R: R ggplot2

Q: What’s the difference between inside and outside of aes()?

Inside aes() is a variable mapping (e.g., color = group where each group has a different color); outside aes() is a fixed value (e.g., color = "red" where everything is red). Placing "red" inside aes() will be treated as a column name.

Q: What is the difference between geom_bar() and geom_col() ?

geom_bar(stat = "identity") uses a y value; geom_col() is its syntactic sugar (more concise). geom_bar() counts by default (no y required).

Q: How do I add comments?

Use geom_text() / geom_label() :

Q: How do I remove the legend?

theme(legend.position = "none") .

Q: How do I save high-resolution images?

ggsave("plot.png", dpi = 300, width = 8, height = 6) , 300 dpi is the standard for printing.

Last updated: 2026-08-26

In the previous lesson, we covered basic R plotting—fast but ugly. In this lesson, we’ll dive into R’s true “secret weapon”—ggplot2. Designed by Hadley Wickham, its “Grammar of Graphics” syntax makes plotting as easy as “building with blocks,” produces publication-quality visuals by default, and is used in 90% of R academic papers.

After completing this lesson, you’ll be able to create 5 core types of charts using ggplot2 and understand the core concept of “layering.”

1. What You'll Learn

ggplot2 core: Grammar of Graphics syntax
The "ggplot() + aes() + geom_xxx()" trio
5 basic geoms:point / line / bar / histogram / boxplot
Aesthetic mapping aes(x, y, color, size, shape)
labs() tag + theme_minimal() theme
Key Differences Between ggplot2 and Basic R

2. A Story of Data Visualization

(1) Pain Point: Basic R is too ugly

Bob used basic R to create a chart showing sales trends in four cities:

plot(1:4, sales, type = "b")

The manager glanced at it and asked, "Can this image be included in the report?"

(2) The ggplot2 Solution

library(ggplot2)

ggplot(sales_df, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  labs(title = "Q1-Q4 Sales Trends", x = "Quarter", y = "Sales") +
  theme_minimal()

4 lines of code + default publication-quality visuals. That’s the power of ggplot2.

3. Core Concepts of "Grammar of Graphics"

(1) What is the Grammar of Graphics?

graph TB
    A[ggplot2 Charts] --> B[Data Data<br/>data]
    A --> C[Aesthetic Mapping Aesthetics<br/>aes]
    A --> D[Geometric Objects Geometries<br/>geom_xxx]
    A --> E[Statistical Transformations Statistics<br/>stat_xxx]
    A --> F[Coordinate System Coordinate<br/>coord_xxx]
    A --> G[Sub-section Facet<br/>facet_xxx]
    A --> H[Topic Theme<br/>theme_xxx]
    
    style A fill:#fff3cd
    style B fill:#cce5ff
    style C fill:#d4edda
    style D fill:#f8d7da
    style E fill:#e1d4ff
    style F fill:#ffe1e1
    style G fill:#e1ffe1
    style H fill:#ffe1d4

In a nutshell: ggplot2 breaks down a plot into 7 layers, each of which is independent and replaceable.

(2) The Three Pillars of ggplot2

Function	Purpose	Example
`ggplot()`	Initialization (Specified Data)	`ggplot(df)`
`aes()`	Aesthetic Mapping (Variables → Visuals)	`aes(x, y, color)`
`geom_xxx()`	Geometric Objects (Layers)	`geom_point()`

Minimal Example:

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

4. The First ggplot2 Plot

(1) 5-Step Drawing Process

library(ggplot2)

# 1. Prepare data
df <- data.frame(
  x = 1:10,
  y = (1:10) ^ 2
)

# 2. Initialization(Data)
p <- ggplot(df, aes(x = x, y = y))

# 3. Add a Layer
p <- p + geom_point()

# 4. Add a tag
p <- p + labs(title = "Quadratic Curve", x = "X", y = "Y")

# 5. Add a topic
p <- p + theme_minimal()

# 6. Print
print(p)

Or use + to do it all in one go:

ggplot(df, aes(x = x, y = y)) +
  geom_point() +
  labs(title = "Quadratic Curve", x = "X", y = "Y") +
  theme_minimal()

💡 Tip: In ggplot2, use + to chain layers, similar to how |> is used to chain data operations.

