Pipe Data into ggplot
This lesson is called Pipe Data into ggplot, part of the Going Deeper with R course. This lesson is called Pipe Data into ggplot, part of the Going Deeper with R course.
Transcript
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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() |>
mutate(percent_proficient = case_when(
is.nan(percent_proficient) ~ NA,
.default = percent_proficient
)) |>
filter(is_proficient == TRUE) |>
select(academic_year, school, district, percent_proficient) |>
rename(year = academic_year)
# Plot --------------------------------------------------------------------
third_grade_math_proficiency |>
filter(year == "2021-2022") |>
filter(district == "Portland SD 1J") |>
ggplot(aes(x = percent_proficient,
y = school)) +
geom_col()
Your Turn
Create a new R script file.
Download the enrollment data by race/ethnicity and create a data frame called
enrollment_by_race_ethnicity
using the starter code below.Pipe your data into a bar chart that shows the breakdown of race/ethnicity among students in Beaverton SD 48J in 2022-2023.
# 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/enrollment_by_race_ethnicity.rds",
mode = "wb",
destfile = "data/enrollment_by_race_ethnicity.rds")
# Import Data -------------------------------------------------------------
enrollment_by_race_ethnicity <-
read_rds("data/enrollment_by_race_ethnicity.rds") |>
select(-district_institution_id) |>
select(year, district, everything()) |>
mutate(year = case_when(
year == "School 2021-22" ~ "2021-2022",
year == "School 2022-23" ~ "2022-2023",
))
Have any questions? Put them below and we will help you out!
Course Content
44 Lessons
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
Changing Variable Types
04:51
7
Dealing with Missing Data
04:55
8
Advanced Summarizing
06:25
9
Binding Data Frames
07:17
10
Functions
15:06
11
Data Merging
09:27
12
Exporting Data
04:38
13
Bring It All Together (Advanced Data Wrangling)
13:03
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
Tweak Spacing
05:11
12
Create a Custom Theme
03:47
13
Customize Your Fonts
08:32
14
Try New Plot Types
03:24
15
Bring it All Together (Advanced Data Visualization)
14:30
1
Advanced Markdown
06:43
2
Advanced YAML and Code Chunk Options
05:53
3
Tables
18:36
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
Wrapping up Going Deeper with R
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