Week 11 Live Session (Fall 2025)

library(tidyverse)
library(palmerpenguins)

# Theme -------------------------------------------------------------------

theme_dk <- function(hide_gridlines = FALSE, hide_legend = FALSE) {
  my_theme <- theme_minimal() +
    theme(
      axis.title = element_blank(),
      axis.text = element_text(
        color = "grey60",
        size = 18
      )
    )
  
  if (hide_gridlines == TRUE) {
    my_theme <- my_theme +
      theme(panel.grid = element_blank())
  }
  
  if (hide_legend == TRUE) {
    my_theme <- my_theme +
      theme(legend.position = "none")
  }
  
  my_theme
}


# Plots -------------------------------------------------------------------

ggplot(
  data = penguins,
  aes(
    x = bill_length_mm,
    y = bill_depth_mm
  )
) +
  geom_point()

ggplot(
  data = penguins,
  aes(
    x = bill_length_mm,
    y = bill_depth_mm,
    color = island
  )
) +
  geom_point() +
  theme_dk(hide_legend = TRUE) 

penguins |> 
  count(island) |> 
  ggplot(
    aes(
      x = island,
      y = n
    )
  ) +
  geom_col() +
  theme_dk(hide_gridlines = TRUE)

library(tidyverse)
library(janitor)
library(sf)

# Portland ----------------------------------------------------------------

portland_boundaries <-
  read_sf("data-raw/City_Boundaries.geojson") |>
  clean_names() |>
  filter(cityname == "Portland")

portland_boundaries

portland_boundaries |>
  ggplot() +
  geom_sf()

traffic_signals <-
  read_sf("data-raw/Traffic_Signals.geojson") |>
  clean_names()

traffic_signals

traffic_signals |>
  ggplot() +
  geom_sf(size = 0.25)

snow_and_ice_routes <-
  read_sf("data-raw/Snow_and_Ice_Routes.geojson") |>
  clean_names()

snow_and_ice_routes

snow_and_ice_routes |>
  ggplot() +
  geom_sf(aes(color = priority))

ggplot() +
  geom_sf(
    data = portland_boundaries,
    fill = "gray80",
    alpha = 0.5
  ) +
  geom_sf(
    data = snow_and_ice_routes,
    alpha = 0.5
  ) +
  geom_sf(
    data = traffic_signals,
    aes(color = software_type),
    alpha = 0.5,
    size = 1
  ) +
  theme_dk(hide_gridlines = TRUE, hide_legend = TRUE)


# Tigris ------------------------------------------------------------------

library(tigris)

us_states <- states()

us_states

us_states |>
  shift_geometry() |>
  ggplot() +
  geom_sf()

kentucky_counties <- counties(state = "Kentucky")

kentucky_counties

kentucky_counties |>
  ggplot() +
  geom_sf()

# Median Income -----------------------------------------------------------

library(tidycensus)
library(scales)

median_income <-
  get_acs(
    state = "Illinois",
    geography = "county",
    variables = "B19013_001",
    geometry = TRUE
  )

median_income

median_income |>
  ggplot(aes(fill = estimate)) +
  geom_sf()


# International Data ------------------------------------------------------

library(rnaturalearth)

?rnaturalearth::ne_countries()


ne_countries(
  country = c("Ukraine"),
  scale = "large",
  returnclass = "sf"
) |>
  select(sovereignt) |>
  ggplot() +
  geom_sf()


# Mapview -----------------------------------------------------------------

library(mapview)

mapview(us_states)


# Interactive -------------------------------------------------------------

library(ggiraph)

median_income_interactive_plot <-
  median_income |>
  ggplot(aes(
    fill = estimate,
    tooltip = estimate |> dollar()
  )) +
  geom_sf_interactive()

girafe(ggobj = median_income_interactive_plot)

---
title: "Interactive Data Viz Example"
format: html
execute: 
  warning: false
  message: false
  echo: false
editor_options: 
  chunk_output_type: console
---

```{r}
library(tidyverse)
library(scales)
library(ggiraph)
```


```{r}
cbem <-
  read_csv(here::here("data-raw/cbem.csv"))
```

## Static Plot

```{r}
cbem |>
  filter(location == "Oregon") |>
  filter(age_group == "Under 18") |>
  filter(group != "All Persons") |>
  mutate(
    group = fct(
      group,
      levels = c(
        "American Indian or Alaska Native",
        "Asian or Pacific Islander",
        "Black or African American",
        "White",
        "Hispanic or Latino"
      )
    )
  ) |>
  mutate(x_position = row_number()) |>
  mutate(
    x_position = case_when(
      group == "Hispanic or Latino" ~ 5.5,
      .default = x_position
    )
  ) |>
  mutate(percent_formatted = percent(percent)) |>
  ggplot(
    aes(
      x = x_position,
      y = percent,
      fill = group,
      label = percent_formatted
    )
  ) +
  geom_col() +
  geom_text(
    vjust = 1.5,
    color = "white"
  ) +
  scale_fill_manual(
    values = c(
      "American Indian or Alaska Native" = "#9CC892",
      "Asian or Pacific Islander" = "#0066cc",
      "Black or African American" = "#477A3E",
      "White" = "#6CC5E9",
      "Hispanic or Latino" = "#ff7400"
    )
  ) +
  theme_void() +
  theme(legend.position = "none")
```


## Interactive Plot

```{r}
cbem_interactive_plot <-
  cbem |>
  filter(location == "Oregon") |>
  filter(age_group == "Under 18") |>
  filter(group != "All Persons") |>
  mutate(
    group = fct(
      group,
      levels = c(
        "American Indian or Alaska Native",
        "Asian or Pacific Islander",
        "Black or African American",
        "White",
        "Hispanic or Latino"
      )
    )
  ) |>
  mutate(x_position = row_number()) |>
  mutate(
    x_position = case_when(
      group == "Hispanic or Latino" ~ 5.5,
      .default = x_position
    )
  ) |>
  mutate(percent_formatted = percent(percent)) |>
  ggplot(
    aes(
      x = x_position,
      y = percent,
      fill = group,
      label = percent_formatted,
      tooltip = group
    )
  ) +
  geom_col_interactive() +
  geom_text(
    vjust = 1.5,
    color = "white"
  ) +
  scale_fill_manual(
    values = c(
      "American Indian or Alaska Native" = "#9CC892",
      "Asian or Pacific Islander" = "#0066cc",
      "Black or African American" = "#477A3E",
      "White" = "#6CC5E9",
      "Hispanic or Latino" = "#ff7400"
    )
  ) +
  theme_void() +
  theme(legend.position = "none")

girafe(ggobj = cbem_interactive_plot)
```