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R in 3 Months (Spring 2026)

Reorder Plots to Highlight Findings

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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-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 --------------------------------------------------------------------

third_grade_math_proficiency |>
  filter(year == "2018-2019") |>
  filter(district == "Portland SD 1J") |>
  ggplot(aes(x = percent_proficient, y = school)) +
  geom_col()

third_grade_math_proficiency |>
  filter(year == "2018-2019") |>
  filter(district == "Portland SD 1J") |>
  ggplot(aes(
    x = percent_proficient,
    y = reorder(school, percent_proficient)
  )) +
  geom_col()

third_grade_math_proficiency |>
  filter(year == "2018-2019") |>
  filter(district == "Portland SD 1J") |>
  mutate(school = fct_reorder(school, percent_proficient)) |>
  ggplot(aes(
    x = percent_proficient,
    y = school
  )) +
  geom_col()

Your Turn

  1. 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:

  2. Using the reorder() function, make a bar chart that shows the percent of race/ethnicity groups in descending order

  3. 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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