Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.
So you want to learn data skills. That’s great! But we offer tons of data science courses. Why should you learn R programming specifically? Would it be better to learn Python?
If you really want to dig into that question, we’ve demonstrated Python vs. R to show how each language handles common data science tasks. And while the the bottom line is that each language has its own strengths, and both are great choices for data science, R does have unique strengths that are worth considering!
R was originally designed by statisticians for doing statistical analysis, and it remains the programming choice of most statisticians today. R’s syntax makes it easy to create complex statistical models with just a few lines of code. Since so many statisticians use and contribute to R packages, you’re likely to be able to find support for any statistical analysis you need to perform.
For related reasons, R is the statistical and data analysis language of course in many academic settings. If you aspire to work in academia — or if you'd just like to read academic papers and then be able to dig into the code behind them — having R programming skills can be a must.
Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. Twitter uses R for data visualization and semantic clustering. Microsoft, Uber, AirBnb, IBM, HP – they all hire data scientists who can program in R.
And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. Even the New York Times uses R!
Python may be one of the most beginner-friendly programming languages, but once you get past the syntax, R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
Because of that, learning the core skills of data science – data manipulation, data visualization, and machine learning – can actually be easier in R once you’ve gotten through the basic fundamentals. Check out, for example, how straightforward it is to create these common data visualization styles in R.
And of course, there's the tidyverse, a group of packages that's built specifically to make data work in R quicker, easier, and more accessible. In fact, that's really an advantage in and of itself:
Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. The dplyr package, for example, makes data manipulation a breeze, and ggplot2 is a fantastic tool for data visualization.
These packages are part of the tidyverse, a growing collection of packages maintained by RStudio, a certifed B-corp that also creates a free-to-use R environment of the same name that's perfect for data work. These packages are powerful, easy to access, and have great documentation.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
My journey into the vast world of data has been a fun and enthralling ride. I have been glued to my courses, waiting to finish one so I can proceed to the next.
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