Functions
This lesson is called Functions, part of the Going Deeper with R course. This lesson is called Functions, part of the Going Deeper with R course.
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# 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, Tidy, and Clean Data --------------------------------------------
# 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, Tidy, and Clean Data -------------------------------------------
clean_math_proficiency_data <- function(raw_data) {
read_excel(
path = raw_data
) |>
clean_names() |>
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)) |>
mutate(
pct = number_of_students / sum(number_of_students, na.rm = TRUE),
.by = school_id
)
}
third_grade_math_proficiency_2021_2022 <-
clean_math_proficiency_data(
raw_data = "data-raw/pagr_schools_math_tot_raceethnicity_2122.xlsx"
)
third_grade_math_proficiency_2018_2019 <-
clean_math_proficiency_data(
raw_data = "data-raw/pagr_schools_math_tot_raceethnicity_1819.xlsx"
)
third_grade_math_proficiency <-
bind_rows(
third_grade_math_proficiency_2021_2022,
third_grade_math_proficiency_2018_2019
)Your Turn
Create a function to clean each year of enrollment data.
To check that your function works, create
enrollment_by_race_ethnicity_2021_2022andenrollment_by_race_ethnicity_2022_2023data frames and then bind them together withbind_rows().
This exercise is challenging! Use the starter code below to help you if you need to.
# 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"
# )
# Tidy and Clean Function ------------------------------------------------
clean_enrollment_data <- function(excel_file, sheet_name) {
read_excel(
path = YOURCODEHERE,
sheet = YOURCODEHERE
) |>
clean_names() |>
select(
1,
3,
7:19
) |>
select(-contains("percent")) |>
set_names(
"district_institution_id",
YOURCODEHERE,
YOURCODEHERE,
YOURCODEHERE,
YOURCODEHERE,
YOURCODEHERE,
YOURCODEHERE,
YOURCODEHERE,
YOURCODEHERE,
) |>
pivot_longer(
cols = -c(district_institution_id, school_institution_id),
names_to = "race_ethnicity",
values_to = "number_of_students"
) |>
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)) |>
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() |>
mutate(year = sheet_name) |>
mutate(year = str_remove(year, "School "))
}
# Tidy and Clean Data ----------------------------------------------------
enrollment_by_race_ethnicity_2022_2023 <-
clean_enrollment_data(
excel_file = YOURCODEHERE,
sheet_name = YOURCODEHERE
)
enrollment_by_race_ethnicity_2021_2022 <-
clean_enrollment_data(
excel_file = YOURCODEHERE,
sheet_name = YOURCODEHERE
)
bind_rows(
YOURCODEHERE,
YOURCODEHERE
)Learn More
Have any questions? Put them below and we will help you out!
Course Content
44 Lessons
1
Downloading and Importing Data
08:13
2
Overview of Tidy Data
05:03
3
Tidy Data Rule #1: Every Column is a Variable
06:26
4
Tidy Data Rule #3: Every Cell is a Single Value
09:27
5
Tidy Data Rule #2: Every Row is an Observation
04:05
6
Changing Variable Types
05:13
7
Dealing With Missing Data
04:41
8
Advanced Summarizing
07:52
9
Binding Data Frames
06:56
10
Functions
11:59
11
Data Merging
09:24
12
Exporting Data
04:20
13
Bring It All Together (Advanced Data Wrangling)
14:22
1
Best Practices in Data Visualization
03:38
2
Tidy Data
02:25
3
Pipe Data in ggplot
08:18
4
Reorder Plots to Highlight Findings
03:50
5
Line Charts
04:13
6
Use Color to Highlight Findings
08:23
7
Declutter
07:53
8
Add Descriptive Labels to Your Plots
09:18
9
Use Titles to Highlight Findings
08:30
10
Use Annotations to Explain
06:35
11
Tweak Spacing
05:36
12
Create a Custom Theme
03:20
13
Customize Your Fonts
04:42
14
Try New Plot Types
03:24
15
Bring it All Together (Advanced Data Visualization)
11:04
1
Advanced Markdown
07:10
2
Tables
15:48
3
Advanced YAML and Code Chunk Options
05:42
4
Inline R Code
03:42
5
Making Your Reports Shine: Word Edition
05:08
6
Making Your Reports Shine: PDF Edition
07:37
7
Making Your Reports Shine: HTML Edition
06:08
8
Presentations
11:12
9
Dashboards
06:20
10
Websites
08:11
11
Publishing Your Work
02:37
12
Quarto Extensions
06:38
13
Parameterized Reporting, Part 1
07:02
14
Parameterized Reporting, Part 2
04:03
15
Parameterized Reporting, Part 3
06:22
16
Wrapping up Going Deeper with R
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Alberto Cabrera • April 20, 2026
The starter code is in error. It assumes that the 7 columns reporting enrollment by race/ethnicity are in numeric order. After several trials, I realized that each enrollment column is followed by a column reporting enrollment by percentages. The correct select script should read: select(1, 3, 7, 9, 11, 13, 15, 17, 19) |> instead of select( 1, 3, 7:19 ) |>
Below, please find the function that worked:
clean_enrollment_data <- function(excel_file, sheet_name) { read_excel( path = excel_file, sheet = sheet_name ) |> clean_names() |> select(1, 3, 7, 9, 11, 13, 15, 17, 19) |> select(-contains("percent")) |> set_names( "district_institution_id", "school_institution_id", "american_indian_alaska_native", #(7) "asian", #(9) "native_hawaiian_pacific_islander", #(11) "black_african_american", #(13) "hispanic_latino", #(15) "white", #(17) "multiracial", #(19) ) |> pivot_longer( cols = -c(district_institution_id, school_institution_id), names_to = "race_ethnicity", values_to = "number_of_students" ) |> 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)) |> 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, na.rm = TRUE)) |> ungroup() |> mutate(year = sheet_name) |> mutate(year = str_remove(year, "School ")) }
Alberto Cabrera • April 20, 2026
Correction to my previous note.
Watching the solution, I realized the select(1, 3, 7:19) |> option suggested works provided it is followed by select(-contains("percent")), which excludes all columns in between that report enrollment in percentages. I should have watched the solution. It would have made me realize I forgot to include select().
Sorry for the confusion.