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ggplot2 Exercises in R: 50 Real-World Practice Problems

Fifty scenario-based ggplot2 exercises spanning geoms, aesthetics, scales, facets, themes, and multi-layer compositions. Solutions hidden behind reveal toggles so you actually build the plot first.

By Selva Prabhakaran  ·  Published May 11, 2026  ·  Last updated May 11, 2026
RRun this once before any exercise
library(ggplot2) library(dplyr) library(scales) library(forcats) library(tibble)

  

  





Section 1. Basic geoms (8 problems)



Exercise 1.1: Scatter plot of weight vs mpg



Scenario: A car magazine wants to show the relationship between weight and fuel economy. Build a basic scatter plot of mtcars$wt (x) vs mtcars$mpg (y).



Difficulty: Beginner




  

    
RYour turn

    
ex_1_1 <- # your code here
ex_1_1

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

ex_1_1

    

      
      
    

    

  

  





Explanation: ggplot() declares data and aesthetic mapping; geom_point() draws the points. Every plot starts this way.






Exercise 1.2: Histogram of diamond prices



Scenario: A jeweller wants the price distribution. Plot a histogram of diamonds$price with 40 bins.



Difficulty: Beginner




  

    
RYour turn

    
ex_1_2 <- # your code here
ex_1_2

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_2 <- ggplot(diamonds, aes(x = price)) +
  geom_histogram(bins = 40)

ex_1_2

    

      
      
    

    

  

  





Explanation: geom_histogram only needs an x mapping; it counts rows per bin. bins = 40 is a sensible default for moderate data; binwidth = is the alternative when you know the unit.






Exercise 1.3: Bar chart of diamond counts by cut



Scenario: A merchandising team wants the inventory mix. Plot a bar chart showing the count of diamonds for each cut category.



Difficulty: Beginner




  

    
RYour turn

    
ex_1_3 <- # your code here
ex_1_3

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_3 <- ggplot(diamonds, aes(x = cut)) +
  geom_bar()

ex_1_3

    

      
      
    

    

  

  





Explanation: geom_bar counts rows per x category by default. Use geom_col when you already have the heights as a y column.






Exercise 1.4: Boxplot of sepal length per species



Scenario: A botanist needs to compare sepal length distributions across the three iris species. Build a boxplot with Species on x and Sepal.Length on y.



Difficulty: Intermediate




  

    
RYour turn

    
ex_1_4 <- # your code here
ex_1_4

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_4 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot()

ex_1_4

    

      
      
    

    

  

  





Explanation: geom_boxplot summarises the distribution into median, IQR, whiskers, outliers per group. Useful first comparison across categories.






Exercise 1.5: Overlapping density curves by species



Scenario: A researcher wants smoothed distributions, not boxes. Plot overlapping density curves of Sepal.Length colored by Species, with 50% transparency.



Difficulty: Intermediate




  

    
RYour turn

    
ex_1_5 <- # your code here
ex_1_5

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_5 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
  geom_density(alpha = 0.5)

ex_1_5

    

      
      
    

    

  

  





Explanation: geom_density needs only x; fill maps Species to colored regions. alpha < 1 makes overlap legible. Compare to faceted densities when overlap is too much.






Exercise 1.6: Line chart of unemployment over time



Scenario: An economist wants the long-term unemployment trend. From economics, plot a line chart of unemploy over date.



Difficulty: Intermediate




  

    
RYour turn

    
ex_1_6 <- # your code here
ex_1_6

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_6 <- ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line()

ex_1_6

    

      
      
    

    

  

  





Explanation: geom_line connects points by x order; ideal for time series. economics is a built-in monthly dataset with date already in Date format, so the x-axis formats nicely.






Exercise 1.7: Scatter with linear smoother



Scenario: A data analyst presenting the wt-vs-mpg trend wants the line of best fit overlaid. Add a linear smoother to a scatter plot of mtcars.



Difficulty: Intermediate




  

    
RYour turn

    
ex_1_7 <- # your code here
ex_1_7

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_7 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE)

ex_1_7

    

      
      
    

    

  

  





Explanation: geom_smooth fits a smoother per group; method="lm" is straight-line regression. se=TRUE shows the 95% confidence ribbon. Use method="loess" for a curve.






