Now enrolling — Batch 2025

Master Data Visualization
with R Graphics

plot(data, type = "l", col = "#58A6FF")

A hands-on course covering base R graphics, the plot() function, graph customization, and exporting publication-ready charts.

Explore the syllabus Learn more
6
Core modules
20+
Hands-on exercises
100%
Project-based
// about the course

Why learn R graphics?

R is the language of data science. Its base graphics engine is powerful, flexible, and built into every R installation — no extra packages needed.

This course takes you from zero to confident: you'll understand how R's graphics engine works, how to build any common chart type, and how to make it look exactly the way you want.

You'll work with real datasets, write real R code, and finish each module with a graph you're proud to share. Whether you're a student, researcher, or analyst — these skills translate directly to your work.

By the end, you can produce print-ready visualizations and export them to PNG, PDF, SVG, or JPEG directly from R.

scatter_plot.R
# Load and prepare data
data <- read.csv("scores.csv")

# Open PNG device
png("output.png", width = 800)

# Create customized plot
plot(data$x, data$y,
  main = "Score Distribution",
  xlab = "Students",
  ylab = "Score",
  col = "#58A6FF",
  pch = 19, cex = 1.2)

dev.off()
// syllabus

What you'll learn

Six focused modules, each building on the last — from your first graph to polished, exportable visualizations.

01
Introduction to R Graphics
Understand the graphics pipeline in R, how devices work, and where base graphics fits alongside ggplot2 and lattice.
graphics() devices overview
02
Creating Graphs
Build scatter plots, bar charts, histograms, line graphs, and box plots using R's built-in functions from scratch.
barplot() hist() boxplot()
03
Base Graphics — The Workhorse
Deep-dive into R's base graphics system: the graphics state, par() settings, coordinate systems, and multi-panel layouts.
par() mfrow layout()
04
The plot() Function
Master the most versatile function in R graphics. Learn every argument, method dispatch, and how plot() adapts to different data types.
plot() type= xlim / ylim
05
Customizing Graphs
Take full control: colors, point shapes, line types, fonts, legends, annotations, and adding layers with lines(), points(), and text().
col pch / lty legend()
06
Saving Graphs to Files
Export publication-ready graphics using R's device functions. Control resolution, dimensions, and file format for any workflow.
png() pdf() svg()
// get in touch

Enroll or ask a question

Ready to start? Fill in the form and we'll get back to you within 24 hours.

Contact details

Whether you're a student, faculty, or institution looking to run this course — reach out and we'll help you get started.

📧
hello@rlab-course.edu
🕐
Mon–Fri, 9 AM – 6 PM IST
📍
Hyderabad, Telangana
// test yourself

R Graphics Quiz

10 questions covering everything from plot() basics to saving graphs. See how much you know!

Question 1 of 10 Score: 0
out of 10