Getting started with R

R is a programming language designed for statistical computing and data visualization. It’s widely used in data science, research, and academia. This guide walks you through installing R, exploring its basics, and performing a simple analysis.

Installing R

1. Install R

Download R from the Comprehensive R Archive Network (CRAN):
https://cran.r-project.org

Choose the installer for your operating system and follow the prompts.

2. Install RStudio (Recommended)

RStudio is a popular integrated development environment (IDE) for R. Download it here:
https://posit.co/download/rstudio-desktop/

Running R

You can run R:

In the RStudio console
Directly in the terminal by typing R
In scripts saved as .R files

Basic Syntax

# Assign variables
x <- 5
y <- 10

# Print output
print(x + y)

# Create a vector
numbers <- c(1, 2, 3, 4, 5)

# Get the mean
mean(numbers)

Data Structures

Vector: One-dimensional array of elements of the same type.
Matrix: Two-dimensional array.
Data Frame: Table-like structure (similar to a spreadsheet).
List: Collection of elements of different types.

Example of a data frame:

data <- data.frame(
    name = c("Alice", "Bob"),
    age = c(25, 30)
)

print(data)

Installing and Loading Packages

Packages extend R’s functionality.

install.packages("ggplot2")
library(ggplot2)

Plotting Data

library(ggplot2)

df <- data.frame(
    x = 1:10,
    y = c(2, 3, 5, 7, 11, 13, 17, 19, 23, 29)
)

ggplot(df, aes(x = x, y = y)) +
    geom_point() +
    geom_line() +
    labs(title = "Simple Plot", x = "X values", y = "Y values")

Reading Data from a CSV

data <- read.csv("data.csv")
head(data)

Performing a Simple Analysis

summary(data)
cor(data$x, data$y)

Tips for Learning R

Practice with built-in datasets (mtcars, iris).
Learn vectorized operations instead of loops when possible.
Explore R’s extensive package ecosystem for specialized analysis.

Final Thoughts

R is an excellent choice for statistics and visualization. Once you’re comfortable with the basics, explore tidyverse packages for modern, streamlined data analysis.

# Getting started with R