Exercise 1.8: Violin + boxplot combination



Scenario: A reviewer wants the distribution shape AND quartiles for each cylinder count. Combine a violin plot with a thin boxplot inside, showing mpg by factor(cyl).



Difficulty: Intermediate




  

    
RYour turn

    
ex_1_8 <- # your code here
ex_1_8

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_1_8 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_violin(fill = "lightblue") +
  geom_boxplot(width = 0.15, fill = "white")

ex_1_8

    

      
      
    

    

  

  





Explanation: Layering geoms shows two summaries at once. Coerce cyl to factor so it is treated as discrete groups. Narrow the boxplot with width so it nests inside the violin.






Section 2. Aesthetics and mapping (8 problems)



Exercise 2.1: Color points by species



Scenario: A botanist wants the three iris species color-coded. Build a scatter plot of Sepal.Length vs Petal.Length with points colored by Species.



Difficulty: Beginner




  

    
RYour turn

    
ex_2_1 <- # your code here
ex_2_1

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_1 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point()

ex_2_1

    

      
      
    

    

  

  





Explanation: color is mapped INSIDE aes() because it depends on the data column. ggplot picks distinct hues automatically and adds a legend.






Exercise 2.2: Size points by horsepower



Scenario: Engineering wants horsepower visible at a glance. Plot mtcars wt vs mpg with point size proportional to hp.



Difficulty: Beginner




  

    
RYour turn

    
ex_2_2 <- # your code here
ex_2_2

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_2 <- ggplot(mtcars, aes(x = wt, y = mpg, size = hp)) +
  geom_point(alpha = 0.7)

ex_2_2

    

      
      
    

    

  

  





Explanation: size mapped inside aes() encodes a third numeric variable. alpha helps when bigger points overlap.






Exercise 2.3: Distinguish categories by shape



Scenario: A printer who can only print grayscale needs categories distinguishable without color. Use shape (not color) for Species in the iris scatter.



Difficulty: Intermediate




  

    
RYour turn

    
ex_2_3 <- # your code here
ex_2_3

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_3 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, shape = Species)) +
  geom_point(size = 3)

ex_2_3

    

      
      
    

    

  

  





Explanation: shape maps a discrete variable to point glyph. Default ggplot has 6 distinct shapes; for 3 species this works cleanly. Pair with size for grayscale reports.






Exercise 2.4: Use alpha to handle overplotting



Scenario: A scatter of diamonds carat vs price has 53,940 points; many overlap. Use alpha = 0.05 so density becomes visible.



Difficulty: Intermediate




  

    
RYour turn

    
ex_2_4 <- # your code here
ex_2_4

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_4 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.05)

ex_2_4

    

      
      
    

    

  

  





Explanation: alpha is set OUTSIDE aes() because it is a fixed value, not a data mapping. With heavy overlap, low alpha turns density into shading. For really dense data, prefer geom_hex or geom_bin2d.






Exercise 2.5: Group lines by panel id



Scenario: Plot weight trajectories for each chick in ChickWeight. Use Time on x, weight on y, and one line per chick.



Difficulty: Intermediate




  

    
RYour turn

    
ex_2_5 <- # your code here
ex_2_5

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_5 <- ggplot(ChickWeight, aes(x = Time, y = weight, group = Chick)) +
  geom_line(alpha = 0.4)

ex_2_5

    

      
      
    

    

  

  





Explanation: Without group = Chick, geom_line connects across chicks in time order, drawing zigzags. The group aesthetic tells ggplot which observations belong to the same line.






Exercise 2.6: Color vs fill on a bar chart



Scenario: A bar chart with bordered bars. Make a bar chart of diamonds cut where the bar BORDER is black and the bar INTERIOR is colored by clarity.



Difficulty: Intermediate




  

    
RYour turn

    
ex_2_6 <- # your code here
ex_2_6

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_6 <- ggplot(diamonds, aes(x = cut, fill = clarity)) +
  geom_bar(color = "black")

ex_2_6

    

      
      
    

    

  

  





Explanation: color is the outline of a polygon-like geom (bar, point, etc.); fill is the interior. Mapping fill inside aes() splits the bars by clarity (stacked by default). Setting color outside makes a fixed border.






