r‑statistics.co by Selva Prabhakaran
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ggplot2 Exercises: 15 Chart-Building Practice Problems (With Solutions)

Fifteen hands-on ggplot2 exercises covering scatter plots, bar charts, line charts, heatmaps, facets, themes, and more, each with a worked solution and runnable code.

Introduction

Reading about ggplot2 is useful. Writing ggplot2 code without looking at notes is how you actually learn it.

These 15 exercises cover the full core ggplot2 toolkit, geoms, aesthetics, scales, facets, themes, and coordinate systems, using built-in R datasets so no data download is needed. Each exercise has a clear goal, optional hints, and a complete worked solution.

Work through them in order (they progress from easier to harder) or jump to the topics you want to reinforce.

All exercises use only base R datasets, mtcars, iris, airquality, mpg, diamonds, economics, so you can run them anywhere.

Exercise 1: Basic Scatter Plot

Dataset: iris

Task: Create a scatter plot of Sepal.Length (x) vs Petal.Length (y). Color points by Species. Set point size to 3 and transparency to 0.7.

Expected output: Three colored clusters of points, clearly separated by species.

RExercise 1: Iris scatter starter
library(ggplot2) # Your code here # Hint: use aes(color = Species) inside ggplot() p1 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point(size = 3, alpha = 0.7) p1

  

  






Show solution



  

    
RIris scatter by species solution

    
library(ggplot2)

p1 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 3, alpha = 0.7) +
  scale_color_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
  labs(
    title = "Sepal vs Petal Length by Species",
    x     = "Sepal Length (cm)",
    y     = "Petal Length (cm)"
  ) +
  theme_minimal()
p1

    

      
      
    

    

  

  











Exercise 2: Scatter Plot with Trend Line



Dataset: mtcars



Task: Create a scatter plot of wt (x) vs mpg (y). Add a linear regression smooth line (method = "lm") with a 95% confidence band. Color the points by cyl (treated as a factor).




  

    
RExercise 2: wt-mpg starter

    
# Your code here
# Hint: factor(cyl) converts cyl to discrete for coloring
# Hint: geom_smooth(method = "lm", formula = y ~ x)

    

      
      
    

    

  

  






Show solution



  

    
Rwt-mpg scatter with lm solution

    
library(ggplot2)

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3, alpha = 0.8) +
  geom_smooth(method = "lm", formula = y ~ x,
              color = "grey30", se = TRUE, linewidth = 0.8) +
  scale_color_manual(
    values = c("4" = "#2196F3", "6" = "#FF9800", "8" = "#F44336"),
    labels = c("4 cyl", "6 cyl", "8 cyl")
  ) +
  labs(
    title  = "Car Weight vs Fuel Efficiency",
    x      = "Weight (1,000 lbs)", y = "Miles per Gallon",
    color  = "Cylinders",
    caption = "Shaded area = 95% confidence interval"
  ) +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 3: Ordered Bar Chart



Dataset: mpg (from ggplot2)



Task: Compute the average highway MPG (hwy) for each manufacturer. Create a horizontal bar chart ordered from highest to lowest average MPG. Show bars in a single color of your choice.




  

    
RExercise 3: Ordered bar starter

    
# Hint: use dplyr to summarise, then reorder()
# Hint: coord_flip() for horizontal bars

    

      
      
    

    

  

  






Show solution



  

    
ROrdered manufacturer MPG bar solution

    
library(ggplot2)
library(dplyr)

mpg_avg <- mpg |>
  group_by(manufacturer) |>
  summarise(avg_hwy = mean(hwy)) |>
  mutate(manufacturer = reorder(manufacturer, avg_hwy))

ggplot(mpg_avg, aes(x = manufacturer, y = avg_hwy)) +
  geom_col(fill = "#1565C0", alpha = 0.85, width = 0.7) +
  coord_flip() +
  labs(
    title = "Average Highway MPG by Manufacturer",
    x = NULL, y = "Average Highway MPG"
  ) +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 4: Grouped Bar Chart



Dataset: mtcars



Task: Create a grouped bar chart showing the average mpg for each combination of cyl (x-axis) and am (0 = automatic, 1 = manual, used for fill grouping). Use position_dodge().




