4 Reasons Why You Should Learn R for Data Science

Mavis LohUncategorized

Keen to pick up some data science skills? Great! While there’re tons of data science courses out in the market, you probably want to first identify what programming language would you like to learn specifically – R or Python? Bottom line is that each language has its own strengths, and both are great choices for data science. We previously talked about the 6 Advantages of Learning Python, so today let’s focus on the unique strengths of R programming that are worth considering!

1. R is built for Statistics

R was originally designed by statisticians for statistical analysis tasks and it remains as the programming choice of most statisticians today. Why? This is because R’s syntax makes it easy to create complex statistical models with simply 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.

2. R has a huge online community of data scientists and statisticians

Along with the exponential growth of the use of data science, R followed suit and became one of the fastest-growing languages in the world. This translates to the ease of finding answers and a huge community guidance as you work your way through projects in R. Furthermore, because there are so many enthusiastic R users, you can find R packages integrating almost any app you can think of!

3. Packages that your data analysis a whole lot efficient

Because R was designed for statistical analyses, 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 visualisation. 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.

4. Put another tool in your toolkit.

Even if you’re already a Python expert, there’s no harm in mastering another programming language! Instead, adding R programming to your repertoire will make you a much more efficient, flexible and marketable employee when you’re looking for jobs in data science. Even if you don’t want to use R yourself, learning the basics will make it easier for you to follow someone else’s R code if you ever have to take over a colleague’s project. Having the ability to interpret R and translate it into Python means that the amazing resources of both languages are open to you. Plus, did we mention that R is also a popular language for data science at top tech firms? This gives you even more reasons to upskill yourself with R to stay employable and in-demand.

Long story short: there are lots of great reasons why you should learn R, because it’s a fantastic language for data science.