Exercise 2.7: Multiple aesthetics combined



Scenario: An analyst wants weight, mpg, hp, and cylinder count visible all at once. Build a scatter where x = wt, y = mpg, color = factor(cyl), size = hp.



Difficulty: Intermediate




  

    
RYour turn

    
ex_2_7 <- # your code here
ex_2_7

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_7 <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl), size = hp)) +
  geom_point(alpha = 0.7)

ex_2_7

    

      
      
    

    

  

  





Explanation: Four aesthetics encode four variables. color and size each generate their own legend automatically. factor(cyl) treats cyl as discrete (3 colors instead of a gradient).






Exercise 2.8: Fixed value vs mapped (the gotcha)



Scenario: A trainee writes aes(color = "red") and is confused why all points are pink, with a "red" legend. Diagnose and fix.



Difficulty: Advanced




  

    
RYour turn

    
# WRONG: color is mapped to a constant string "red"
wrong <- ggplot(mtcars, aes(x = wt, y = mpg, color = "red")) + geom_point()

# fix it: make every point actually red, no legend
ex_2_8 <- # your code here
ex_2_8

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_2_8 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(color = "red")

ex_2_8

    

      
      
    

    

  

  





Explanation: When you put color = "red" INSIDE aes(), ggplot maps every row to the literal value "red" and assigns it the first hue from the palette (pink), with a legend. To set a fixed color, put it OUTSIDE aes() in the geom layer.






Section 3. Scales and coordinates (10 problems)



Exercise 3.1: Set axis limits



Scenario: Zoom the mtcars scatter to mpg between 15 and 30. Use scale_y_continuous limits.



Difficulty: Beginner




  

    
RYour turn

    
ex_3_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_3_1

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  scale_y_continuous(limits = c(15, 30))

ex_3_1

    

      
      
    

    

  

  





Explanation: scale_y_continuous(limits = c(lo, hi)) clips the data and removes points outside. To zoom WITHOUT removing data, use coord_cartesian(ylim = ...).






Exercise 3.2: Format y-axis as currency



Scenario: A finance team needs the y-axis in dollar format with thousands separators. Plot diamonds carat vs price and label y as "$1,000", "$5,000", etc.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_2 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.05) +
  # your code here
ex_3_2

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_2 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.05) +
  scale_y_continuous(labels = scales::dollar_format())

ex_3_2

    

      
      
    

    

  

  





Explanation: The scales package provides label formatters: dollar_format(), comma_format(), percent_format(). Pass to the labels argument of any continuous scale.






Exercise 3.3: Log10 axis for skewed data



Scenario: Diamond carat is right-skewed, making the linear plot crowded near zero. Apply log10 to the x-axis.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_3 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.05) +
  # your code here
ex_3_3

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_3 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.05) +
  scale_x_log10()

ex_3_3

    

      
      
    

    

  

  





Explanation: scale_x_log10() transforms before plotting and labels axis ticks at 0.1, 1, 10, etc. Equivalent to scale_x_continuous(trans = "log10").






Exercise 3.4: Manual color palette



Scenario: Marketing requires brand colors: green for setosa, navy for versicolor, gold for virginica. Apply these via scale_color_manual.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_4 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  # your code here
ex_3_4

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_4 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  scale_color_manual(values = c(setosa = "darkgreen",
                                versicolor = "navy",
                                virginica = "goldenrod"))

ex_3_4

    

      
      
    

    

  

  





Explanation: Named vector in values = maps each level to a specific color. Order in the vector does not matter when names are used.






Exercise 3.5: ColorBrewer palette



Scenario: A scientific publication style guide requires the ColorBrewer "Set2" palette for categorical data. Apply it to the iris scatter.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_5 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  # your code here
ex_3_5

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_5 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  scale_color_brewer(palette = "Set2")

ex_3_5

    

      
      
    

    

  

  





Explanation: scale_color_brewer takes a palette name. "Set1"/"Set2"/"Set3" are qualitative; "Blues"/"Reds" are sequential; "RdBu" is diverging. Use scale_fill_brewer for fill aesthetic.






