned earlier and visit www.latex-project.org.
22.4 Creating dynamic reports with R and Open Document
Although LaTeX is powerful, it requires significant study to use effectively and creates documents in formats (PDF, DVI, PS) that can’t be edited. You can also output R results to a word-processing document. Two of the most popular formats are Microsoft Word (.docx) and Open Document (.odf).
Open Document Format for Office Applications (ODF) is an open source, XML- based file format that’s compatible with many software suites. Two popular and freely available office suites are OpenOffice (www.openoffice.org) and LibreOffice (www.libreoffice.org). Both are available for Windows, Mac OS X, and Linux environments, and either can be used in this section.
The odfWeave package provides a mechanism for embedding R code and output in an Open Document file. In this section, you’ll create a report exploring salary differences between male and female professors.
After installing OpenOffice (or LibreOffice) and the odfWeave package, create a document named salaryTemplate.odt (see figure 22.4). The document contains formatted text and R code chunks. The text is formatted using the OpenOffice (or LibreOffice) GUI. The code chunks are delimited as follows:
<<options>>= R statements
@
Code chunk options are given in table 22.3. Inline R code results (numbers or strings) are included using \Sexpr{R code}.
Table 22.3 R code chunk options in odfWeave
Option |
Action |
|
|
echo |
Includes R code in the output file (TRUE/FALSE) |
results |
Outputs results as is (verbatim), as XML code (xml), or suppress output (hide) |
fig |
Code chunk produces graphical output (TRUE/FALSE) |
|
|
Once the document is saved, process it using odfWeave() in the odfWeave package:
library(odfWeave)
infile <- "salaryTemplate.odt" outfile <- "salaryReport.odf" odfWeave(infile, outfile)
This takes the salaryTemplate.odt file displayed in figure 22.4 and produces the salaryReport.odf file displayed in figure 22.5.
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CHAPTER 22 Creating dynamic reports |
Figure 22.4 OpenOffice Writer file
(salaryTemplate.odt) with embedded R code chunks. After the file is processed by the odfWeave() function, the report in figure 22.7 (salaryReport.odf) is produced.
Figure 22.5 Final report in ODF format (salaryReport.odf)