---
title: "Areal Data and Creating High-Quality Graphics"
subtitle: "Lecture 10"
author: "Shannon Gallagher"
date: "September 28, 2026"
date-format: long
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    footer: "MaDS Data Visualization · Fall 2026"
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---

```{r setup}
source("_setup.R")
us <- ggplot2::map_data("state")
state_key <- tibble(region = str_to_lower(state.name), state = state.name)
state_counts <- events |> count(state, name = "event_records")
map_counts <- us |> left_join(state_key, by = "region") |> left_join(state_counts, by = "state")
```

## Reminders, previously, and today...

- join areal data to map geometry safely,
- distinguish totals from normalized rates,
- choose color scales that match the quantity,
- combine and annotate graphics without turning them into decoration.

## Thinking about areal data

You need:

1. one row per geographic unit,
2. a stable geographic key,
3. polygon geometry for the same units,
4. a successful join,
5. an explicit treatment of unmatched regions.

## High-level overview of steps

```r
summary_by_region <- records |>
  group_by(region_id) |>
  summarise(value = ...)

map_data <- geometry |>
  left_join(summary_by_region, by = "region_id")

ggplot(map_data, aes(long, lat, group = group, fill = value)) +
  geom_polygon()
```

Do the aggregation and validate the join before choosing colors.

## Typical workflow for plotting areal data

```{r map-join-check}
state_counts |>
  anti_join(state_key, by = "state")
```

Territories and non-state codes are legitimate data. They simply are not in the
contiguous-state polygon table.

## Create a choropleth map with `geom_polygon()`

```{r raw-choropleth, fig.alt="Choropleth map of NOAA event record counts by state."}
ggplot(map_counts, aes(long, lat, group = group, fill = event_records)) +
  geom_polygon(color = "white", linewidth = 0.2) +
  coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, labels = comma, na.value = "gray90") +
  labs(title = "NOAA event-record totals vary widely by state", fill = "Records") +
  theme_void() +
  theme(legend.position = "bottom")
```

## Raw totals answer a narrow question

The map shows where many records were reported. It does not directly show:

- resident risk,
- land-area-adjusted frequency,
- probability of an event,
- reporting completeness,
- causal climate differences.

## Checkpoint 1 · Pick the denominator

::: {.checkpoint}
Choose a denominator for each question: risk to residents, event concentration by land
area, and share of national records. Which denominator cannot be obtained from NOAA alone?
:::

## Never map counts when the question asks for rates

A rate needs a defensible numerator and denominator measured for compatible geography and
time.

Examples:

- deaths per million residents,
- events per 10,000 square miles,
- damaging events per 1,000 event records.

Each rate answers a different question.

## Normalize by land area when concentration is the question

```{r area-normalized-map, fig.alt="Choropleth map of NOAA event records per thousand square miles."}
area_lookup <- tibble(state = state.name, area_sq_miles = state.area)
area_rates <- state_counts |>
  left_join(area_lookup, by = "state") |>
  mutate(records_per_1000_sq_miles = 1000 * event_records / area_sq_miles)

map_rates <- us |>
  left_join(state_key, by = "region") |>
  left_join(area_rates, by = "state")

ggplot(map_rates, aes(long, lat, group = group, fill = records_per_1000_sq_miles)) +
  geom_polygon(color = "white", linewidth = 0.2) +
  coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, na.value = "gray90") +
  labs(title = "Area normalization changes the geographic comparison", fill = "Records per\n1,000 sq mi") +
  theme_void() +
  theme(legend.position = "bottom")
```

This is still record concentration, not risk to people or property.

## Put totals and rates side by side

```{r totals-rates-comparison, fig.alt="Side-by-side maps of raw NOAA totals and area-normalized record rates."}
#| layout-ncol: 2
ggplot(map_counts, aes(long, lat, group = group, fill = event_records)) +
  geom_polygon(color = "white", linewidth = 0.15) +
  coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, na.value = "gray90") +
  labs(title = "Raw totals", fill = NULL) + theme_void()

ggplot(map_rates, aes(long, lat, group = group, fill = records_per_1000_sq_miles)) +
  geom_polygon(color = "white", linewidth = 0.15) +
  coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, na.value = "gray90") +
  labs(title = "Per 1,000 sq mi", fill = NULL) + theme_void()
```

## Color scale follows meaning

- sequential scale: low to high quantity,
- diverging scale: deviations around a meaningful center,
- categorical palette: distinct unordered groups.

Rainbow scales manufacture boundaries and are difficult to read accurately.

## Bins or continuous color?

Bins can make policy thresholds visible, but boundary choices become part of the claim.
Continuous scales preserve ordering but can make exact comparisons difficult.

Show the legend. State the transformation. Explain missing values.