Exercise 3.6: Viridis perceptually-uniform palette



Scenario: An accessibility-conscious team wants a palette that works in greyscale and for color-blind viewers. Apply scale_color_viridis_d() to the iris scatter.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_6 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  # your code here
ex_3_6

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_6 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  scale_color_viridis_d()

ex_3_6

    

      
      
    

    

  

  





Explanation: viridis is the gold-standard palette for accessibility. _d suffix is the discrete variant; _c for continuous. Default option "D" (viridis); also "A" (magma), "B" (inferno), "C" (plasma).






Exercise 3.7: Reorder bars by frequency



Scenario: A bar chart of diamonds cut should be sorted with the most-frequent cut on the LEFT, not in factor level order. Use forcats::fct_infreq.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_7 <- # your code here
ex_3_7

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_7 <- ggplot(diamonds, aes(x = forcats::fct_infreq(cut))) +
  geom_bar() +
  labs(x = "cut")

ex_3_7

    

      
      
    

    

  

  





Explanation: Bar charts respect factor level order. fct_infreq sorts levels by frequency (descending). For a custom order use fct_relevel; for ascending use fct_rev(fct_infreq()).






Exercise 3.8: Reverse the y-axis



Scenario: A medical chart needs lower values at the top (e.g., cancer staging). Reverse the y-axis with scale_y_reverse().



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_8 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_3_8

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_8 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  scale_y_reverse()

ex_3_8

    

      
      
    

    

  

  





Explanation: scale_y_reverse() flips the y-axis without changing data. Use scale_x_reverse() for x.






Exercise 3.9: Horizontal bar chart with coord_flip



Scenario: A long list of categories cramped on the x-axis becomes readable when bars are horizontal. Make the diamonds cut bar chart horizontal using coord_flip.



Difficulty: Intermediate




  

    
RYour turn

    
ex_3_9 <- ggplot(diamonds, aes(x = cut)) +
  geom_bar() +
  # your code here
ex_3_9

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_9 <- ggplot(diamonds, aes(x = cut)) +
  geom_bar() +
  coord_flip()

ex_3_9

    

      
      
    

    

  

  





Explanation: coord_flip swaps x and y after the geom is computed. Modern ggplot2 also accepts aes(y = cut) directly with geom_bar; coord_flip remains useful when the geom is defined with x and you don't want to rewrite the call.






Exercise 3.10: Zoom without filtering



Scenario: A boxplot of mtcars mpg by cyl loses the IQR detail because outliers stretch the y-axis. Zoom y to 10-30 WITHOUT removing the outlier rows.



Difficulty: Advanced




  

    
RYour turn

    
ex_3_10 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_boxplot() +
  # your code here
ex_3_10

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_3_10 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_boxplot() +
  coord_cartesian(ylim = c(10, 30))

ex_3_10

    

      
      
    

    

  

  





Explanation: coord_cartesian(ylim) zooms the visible region without dropping data; the boxplot whiskers and outliers are still computed from full data. scale_y_continuous(limits) would drop outside points and recompute statistics.






Section 4. Faceting (6 problems)



Exercise 4.1: facet_wrap by Species



Scenario: A researcher wants three side-by-side scatter plots, one per iris species. Use facet_wrap.



Difficulty: Beginner




  

    
RYour turn

    
ex_4_1 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
  geom_point() +
  # your code here
ex_4_1

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_4_1 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
  geom_point() +
  facet_wrap(~ Species)

ex_4_1

    

      
      
    

    

  

  





Explanation: facet_wrap takes a one-sided formula. Each species gets its own panel; same axes by default.






Exercise 4.2: facet_wrap with custom number of columns



Scenario: With 5 levels of cut in diamonds, the default 3x2 layout is awkward. Lay out a faceted price histogram with ncol = 5 (one row).