(2) Output to a file

# Save as PNG
ggsave("myplot.png", width = 8, height = 6, dpi = 300)

# Save as PDF(For a thesis)
ggsave("myplot.pdf", width = 8, height = 6)

# Custom Size
ggsave("myplot.png", width = 10, height = 8, units = "cm", dpi = 300)

5. Aesthetic Mapping aes()

(1) Common Mappings

Mapping	Function	Example
`x`, `y`	X / Y coordinates	`aes(x = age, y = height)`
`color` / `colour`	color (outer)	`aes(color = gender)`
`fill`	Fill Color (Bar/Area)	`aes(fill = region)`
`size`	Font Size	`aes(size = amount)`
`shape`	Dot Shape	`aes(shape = category)`
`alpha`	Transparency	`aes(alpha = weight)`
`linetype`	Linear	`aes(linetype = type)`

(2) Practical Application: 3-D Mappings

# Color + Size + Shape 3 dimensions
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl), size = hp)) +
  geom_point(alpha = 0.7) +
  labs(title = "Vehicle Weight vs Fuel Consumption", color = "Number of cylinders", size = "Horsepower")

(3) ⚠️ Inside vs. Outside of aes()

# aes() Inside:Map to a variable(Each dot is a different color)
ggplot(df, aes(x, y, color = group)) + geom_point()
# → Different group Display different colors

# aes() Outside:Fixed value(All dots are the same color)
ggplot(df, aes(x, y)) + geom_point(color = "red")
# → All the dots are red

💡 Tip: color = "red" inside aes() will be interpreted as the "red column" (if the data contains a red column). Fixed colors must be placed outside aes().

6. 5 Basic Geoms

(1) geom_point() Scatter Plot

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  labs(title = "Vehicle Weight vs Fuel Consumption", x = "Weight", y = "Fuel Consumption")

(2) geom_line() Line Chart

df <- data.frame(
  x = 1:10,
  y = c(2, 4, 3, 5, 4, 6, 5, 7, 6, 8),
  group = "A"
)

ggplot(df, aes(x = x, y = y)) +
  geom_line(color = "blue", linewidth = 1) +
  geom_point(color = "blue", size = 3)

(3) geom_bar() Bar Chart

# Data
df <- data.frame(
  city = c("Beijing", "Shanghai", "Guangzhou", "Shenzhen"),
  sales = c(4800, 7000, 3900, 4500)
)

# Bar Chart
ggplot(df, aes(x = city, y = sales, fill = city)) +
  geom_bar(stat = "identity") +  # identity = Use y values
  labs(title = "Total Sales by City", x = "City", y = "Sales") +
  theme_minimal()

⚠️ Note: geom_bar(stat = "identity") uses the y-values from the data; geom_bar() counts by default.

(4) geom_histogram() Histogram

# 1000 A random number from a normal distribution
data <- data.frame(x = rnorm(1000, mean = 100, sd = 15))

ggplot(data, aes(x = x)) +
  geom_histogram(bins = 30, fill = "skyblue", color = "white") +
  labs(title = "Normal Distribution", x = "Value", y = "Frequency")

(5) geom_boxplot() Box Plot

# 4 Class Test Scores
scores <- data.frame(
  class = rep(c("1Cls", "2Cls", "3Cls", "4Cls"), each = 10),
  score = c(rnorm(10, 80, 8), rnorm(10, 75, 10),
            rnorm(10, 85, 7), rnorm(10, 70, 12))
)

ggplot(scores, aes(x = class, y = score, fill = class)) +
  geom_boxplot() +
  labs(title = "4 Class Grade Distribution", x = "Class", y = "Fractions")

7. Tags and Topics

(1) labs() tag

ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    title = "Main Title",
    subtitle = "Subtitle",
    caption = "Data Sources:xxx",
    x = "X Axis",
    y = "Y Axis",
    color = "Color Mapping",
    fill = "Fill Mapping"
  )

(2) Theme theme_xxx()

Theme	Style
`theme_minimal()`	Minimalist (Most Common)
`theme_bw()`	Black and White
`theme_classic()`	Classics (Academic)
`theme_void()`	Blank
`theme_light()`	Light
`theme_dark()`	Dark

# Comparison 4 Topics
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
(p + theme_minimal())  # Default Recommendation
(p + theme_bw())
(p + theme_classic())
(p + theme_void())

(3) Fine-Tuning the Theme

p + theme(
  plot.title = element_text(size = 16, face = "bold"),
  axis.text = element_text(size = 10),
  legend.position = "bottom"  # Legend Location
)

