Reorder Plots to Highlight Findings

This lesson is called Reorder Plots to Highlight Findings, part of the Going Deeper with R course. This lesson is called Reorder Plots to Highlight Findings, part of the Going Deeper with R course.

Transcript

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View code shown in video

# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)

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

dir_create("data")

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

# download.file("https://github.com/rfortherestofus/going-deeper-v2/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) |> 
  mutate(percent_proficient = case_when(
    is.nan(percent_proficient) ~ NA,
    .default = percent_proficient
  ))

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

third_grade_math_proficiency |> 
  filter(year == "2021-2022") |> 
  filter(district == "Portland SD 1J") |> 
  ggplot(aes(x = percent_proficient, 
             y = school)) +
  geom_col()

third_grade_math_proficiency |> 
  filter(year == "2021-2022") |> 
  filter(district == "Portland SD 1J") |> 
  ggplot(aes(x = percent_proficient, 
             y = reorder(school, percent_proficient))) +
  geom_col()

third_grade_math_proficiency |> 
  filter(year == "2021-2022") |> 
  filter(district == "Portland SD 1J") |> 
  mutate(school = fct_reorder(school, percent_proficient)) |> 
  ggplot(aes(x = percent_proficient, 
             y = school)) +
  geom_col()

Your Turn

Solution

# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)

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

dir_create("data")

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

# download.file("https://github.com/rfortherestofus/going-deeper-v2/raw/main/data/enrollment_by_race_ethnicity.rds",
#               mode = "wb",
#               destfile = "data/enrollment_by_race_ethnicity.rds")

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

enrollment_by_race_ethnicity <-
  read_rds("data/enrollment_by_race_ethnicity.rds") |> 
  select(-district_institution_id)  |> 
  select(year, district, everything()) |> 
  mutate(year = case_when(
    year == "School 2021-22" ~ "2021-2022",
    year == "School 2022-23" ~ "2022-2023",
  ))

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

enrollment_by_race_ethnicity |> 
  filter(year == "2022-2023") |> 
  filter(district == "Beaverton SD 48J") |> 
  ggplot(aes(x = pct, 
             y = race_ethnicity)) +
  geom_col()

enrollment_by_race_ethnicity |> 
  filter(year == "2022-2023") |> 
  filter(district == "Beaverton SD 48J") |> 
  ggplot(aes(x = pct, 
             y = reorder(race_ethnicity, pct))) +
  geom_col()

enrollment_by_race_ethnicity |> 
  filter(year == "2022-2023") |> 
  filter(district == "Beaverton SD 48J") |> 
  mutate(race_ethnicity = fct_reorder(race_ethnicity, pct)) |> 
  ggplot(aes(x = pct, 
             y = race_ethnicity)) +
  geom_col()

Make a bar chart that shows race/ethnicity in Beaverton SD 48J. As before, filter your data to only include 2022-2023 data and only include Beaverton SD 48J. Then, do the following:
Using the reorder() function, make a bar chart that shows the percent of race/ethnicity groups in descending order
Make the same bar chart using mutate() and fct_reorder() to reorder the race/ethnicity groups

Learn More

R for the Rest of Us consultant Albert Rapp has written a blog post on reordering items in plots.

If you want to delve deeper into factors, check out:

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

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Myles Kwitny • May 20, 2025

When I ran my code, I did the select and filter functions during the creation of my data set instead of during the plotting stage. Is this an okay way to do it or could it cause problems if I was doing more complex data plotting? Below is the code I used for reference:

third_grade_math_proficiency_by_race_2022_2023 <- read_rds("data/enrollment_by_race_ethnicity.rds") |> select(year, district, number_of_students,race_ethnicity, pct) |> filter(district == "Beaverton SD 48J", year == "School 2022-23")

third_grade_math_proficiency_by_race_2022_2023 |> ggplot(aes(x = pct, y = reorder(race_ethnicity, pct))) + geom_col()

Gracielle Higino Coach • May 20, 2025

Hi Myles! That's not a problem as long as you have descriptive object names, as you do in your example! However, if you want to do that for several years, you'll end up with a bunch of objects on your environment and this can make your computer slower depending on how big your datasets are. Just keep in mind that the method that David is teaching here can be useful in the future when you're learning about parameterized reports, or when you put that into a function.