Creating Treemaps with viz

📖 Introduction

Treemaps display hierarchical data as nested rectangles. The size of each rectangle represents its value - larger rectangles mean larger values. They’re excellent for showing composition and proportions across many categories at once.

library(dashboardr)
library(dplyr)
library(gssr)
library(haven)

# Load GSS data
data(gss_all)
gss <- gss_all %>%
  select(year, age, sex, race, degree, happy, polviews, region) %>%
  filter(year == max(year, na.rm = TRUE), 
         !is.na(degree), !is.na(sex), !is.na(race), !is.na(region)) %>%
  mutate(
    degree = droplevels(as_factor(degree)),
    sex = droplevels(as_factor(sex)),
    race = droplevels(as_factor(race)),
    region = droplevels(as_factor(region))
  )

Important: Treemaps require pre-aggregated data with a value column. You typically need to count() or summarize() your data first.

📊 Basic Treemaps

Simple Category Sizes

First, aggregate your data to get counts per category:

# Aggregate - treemaps need pre-summarized data!
degree_counts <- gss %>%
  count(degree, name = "n")

degree_counts
#> # A tibble: 5 × 2
#>   degree                       n
#>   <fct>                    <int>
#> 1 less than high school      287
#> 2 high school               1476
#> 3 associate/junior college   290
#> 4 bachelor's                 708
#> 5 graduate                   465

Now create the treemap:

plot <- viz_treemap(
  data = degree_counts,
  group_var = "degree",
  value_var = "n",
  title = "Education Level Distribution"
)

plot

Race Distribution

race_counts <- gss %>%
  count(race, name = "n")

plot <- viz_treemap(
  data = race_counts,
  group_var = "race",
  value_var = "n",
  title = "Race Distribution",
  color_palette = c("#3498DB", "#E74C3C", "#27AE60")
)

plot

🏗️ Hierarchical Treemaps

Treemaps shine when showing nested categories. Use subgroup_var for a second level:

# Two-level: degree within sex
degree_by_sex <- gss %>%
  count(sex, degree, name = "n")

degree_by_sex
#> # A tibble: 10 × 3
#>    sex    degree                       n
#>    <fct>  <fct>                    <int>
#>  1 male   less than high school      110
#>  2 male   high school                692
#>  3 male   associate/junior college   112
#>  4 male   bachelor's                 322
#>  5 male   graduate                   203
#>  6 female less than high school      177
#>  7 female high school                784
#>  8 female associate/junior college   178
#>  9 female bachelor's                 386
#> 10 female graduate                   262

plot <- viz_treemap(
  data = degree_by_sex,
  group_var = "sex",
  subgroup_var = "degree",
  value_var = "n",
  title = "Education by Sex"
)

plot

Region by Race

region_race <- gss %>%
  count(region, race, name = "n")

plot <- viz_treemap(
  data = region_race,
  group_var = "region",
  subgroup_var = "race",
  value_var = "n",
  title = "Race Distribution by Region"
)

plot

🎨 Custom Colors

plot <- viz_treemap(
  data = degree_counts,
  group_var = "degree",
  value_var = "n",
  title = "Education Levels",
  color_palette = c("#1ABC9C", "#3498DB", "#9B59B6", "#E74C3C", "#F39C12")
)

plot

🏷️ Labels and Tooltips

Customize what appears in the treemap cells and hover tooltips:

plot <- viz_treemap(
  data = degree_counts,
  group_var = "degree",
  value_var = "n",
  title = "Education Distribution",
  value_label = "Respondents",  # Label for the value in tooltips
  tooltip_suffix = " people"
)

plot

Parameter	Description	Example
`value_label`	Label for value in tooltip	`"Count"`, `"Revenue"`
`tooltip_prefix`	Text before value	`"$"`, `"N = "`
`tooltip_suffix`	Text after value	`" units"`, `"%"`

For hierarchical treemaps, both group and subgroup appear in tooltips automatically.

📁 Using with create_content()

Basic Integration

content <- create_content(data = degree_counts, type = "treemap") %>%
  add_viz(
    group_var = "degree",
    value_var = "n",
    title = "Education Breakdown"
  )

content %>% preview()

Preview

Education Breakdown

Multiple Treemaps

# Prepare different aggregations
sex_counts <- gss %>% count(sex, name = "n")
race_counts <- gss %>% count(race, name = "n")
region_counts <- gss %>% count(region, name = "n")

# Combine into one dataset with a category indicator
all_counts <- bind_rows(
  sex_counts %>% rename(category = sex) %>% mutate(type = "Sex"),
  race_counts %>% rename(category = race) %>% mutate(type = "Race"),
  region_counts %>% rename(category = region) %>% mutate(type = "Region")
)

content <- create_content(data = all_counts, type = "treemap") %>%
  add_viz(
    group_var = "category",
    value_var = "n",
    title = "By Sex",
    filter = ~ type == "Sex",
    tabgroup = "Demographics"
  ) %>%
  add_viz(
    group_var = "category",
    value_var = "n",
    title = "By Race",
    filter = ~ type == "Race",
    tabgroup = "Demographics"
  ) %>%
  add_viz(
    group_var = "category",
    value_var = "n",
    title = "By Region",
    filter = ~ type == "Region",
    tabgroup = "Demographics"
  )

content %>% preview()

Preview

By Sex

By Race

By Region

💡 Data Preparation Tips

Treemaps need aggregated data. Here’s how to prepare GSS data:

Simple Counts

# Count by one variable
gss %>% count(degree, name = "n")
#> # A tibble: 5 × 2
#>   degree                       n
#>   <fct>                    <int>
#> 1 less than high school      287
#> 2 high school               1476
#> 3 associate/junior college   290
#> 4 bachelor's                 708
#> 5 graduate                   465

Hierarchical Counts

# Count by two variables for nested treemap
gss %>% count(sex, degree, name = "n")
#> # A tibble: 10 × 3
#>    sex    degree                       n
#>    <fct>  <fct>                    <int>
#>  1 male   less than high school      110
#>  2 male   high school                692
#>  3 male   associate/junior college   112
#>  4 male   bachelor's                 322
#>  5 male   graduate                   203
#>  6 female less than high school      177
#>  7 female high school                784
#>  8 female associate/junior college   178
#>  9 female bachelor's                 386
#> 10 female graduate                   262

With Calculated Values

# Average age by education level
gss %>%
  group_by(degree) %>%
  summarize(avg_age = mean(age, na.rm = TRUE), .groups = "drop")
#> # A tibble: 5 × 2
#>   degree                   avg_age
#>   <fct>                      <dbl>
#> 1 less than high school       49.9
#> 2 high school                 49.8
#> 3 associate/junior college    49.8
#> 4 bachelor's                  51.1
#> 5 graduate                    52.4

🔍 When to Use Treemaps

Use treemaps when: - Showing part-to-whole relationships - Comparing sizes across many categories - Displaying hierarchical data - Space is limited (more compact than bar charts)

Use bar charts instead when: - Precise comparisons are important - You have few categories (< 5) - Order matters (rankings)

📚 See Also

?viz_treemap - Full function documentation
vignette("bar_vignette") - For categorical comparisons
vignette("content-collections") - For dashboard integration

Creating Treemaps with viz_treemap()