## A binned scale makes thresholds explicit

```{r binned-map, fig.alt="Binned choropleth map of NOAA event-record totals."}
ggplot(map_counts, aes(long, lat, group = group, fill = cut(event_records, breaks = c(0, 500, 1000, 2000, 4000, Inf)))) +
  geom_polygon(color = "white", linewidth = 0.2) +
  coord_quickmap() +
  scale_fill_brewer(palette = "YlOrRd", na.value = "gray90", drop = FALSE) +
  labs(title = "Bins simplify comparison but make the cut points consequential", fill = "Event records") +
  theme_void() +
  theme(legend.position = "bottom")
```

## Visual randomization test

```{r randomized-map, fig.alt="Maps comparing observed state totals with randomly reassigned totals."}
#| layout-ncol: 2
set.seed(36613)
shuffled_counts <- state_counts |>
  mutate(event_records = sample(event_records))
shuffled_map <- us |>
  left_join(state_key, by = "region") |>
  left_join(shuffled_counts, by = "state")

ggplot(map_counts, aes(long, lat, group = group, fill = event_records)) +
  geom_polygon(color = "white", linewidth = 0.15) + coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, na.value = "gray90") +
  labs(title = "Observed") + theme_void() + theme(legend.position = "none")

ggplot(shuffled_map, aes(long, lat, group = group, fill = event_records)) +
  geom_polygon(color = "white", linewidth = 0.15) + coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, na.value = "gray90") +
  labs(title = "One random reassignment") + theme_void() + theme(legend.position = "none")
```

The comparison asks whether the observed spatial arrangement looks unusual relative to
a deliberately chosen null mechanism.

## Checkpoint 2 · Audit the legend

::: {.checkpoint}
For the raw-count map, propose a better title, a missing-data label, and either a
continuous or binned scale. Explain the decision your legend helps the reader make.
:::

## Infographics vs. figures in papers/reports

A client graphic should make the reading order obvious:

1. finding-oriented title,
2. visual evidence,
3. annotation for the key comparison,
4. units and source,
5. caveat or scope note.

Everything else competes for attention.

## Annotation

Use direct labels and short notes to point to:

- a change point,
- an outlier,
- a threshold,
- the comparison that supports the conclusion.

Do not annotate every data point.

## Annotate the comparison that matters

```{r annotated-bars, fig.alt="Annotated bar chart of the ten states with the most NOAA event records."}
top_states <- state_counts |>
  slice_max(event_records, n = 10) |>
  mutate(state = fct_reorder(state, event_records))
focus <- top_states |> slice_max(event_records, n = 1)

ggplot(top_states, aes(event_records, state)) +
  geom_col(fill = gold) +
  geom_text(data = focus, aes(label = paste(comma(event_records), "records")), hjust = 1.05, color = "white", fontface = "bold") +
  scale_x_continuous(labels = comma) +
  labs(title = "The leading state accounts for the largest observed record total", x = "Event records", y = NULL)
```

## Creating compound figures

Two panels belong together when they answer complementary parts of one question—for
example, a map showing **where** and a time series showing **when**.

A dashboard of unrelated charts is not automatically a story.

## Combine where and when

```{r compound-map-time, fig.alt="A choropleth map beside a monthly line chart of NOAA event records."}
#| layout-ncol: 2
ggplot(map_counts, aes(long, lat, group = group, fill = event_records)) +
  geom_polygon(color = "white", linewidth = 0.15) + coord_quickmap() +
  scale_fill_gradient(low = pale_gold, high = deep_gold, na.value = "gray90") +
  labs(title = "Where", fill = NULL) + theme_void()

events |>
  count(month) |>
  ggplot(aes(month, n)) +
  geom_line(color = deep_gold, linewidth = 1) +
  geom_point(color = deep_gold) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  labs(title = "When", x = NULL, y = "Event records")
```

## Creating the same type of plot many times

```r
make_event_plot <- function(type) {
  events |>
    filter(event_type == type) |>
    count(month) |>
    ggplot(aes(month, n)) +
    geom_line() +
    labs(title = type)
}

purrr::map(c("Hail", "Tornado", "Heat"), make_event_plot)
```

A function keeps axes, labels, and styling consistent across repeated figures.

## Thinking about themes...

- Use one text family and a small number of sizes.
- Keep grid lines only when they aid comparison.
- Reserve saturated color for the main evidence.
- Align titles, panels, and captions.
- Preserve enough whitespace to separate ideas.

Consistency makes differences in the data—not accidental formatting—the primary signal.

## Checkpoint 3 · Remove one thing

::: {.checkpoint}
Take one graphic from prior work. Identify the single element the reader should notice
first, then remove or mute one element that competes with it. Explain your revision.
:::

## Save for the destination

Before exporting, set:

- physical size,
- aspect ratio,
- resolution,
- font size,
- background,
- file type.

Test the actual medium: slides, web, or print.

## Saving plots

```r
ggsave(
  filename = "state-event-rate.png",
  plot = final_plot,
  width = 10,
  height = 6,
  units = "in",
  dpi = 300,
  bg = "white"
)
```

Do not rely on the size of the RStudio plot pane to determine the exported result.

## Recap and next steps

- A map is a data join plus a projection.
- Totals and rates answer different questions.
- Color and bins encode substantive choices.
- Design should clarify the argument and evidence trail.

Next: text as data.