R

R is both a programming language and an environment built specifically for statistical computing. This definition has been taken from the R website, r-project.org/about.html:

In other words, one of the major differences between R and Python is that some of the most common functionalities for working with data--data handling and storage, visualization, statistical computation, and so on--come built in. A good example of this is linear modeling, a basic statistical method for modelling numerical data.

In R, linear modeling is a built-in functionality that is made very intuitive and straightforward, as we will see in Chapter 5, Manipulating Text Data - An Introduction to Regular Expressions. There are a number of ways to do linear modeling in Python, but they all require using external libraries and often doing extra work to get the data in the right format.

R also has a built-in data structure called a dataframe that can make manipulation of tabular data more intuitive. 

The big takeaway here is that there are benefits and trade-offs to both languages. In general, being able to use the right tool for the job can save an immense amount of time spent on data wrangling. It is therefore quite useful as a data programmer to have a good working knowledge of each language and know when to use one or the other.