Skip to content
R for the Rest of Us Logo

Going Deeper with R

Use Annotations to Explain

Transcript

Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.

View code shown in video 
# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)
library(scales)
library(ggrepel)
library(marquee)

# Create Directory --------------------------------------------------------

dir_create("data")

# Download Data -----------------------------------------------------------

download.file(
  "https://github.com/rfortherestofus/going-deeper-positron/raw/main/data/third_grade_math_proficiency.rds",
  mode = "wb",
  destfile = "data/third_grade_math_proficiency.rds"
)

# Import Data -------------------------------------------------------------

third_grade_math_proficiency <-
  read_rds("data/third_grade_math_proficiency.rds") |>
  select(
    academic_year,
    school,
    school_id,
    district,
    proficiency_level,
    number_of_students
  ) |>
  mutate(
    is_proficient = case_when(
      proficiency_level >= 3 ~ TRUE,
      .default = FALSE
    )
  ) |>
  group_by(academic_year, school, district, school_id, is_proficient) |>
  summarize(number_of_students = sum(number_of_students, na.rm = TRUE)) |>
  ungroup() |>
  group_by(academic_year, school, district, school_id) |>
  mutate(
    percent_proficient = number_of_students /
      sum(number_of_students, na.rm = TRUE)
  ) |>
  ungroup() |>
  filter(is_proficient == TRUE) |>
  select(academic_year, school, district, percent_proficient) |>
  rename(year = academic_year)

# Plot --------------------------------------------------------------------

top_growth_school <-
  third_grade_math_proficiency |>
  filter(district == "Portland SD 1J") |>
  group_by(school) |>
  mutate(
    growth_from_previous_year = percent_proficient - lag(percent_proficient)
  ) |>
  ungroup() |>
  slice_max(
    order_by = growth_from_previous_year,
    n = 1
  ) |>
  pull(school)


plot_title <-
  marquee_glue(
    "{.orange **{top_growth_school}**} showed large growth 
        in math proficiency over the last two years"
  )

third_grade_math_proficiency |>
  filter(district == "Portland SD 1J") |>
  mutate(
    highlight_school = case_when(
      school == top_growth_school ~ "Y",
      .default = "N"
    )
  ) |>
  mutate(
    school = fct_relevel(
      school,
      top_growth_school,
      after = Inf
    )
  ) |>
  mutate(
    percent_proficient_formatted = percent(percent_proficient, accuracy = 1)
  ) |>
  mutate(
    percent_proficient_formatted = case_when(
      highlight_school == "Y" & year == "2021-2022" ~
        str_glue(
          "{percent_proficient_formatted} of students
          were proficient
          in {year}"
        ),
      highlight_school == "Y" & year == "2018-2019" ~
        percent_proficient_formatted
    )
  ) |>
  ggplot(
    aes(
      x = year,
      y = percent_proficient,
      color = highlight_school,
      group = school,
      label = percent_proficient_formatted
    )
  ) +
  geom_line() +
  geom_text_repel(
    hjust = 0,
    lineheight = 0.9,
    direction = "x"
  ) +
  scale_color_manual(
    values = c(
      "Y" = "orange",
      "N" = "gray80"
    )
  ) +
  scale_y_continuous(
    labels = percent_format()
  ) +
  annotate(
    geom = "text",
    x = 2.02,
    y = 0.6,
    hjust = 0,
    lineheight = 0.9,
    color = "gray70",
    label = str_glue(
      "Each gray line
    represents one school"
    )
  ) +
  labs(
    title = plot_title
  ) +
  theme_minimal() +
  theme(
    axis.title = element_blank(),
    legend.position = "none",
    panel.grid = element_blank(),
    plot.title = element_marquee(width = 1),
    plot.title.position = "plot"
  )

Your Turn

Add an annotation to explain what the grey lines represent

(Optional) You're welcome to add other annotations as well in case you want to test out the power of the annotate function.

Learn More

The article How to add annotations in ggplot: should you use geoms or annotations? by Albert Rapp is a good overview of how to select between using geom_text() and annotate().

Cara Thompson's blog post Level Up Your Labels: Tips and Tricks for Annotating Plots is a masterclass in using annotations to improve the quality of your plots.

This video, titled How to add annotations to ggplots in R, is a good walkthrough of using annotations in ggplot.

If you want to learn more about the importance of annotation in data visualization, check out this article from Elijah Meeks titled Making Annotations First-Class Citizens in Data Visualization. Also check out this article from Alberto Cairo discussing another example of work from the Financial Times that uses annotations well (folks at the FT are experts at annotations, in case you haven’t yet picked that up!).

Have any questions? Put them below and we will help you out!

You need to be signed-in to comment on this post. Login.

Course Content

44 Lessons