book

R for Everyone: Advanced Analytics and Graphics, 2nd Edition

by Jared P. Lander

June 2017

Beginner to intermediate

560 pages

17h 32m

English

Addison-Wesley Professional

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Acknowledgments for the Second EditionAcknowledgments for the First Edition
1.1 Downloading R1.2 R Version1.3 32-bit vs. 64-bit1.4 Installing1.4.1 Installing on Windows1.4.2 Installing on Mac OS X1.4.3 Installing on Linux1.5 Microsoft R Open1.6 Conclusion

2.1 Command Line Interface2.2 RStudio2.2.1 RStudio Projects2.2.2 RStudio Tools2.2.3 Git Integration2.3 Microsoft Visual Studio2.4 Conclusion
3.1 Installing Packages3.1.1 Uninstalling Packages3.2 Loading Packages3.2.1 Unloading Packages3.3 Building a Package3.4 Conclusion
4.1 Basic Math4.2 Variables4.2.1 Variable Assignment4.2.2 Removing Variables4.3 Data Types4.3.1 Numeric Data4.3.2 Character Data4.3.3 Dates4.3.4 Logical4.4 Vectors4.4.1 Vector Operations4.4.2 Factor Vectors4.5 Calling Functions4.6 Function Documentation4.7 Missing Data4.7.1 NA4.7.2 NULL4.8 Pipes4.9 Conclusion
5.1 data.frames5.2 Lists5.3 Matrices5.4 Arrays5.5 Conclusion
6.1 Reading CSVs6.1.1 read_delim6.1.2 fread6.2 Excel Data6.3 Reading from Databases6.4 Data from Other Statistical Tools6.5 R Binary Files6.6 Data Included with R6.7 Extract Data from Web Sites6.7.1 Simple HTML Tables6.7.2 Scraping Web Data6.8 Reading JSON Data6.9 Conclusion
7.1 Base Graphics7.1.1 Base Histograms7.1.2 Base Scatterplot7.1.3 Boxplots7.2 ggplot27.2.1 ggplot2 Histograms and Densities7.2.2 ggplot2 Scatterplots7.2.3 ggplot2 Boxplots and Violins Plots7.2.4 ggplot2 Line Graphs7.2.5 Themes7.3 Conclusion
8.1 Hello, World!8.2 Function Arguments8.2.1 Default Arguments8.2.2 Extra Arguments8.3 Return Values8.4 do.call8.5 Conclusion
9.1 if and else9.2 switch9.3 ifelse9.4 Compound Tests9.5 Conclusion
10.1 for Loops10.2 while Loops10.3 Controlling Loops10.4 Conclusion
11.1 Apply Family11.1.1 apply11.1.2 lapply and sapply11.1.3 mapply11.1.4 Other apply Functions11.2 aggregate11.3 plyr11.3.1 ddply11.3.2 llply11.3.3 plyr Helper Functions11.3.4 Speed versus Convenience11.4 data.table11.4.1 Keys11.4.2 data.table Aggregation11.5 Conclusion
12.1 Pipes12.2 tbl12.3 select12.4 filter12.5 slice12.6 mutate12.7 summarize12.8 group_by12.9 arrange12.10 do12.11 dplyr with Databases12.12 Conclusion
13.1 map13.2 map with Specified Types13.2.1 map_int13.2.2 map_dbl13.2.3 map_chr13.2.4 map_lgl13.2.5 map_df13.2.6 map_if13.3 Iterating over a data.frame13.4 map with Multiple Inputs13.5 Conclusion
14.1 cbind and rbind14.2 Joins14.2.1 merge14.2.2 plyr join14.2.3 data.table merge14.3 reshape214.3.1 melt14.3.2 dcast14.4 Conclusion
15.1 Binding Rows and Columns15.2 Joins with dplyr15.3 Converting Data Formats15.4 Conclusion
16.1 paste16.2 sprintf16.3 Extracting Text16.4 Regular Expressions16.5 Conclusion
17.1 Normal Distribution17.2 Binomial Distribution17.3 Poisson Distribution17.4 Other Distributions17.5 Conclusion
18.1 Summary Statistics18.2 Correlation and Covariance18.3 T-Tests18.3.1 One-Sample T-Test18.3.2 Two-Sample T-Test18.3.3 Paired Two-Sample T-Test18.4 ANOVA18.5 Conclusion
19.1 Simple Linear Regression19.1.1 ANOVA Alternative19.2 Multiple Regression19.3 Conclusion
20.1 Logistic Regression20.2 Poisson Regression20.3 Other Generalized Linear Models20.4 Survival Analysis20.5 Conclusion
21.1 Residuals21.2 Comparing Models21.3 Cross-Validation21.4 Bootstrap21.5 Stepwise Variable Selection21.6 Conclusion
22.1 Elastic Net22.2 Bayesian Shrinkage22.3 Conclusion
23.1 Nonlinear Least Squares23.2 Splines23.3 Generalized Additive Models23.4 Decision Trees23.5 Boosted Trees23.6 Random Forests23.7 Conclusion
24.1 Autoregressive Moving Average24.2 VAR24.3 GARCH24.4 Conclusion
25.1 K-means25.2 PAM25.3 Hierarchical Clustering25.4 Conclusion
26.1 Caret Basics26.2 Caret Options26.2.1 caret Training Controls26.2.2 Caret Search Grid26.3 Tuning a Boosted Tree26.4 Conclusion
27.1 Installing a LATEX Program27.2 LATEX Primer27.3 Using knitr with LATEX27.4 Conclusion
28.1 Document Compilation28.2 Document Header28.3 Markdown Primer28.4 Markdown Code Chunks28.5 htmlwidgets28.5.1 datatables28.5.2 leaflet28.5.3 dygraphs28.5.4 threejs28.5.5 d3heatmap28.6 RMarkdown Slideshows28.7 Conclusion
29.1 Shiny in RMarkdown29.2 Reactive Expressions in Shiny29.3 Server and UI29.4 Conclusion
30.1 Folder Structure30.2 Package Files30.2.1 DESCRIPTION File30.2.2 NAMESPACE File30.2.3 Other Package Files30.3 Package Documentation30.4 Tests30.5 Checking, Building and Installing30.6 Submitting to CRAN30.7 C++ Code30.7.1 sourceCpp30.7.2 Compiling Packages30.8 Conclusion
A.1 MeetupsA.2 Stack OverflowA.3 TwitterA.4 ConferencesA.5 Web SitesA.6 DocumentsA.7 BooksA.8 Conclusion

Content preview from R for Everyone: Advanced Analytics and Graphics, 2nd Edition

28. Rich Documents with RMarkdown

RMarkdown has quickly become the preferred means of communicating results from R, eclipsing LaTeX due to RMarkdown’s simplicity to write and that it compiles to many different formats, including HTML, PDF, Microsoft Word, slideshows and Shiny apps. The RMarkdown format is extendable and templated, enabling customized documents. The workflow for writing RMarkdown is similar to that for LaTeX as detailed in Chapter 27: Normal text (flavored with Markdown) is written and R code is put in chunks. The style of the chunk delineation is different, but the idea is the same. The files are typically saved as .Rmd files.

28.1 Document Compilation

RMarkdown depends on knitr for evaluating R code and pandoc for converting ...

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R for Everyone: Advanced Analytics and Graphics

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ISBN: 9780134546988Purchase book

R for Everyone: Advanced Analytics and Graphics, 2nd Edition

by Jared P. Lander

28. Rich Documents with RMarkdown

28.1 Document Compilation

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