Get access to all lessons in this course.
-
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
Advanced YAML and Code Chunk Options
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This lesson is called Advanced YAML and Code Chunk Options, part of the Going Deeper with R course. This lesson is called Advanced YAML and Code Chunk Options, part of the Going Deeper with R course.
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Transcript
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View code shown in video
---
title: "Advanced YAML and Code Chunk Options"
format:
html:
fig-height: 5
fig-width: 5
toc: true
toc-location: right
toc-depth: 1
execute:
echo: false
warning: false
message: false
editor_options:
chunk_output_type: console
---
```{r}
library(tidyverse)
library(fs)
library(scales)
library(ggrepel)
library(ggtext)
library(ragg)
library(here)
library(gt)
```
```{r}
third_grade_math_proficiency <-
read_rds(here("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))
```
```{r}
theme_dk <- function() {
theme_minimal(base_family = "IBM Plex Mono") +
theme(axis.title = element_blank(),
axis.text = element_text(color = "grey60",
size = 10),
plot.title = element_markdown(),
plot.title.position = "plot",
panel.grid = element_blank(),
legend.position = "none")
}
```
## Chart
The chart below shows math proficiency for all PPS schools.
```{r}
#| fig-alt: A line chart showing math proficiency rates among all PPS schools in 2018-2019 and 2021-2022
#| fig-cap: A line chart showing math proficiency rates among all PPS schools in 2018-2019 and 2021-2022
#| fig-height: 10
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,
family = "IBM Plex Mono",
direction = "x") +
scale_color_manual(values = c(
"N" = "grey90",
"Y" = "orange"
)) +
scale_y_continuous(labels = percent_format()) +
scale_x_discrete(expand = expansion(mult = c(0.05, 0.5))) +
annotate(geom = "text",
x = 2.02,
y = 0.6,
hjust = 0,
lineheight = 0.9,
color = "grey70",
family = "IBM Plex Mono",
label = str_glue("Each grey line
represents one school")) +
labs(title = str_glue("<b style='color: orange;'>{top_growth_school}</b> showed large growth<br>in math proficiency over the last two years")) +
theme_dk()
```
# Table
The following table shows math proficiency in the last two years for five key schools.
```{r}
gt_data_interactive <-
read_rds(here("data/third_grade_math_proficiency_dichotomous.rds")) |>
filter(district == "Portland SD 1J") |>
select(year, school, percent_proficient) |>
arrange(school) |>
pivot_wider(id_cols = school,
names_from = year,
values_from = percent_proficient)
gt_data_interactive |>
gt() |>
cols_label(school = "School") |>
cols_width(
school ~ pct(50)
) |>
cols_align(
columns = `2018-2019`,
align = "center"
) |>
fmt_percent(
columns = 2:3,
decimals = 0
) |>
tab_caption("Math proficiency among third graders in five Portland schools") |>
opt_interactive(
use_search = TRUE,
use_highlight = TRUE
)
```
Your Turn
Working in the Quarto document you created in the last lesson:
Add a table of contents and adjust where it goes
Set default figure width and height (you'll need to add a plot before you do so)
Change the figure width and height in an individual code chunk
Add a figure caption and alt text
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