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Going Deeper with R

Advanced Summarizing

This lesson is called Advanced Summarizing, part of the Going Deeper with R course. This lesson is called Advanced Summarizing, part of the Going Deeper with R course.

Transcript

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View code shown in video

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

library(tidyverse)
library(fs)
library(readxl)
library(janitor)

# Create Directories ------------------------------------------------------

dir_create("data-raw")

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

# https://www.oregon.gov/ode/educator-resources/assessment/Pages/Assessment-Group-Reports.aspx

# download.file("https://www.oregon.gov/ode/educator-resources/assessment/Documents/TestResults2122/pagr_schools_math_tot_raceethnicity_2122.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_tot_raceethnicity_2122.xlsx")
#
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/Documents/TestResults2122/TestResults2019/pagr_schools_math_tot_raceethnicity_1819.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_tot_raceethnicity_1819.xlsx")
#
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/TestResults2018/pagr_schools_math_raceethnicity_1718.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_raceethnicity_1718.xlsx")
#
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/TestResults2017/pagr_schools_math_raceethnicity_1617.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_raceethnicity_1617.xlsx")
#
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/TestResults2016/pagr_schools_math_raceethnicity_1516.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_raceethnicity_1516.xlsx")

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

math_scores_2021_2022 <-
  read_excel(path = "data-raw/pagr_schools_math_tot_raceethnicity_2122.xlsx") |>
  clean_names()


# Tidy and Clean Data -----------------------------------------------------

third_grade_math_proficiency_2021_2022 <-
  math_scores_2021_2022 |>
  filter(student_group == "Total Population (All Students)") |>
  filter(grade_level == "Grade 3") |>
  select(academic_year, school_id, contains("number_level")) |>
  pivot_longer(
    cols = starts_with("number_level"),
    names_to = "proficiency_level",
    values_to = "number_of_students"
  ) |>
  mutate(proficiency_level = parse_number(proficiency_level)) |>
  mutate(number_of_students = parse_number(number_of_students))

third_grade_math_proficiency_2021_2022 |>
  # group_by(school_id) |>
  mutate(
    pct = number_of_students / sum(number_of_students, na.rm = TRUE),
    .by = school_id
  ) |>
  # ungroup() |>
  filter(proficiency_level == 4) |>
  slice_max(order_by = pct, n = 1)

Your Turn

Solution

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

library(tidyverse)
library(fs)
library(readxl)
library(janitor)

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

dir_create("data-raw")

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

# https://www.oregon.gov/ode/reports-and-data/students/Pages/Student-Enrollment-Reports.aspx

# download.file(
#   url = "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20222023.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20222023.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20212022.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20212022.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20202021.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20202021.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20192020.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20192020.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20182019.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20182019.xlsx"
# )

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

enrollment_2022_2023 <-
  read_excel(
    path = "data-raw/fallmembershipreport_20222023.xlsx",
    sheet = "School 2022-23"
  ) |>
  clean_names()

# Tidy and Clean Data ----------------------------------------------------

enrollment_by_race_ethnicity_2022_2023 <-
  enrollment_2022_2023 |>
  select(
    district_institution_id,
    school_institution_id,
    x2022_23_american_indian_alaska_native:x2022_23_multi_racial
  ) |>
  select(-contains("percent")) |>
  pivot_longer(
    cols = -c(district_institution_id, school_institution_id),
    names_to = "race_ethnicity",
    values_to = "number_of_students"
  ) |>
  mutate(race_ethnicity = str_remove(race_ethnicity, "x2022_23_")) |>
  mutate(
    race_ethnicity = recode_values(
      race_ethnicity,
      "american_indian_alaska_native" ~ "American Indian Alaska Native",
      "asian" ~ "Asian",
      "black_african_american" ~ "Black/African American",
      "hispanic_latino" ~ "Hispanic/Latino",
      "multiracial" ~ "Multi-Racial",
      "native_hawaiian_pacific_islander" ~ "Native Hawaiian Pacific Islander",
      "white" ~ "White",
      "multi_racial" ~ "Multiracial"
    )
  ) |>
  mutate(number_of_students = parse_number(number_of_students))

enrollment_by_race_ethnicity_2022_2023 |>
  group_by(district_institution_id, race_ethnicity) |>
  summarize(number_of_students = sum(number_of_students, na.rm = TRUE)) |>
  ungroup() |>
  group_by(district_institution_id) |>
  mutate(pct = number_of_students / sum(number_of_students)) |>
  ungroup()

