R ggplot2 Basics

ggplot2 is the most popular visualization package in R. It is built on a system called the Grammar of Graphics — every plot is assembled from layers: data, aesthetic mappings, and geometric shapes. This layered approach makes plots highly customizable and consistent in style.

Installing and Loading ggplot2

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

The Grammar of Graphics

Every ggplot2 chart has these components:

ggplot(data, aes(x, y)) +   ← Data and aesthetic mapping
  geom_*()               +   ← Geometric layer (what shape to draw)
  labs()                 +   ← Labels (title, axis names)
  theme_*()                  ← Visual theme (background, fonts)

Layer diagram:
  [Theme]       ← background, grid, fonts
  [Labels]      ← title, axis titles, legend
  [Statistics]  ← optional transformations
  [Geometries]  ← bars, points, lines, boxes
  [Aesthetics]  ← x, y, color, size, shape
  [Data]        ← your data frame

Your First ggplot2 Chart

library(ggplot2)

students <- data.frame(
  name  = c("Asha","Balu","Cena","Dev","Eva"),
  score = c(88, 72, 95, 65, 80),
  hours = c(5, 3, 7, 2, 6)
)

ggplot(students, aes(x=hours, y=score)) +
  geom_point(size=3, color="steelblue") +
  labs(title="Study Hours vs Score",
       x="Hours Studied",
       y="Score") +
  theme_minimal()

Aesthetic Mappings — aes()

# Map data columns to visual properties
aes(x=hours,          # position on x-axis
    y=score,          # position on y-axis
    color=department, # color by group
    size=salary,      # size by value
    shape=gender,     # shape by category
    fill=region,      # fill color for bars/boxes
    alpha=0.7)        # transparency (0=invisible, 1=solid)

# Fixed vs mapped aesthetics:
geom_point(aes(color=dept))   # mapped — changes with data
geom_point(color="red")       # fixed  — same for all points

Common geom Functions

geom_point()      Scatter plot
geom_line()       Line chart
geom_bar()        Bar chart (counts)
geom_col()        Bar chart (values)
geom_histogram()  Histogram
geom_boxplot()    Box plot
geom_violin()     Violin plot
geom_smooth()     Trend line / regression line
geom_text()       Text labels on plot
geom_hline()      Horizontal reference line
geom_vline()      Vertical reference line

Adding Multiple Layers

ggplot(students, aes(x=hours, y=score)) +
  geom_point(size=3, color="steelblue") +
  geom_smooth(method="lm", se=FALSE, color="red") +   # add regression line
  geom_text(aes(label=name), vjust=-1, size=3) +       # add name labels
  labs(title="Study Hours vs Score",
       x="Hours Studied", y="Score",
       caption="Source: Class Data") +
  theme_minimal()

Faceting — Small Multiples

# Create separate panels per category
ggplot(students, aes(x=hours, y=score)) +
  geom_point() +
  facet_wrap(~department)     # one panel per department

# Two-way facet
ggplot(data, aes(x=x, y=y)) +
  geom_point() +
  facet_grid(gender ~ region)  # rows=gender, cols=region

Saving ggplot2 Charts

p <- ggplot(students, aes(x=hours, y=score)) +
       geom_point() +
       labs(title="Study Chart")

ggsave("study_chart.png", plot=p, width=8, height=5, dpi=300)
ggsave("study_chart.pdf", plot=p, width=8, height=5)

Storing a Plot in a Variable

base_plot <- ggplot(students, aes(x=hours, y=score))

# Add different layers to the same base
base_plot + geom_point()
base_plot + geom_line()
base_plot + geom_smooth()

ggplot2's layered system makes complex charts as easy to build as simple ones — you just add layers. The consistent syntax means every chart type follows the same pattern: data → aesthetics → geometry → labels → theme. Once you learn this structure, every new chart type is just a matter of choosing the right geom.