R ggplot2 Bar Chart

Bar charts display quantities for different categories. In ggplot2, two functions create bar charts: geom_bar() counts rows automatically, and geom_col() uses pre-computed values you supply. Knowing when to use each saves time and prevents errors.

geom_bar() vs geom_col()

geom_bar()   → counts rows in your data automatically (stat="count")
geom_col()   → uses a value column you provide (stat="identity")

Use geom_bar() when:   your data has one row per observation
Use geom_col() when:   your data already has summary totals

geom_bar() — Count Automatically

library(ggplot2)

orders <- data.frame(
  region  = c("North","South","North","East","South","North","East","South"),
  product = c("A","A","B","A","B","A","B","A")
)

# Count orders per region
ggplot(orders, aes(x=region)) +
  geom_bar(fill="steelblue", color="white") +
  labs(title="Orders by Region", x="Region", y="Number of Orders") +
  theme_minimal()

geom_col() — Use Pre-computed Values

sales_summary <- data.frame(
  region  = c("North","South","East","West"),
  revenue = c(45000, 62000, 38000, 51000)
)

ggplot(sales_summary, aes(x=region, y=revenue)) +
  geom_col(fill="tomato", color="white", width=0.6) +
  labs(title="Revenue by Region", x="Region", y="Revenue (₹)") +
  scale_y_continuous(labels=scales::comma) +
  theme_minimal()

Horizontal Bar Chart

ggplot(sales_summary, aes(x=revenue, y=reorder(region, revenue))) +
  geom_col(fill="steelblue") +
  labs(title="Revenue by Region (Ranked)", x="Revenue (₹)", y="Region") +
  theme_minimal()

Use reorder(region, revenue) to sort bars by value — always do this for horizontal bar charts so the longest bar is at top.

Grouped Bar Chart

dept_data <- data.frame(
  dept     = rep(c("HR","IT","Finance"), each=2),
  year     = rep(c("2023","2024"), 3),
  budget   = c(120,140, 300,350, 200,220)
)

ggplot(dept_data, aes(x=dept, y=budget, fill=year)) +
  geom_col(position="dodge", color="white") +  # "dodge" = side by side
  scale_fill_manual(values=c("2023"="steelblue","2024"="tomato")) +
  labs(title="Department Budget 2023 vs 2024",
       x="Department", y="Budget (₹ thousands)", fill="Year") +
  theme_minimal()

Stacked Bar Chart

ggplot(dept_data, aes(x=year, y=budget, fill=dept)) +
  geom_col(position="stack") +      # "stack" = stacked
  labs(title="Total Budget by Year", x="Year", y="Budget", fill="Department") +
  theme_minimal()

# 100% stacked
ggplot(dept_data, aes(x=year, y=budget, fill=dept)) +
  geom_col(position="fill") +       # "fill" = proportional (0 to 1)
  scale_y_continuous(labels=scales::percent) +
  labs(title="Budget Share by Year") +
  theme_minimal()

Adding Value Labels

ggplot(sales_summary, aes(x=region, y=revenue)) +
  geom_col(fill="steelblue") +
  geom_text(aes(label=paste0("₹",revenue/1000,"K")),
            vjust=-0.5, size=3.5, fontface="bold") +
  labs(title="Revenue by Region") +
  ylim(0, 70000) +
  theme_minimal()

Flipping Coordinates

ggplot(sales_summary, aes(x=reorder(region,-revenue), y=revenue)) +
  geom_col(fill="steelblue") +
  coord_flip() +    # swap x and y axes
  labs(title="Revenue by Region", x=NULL, y="Revenue") +
  theme_minimal()

Color by Category

ggplot(sales_summary, aes(x=region, y=revenue, fill=region)) +
  geom_col(show.legend=FALSE) +
  scale_fill_brewer(palette="Set2") +   # ColorBrewer palette
  labs(title="Revenue by Region") +
  theme_minimal()

Bar charts are the most common chart type in business reporting. Use vertical bars for few categories, horizontal bars for many or long names, grouped bars for comparisons across two dimensions, and stacked bars for part-to-whole relationships. Always sort bars when the order is not inherently meaningful.