  

    
RExercise 4: Grouped bar starter

    
# Hint: am should be a factor for discrete fill
# Hint: position_dodge(0.8) separates grouped bars

    

      
      
    

    

  

  






Show solution



  

    
RGrouped bar by cylinder solution

    
library(ggplot2)
library(dplyr)

mt_sum <- mtcars |>
  group_by(cyl, am) |>
  summarise(avg_mpg = mean(mpg), .groups = "drop") |>
  mutate(
    cyl = paste(cyl, "cylinders"),
    am  = factor(am, labels = c("Automatic", "Manual"))
  )

ggplot(mt_sum, aes(x = cyl, y = avg_mpg, fill = am)) +
  geom_col(position = position_dodge(0.8), width = 0.7, alpha = 0.9) +
  scale_fill_manual(values = c("Automatic" = "#FF9800", "Manual" = "#1565C0")) +
  labs(
    title = "Average MPG: Automatic vs Manual by Cylinder Count",
    x = NULL, y = "Average MPG", fill = "Transmission"
  ) +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 5: Time Series Line Chart



Dataset: economics (from ggplot2)



Task: Plot unemploy (number unemployed) over date as a line chart. Add a horizontal reference line at the mean of unemploy. Color the area below the line using geom_area().




  

    
RExercise 5: Time series starter

    
# Hint: geom_area(alpha = 0.2) fills under the line
# Hint: geom_hline(yintercept = mean(economics$unemploy), ...)

    

      
      
    

    

  

  






Show solution



  

    
RUnemployment with mean line solution

    
library(ggplot2)

mean_unemploy <- mean(economics$unemploy)

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_area(fill = "#1565C0", alpha = 0.2) +
  geom_line(color = "#1565C0", linewidth = 0.8) +
  geom_hline(yintercept = mean_unemploy, linetype = "dashed",
             color = "#C62828", linewidth = 0.7) +
  annotate("text", x = min(economics$date), y = mean_unemploy + 300,
           label = paste0("Mean: ", round(mean_unemploy/1000, 0), "K"),
           color = "#C62828", hjust = 0, size = 3.5) +
  scale_y_continuous(labels = function(x) paste0(x/1000, "K")) +
  labs(
    title = "US Unemployment Over Time",
    x = NULL, y = "Unemployed (thousands)"
  ) +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 6: Faceted Line Chart



Dataset: airquality



Task: For each month (5-9), plot Temp (y) vs day number (x) as a line. Use facet_wrap(~ Month) with 1 row. Give each month a descriptive label using labeller.




  

    
RExercise 6: Faceted line starter

    
# Hint: airquality already has Day and Month columns
# Hint: labeller = labeller(Month = c("5"="May","6"="Jun",...))

    

      
      
    

    

  

  






Show solution



  

    
RFaceted temperature by month solution

    
library(ggplot2)

month_labels <- c("5" = "May", "6" = "Jun", "7" = "Jul",
                  "8" = "Aug", "9" = "Sep")

ggplot(airquality, aes(x = Day, y = Temp)) +
  geom_line(color = "#E53935", linewidth = 0.7) +
  geom_smooth(method = "loess", formula = y ~ x,
              se = FALSE, color = "grey40", linewidth = 0.5, linetype = "dashed") +
  facet_wrap(~ Month, nrow = 1,
             labeller = labeller(Month = month_labels)) +
  labs(
    title = "Daily Temperature by Month (New York 1973)",
    x = "Day of Month", y = "Temperature (°F)"
  ) +
  theme_minimal() +
  theme(strip.text = element_text(face = "bold"))

    

      
      
    

    

  

  











Exercise 7: Histogram with Density Curve



Dataset: diamonds (from ggplot2)



Task: Plot a histogram of price (log10-transformed for readability). Overlay a density curve using geom_density(). Use aes(y = after_stat(density)) to put histogram and density on the same scale.




  

    
RExercise 7: Histogram starter

    
# Hint: scale_x_log10() transforms the x axis
# Hint: after_stat(density) rescales histogram counts to density

    

      
      
    

    

  

  






Show solution



  

    
RLog10 histogram with density solution

    
library(ggplot2)

ggplot(diamonds, aes(x = price)) +
  geom_histogram(aes(y = after_stat(density)),
                 bins = 50, fill = "#1565C0", alpha = 0.6, color = "white") +
  geom_density(color = "#C62828", linewidth = 1) +
  scale_x_log10(labels = scales::dollar) +
  labs(
    title    = "Diamond Price Distribution (log10 scale)",
    subtitle = "Histogram with overlaid density curve",
    x        = "Price (log10 scale)", y = "Density"
  ) +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 8: Boxplot with Jitter



Dataset: iris



Task: Create a boxplot of Sepal.Width for each Species. Overlay individual data points using geom_jitter(). Color by Species, use a custom palette.




