Accessible data visualizations

Application exercise
Modified

March 4, 2024

library(tidyverse)
library(readxl)
library(scales)
library(colorblindr)

theme_set(theme_minimal())

Import nursing data

nurses <- read_csv("data/nurses.csv") |> janitor::clean_names()
Rows: 1242 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (1): State
dbl (21): Year, Total Employed RN, Employed Standard Error (%), Hourly Wage ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# subset to three states
nurses_subset <- nurses |>
  filter(state %in% c("California", "New York", "North Carolina"))

# unemployment data
unemp_state <- read_excel(
  path = "data/emp-unemployment.xls",
  sheet = "States",
  skip = 5
) |>
  pivot_longer(
    cols = -c(Fips, Area),
    names_to = "Year",
    values_to = "unemp"
  ) |>
  rename(state = Area, year = Year) |>
  mutate(year = parse_number(year)) |>
  filter(state != "United States") |>
  # calculate mean unemp rate per state and year
  group_by(state, year) |>
  summarize(unemp_rate = mean(unemp, na.rm = TRUE))
`summarise()` has grouped output by 'state'. You can override using the
`.groups` argument.

Developing alternative text

Bar chart

Demonstration: The following code chunk demonstrates how to add alternative text to a bar chart. The alternative text is added to the chunk header using the fig-alt chunk option. The text is written in Markdown and can be as long as needed. Note that fig-cap is not the same as fig-alt.

```{r}
#| label: nurses-bar
#| fig-cap: "Total employed Registered Nurses"
#| fig-alt: "The figure is a bar chart titled 'Total employed Registered
#| Nurses' that displays the numbers of registered nurses in three states
#| (California, New York, and North Carolina) over a 20 year period, with data
#| recorded in three time points (2000, 2010, and 2020). In each state, the
#| numbers of registered nurses increase over time. The following numbers are
#| all approximate. California started off with 200K registered nurses in 2000,
#| 240K in 2010, and 300K in 2020. New York had 150K in 2000, 160K in 2010, and
#| 170K in 2020. Finally North Carolina had 60K in 2000, 90K in 2010, and 100K
#| in 2020."

nurses_subset |>
  filter(year %in% c(2000, 2010, 2020)) |>
  ggplot(aes(x = state, y = total_employed_rn, fill = factor(year))) +
  geom_col(position = "dodge") +
  scale_fill_viridis_d(option = "E") +
  scale_y_continuous(labels = label_number(scale = 1/1000, suffix = "K")) +
  labs(
    x = "State", y = "Number of Registered Nurses", fill = "Year",
    title = "Total employed Registered Nurses"
  ) +
  theme(
    legend.background = element_rect(fill = "white", color = "white"),
    legend.position = c(0.85, 0.75)
    )
```

The figure is a bar chart titled 'Total employed Registered Nurses' that displays the numbers of registered nurses in three states (California, New York, and North Carolina) over a 20 year period, with data recorded in three time points (2000, 2010, and 2020). In each state, the numbers of registered nurses increase over time. The following numbers are all approximate. California started off with 200K registered nurses in 2000, 240K in 2010, and 300K in 2020. New York had 150K in 2000, 160K in 2010, and 170K in 2020. Finally North Carolina had 60K in 2000, 90K in 2010, and 100K in 2020.

Total employed Registered Nurses

Line chart

Your turn: Add alternative text to the following line chart.

nurses_subset |>
  ggplot(aes(x = year, y = annual_salary_median, color = state)) +
  geom_line(show.legend = FALSE) +
  geom_text(
    data = nurses_subset |> filter(year == max(year)),
    aes(label = state), hjust = 0, nudge_x = 1,
    show.legend = FALSE
  ) +
  scale_color_viridis_d(option = "C", end = 0.5) +
  scale_y_continuous(labels = label_dollar(scale = 1/1000, suffix = "K")) +
  labs(
    x = "Year", y = "Annual median salary", color = "State",
    title = "Annual median salary of Registered Nurses"
  ) +
  coord_cartesian(clip = "off") +
  theme(
    plot.margin = margin(0.1, 0.9, 0.1, 0.1, "in")
    )

Scatterplot

Your turn: Add alternative text to the following scatterplot.

nurses |>
  left_join(unemp_state) |>
  drop_na(unemp_rate) |>
  ggplot(aes(x = unemp_rate, y = hourly_wage_median)) +
  geom_point(size = 2, alpha = .5) +
  scale_x_continuous(labels = label_percent(scale = 1)) +
  scale_y_continuous(labels = label_dollar()) +
  labs(
    x = "Unemployment rate", y = "Median hourly wage",
    title = "Median hourly wage of Registered Nurses (1998-2018)",
    subtitle = "By state"
  )
Joining with `by = join_by(state, year)`

Acknowledgments