Data Visualization

How to Make Dynamic Figures

How to render with/without answers

• With answers (default): quarto render viz-handout.qmd
• Without answers (figures only): quarto render viz-handout.qmd -P show_answers:false

1) Distribution: Histogram + Density Overlay (Diamonds)

You are given ggplot2::diamonds (>50k rows). Create a histogram of price (restrict to $10,000 or less for visibility), overlay a scaled density curve so both share a comparable y-scale, and style axes and labels.

Requirements

• Filter to price <= 10000.
• Histogram with a binwidth of 250.
• Overlay a density where y = after_stat(count) * 250 so peaks align with histogram counts.
• Label axes and apply a dollar formatter to x-axis.
• Use theme_bw() and a concise title/subtitle.

library(ggplot2)

p1 <- diamonds |>
  filter(price <= 10000) |>
  ggplot(aes(price)) +
  geom_histogram(binwidth = 250, boundary = 0, closed = "left", color = "white") +
  geom_density(aes(y = after_stat(count) * 250), linewidth = 1, alpha = 0.15, fill = NA) +
  scale_x_continuous(labels = scales::dollar_format(accuracy = 1)) +
  labs(
    title = "Diamond Prices (≤ $10k)",
    subtitle = "Histogram with density overlay scaled to counts",
    x = "Price (USD)", y = "Count"
  ) +
  theme(legend.position = "none")

p1

Figure 1: Histogram and scaled density of diamond prices (≤ $10k).

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1.1) Make the above graph better

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2) Bivariate: Scatter with LOESS Trend + Facets (MPG)

Using ggplot2::mpg, visualize highway mileage (hwy) vs engine displacement (displ), colored by class. Add a LOESS smooth and facet by drive (drv).

Requirements

• geom_point(alpha = 0.6) and geom_smooth(se = FALSE, method = "loess").
• Axis limits: y from 10 to 45.
• Facet by drv with facet_wrap(~ drv, nrow = 1).
• Place legend at the bottom.

mpg |>
  ggplot(aes(displ, hwy, color = class)) +
  geom_point(alpha = 0.6) +
  geom_smooth(se = FALSE, method = "loess") +
  scale_y_continuous(limits = c(10, 45), breaks = seq(10, 45, 5)) +
  labs(
    title = "Fuel Economy by Engine Size",
    subtitle = "LOESS trends by vehicle class",
    x = "Engine displacement (L)", y = "Highway MPG"
  ) +
  facet_wrap(~ drv, nrow = 1) +
  theme(legend.position = "bottom")

Figure 2: Highway MPG vs. engine displacement, colored by class, faceted by drive (mpg).

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2.1) Make the above graph better

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3) Small Multiples: Facet Wrap by Class (MPG)

Produce small multiples of hwy vs displ but facet by class to compare patterns across vehicle classes.

Requirements

• Same base mapping as above; facet by class (ncol = 4).
• Use a consistent y-scale.
• Add a translucent linear fit per panel (method = "lm").

mpg |>
  ggplot(aes(displ, hwy)) +
  geom_point(alpha = 0.6, color = "grey30") +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.8, alpha = 0.9) +
  scale_y_continuous(limits = c(10, 45), breaks = seq(10, 45, 5)) +
  labs(
    title = "Fuel Economy vs Engine Size by Vehicle Class",
    x = "Engine displacement (L)", y = "Highway MPG"
  ) +
  facet_wrap(~ class, ncol = 4)

Figure 3: Small multiples of hwy vs displ by vehicle class (linear fits).

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3.1) Make the above graph better

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4) Time Series: Unemployment Rate with 12-Month Moving Average (economics)

Using ggplot2::economics, compute the unemployment rate as 100 * unemploy / pop. Plot the monthly rate and a 12-month moving average.

