# What R Is For — R Programming

Source: https://www.geekswithgeeks.com/en/r-programming/f-what

> Describe R's strengths, its ecosystem and when to choose it.

## A language built for data

**R** is a free, open-source language and environment for **statistical computing and graphics**, descended from the S language developed at Bell Labs. It is widely used in **statistics, data analysis, bioinformatics, epidemiology, economics, finance and academic research**, and in industry teams doing analytics and reporting. R's strengths: thousands of statistical methods implemented by statisticians themselves, excellent **data visualisation** (especially with **ggplot2**), the **tidyverse** collection of packages for consistent data manipulation, and first-class tools for **reproducible reports** (R Markdown and Quarto) and **interactive dashboards** (Shiny). More than 20,000 packages are available on **CRAN** (the Comprehensive R Archive Network), plus many more on **Bioconductor** for genomics. R is **vectorised**: most operations work on whole vectors at once, and it is usually used interactively in a console or notebook-style editor. Compared with Python, R is often preferred for classical statistics and publication-quality graphics, while Python dominates general software engineering and deep learning; many data scientists use both.

## From raw data to insight

A typical R workflow: import, tidy, transform, visualise, model and communicate.

![A horizontal flow of six connected stages from a table icon to a chart icon and finally a document icon.](assets/figures/r-programming/section-1-map.svg) — Figure 1.1 — The data analysis workflow in R.

## A first look at R

Vectors, summary statistics and a quick plot in a few lines.

```r
marks <- c(72, 45, 90, 38, 66, 81)       # a numeric vector

mean(marks)                               # average
summary(marks)                            # min, quartiles, median, mean, max
sum(marks >= 40)                          # how many passed (TRUE counts as 1)

passed <- marks[marks >= 40]              # keep only passing marks
round(sd(passed), 1)                      # standard deviation

hist(marks, main = "Distribution of marks", xlab = "Marks")   # base R plot
```

## Think in vectors, not loops

Newcomers from C or Java write loops for everything. In R, `marks >= 40` compares every element at once. Vectorised code is shorter, clearer and usually much faster.

**Quiz:** For which kind of work is R especially well known?

- [x] Statistical analysis and data visualisation
- [ ] Operating system kernels
- [ ] Mobile game engines
- [ ] Device drivers

*Answer:* Statistical analysis and data visualisation. R was designed for statistics and graphics and excels at them.