  

    
RExercise 8: Boxplot jitter starter

    
# Hint: add geom_jitter() after geom_boxplot()
# Hint: width = 0.2 in geom_jitter() reduces horizontal spread

    

      
      
    

    

  

  






Show solution



  

    
RBoxplot with jitter solution

    
library(ggplot2)

ggplot(iris, aes(x = Species, y = Sepal.Width, fill = Species, color = Species)) +
  geom_boxplot(alpha = 0.3, outlier.shape = NA, width = 0.5) +
  geom_jitter(width = 0.15, alpha = 0.6, size = 1.8) +
  scale_fill_manual(values  = c("#E69F00", "#56B4E9", "#009E73")) +
  scale_color_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
  labs(
    title = "Sepal Width Distribution by Species",
    x = NULL, y = "Sepal Width (cm)"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

    

      
      
    

    

  

  











Exercise 9: Heatmap



Dataset: airquality



Task: Create a heatmap showing average daily temperature by month (rows) and week of month (columns). Compute week of month as ceiling(Day / 7). Use geom_tile() with a sequential blue color scale.




  

    
RExercise 9: Heatmap starter

    
# Hint: mutate(Week = ceiling(Day / 7))
# Hint: scale_fill_gradient(low = "white", high = "#1565C0")

    

      
      
    

    

  

  






Show solution



  

    
RTemperature heatmap solution

    
library(ggplot2)
library(dplyr)

air_heat <- airquality |>
  mutate(
    Week      = ceiling(Day / 7),
    Month_lab = month.abb[Month]
  ) |>
  group_by(Month_lab, Week) |>
  summarise(avg_temp = mean(Temp, na.rm = TRUE), .groups = "drop")

air_heat$Month_lab <- factor(air_heat$Month_lab,
                             levels = month.abb[5:9])

ggplot(air_heat, aes(x = factor(Week), y = Month_lab, fill = avg_temp)) +
  geom_tile(color = "white", linewidth = 0.5) +
  scale_fill_gradient(low = "#E3F2FD", high = "#1565C0", name = "°F") +
  labs(
    title = "Average Temperature by Month and Week",
    x     = "Week of Month", y = NULL
  ) +
  theme_minimal() +
  theme(panel.grid = element_blank())

    

      
      
    

    

  

  











Exercise 10: Faceted Scatter Plot



Dataset: mpg



Task: Create scatter plots of displ (x) vs hwy (y) faceted by drv (drive type). Add a LOESS smooth to each facet. Use facet_grid(drv ~ .) for a vertical layout.




  

    
RExercise 10: Faceted scatter starter

    
# Hint: facet_grid(drv ~ .) creates one row per drv level

    

      
      
    

    

  

  






Show solution



  

    
RFaceted displ-hwy scatter solution

    
library(ggplot2)

drv_labels <- c("4" = "4-Wheel Drive", "f" = "Front-Wheel", "r" = "Rear-Wheel")

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(alpha = 0.5, color = "#1565C0") +
  geom_smooth(method = "loess", formula = y ~ x,
              color = "#C62828", se = FALSE, linewidth = 0.8) +
  facet_grid(drv ~ ., labeller = labeller(drv = drv_labels)) +
  labs(
    title = "Engine Displacement vs Highway MPG by Drive Type",
    x     = "Displacement (L)", y = "Highway MPG"
  ) +
  theme_minimal() +
  theme(strip.text.y = element_text(face = "bold"))

    

      
      
    

    

  

  











Exercise 11: Lollipop Chart



Dataset: mtcars



Task: Compute the average qsec (quarter-mile time) per number of cylinders. Create a horizontal lollipop chart ordered by qsec descending (slowest cars at top). Color lollipops by whether qsec is above or below the overall mean.