8. Colors and Mappings

(1) Discrete Colors

# 1. Built-in Color Palette
p + scale_color_brewer(palette = "Set1")    # Classic
p + scale_color_brewer(palette = "Dark2")    # Dark
p + scale_color_brewer(palette = "Pastel1")  # Light-colored

# 2. Specify manually
p + scale_color_manual(values = c("red", "blue", "green"))

# 3. rainbow
p + scale_color_manual(values = rainbow(4))

(2) Continuous Color

# Gradient(For continuous variables)
p + scale_color_gradient(low = "blue", high = "red")
p + scale_color_viridis_c()   # viridis(Recommendations)
p + scale_color_distiller(palette = "RdYlBu")

9. Complete Example: Multi-Image Sales in 4 Cities

Below is an example of a complete workflow that ties together all the concepts covered in this lesson.

▶ Example: 4 Cities, 5 Photos Collection

R 📖 Display only

# ============================================
# 4 City Sales 5 Image Gallery
# Features:Use ggplot2 to create 5 publication-quality charts
# ============================================

library(ggplot2)
library(dplyr)
library(tidyr)

# 1. Prepare data
sales_wide <- tibble(
  city = c("Beijing", "Shanghai", "Guangzhou", "Shenzhen"),
  Q1 = c(1000, 1500, 800, 1200),
  Q2 = c(1200, 1800, 900, 1400),
  Q3 = c(1100, 1700, 1000, 1300),
  Q4 = c(1500, 2000, 1200, 1600)
)

sales_long <- sales_wide |>
  pivot_longer(cols = -city, names_to = "quarter", values_to = "sales")

cat("=== Sales Data ===\n")
print(sales_long)

# 2. Plot 1:Linear Trend(By City)
p1 <- ggplot(sales_long, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  labs(title = "Q1-Q4 Sales Trends", x = "Quarter", y = "Sales", color = "City") +
  scale_color_brewer(palette = "Set1") +
  theme_minimal()

print(p1)

# 3. Plot 2:Total Sales Bar Chart
total_sales <- sales_wide |>
  mutate(total = Q1 + Q2 + Q3 + Q4) |>
  select(city, total)

p2 <- ggplot(total_sales, aes(x = reorder(city, total), y = total, fill = city)) +
  geom_bar(stat = "identity") +
  coord_flip() +  # Sideways
  labs(title = "Total Sales by City", x = "City", y = "Total Sales") +
  theme_minimal() +
  theme(legend.position = "none")

print(p2)

# 4. Plot 3:Pie Chart of Percentage Distributions(geom_bar + coord_polar)
p3 <- ggplot(total_sales, aes(x = "", y = total, fill = city)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar(theta = "y") +  # Polar Coordinates(Pie Chart)
  labs(title = "Share of Sales") +
  theme_void() +
  scale_fill_brewer(palette = "Set2")

print(p3)

# 5. Plot 4:Histogram(All Sales Data)
p4 <- ggplot(sales_long, aes(x = sales, fill = city)) +
  geom_histogram(bins = 10, alpha = 0.7, position = "identity") +
  labs(title = "Sales Distribution", x = "Sales", y = "Frequency") +
  scale_fill_brewer(palette = "Set1") +
  theme_minimal()

print(p4)

# 6. Plot 5:Box-and-Whisker Plot(By City)
p5 <- ggplot(sales_long, aes(x = city, y = sales, fill = city)) +
  geom_boxplot() +
  labs(title = "Sales Distribution by City", x = "City", y = "Sales") +
  theme_minimal() +
  theme(legend.position = "none")

print(p5)

# 7. Save All Images
ggsave("01_trend.png", p1, width = 8, height = 6, dpi = 300)
ggsave("02_total.png", p2, width = 8, height = 6, dpi = 300)
ggsave("03_pie.png", p3, width = 8, height = 6, dpi = 300)
ggsave("04_hist.png", p4, width = 8, height = 6, dpi = 300)
ggsave("05_box.png", p5, width = 8, height = 6, dpi = 300)

cat("\n=== 5 The image has been saved ===\n")
cat("  01_trend.png - Linear Trend\n")
cat("  02_total.png - Bar Chart Comparison\n")
cat("  03_pie.png - Pie Chart of Percentage Distributions\n")
cat("  04_hist.png - Histogram\n")
cat("  05_box.png - Urban Box Lines\n")

61 logic lines (exceeds 40-line limit, display only)

Expected output: 5 publication-quality charts (the default "theme_minimal" is already attractive enough).

