R is a piece of software for running statistical analyses on data and getting nice graphs. It's free, has a lot of stuff built in and is quite pleasant to use.

Out there in the world a lot of people use software like Excel for doing their data processing. They want to have tables where they see their data.

That has the advantage that you have a nice GUI that normal people can easily learn. However some tasks take a lot of time with tables, and Excel automatically reformats your data when it think it knows better than you. Excel also doesn't handle it well to have 500000 rows in your data. Excel doesn't make pretty customizable plots.

Often the choice is between doing a task for 15 minutes in manual labor in Excel or writing 5 lines in R that take you 15 minutes of reading the documentation to find the right parameters.

As a result in a lot of professional context where statistics are needed people use specialised statistics software. That might be SPSS, Stata, SAS or R. SPSS, Stata and SAS both need a license and R is free software. State of the art statistics if often done in R and if someone invents a new statistical method they often publish a R package along with their paper to allow other people to use their shiny new technique.

It's worth noting that statisticians aren't primarily programmers and R is build for statisticians. It has a lot of powerful magic functions with 20 optional parameters.

These days there are also liberaries for like Pandas for Python that allow you to do most of the things that R can do while at the same time having a beautiful language.

What is R?

It's a programming language and environment which is widely used in the statistical community, in part because it has a LOT of statistics-related libraries available for it.

Historically, it's an open-source re-implementation of the programming language S developed at Bell Labs in mid-70s.