  

    
RExercise 11: Lollipop starter

    
# Hint: geom_segment(aes(x = cyl, xend = cyl, y = mean_qsec, yend = qsec))
#       where mean_qsec is the overall mean (reference line)

    

      
      
    

    

  

  






Show solution



  

    
RLollipop by cylinder solution

    
library(ggplot2)
library(dplyr)

qsec_df <- mtcars |>
  group_by(cyl) |>
  summarise(avg_qsec = mean(qsec)) |>
  mutate(
    cyl   = paste(cyl, "cylinders"),
    cyl   = reorder(cyl, avg_qsec),
    above = avg_qsec >= mean(avg_qsec)
  )

mean_q <- mean(qsec_df$avg_qsec)

ggplot(qsec_df, aes(x = cyl, y = avg_qsec, color = above)) +
  geom_hline(yintercept = mean_q, linetype = "dashed", color = "grey50") +
  geom_segment(aes(x = cyl, xend = cyl, y = mean_q, yend = avg_qsec),
               linewidth = 0.9) +
  geom_point(size = 5) +
  scale_color_manual(values = c("TRUE" = "#1565C0", "FALSE" = "#C62828"),
                     guide = "none") +
  coord_flip() +
  labs(title = "Quarter-Mile Time by Cylinder Count",
       x = NULL, y = "Average Quarter-Mile Time (sec)") +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 12: Custom Theme



Dataset: iris



Task: Recreate the scatter plot from Exercise 1, but apply a fully custom theme. Requirements: dark grey background (#2b2b2b), white text, no gridlines, white axis lines, legend at bottom.




  

    
RExercise 12: Custom theme starter

    
# Hint: theme() with panel.background, text, axis.line, legend.position
# Hint: theme_dark() as a starting point, then override specific elements

    

      
      
    

    

  

  






Show solution



  

    
RDark custom theme solution

    
library(ggplot2)

dark_theme <- theme(
  plot.background  = element_rect(fill = "#2b2b2b", color = NA),
  panel.background = element_rect(fill = "#2b2b2b", color = NA),
  panel.grid       = element_blank(),
  axis.line        = element_line(color = "white", linewidth = 0.5),
  axis.text        = element_text(color = "white", size = 11),
  axis.title       = element_text(color = "white", size = 12),
  plot.title       = element_text(color = "white", face = "bold", size = 14),
  legend.background = element_rect(fill = "#2b2b2b"),
  legend.text      = element_text(color = "white"),
  legend.title     = element_text(color = "white"),
  legend.position  = "bottom"
)

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 3, alpha = 0.8) +
  scale_color_manual(values = c("#F4A620", "#00B4D8", "#80B918")) +
  labs(title = "Iris Scatter Plot, Dark Theme",
       x = "Sepal Length (cm)", y = "Petal Length (cm)") +
  dark_theme

    

      
      
    

    

  

  











Exercise 13: Stacked Area Chart



Dataset: economics_long (from ggplot2)



Task: Filter economics_long to the variables unemploy and pop. Plot a stacked area chart over time using geom_area() with position = "stack". Use a two-color palette.




  

    
RExercise 13: Stacked area starter

    
# Hint: filter(variable %in% c("unemploy", "pop"))
# Hint: geom_area(aes(fill = variable), position = "stack")

    

      
      
    

    

  

  






Show solution



  

    
REconomics stacked area solution

    
library(ggplot2)
library(dplyr)

econ_sub <- economics_long |>
  filter(variable %in% c("unemploy", "pop"))

ggplot(econ_sub, aes(x = date, y = value01, fill = variable)) +
  geom_area(position = "stack", alpha = 0.8) +
  scale_fill_manual(
    values = c("unemploy" = "#C62828", "pop" = "#1565C0"),
    labels = c("unemploy" = "Unemployment", "pop" = "Population (normalized)")
  ) +
  labs(
    title = "Stacked Area: US Economic Indicators",
    x = NULL, y = "Normalized Value (0-1)", fill = NULL
  ) +
  theme_minimal() +
  theme(legend.position = "top")

    

      
      
    

    

  

  











Exercise 14: Annotated Plot



Dataset: mtcars



Task: Create a scatter plot of wt vs mpg. Label the 3 most fuel-efficient and 3 least fuel-efficient cars using geom_text() or ggrepel::geom_text_repel(). Add geom_smooth(method = "lm").