❓ FAQ

Q Should I use ggplot2 or basic R?

A Use ggplot2 for publications, papers, and reports (publication-quality visuals + consistent style); use basic R for a quick look at data (one-line plotting). This tutorial focuses primarily on ggplot2.

Q What’s the difference between inside and outside of aes()?

A Inside aes() is a variable mapping (e.g., color = group where each group has a different color); outside aes() is a fixed value (e.g., color = "red" where everything is red). Placing "red" inside aes() will be treated as a column name.

Q What is the difference between geom_bar() and geom_col()?

A geom_bar(stat = "identity") uses a y value; geom_col() is its syntactic sugar (more concise). geom_bar() counts by default (no y required).

Q How do I add comments?

A Use geom_text() / geom_label():

ggplot(df, aes(x, y)) + geom_point() +
  geom_text(aes(label = name), vjust = -0.5)  # Add labels above the data points

Q How do I remove the legend?

A theme(legend.position = "none").

Q How do I save high-resolution images?

A ggsave("plot.png", dpi = 300, width = 8, height = 6), 300 dpi is the standard for printing.

📖 Summary

ggplot2 is based on the Grammar of Graphics and breaks down plots into 7 layers (data, aesthetics, geometry, statistics, coordinates, facets, and themes)
The Three Pillars: ggplot() (Data) + aes() (Mapping) + geom_xxx() (Layers)
5 basic geom types: point / line / bar / histogram / boxplot Aesthetic Mapping: color fill size shape alpha linetype
aes() Inside = variable mapping; aes() Outside = fixed value
Use theme_minimal(), theme_bw(), etc., for themes; use scale_color_brewer(palette = "Set1") for color palettes
ggsave() Save image (dpi = 300, printing standard)
ggplot2 offers publication-quality visuals by default—it’s used in all professional R reports and papers

📝 Exercises

Basic Exercise: Create a data frame (10 rows, 2 columns of x and y values), use ggplot + geom_point + labs + theme_minimal to plot a scatter plot, and verify the difference between color = "red" (inside) and color = "red" (outside) in aes().
Basic Exercise: Create a scatter plot using the mtcars dataset (wt vs mpg), color the points according to cyl (color = factor(cyl)), and add a title and theme using labs().
Basic Exercise: Create a data frame (5 cities + sales figures), use geom_col() to plot a bar chart, sort the data using reorder(city, sales), and verify the sorting results.
Advanced Exercise: Simulate the scores for Class 4 with 30 students, use geom_boxplot() to plot a score distribution chart for Class 4, and add fill = class for fill color and theme_minimal() for a theme.
Challenge: Use mtcars to create 6 figures (combining the par(mfrow) style with ggplot2): ① Scatter plot of wt vs. mpg ② Histogram of wt distribution ③ Box plot of mpg ④ wt vs. mpg by cylinder ⑤ Color-mapped scatter plot ⑥ Complete theme customization. Save the 6 plots as a PDF.

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R: R ggplot2

1. What You'll Learn

2. A Story of Data Visualization

(1) Pain Point: Basic R is too ugly

(2) The ggplot2 Solution

3. Core Concepts of "Grammar of Graphics"

(1) What is the Grammar of Graphics?

(2) The Three Pillars of ggplot2

4. The First ggplot2 Plot

(1) 5-Step Drawing Process

(2) Output to a file

5. Aesthetic Mapping aes()

(1) Common Mappings

(2) Practical Application: 3-D Mappings

(3) ⚠️ Inside vs. Outside of aes()

6. 5 Basic Geoms

(1) geom_point() Scatter Plot

(2) geom_line() Line Chart

(3) geom_bar() Bar Chart

(4) geom_histogram() Histogram

(5) geom_boxplot() Box Plot

7. Tags and Topics

(1) labs() tag

(2) Theme theme_xxx()

(3) Fine-Tuning the Theme

8. Colors and Mappings

(1) Discrete Colors

(2) Continuous Color

9. Complete Example: Multi-Image Sales in 4 Cities

▶ Example: 4 Cities, 5 Photos Collection

❓ FAQ

📖 Summary

📝 Exercises

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