ggplot2: Data Visualization

The grammar of graphics — R’s most beautiful tool

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⏳ Loading R... (first load takes ~15 seconds)

📖 Concept Recap

ggplot2 builds charts in layers. Each layer adds to the previous one using +:

ggplot(data, aes(...)) — declare data and aesthetic mappings (x, y, color, fill, size, shape)
geom_bar / geom_col — bar charts (geom_bar counts rows, geom_col uses a y value)
geom_line / geom_point — line and scatter plots
geom_histogram / geom_boxplot — distributions
labs() — title, x, y labels
theme_minimal() / theme_classic() — clean built-in themes
facet_wrap(~var) — separate panels per group

Charts render directly on the page when you click Run — look for the image below the output box!

👀 Worked Example

A polished horizontal bar chart with labels and a custom theme:

library(ggplot2) sales <- data.frame( category = c("Electronics","Clothing","Food","Sports","Books"), revenue = c(45000, 32000, 28000, 19000, 12000) ) ggplot(sales, aes(x = reorder(category, revenue), y = revenue, fill = category)) + geom_col(show.legend = FALSE) + geom_text(aes(label = paste0("$", format(revenue, big.mark=","))), hjust = -0.1, size = 3.5) + coord_flip() + scale_fill_brewer(palette = "Set2") + labs(title = "Revenue by Category", x = NULL, y = "Revenue ($)") + theme_minimal() + expand_limits(y = 55000)

✏️ Guided

Exercise 1 — Scatter Plot: GPA by Year

Run this scatter plot that shows GPA vs. year, colored by major. The code is complete — run it, then try adjusting the point size or color palette.

library(ggplot2)
students <- data.frame(
  name  = c("Alex","Beth","Carlos","Diana","Ethan","Fiona","George","Hana"),
  major = c("CS","Math","CS","English","CS","Math","English","CS"),
  gpa   = c(3.8, 3.2, 3.9, 2.9, 3.5, 3.7, 3.1, 3.6),
  year  = c(3, 2, 4, 1, 2, 3, 4, 1)
)
ggplot(students, aes(x = year, y = gpa, color = major)) +
  geom_point(size = 4, alpha = 0.8) +
  geom_text(aes(label = name), vjust = -1, size = 3) +
  scale_y_continuous(limits = c(2.5, 4.2)) +
  scale_color_manual(values = c("CS"="#C62828","Math"="#1565C0","English"="#2E7D32")) +
  labs(title = "Student GPA by Year", x = "Year", y = "GPA", color = "Major") +
  theme_minimal()

Output will appear here...

💡 Hint: aes(color = major) maps the major variable to point color. geom_text(aes(label = name)) adds student name labels. vjust = -1 positions labels above the points.

💪 Independent

Exercise 2 — Grouped Bar Chart

Create a grouped bar chart comparing average GPA by major AND year using the students data. Use stat="identity" after computing averages with dplyr, and position="dodge" to place bars side-by-side.

library(ggplot2)
library(dplyr)
students <- data.frame(
  major = c("CS","Math","CS","English","CS","Math","English","CS"),
  gpa   = c(3.8, 3.2, 3.9, 2.9, 3.5, 3.7, 3.1, 3.6),
  year  = c(3, 2, 4, 1, 2, 3, 4, 1)
)

# Compute averages first
avg_data <- students |>
  group_by(major, year) |>
  summarize(avg_gpa = mean(gpa), .groups = "drop")

# Create the grouped bar chart
ggplot(avg_data, aes(x = major, y = avg_gpa, fill = factor(year))) +
  geom_col(position = "dodge") +
  labs(title = "Average GPA by Major and Year",
       x = "Major", y = "Average GPA", fill = "Year") +
  scale_fill_brewer(palette = "RdYlBu") +
  theme_minimal()

Output will appear here...

💡 Hint: factor(year) tells ggplot to treat year as a discrete category rather than a continuous number, which gives each year its own bar color. position="dodge" places bars side by side instead of stacked.

🔥 Challenge

Exercise 3 — Faceted Distribution

Create a 2-panel figure using facet_wrap(~major) showing the GPA distribution (histogram or density) per major, with a vertical dashed line marking the mean GPA for each panel.

library(ggplot2)
library(dplyr)
students <- data.frame(
  major = c("CS","Math","CS","English","CS","Math","English","CS"),
  gpa   = c(3.8, 3.2, 3.9, 2.9, 3.5, 3.7, 3.1, 3.6),
  year  = c(3, 2, 4, 1, 2, 3, 4, 1)
)

# Compute mean GPA per major for the vertical line
means <- students |> group_by(major) |> summarize(mean_gpa = mean(gpa), .groups="drop")

ggplot(students, aes(x = gpa, fill = major)) +
  geom_histogram(bins = 5, color = "white", alpha = 0.8) +
  geom_vline(data = means, aes(xintercept = mean_gpa),
             linetype = "dashed", color = "#C62828", linewidth = 1) +
  facet_wrap(~major, ncol = 1) +
  labs(title = "GPA Distribution by Major",
       subtitle = "Dashed line = group mean",
       x = "GPA", y = "Count") +
  theme_minimal() +
  theme(legend.position = "none")

Output will appear here...