Requirements

• Create urate = 100 * unemploy / pop.
• Compute a 12-month moving average using stats::filter.
• Plot urate as a faint line and overlay the moving average as a thicker line.
• Limit the x-axis to 1970–2010 for focus.

library(stats)

economics |>
  mutate(
    urate = 100 * unemploy / pop,
    ma12 = as.numeric(stats::filter(urate, rep(1/12, 12), sides = 1))
  ) |>
  filter(date >= as.Date("1970-01-01"), date <= as.Date("2010-12-31")) |>
  ggplot(aes(date, urate)) +
  geom_line(alpha = 0.4) +
  geom_line(aes(y = ma12), linewidth = 1) +
  labs(
    title = "US Unemployment Rate",
    subtitle = "Monthly values with 12-month moving average",
    x = NULL, y = "Percent"
  )

Figure 4: US unemployment rate with 12-month moving average (1970–2010).

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5) Distribution by Group: Violin + Box + Jitter (iris)

Using datasets::iris, compare Sepal.Length by Species with violins, embedded boxplots, and jittered points. Reorder species by median sepal length.

Requirements

• Reorder x-axis by median Sepal.Length.
• Use geom_violin(trim = FALSE, alpha = 0.3) + geom_boxplot(width = 0.1, outlier.shape = NA).
• Add geom_jitter(width = 0.1, alpha = 0.5, size = 0.8).
• Hide the fill legend and label clearly.

iris |>
  as_tibble() |>
  mutate(Species = fct_reorder(Species, Sepal.Length, .fun = median)) |>
  ggplot(aes(Species, Sepal.Length, fill = Species)) +
  geom_violin(trim = FALSE, alpha = 0.3) +
  geom_boxplot(width = 0.1, outlier.shape = NA) +
  geom_jitter(width = 0.1, alpha = 0.5, size = 0.8) +
  guides(fill = "none") +
  labs(
    title = "Sepal Length by Species",
    x = NULL, y = "Sepal length (cm)"
  )

Figure 5: Sepal length distributions by species (iris).

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5.1) Make the above graph better

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6) Correlation Heatmap with Clustering (mtcars)

Compute a Spearman correlation matrix for numeric variables in mtcars, cluster variables, and display a heatmap with correlation values.

Requirements

• Compute Spearman correlations on numeric columns.
• Order variables using hierarchical clustering on 1 - r.
• Use geom_tile() and scale_fill_gradient2() with limits c(-1, 1).
• Add rounded correlation labels.

num <- mtcars |> select(where(is.numeric))
R <- cor(num, method = "spearman", use = "pairwise.complete.obs")
ord <- hclust(as.dist(1 - R))$order
lvl <- colnames(R)[ord]

corr_df <- as.data.frame(R) |>
  rownames_to_column("var1") |>
  pivot_longer(-var1, names_to = "var2", values_to = "r") |>
  mutate(
    var1 = factor(var1, levels = lvl),
    var2 = factor(var2, levels = lvl)
  )

ggplot(corr_df, aes(var2, var1, fill = r)) +
  geom_tile() +
  geom_text(aes(label = sprintf("%.2f", r)), size = 3) +
  scale_fill_gradient2(limits = c(-1, 1)) +
  coord_fixed() +
  labs(x = NULL, y = NULL, fill = "rho",
       title = "Spearman Correlation (mtcars)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Figure 6: Spearman correlation heatmap with hierarchical clustering (mtcars).

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6.1) Make the above graph better

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7) Tile Heatmap: Housing Prices by Month × Year (txhousing)

Using ggplot2::txhousing, create a month × year heatmap of median home prices for Austin.

Requirements

• Filter city to "Austin".
• Limit years to 2000–2015.
• Y-axis should be month (Jan–Dec).
• Fill tiles by median (median sales price).
• Add a readable color scale and labels.

# subset and prepare month labels
au <- txhousing |>
  filter(city == "Austin", year >= 2000, year <= 2015) |>
  mutate(Month = factor(month, levels = 1:12, labels = month.abb))

ggplot(au, aes(year, Month, fill = median)) +
  geom_tile() +
  scale_fill_gradient(labels = scales::dollar, name = "Median price") +
  scale_x_continuous(breaks = seq(2000, 2015, by = 3)) +
  labs(
    title = "Austin Housing — Median Price by Month and Year",
    x = NULL, y = NULL
  ) +
  theme(legend.position = "right")

Figure 7: Median home price (Austin): month × year heatmap (txhousing).

7.1) Make the above graph better

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Appendix: Session Info (hidden)