Binding Data Frames
This lesson is called Binding Data Frames, part of the R in 3 Months (Fall 2025) course. This lesson is called Binding Data Frames, part of the R in 3 Months (Fall 2025) 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 Data -------------------------------------------------------------
math_scores_2021_2022 <-
read_excel(path = "data-raw/pagr_schools_math_tot_raceethnicity_2122.xlsx") |>
clean_names()
math_scores_2018_2019 <-
read_excel(path = "data-raw/pagr_schools_math_tot_raceethnicity_1819.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 = case_when(
proficiency_level == "number_level_4" ~ "4",
proficiency_level == "number_level_3" ~ "3",
proficiency_level == "number_level_2" ~ "2",
proficiency_level == "number_level_1" ~ "1"
)) |>
mutate(number_of_students = parse_number(number_of_students)) |>
group_by(school_id) |>
mutate(pct = number_of_students / sum(number_of_students, na.rm = TRUE)) |>
ungroup()
third_grade_math_proficiency_2018_2019 <-
math_scores_2018_2019 |>
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 = case_when(
proficiency_level == "number_level_4" ~ "4",
proficiency_level == "number_level_3" ~ "3",
proficiency_level == "number_level_2" ~ "2",
proficiency_level == "number_level_1" ~ "1"
)) |>
mutate(number_of_students = parse_number(number_of_students)) |>
group_by(school_id) |>
mutate(pct = number_of_students / sum(number_of_students, na.rm = TRUE)) |>
ungroup()
bind_rows(third_grade_math_proficiency_2018_2019,
third_grade_math_proficiency_2021_2022) |>
view()
third_grade_math_proficiency_2018_2019 |>
bind_rows(third_grade_math_proficiency_2021_2022)Your Turn
Import 2021-2022 enrollment data into a data frame called
enrollment_by_race_ethnicity_2021_2022and clean it using the code you used for the 2022-2023 data. You’ll need to change bits of your code to make it work.Add a
yearvariable to theenrollment_by_race_ethnicity_2021_2022andenrollment_by_race_ethnicity_2022_2023data frames.Use bind_rows() to make a
enrollment_by_race_ethnicitydata frame by combining theenrollment_by_race_ethnicity_2021_2022andenrollment_by_race_ethnicity_2022_2023data frames.
Here is code to get started:
# Load Packages -----------------------------------------------------------
library(tidyverse)
library(fs)
library(readxl)
library(janitor)
# Create Directories ------------------------------------------------------
dir_create("data-raw")
# Download Data -----------------------------------------------------------
# https://www.oregon.gov/ode/reports-and-data/students/Pages/Student-Enrollment-Reports.aspx
# download.file("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()
enrollment_2021_2022 <- read_excel(path = "data-raw/fallmembershipreport_20212022.xlsx",
sheet = "School 2021-22") |>
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, pattern = "x2022_23_")) |>
mutate(race_ethnicity = case_when(
race_ethnicity == "american_indian_alaska_native" ~ "American Indian Alaska Native",
race_ethnicity == "asian" ~ "Asian",
race_ethnicity == "black_african_american" ~ "Black/African American",
race_ethnicity == "hispanic_latino" ~ "Hispanic/Latino",
race_ethnicity == "multiracial" ~ "Multi-Racial",
race_ethnicity == "native_hawaiian_pacific_islander" ~ "Native Hawaiian Pacific Islander",
race_ethnicity == "white" ~ "White",
race_ethnicity == "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 = YOURCODEHERE)
enrollment_by_race_ethnicity_2021_2022 <-
enrollment_2021_2022 |>
select(attending_district_institution_id, attending_school_institution_id,
YOURCODEHERE:YOURCODEHERE) |>
rename(YOURCODEHERE = attending_district_institution_id,
YOURCODEHERE = attending_school_institution_id) |>
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, pattern = YOURCODEHERE)) |>
mutate(race_ethnicity = case_when(
race_ethnicity == "american_indian_alaska_native" ~ "American Indian Alaska Native",
race_ethnicity == "asian" ~ "Asian",
race_ethnicity == "black_african_american" ~ "Black/African American",
race_ethnicity == "hispanic_latino" ~ "Hispanic/Latino",
race_ethnicity == "multiracial" ~ "Multi-Racial",
race_ethnicity == "native_hawaiian_pacific_islander" ~ "Native Hawaiian Pacific Islander",
race_ethnicity == "white" ~ "White",
race_ethnicity == "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 = YOURCODEHERE)
enrollment_by_race_ethnicity <-
bind_rows(YOURCODEHERE,
YOURCODEHERE)Have any questions? Put them below and we will help you out!