Difficulty: Intermediate




  

    
RYour turn

    
ex_4_2 <- ggplot(diamonds, aes(x = price)) +
  geom_histogram(bins = 30) +
  # your code here
ex_4_2

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_4_2 <- ggplot(diamonds, aes(x = price)) +
  geom_histogram(bins = 30) +
  facet_wrap(~ cut, ncol = 5)

ex_4_2

    

      
      
    

    

  

  





Explanation: ncol forces the layout. Use nrow for the other axis. Strip headers show the level name automatically.






Exercise 4.3: facet_grid with two variables



Scenario: An analyst wants a grid of scatter plots: rows by cut, columns by clarity. Use facet_grid.



Difficulty: Intermediate




  

    
RYour turn

    
ex_4_3 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.1) +
  # your code here
ex_4_3

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_4_3 <- ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.1) +
  facet_grid(cut ~ clarity)

ex_4_3

    

      
      
    

    

  

  





Explanation: facet_grid takes rows ~ cols. Result is a matrix of panels, useful for two-way comparisons. facet_wrap drops empty cells; facet_grid keeps them.






Exercise 4.4: Free y-axis scales per facet



Scenario: Faceting by drv on mpg dataset (cars) creates panels where the y-range varies a lot. Allow each panel its own y scale.



Difficulty: Intermediate




  

    
RYour turn

    
ex_4_4 <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  # your code here
ex_4_4

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_4_4 <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_wrap(~ drv, scales = "free_y")

ex_4_4

    

      
      
    

    

  

  





Explanation: scales = "free_y" lets each facet pick its own y-range. Options: "fixed" (default), "free_x", "free_y", "free". Use sparingly; readers easily miss that scales differ.






Exercise 4.5: Custom facet labels with labeller



Scenario: Default strip labels show factor levels (4, 6, 8). The exec reading the chart prefers "4-cyl", "6-cyl", "8-cyl". Customize with labeller.



Difficulty: Advanced




  

    
RYour turn

    
ex_4_5 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_4_5

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
cyl_labels <- c("4" = "4-cyl", "6" = "6-cyl", "8" = "8-cyl")

ex_4_5 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  facet_wrap(~ cyl, labeller = labeller(cyl = cyl_labels))

ex_4_5

    

      
      
    

    

  

  





Explanation: labeller() takes a named vector mapping levels to display labels. For multiple facets you can pass several mappings: labeller(cyl = ..., gear = ...). Use as_labeller() for function-based transforms.






Exercise 4.6: Per-facet annotation



Scenario: Add a panel-specific text label inside each facet showing the count of cars in that cylinder group.



Difficulty: Advanced




  

    
RYour turn

    
ex_4_6 <- # your code here
ex_4_6

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
counts <- mtcars |>
  count(cyl) |>
  mutate(label = paste("n =", n))

ex_4_6 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_text(data = counts, aes(x = 5, y = 32, label = label),
            hjust = 1, color = "red") +
  facet_wrap(~ cyl)

ex_4_6

    

      
      
    

    

  

  





Explanation: Build a per-facet summary tibble; pass it to a NEW geom_text layer with its own data. The layer inherits the facet variable from the main aes if it has the same column. Standard pattern for in-panel annotations.






Section 5. Themes and customization (10 problems)



Exercise 5.1: Add title, subtitle, caption



Scenario: A presentation chart needs a clear title, descriptive subtitle, and source citation. Add all three using labs() to the mtcars scatter.



Difficulty: Beginner




  

    
RYour turn

    
ex_5_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_5_1

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  labs(title    = "Heavier cars consume more fuel",
       subtitle = "1974 Motor Trend US data, 32 cars",
       caption  = "Source: mtcars (built-in R dataset)")

ex_5_1

    

      
      
    

    

  

  





Explanation: labs() handles title, subtitle, caption, x, y, color, fill, etc. in one call. Cleaner than separate ggtitle()/xlab()/ylab() calls.






Exercise 5.2: Custom axis labels



Scenario: Default axis labels are the variable names (wt, mpg). Replace with "Weight (1000 lbs)" and "Miles per gallon".



Difficulty: Beginner




  

    
RYour turn

    
ex_5_2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_5_2

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  labs(x = "Weight (1000 lbs)", y = "Miles per gallon")

ex_5_2

    

      
      
    

    

  

  





Explanation: x and y inside labs() override the auto-generated axis titles. For multi-line labels use "Line 1\nLine 2".






