“I am currently a research assistant and I have a good understanding of Stata. However, if I plan to continue to pursue a career in research and data analysis, should I also learn R?”
My answer? Yes, of course, you should learn R!! Why shouldn’t you? We all are lifelong learners, and skilled data scientists not only should know how to operate in R but also in Python (and Java, Hadoop and Spark, and learn to get comfortable with the command line).
You have to start somewhere and in our opinion, a good place to start is learning R.
Why is that?
First of all, look at what Courtney Brown says about learning R:
I totally agree with Courtney: spend your time learning a useful programming language with a long shelf-life.
R is an open source, free programming language and environment for statistical computing and graphics used worldwide. This means that
- R is easy to download on any computer operating system!
- It is easy to find any kind of resources and help on R;
- Many people are contributing to the development of R – for example, look at the R bloggers website – so R will be around for a long time;
- The graphics you can do with R are amazing! Look at the R graphic demo below:
OK: now, I hope, you are convinced that it is worth learning R, but I bet you are reading this post because you want to become a data scientist … right?
So will R help you becoming a better data scientist? Why do data scientists use R?
As a data scientist, you need to interpret, interact and visualize data, one of the best software to do so, is R!
Hadley Wickham is a major figure in the development of R. Among other things he created ggplot2 for R, and he emphasizes that to do data science you need to do the following things to, and with, data:
- visualize &
And you need a means to connect these actions so your analysis flows smoothly. Hadley, is of course, an evangelist for the use of R in data science:
Some people dispute about whether one should learn R or Python.
For a data scientist, this is a non-question: you should learn both!
Both languages have relatively simple entry points and they serve somewhat different purposes. To become fluent in both, and flexible as to how and when to use them, is important.
I have a Ph.D. in mathematics and I am a Certified Base Programmer for SAS 9, and I am learning R – because it is so important for a data scientist. So, do not waste time: do like me and learn R!