What R Is For

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 first look at R

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

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.

Quick check: For which kind of work is R especially well known?

• Statistical analysis and data visualisation
• Operating system kernels
• Mobile game engines
• Device drivers