-
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)
-
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
Use Titles to Highlight Findings
This lesson is called Use Titles to Highlight Findings, part of the Going Deeper with R course. This lesson is called Use Titles to Highlight Findings, part of the Going Deeper with R course.
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Transcript
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View code shown in video
# Load Packages -----------------------------------------------------------
library(tidyverse)
library(fs)
library(scales)
library(ggrepel)
library(ggtext)
# 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() |>
filter(is_proficient == TRUE) |>
select(academic_year, school, district, percent_proficient) |>
rename(year = academic_year) |>
mutate(percent_proficient = case_when(
is.nan(percent_proficient) ~ NA,
.default = percent_proficient
))
# Plot --------------------------------------------------------------------
top_growth_school <-
third_grade_math_proficiency |>
filter(district == "Portland SD 1J") |>
group_by(school) |>
mutate(growth_from_previous_year = percent_proficient - lag(percent_proficient)) |>
ungroup() |>
drop_na(growth_from_previous_year) |>
slice_max(order_by = growth_from_previous_year,
n = 1) |>
pull(school)
third_grade_math_proficiency |>
filter(district == "Portland SD 1J") |>
mutate(highlight_school = case_when(
school == top_growth_school ~ "Y",
.default = "N"
)) |>
mutate(percent_proficient_formatted = case_when(
school == top_growth_school ~ percent(percent_proficient, accuracy = 1)
)) |>
mutate(percent_proficient_formatted = case_when(
highlight_school == "Y" & year == "2021-2022" ~ str_glue("{percent_proficient_formatted} of students
were proficient
in {year}"),
highlight_school == "Y" & year == "2018-2019" ~ percent_proficient_formatted
)) |>
mutate(school = fct_relevel(school, top_growth_school, after = Inf)) |>
ggplot(aes(x = year,
y = percent_proficient,
group = school,
color = highlight_school,
label = percent_proficient_formatted)) +
geom_line() +
geom_text_repel(hjust = 0,
lineheight = 0.9,
direction = "x") +
scale_color_manual(values = c(
"N" = "grey90",
"Y" = "orange"
)) +
scale_y_continuous(labels = percent_format()) +
labs(title = str_glue("<b style='color: orange;'>{top_growth_school}</b>
showed large growth in math proficiency over the
last two years")) +
theme_minimal() +
theme(axis.title = element_blank(),
legend.position = "none",
plot.title = element_markdown(),
plot.title.position = "plot",
panel.grid = element_blank())
Your Turn
Add a descriptive title to your plot
Use color strategically in your title using the
ggtext
packageAlign your title all the way to the edge of the plot
Learn More
To learn more about ggtext
, check out both the package documentation and this presentation by Cara Thompson.
To learn more about why you should align your plot title all the way to the edge, see this talk by Axios data journalist Will Chase.
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