In this chapter, we will cover the following topics:
- Dealing with regular expressions
- Analyzing PDF reports in a folder with the
tmpackage - Creating wordclouds with the
wordcloudpackage - 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
quantmodepackage - Optimizing portfolio composition and maximizing returns with the Portfolio Analytics package
- Forecasting the stock market
I have to be honest: one of the things I love the most about R is the huge amount of domain-specific packages.
I am sure you know that feeling. You start working on a new topic, let's say financial portfolio optimization. You start studying a bit of theory and then ask yourself, "Will there be an R package for this?"
In 90 percent cases, the answer will be "Yes." Actually, if R can be considered a powerful programming language, it is in domain-specific applications that we can appreciate its true power.
As of June 24, 2015, 6,712 packages for Windows OS were computed. We could even paraphrase that well-known Apple remark into:
"There's a package for that."
Chapter 7, Developing Static Reports, which follows, highlights this R richness, showing 13 domain applications where specific packages play a central role.