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Advanced Data Wrangling
- 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
- Functions
- Data Merging
- Exporting Data
- Bring It All Together (Advanced Data Wrangling)
-
Advanced Data Visualization
- Best Practices in Data Visualization
- Tidy Data
- Pipe Data into ggplot
- Reorder Plots to Highlight Findings
- Line Charts
- Use Color to Highlight Findings
- Declutter
- Add Descriptive Labels to Your Plots
- Use Titles to Highlight Findings
- Use Annotations to Explain
- Tweak Spacing
- Create a Custom Theme
- Customize Your Fonts
- Try New Plot Types
- Bring it All Together (Advanced Data Visualization)
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Quarto
- Advanced Markdown
- Advanced YAML and Code Chunk Options
- Tables
- Inline R Code
- Making Your Reports Shine: Word Edition
- Making Your Reports Shine: PDF Edition
- Making Your Reports Shine: HTML Edition
- Presentations
- Dashboards
- Websites
- Publishing Your Work
- Quarto Extensions
- Parameterized Reporting, Part 1
- Parameterized Reporting, Part 2
- Parameterized Reporting, Part 3
- Wrapping up Going Deeper with R
Going Deeper with R
Pipe Data into ggplot
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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.
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# 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_ethnicityusing 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",
))
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