  

    
RExercise 14: Label top and bottom

    
# Hint: slice_max(mpg, n = 3) and slice_min(mpg, n = 3)
# Hint: bind the two subsets, then label only those rows

    

      
      
    

    

  

  






Show solution



  

    
RAnnotated mtcars scatter solution

    
library(ggplot2)
library(ggrepel)
library(dplyr)

mt <- mtcars
mt$car <- rownames(mtcars)

top3    <- mt |> slice_max(mpg, n = 3)
bottom3 <- mt |> slice_min(mpg, n = 3)
labeled <- bind_rows(top3, bottom3)

mt$label <- ifelse(mt$car %in% labeled$car, mt$car, NA)

ggplot(mt, aes(x = wt, y = mpg)) +
  geom_point(alpha = 0.6, color = "steelblue", size = 3) +
  geom_smooth(method = "lm", formula = y ~ x, se = FALSE,
              color = "#C62828", linewidth = 1) +
  geom_text_repel(aes(label = label), size = 3, color = "grey20",
                  na.rm = TRUE, box.padding = 0.5) +
  labs(title = "Car Weight vs MPG (best and worst labeled)",
       x = "Weight (1,000 lbs)", y = "Miles per Gallon") +
  theme_minimal()

    

      
      
    

    

  

  











Exercise 15: Complete Multi-Layer Plot



Dataset: airquality



Task: Create a polished chart showing the distribution of Temp by Month:



    


  • Violin plot (outer shape) for distribution
    • 


  • Boxplot inside (narrow, white, no outliers)
    • 


  • Jittered points on top (small, semi-transparent)
    • 


  • Color by month using a sequential palette
    • 


  • Properly labeled with a clean theme
    • 





This combines everything, multiple geom layers, color scales, transparency, and theme customization.




  

    
RExercise 15: Violin layer starter

    
# Hint: use geom_violin() + geom_boxplot(width = 0.1) + geom_jitter()
# Hint: all three share aes(x = factor(Month), y = Temp)

    

      
      
    

    

  

  






Show solution



  

    
RViolin, box, and jitter solution

    
library(ggplot2)

airquality$Month_f <- factor(airquality$Month,
                              labels = c("May","Jun","Jul","Aug","Sep"))

ggplot(airquality, aes(x = Month_f, y = Temp, fill = Month_f)) +
  geom_violin(alpha = 0.4, trim = FALSE) +
  geom_boxplot(
    width = 0.1, fill = "white", color = "grey30",
    outlier.shape = NA
  ) +
  geom_jitter(
    aes(color = Month_f),
    width = 0.08, alpha = 0.5, size = 1.5
  ) +
  scale_fill_brewer(palette  = "YlOrRd", guide = "none") +
  scale_color_brewer(palette = "YlOrRd", guide = "none") +
  labs(
    title    = "Temperature Distribution by Month, New York 1973",
    subtitle = "Violin + boxplot + individual observations",
    x        = NULL,
    y        = "Temperature (°F)",
    caption  = "Source: airquality dataset (R base)"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title    = element_text(face = "bold"),
    plot.subtitle = element_text(color = "grey50"),
    panel.grid.major.x = element_blank()
  )

    

      
      
    

    

  

  











Summary







#
Topic
Geoms / Concepts






1
Scatter plot
geom_point(), color aesthetics




2
Scatter + trend
geom_smooth(method = "lm")




3
Ordered bar chart
geom_col(), reorder(), coord_flip()




4
Grouped bar chart
position_dodge(), factor fill




5
Time series
geom_line(), geom_area(), geom_hline()




6
Faceted lines
facet_wrap(), labeller




7
Histogram + density
after_stat(density), geom_density()




8
Boxplot + jitter
geom_boxplot(), geom_jitter()




9
Heatmap
geom_tile(), scale_fill_gradient()




10
Faceted scatter
facet_grid(), per-facet LOESS




11
Lollipop chart
geom_segment(), geom_point()




12
Custom theme
theme() elements, dark background




13
Stacked area
geom_area(position = "stack")




14
Annotated scatter
geom_text_repel(), annotate()




15
Multi-layer violin
geom_violin(), geom_boxplot(), geom_jitter()







Continue Learning



    


  • ggplot2 Getting Started, the full ggplot2 grammar explained from the ground up
    • 


  • ggplot2 Themes in R, master theme() to create professional publication-ready plots
    • 


  • ggplot2 Scales, control color, size, shape, and axis scales precisely
    • 




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© 2016-2026 Selva Prabhakaran.
          This work is licensed under the Creative Commons License.