Exercise 5.3: Apply theme_minimal



Scenario: A clean, gridline-light look matches your report style. Apply theme_minimal() to the iris scatter.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_3 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point() +
  # your code here
ex_5_3

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_3 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point() +
  theme_minimal()

ex_5_3

    

      
      
    

    

  

  





Explanation: Built-in themes include theme_gray (default), theme_bw, theme_minimal, theme_classic, theme_void, theme_dark. Add at the end of the plot to override the global theme.






Exercise 5.4: Rotate x-axis labels 45 degrees



Scenario: Long category names on the x-axis overlap. Rotate them 45 degrees and right-justify.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_4 <- ggplot(diamonds, aes(x = clarity)) +
  geom_bar() +
  # your code here
ex_5_4

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_4 <- ggplot(diamonds, aes(x = clarity)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ex_5_4

    

      
      
    

    

  

  





Explanation: axis.text.x targets the x tick labels; element_text() configures text properties. hjust = 1 anchors labels to their right edge so they line up with the tick.






Exercise 5.5: Move legend to the bottom



Scenario: The default right-side legend wastes vertical space in a wide chart. Move it to the bottom.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_5 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point() +
  # your code here
ex_5_5

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_5 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point() +
  theme(legend.position = "bottom")

ex_5_5

    

      
      
    

    

  

  





Explanation: legend.position takes "left", "right" (default), "top", "bottom", "none", or a numeric c(x, y) pair in [0, 1] for inside-plot placement.






Exercise 5.6: Remove the legend entirely



Scenario: When the color encoding is self-explanatory (e.g., Species names labelled directly), the legend is redundant. Remove it.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_6 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point() +
  # your code here
ex_5_6

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_6 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point() +
  theme(legend.position = "none")

ex_5_6

    

      
      
    

    

  

  





Explanation: "none" hides legends globally. To hide only one legend (e.g., color but keep size), use guides(color = "none").






Exercise 5.7: Bold, centered, larger title



Scenario: A magazine cover-style chart needs a 16pt bold centered title. Customize plot.title with element_text.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_7 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  labs(title = "Fuel economy vs vehicle weight") +
  # your code here
ex_5_7

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_7 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  labs(title = "Fuel economy vs vehicle weight") +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

ex_5_7

    

      
      
    

    

  

  





Explanation: plot.title is the theme element for the main title. face = "bold" / "italic" / "bold.italic". hjust = 0.5 centers; 0 left-aligns; 1 right-aligns.






Exercise 5.8: Remove minor gridlines



Scenario: A minimalist style requires only major gridlines. Remove minor gridlines on both axes.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_8 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_5_8

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_8 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  theme(panel.grid.minor = element_blank())

ex_5_8

    

      
      
    

    

  

  





Explanation: element_blank() removes a theme element. Other targets: panel.grid.major, panel.grid.major.x, panel.grid.minor.y, etc. theme_minimal already minimises gridlines if you want a softer touch.






Exercise 5.9: Custom font family



Scenario: A brand-aligned chart uses serif typography. Apply font family "serif" globally.



Difficulty: Intermediate




  

    
RYour turn

    
ex_5_9 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  labs(title = "Fuel economy vs weight") +
  # your code here
ex_5_9

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_5_9 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  labs(title = "Fuel economy vs weight") +
  theme(text = element_text(family = "serif"))

ex_5_9

    

      
      
    

    

  

  





Explanation: The text theme element is the parent for all text. Setting family there cascades to title, axis text, legend, etc. For specific overrides, target plot.title etc. individually.






Exercise 5.10: Build a reusable custom theme



Scenario: You want every chart in your report to share the same look. Build a function my_theme() that returns theme_minimal + bold centered titles + bottom legend, then apply it to two charts.