💡 Hint: When using geom_vline(data = means, ...), ggplot will correctly draw one line per panel. facet_wrap(~major) splits the plot into separate panels, one per major value.

🏆 Mini Project — Sales Dashboard (4 Charts)

Build 4 Separate ggplot2 Charts

Create all four charts one at a time (run each button separately). Each chart should have a title, clean theme, and proper axis labels.

Chart 1: Bar Chart — Total Revenue by Rep

library(ggplot2); library(dplyr)
sales <- data.frame(
  rep    = c("Alice","Bob","Carol","Alice","David","Bob","Carol","David"),
  region = c("West","East","West","West","South","East","West","South"),
  month  = c("Jan","Jan","Jan","Feb","Jan","Feb","Feb","Feb"),
  revenue= c(18500,12300,22100,19800,15600,16900,25400,13200)
)
totals <- sales |> group_by(rep) |> summarize(total = sum(revenue), .groups="drop")
ggplot(totals, aes(x = reorder(rep, -total), y = total, fill = rep)) +
  geom_col(show.legend=FALSE) +
  labs(title="Total Revenue by Rep", x="Rep", y="Revenue ($)") +
  scale_y_continuous(labels=scales::comma) + theme_minimal()

Output will appear here...

Chart 2: Line Chart — Revenue by Month per Rep

library(ggplot2)
sales <- data.frame(
  rep    = c("Alice","Bob","Carol","Alice","David","Bob","Carol","David"),
  month  = c("Jan","Jan","Jan","Feb","Jan","Feb","Feb","Feb"),
  revenue= c(18500,12300,22100,19800,15600,16900,25400,13200)
)
ggplot(sales, aes(x = month, y = revenue, color = rep, group = rep)) +
  geom_line(linewidth = 1.2) + geom_point(size = 3) +
  labs(title="Revenue by Month per Rep", x="Month", y="Revenue ($)", color="Rep") +
  scale_y_continuous(labels=scales::comma) + theme_minimal()

Output will appear here...

Chart 3: Boxplot — Revenue Distribution by Region

library(ggplot2)
sales <- data.frame(
  region = c("West","East","West","West","South","East","West","South"),
  revenue= c(18500,12300,22100,19800,15600,16900,25400,13200)
)
ggplot(sales, aes(x = region, y = revenue, fill = region)) +
  geom_boxplot(show.legend=FALSE, alpha=0.7) +
  geom_jitter(width=0.15, size=2, alpha=0.6) +
  labs(title="Revenue Distribution by Region", x="Region", y="Revenue ($)") +
  scale_y_continuous(labels=scales::comma) + theme_minimal()

Output will appear here...

Chart 4: Scatter Plot — Revenue colored by Region

library(ggplot2)
sales <- data.frame(
  rep    = c("Alice","Bob","Carol","Alice","David","Bob","Carol","David"),
  region = c("West","East","West","West","South","East","West","South"),
  month  = c("Jan","Jan","Jan","Feb","Jan","Feb","Feb","Feb"),
  revenue= c(18500,12300,22100,19800,15600,16900,25400,13200)
)
sales$month_n <- ifelse(sales$month=="Jan", 1, 2)
ggplot(sales, aes(x = month_n, y = revenue, color = region, label = rep)) +
  geom_point(size = 4, alpha = 0.8) +
  geom_text(vjust = -1, size = 3) +
  scale_x_continuous(breaks=c(1,2), labels=c("Jan","Feb")) +
  labs(title="Revenue by Month (colored by Region)",
       x="Month", y="Revenue ($)", color="Region") +
  scale_y_continuous(labels=scales::comma) + theme_minimal()

Output will appear here...

✅ Lab 6 Complete!

You’ve created bar charts, scatter plots, line charts, boxplots, and faceted figures — the core of professional data visualization in R. ggplot2’s layered grammar makes complex charts surprisingly simple to build.

Continue to Lab 7: Statistical Analysis →

← Lab 5: Strings Lab 6 of 10 Lab 7: Statistics →