Course Content
128 Lessons
1
Welcome to Getting Started with R
00:57
2
Install R
02:05
3
Install RStudio
02:14
4
Files in R
04:33
5
Projects
07:54
6
Packages
02:38
7
Import Data
05:24
8
Objects and Functions
03:16
9
Examine our Data
12:50
10
Import Our Data Again
07:11
11
Getting Help
07:46
12
Week 1 Live Session (Fall 2025)
53:39
1
Welcome to Fundamentals of R
01:36
2
Update Everything
02:45
3
Start a New Project
02:16
4
The Tidyverse
03:34
5
Pipes
04:15
6
select()
07:25
7
mutate()
04:25
8
filter()
10:05
9
summarize()
05:59
10
group_by() and summarize()
05:54
11
arrange()
02:07
12
Create a New Data Frame
03:58
13
Bring it All Together (Data Wrangling)
07:29
14
Week 2 Project Assignment
09:39
15
Week 2 Coworking Session (Fall 2025)
16
Week 2 Live Session (Fall 2025)
59:15
1
The Grammar of Graphics
04:39
2
Scatterplots
03:46
3
Histograms
05:47
4
Bar Charts
06:37
5
Setting color and fill Aesthetic Properties
02:39
6
Setting color and fill Scales
05:40
7
Setting x and y Scales
03:09
8
Adding Text to Plots
07:32
9
Plot Labels
03:57
10
Themes
02:19
11
Facets
03:12
12
Save Plots
02:57
13
Bring it All Together (Data Visualization)
06:42
14
Week 3 Project Assignment
03:30
15
Week 3 Coworking Session (Fall 2025)
16
Week 3 Live Session (Fall 2025)
1:00:07
1
Downloading and Importing Data
10:32
2
Overview of Tidy Data
05:50
3
Tidy Data Rule #1: Every Column is a Variable
07:43
4
Tidy Data Rule #3: Every Cell is a Single Value
10:04
5
Tidy Data Rule #2: Every Row is an Observation
04:42
6
Week 6 Coworking Session (Fall 2025)
7
Week 6 Live Session (Fall 2025)
59:45
1
Best Practices in Data Visualization
03:44
2
Tidy Data
02:25
3
Pipe Data into ggplot
09:54
4
Reorder Plots to Highlight Findings
03:37
5
Line Charts
04:17
6
Use Color to Highlight Findings
09:16
7
Declutter
08:29
8
Add Descriptive Labels to Your Plots
09:10
9
Use Titles to Highlight Findings
08:14
10
Use Annotations to Explain
07:09
11
Week 9 Coworking Session (Fall 2025)
12
Week 9 Live Session (Fall 2025)
56:18
1
Advanced Markdown
06:43
2
Tables
18:36
3
Advanced YAML and Code Chunk Options
05:53
4
Inline R Code
04:42
5
Making Your Reports Shine: Word Edition
04:30
6
Making Your Reports Shine: PDF Edition
06:11
7
Making Your Reports Shine: HTML Edition
06:06
8
Presentations
10:12
9
Dashboards
05:38
10
Websites
06:43
11
Publishing Your Work
04:38
12
Quarto Extensions
05:50
13
Parameterized Reporting, Part 1
10:57
14
Parameterized Reporting, Part 2
05:11
15
Parameterized Reporting, Part 3
07:47
16
Week 12 Coworking Session (Fall 2025)
17
Week 12 Live Session (Fall 2025)
54:14
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