Difficulty: Advanced




  

    
RYour turn

    
my_theme <- function() {
  # your code here
}

p1 <- ggplot(mtcars, aes(wt, mpg)) + geom_point() + my_theme()
p2 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_point() + my_theme()

list(p1 = p1, p2 = p2)

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
my_theme <- function() {
  theme_minimal() +
    theme(plot.title      = element_text(face = "bold", hjust = 0.5),
          legend.position = "bottom")
}

p1 <- ggplot(mtcars, aes(wt, mpg)) + geom_point() + my_theme()
p2 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_point() + my_theme()

    

      
      
    

    

  

  





Explanation: Wrapping theme code in a function gives you single-source-of-truth styling. theme_minimal() + theme(...) chains compose because theme objects support +. Reuse across all your reports for consistency.






Section 6. Annotations and publication-ready (8 problems)



Exercise 6.1: Add a horizontal reference line



Scenario: Mark the average mpg on a scatter as a dashed horizontal line. Use geom_hline.



Difficulty: Intermediate




  

    
RYour turn

    
ex_6_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_6_1

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_6_1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_hline(yintercept = mean(mtcars$mpg), linetype = "dashed", color = "red")

ex_6_1

    

      
      
    

    

  

  





Explanation: geom_hline draws a horizontal line at yintercept; geom_vline at xintercept; geom_abline takes slope and intercept. linetype options: "solid", "dashed", "dotted", "dotdash", "longdash", "twodash".






Exercise 6.2: Annotate a specific point



Scenario: Highlight the most fuel-efficient car with a text label and arrow. Use annotate.



Difficulty: Intermediate




  

    
RYour turn

    
ex_6_2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_6_2

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_6_2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  annotate("point", x = 1.835, y = 33.9, color = "red", size = 4) +
  annotate("text", x = 2.5, y = 33.9, label = "Toyota Corolla\nbest mpg", hjust = 0)

ex_6_2

    

      
      
    

    

  

  





Explanation: annotate() draws ONE element with fixed coordinates, not a layer driven by data. Use it for callouts, arrows, and one-off labels. Pass arrow = grid::arrow() to a "segment" annotation for arrows.






Exercise 6.3: Highlight a region with geom_rect



Scenario: Shade the "high efficiency" region (mpg > 25) on a scatter. Add a translucent green rectangle.



Difficulty: Intermediate




  

    
RYour turn

    
ex_6_3 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  # your code here
ex_6_3

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_6_3 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = 25, ymax = Inf,
           fill = "lightgreen", alpha = 0.3) +
  geom_point()

ex_6_3

    

      
      
    

    

  

  





Explanation: annotate("rect", ...) with -Inf/Inf extends the rectangle to plot edges. Place the rect BEFORE the points so points draw on top. Standard pattern for highlighting threshold zones.






Exercise 6.4: Label specific points with geom_text



Scenario: Label each car name on the scatter, but only those above 25 mpg. Filter inside the geom layer.



Difficulty: Intermediate




  

    
RYour turn

    
mt <- mtcars |> tibble::rownames_to_column("car")

ex_6_4 <- ggplot(mt, aes(x = wt, y = mpg)) +
  geom_point() +
  # your code here
ex_6_4

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
mt <- mtcars |> tibble::rownames_to_column("car")

ex_6_4 <- ggplot(mt, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_text(data = subset(mt, mpg > 25), aes(label = car),
            hjust = -0.1, size = 3)

ex_6_4

    

      
      
    

    

  

  





Explanation: Pass a filtered data argument to a layer to override the inherited data. geom_text needs a label aesthetic. For non-overlapping labels use ggrepel::geom_text_repel.






Exercise 6.5: Smooth lines per facet group



Scenario: A faceted scatter of mpg dataset by drv with a per-facet linear smoother. Combine facet_wrap + geom_smooth.



Difficulty: Advanced




  

    
RYour turn

    
ex_6_5 <- # your code here
ex_6_5

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_6_5 <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", se = FALSE) +
  facet_wrap(~ drv) +
  labs(title = "Fuel economy vs engine size, by drivetrain",
       x = "Engine displacement (L)", y = "Highway mpg") +
  theme_minimal()

ex_6_5

    

      
      
    

    

  

  





Explanation: geom_smooth fits per-facet automatically because facet acts as an implicit grouping. Adding labs and theme_minimal completes a publication-ready look.






