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Going Deeper with R

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-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

  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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