R

R is a powerful programming language tailored for data analysis and statistics. Within the R ecosystem, there are three main dialects: Base R, Tidyverse, and data.table, each bringing its own flavor to data manipulation. Each has their own unique style and approach. 

While we'll mostly be using Base R and Tidyverse, it's good to know how each friend can bring something different to the table. Whether you prefer Base R’s reliability, Tidyverse’s readability, or data.table’s speed, you’ve got a powerful toolkit for any data challenge!

Base R:

• Think of Base R as the classic friend who does things the old-fashioned way.
• Reliable and straightforward, Base R gives you the tools you need to get the job done, but sometimes you need to write a bit more code

Tidyverse:

• Tidyverse is a collection of modern, cohesive R packages designed for data science, making data manipulation, visualization, and analysis intuitive and efficient. Most people who are interested in data manipulation and visualization use Tidyverse language due to its simplicity. Using intuitive functions and a cohesive syntax, Tidyverseturns data manipulation into a breeze.

data.table:

• data.table is the speed demon of the group. Efficient and lightning-fast, data.table handles big data sets with ease, but its syntax can take some getting used to. If you are working with large datasets, we recommend learning data.table. We recommend learning this package for more advanced users of R.

The difference between these three options would look something like this. Below, you can see three different ways of creating a new variable using these languages. As you can notice, you need to use packages for tidyverse and data.table.

If you want to learn more about the differences between three options, we recommend this guide.