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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)
-
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
Create a Custom Theme
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This lesson is called Create a Custom Theme, part of the Going Deeper with R course. This lesson is called Create a Custom Theme, part of the Going Deeper with R course.
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Transcript
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# 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
)) |>
mutate(percent_proficient_formatted = percent(percent_proficient,
accuracy = 1))
# Theme -------------------------------------------------------------------
theme_dk <- function() {
theme_minimal() +
theme(axis.title = element_blank(),
plot.title = element_markdown(),
plot.title.position = "plot",
panel.grid = element_blank(),
axis.text = element_text(color = "grey60",
size = 10),
legend.position = "none")
}
# 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(
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,
.default = NA
)) |>
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()) +
scale_x_discrete(expand = expansion(add = c(0.05, 0.5))) +
annotate(geom = "text",
x = 2.02,
y = 0.6,
hjust = 0,
lineheight = 0.9,
color = "grey70",
label = str_glue("Each grey line
represents one school")) +
labs(title = str_glue("<b style='color: orange;'>{top_growth_school}</b> showed large growth in math proficiency over the last two years")) +
theme_dk()
Your Turn
Make your own custom theme and apply it to your plot.
If you want to confirm that it works with other plots, copy it to another project and try it there.
Learn More
If you want to see a bunch of complete themes ready for your use, I've written a blog post that may be of interest to you. If you want to dive deep on creating custom themes, Andrew Heiss has some great materials here. This talk by Cara Thompson is also great.
If you have trouble, as I do, remembering the various theme elements, this blog post from Henry Wang has a nice overview to help you.
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