Exercise 6.6: Publication-ready chart with all the polish



Scenario: A final-form chart for a journal: titled, captioned, custom palette, theme_minimal, legend at bottom, dollar y-axis. Make it from diamonds carat vs price colored by cut.



Difficulty: Advanced




  

    
RYour turn

    
ex_6_6 <- # your code here
ex_6_6

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
ex_6_6 <- ggplot(diamonds |> sample_n(2000),
                 aes(x = carat, y = price, color = cut)) +
  geom_point(alpha = 0.6) +
  scale_y_continuous(labels = scales::dollar_format()) +
  scale_color_brewer(palette = "Set2") +
  labs(title    = "Diamond price increases steeply with carat",
       subtitle = "Sample of 2,000 diamonds, colored by cut quality",
       caption  = "Source: ggplot2::diamonds",
       x = "Carat", y = "Price", color = "Cut") +
  theme_minimal() +
  theme(plot.title      = element_text(face = "bold"),
        legend.position = "bottom")

ex_6_6

    

      
      
    

    

  

  





Explanation: Sample reduces overplot. Build up: scales for axis format and palette, labs for text, theme for the look. This template adapts to most reports with two changes.






Exercise 6.7: Bar chart with value labels



Scenario: A finance summary bar chart needs value labels above each bar. Compute cut counts, plot, label.



Difficulty: Advanced




  

    
RYour turn

    
ex_6_7 <- # your code here
ex_6_7

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
counts <- diamonds |> count(cut)

ex_6_7 <- ggplot(counts, aes(x = cut, y = n)) +
  geom_col(fill = "steelblue") +
  geom_text(aes(label = scales::comma(n)), vjust = -0.5, size = 3.5) +
  scale_y_continuous(labels = scales::comma_format(),
                     expand = expansion(mult = c(0, 0.1))) +
  labs(title = "Diamond inventory by cut quality", y = "Count", x = "Cut") +
  theme_minimal()

ex_6_7

    

      
      
    

    

  

  





Explanation: geom_col uses precomputed heights (vs geom_bar which counts). Labels above bars: vjust = -0.5. expansion(mult = c(0, 0.1)) adds 10% headroom on top so labels are not clipped.






Exercise 6.8: Before/after comparison via faceting



Scenario: Compare mpg distributions for automatic vs manual transmission cars. Build a violin + boxplot with two facets, custom labels, and a clear theme.



Difficulty: Advanced




  

    
RYour turn

    
ex_6_8 <- # your code here
ex_6_8

    

      
      
    

    

  

  






Click to reveal solution



  

    
RSolution

    
mt <- mtcars |>
  mutate(transmission = if_else(am == 0, "Automatic", "Manual"))

ex_6_8 <- ggplot(mt, aes(x = transmission, y = mpg, fill = transmission)) +
  geom_violin(alpha = 0.5) +
  geom_boxplot(width = 0.15, fill = "white") +
  scale_fill_manual(values = c(Automatic = "tomato", Manual = "steelblue")) +
  labs(title = "Manual transmissions show higher mpg",
       subtitle = "1974 Motor Trend, 32 cars",
       x = NULL, y = "Miles per gallon") +
  theme_minimal() +
  theme(legend.position = "none",
        plot.title      = element_text(face = "bold"))

ex_6_8

    

      
      
    

    

  

  





Explanation: Decode am to readable labels first (analyst-friendly axis). Layer violin + narrow boxplot for distribution + summary. legend.position none removes redundant fill legend (x-axis already labels). x = NULL drops the x-axis title.






What to do next



After 50 exercises, you should be composing publication-ready ggplot2 charts from memory. Natural follow-ups:



    


  • ggplot2 function-deep posts in this site: geom_point, geom_bar, geom_line, scale_color_brewer, scale_y_continuous, theme, facet_wrap, and more. Each covers one function's full API.
    • 


  • Topic sub-hubs (coming): ggplot2-Themes-Exercises, ggplot2-Facets-Exercises, ggplot2-Color-Scales-Exercises for targeted drilling.
    • 


  • Combine with dplyr: most production charts start with dplyr summaries; the dplyr exercises hub drills the upstream pipeline.
    • 





        


        
      


      

        

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

        

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