Create a new variable called pct that shows each race/ethnicity as a percentage of all students in each district. This will require two steps.
You'll need to use group_by() and summarize() to calculate the number of students in each race/ethnicity group in each district.
You’ll need to use group_by() and mutate() to calculate the percentage of students in each race/ethnicity group in each district.

Don’t forget to ungroup() at the end of each step. Use this code to get started:

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

library(tidyverse)
library(fs)
library(readxl)
library(janitor)

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

dir_create("data-raw")

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

# https://www.oregon.gov/ode/reports-and-data/students/Pages/Student-Enrollment-Reports.aspx

# download.file(
#   url = "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20222023.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20222023.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20212022.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20212022.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20202021.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20202021.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20192020.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20192020.xlsx"
# )

# download.file(
#   "https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20182019.xlsx",
#   mode = "wb",
#   destfile = "data-raw/fallmembershipreport_20182019.xlsx"
# )

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

enrollment_2022_2023 <-
  read_excel(
    path = "data-raw/fallmembershipreport_20222023.xlsx",
    sheet = "School 2022-23"
  ) |>
  clean_names()

# Tidy and Clean Data ----------------------------------------------------

enrollment_by_race_ethnicity_2022_2023 <-
  enrollment_2022_2023 |>
  select(
    district_institution_id,
    school_institution_id,
    x2022_23_american_indian_alaska_native:x2022_23_multi_racial
  ) |>
  select(-contains("percent")) |>
  pivot_longer(
    cols = -c(district_institution_id, school_institution_id),
    names_to = "race_ethnicity",
    values_to = "number_of_students"
  ) |>
  mutate(race_ethnicity = str_remove(race_ethnicity, "x2022_23_")) |>
  mutate(
    race_ethnicity = recode_values(
      race_ethnicity,
      "american_indian_alaska_native" ~ "American Indian Alaska Native",
      "asian" ~ "Asian",
      "black_african_american" ~ "Black/African American",
      "hispanic_latino" ~ "Hispanic/Latino",
      "multiracial" ~ "Multi-Racial",
      "native_hawaiian_pacific_islander" ~ "Native Hawaiian Pacific Islander",
      "white" ~ "White",
      "multi_racial" ~ "Multiracial"
    )
  ) |>
  mutate(number_of_students = parse_number(number_of_students))

Learn More

Daniel Carter has a nice walkthrough of using group_by() and mutate().

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

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Course Content

44 Lessons

Downloading and Importing Data

Overview of Tidy Data

Tidy Data Rule #1: Every Column is a Variable

Tidy Data Rule #3: Every Cell is a Single Value

Tidy Data Rule #2: Every Row is an Observation

Changing Variable Types

Dealing With Missing Data

Advanced Summarizing

Binding Data Frames

Bring It All Together (Advanced Data Wrangling)

Best Practices in Data Visualization

Pipe Data in ggplot

Reorder Plots to Highlight Findings

Use Color to Highlight Findings

Add Descriptive Labels to Your Plots

Use Titles to Highlight Findings

Use Annotations to Explain

Create a Custom Theme

Customize Your Fonts

Try New Plot Types

Bring it All Together (Advanced Data Visualization)

Advanced Markdown

Advanced YAML and Code Chunk Options

Making Your Reports Shine: Word Edition

Making Your Reports Shine: PDF Edition

Making Your Reports Shine: HTML Edition

Publishing Your Work

Quarto Extensions

Parameterized Reporting, Part 1

Parameterized Reporting, Part 2

Parameterized Reporting, Part 3

Wrapping up Going Deeper with R