- RStudio for R Statistical Computing Cookbook
- Table of Contents
- RStudio for R Statistical Computing Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Preface
- 1. Acquiring Data for Your Project
- Introduction
- Acquiring data from the Web – web scraping tasks
- Accessing an API with R
- Getting data from Twitter with the twitteR package
- Getting data from Facebook with the Rfacebook package
- Getting data from Google Analytics
- Loading your data into R with rio packages
- Converting file formats using the rio package
- 2. Preparing for Analysis – Data Cleansing and Manipulation
- 3. Basic Visualization Techniques
- Introduction
- Looking at your data using the plot() function
- Using pairs.panel() to look at (visualize) correlations between variables
- Adding text to a ggplot2 plot at a custom location
- Changing axes appearance to ggplot2 plot (continous axes)
- Producing a matrix of graphs with ggplot2
- Drawing a route on a map with ggmap
- Making use of the igraph package to draw a network
- Showing communities in a network with the linkcomm package
- 4. Advanced and Interactive Visualization
- 5. Power Programming with R
- Introduction
- Writing modular code in RStudio
- Implementing parallel computation in R
- Creating custom objects and methods in R using the S3 system
- Evaluating your code performance using the profvis package
- Comparing an alternative function's performance using the microbenchmarking package
- Using GitHub with RStudio
- 6. Domain-specific Applications
- Introduction
- Dealing with regular expressions
- Analyzing PDF reports in a folder with the tm package
- Creating word clouds with the wordcloud package
- Performing a Twitter sentiment analysis
- Detecting fraud in e-commerce orders with Benford's law
- Measuring customer retention using cohort analysis in R
- Making a recommendation engine
- Performing time series decomposition using the stl() function
- Exploring time series forecasting with forecast()
- Tracking stock movements using the quantmod package
- Optimizing portfolio composition and maximising returns with the Portfolio Analytics package
- Forecasting the stock market
- 7. Developing Static Reports
- 8. Dynamic Reporting and Web Application Development
- Index
This book was developed and written keeping in mind an average R and RStudio user who would like to make the move from good to great in the field of their programming skills on the language.
If you think you are quite good at R and RStudio but you are still missing something in order to be great, this book is exactly what you need to read.
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