Customize Your Fonts

This lesson is called Customize Your Fonts, part of the Going Deeper with R course. This lesson is called Customize Your Fonts, 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(family = "IBM Plex Mono"),
          plot.title.position = "plot",
          panel.grid = element_blank(),
          axis.text = element_text(color = "grey60",
                                   family = "IBM Plex Mono",
                                   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,
                  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(add = c(0.05, 0.5))) +
  annotate(geom = "text",
           x = 2.02,
           y = 0.6,
           hjust = 0,
           lineheight = 0.9,
           family = "IBM Plex Mono",
           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

Solution

# 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/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",
  )) |> 
  mutate(pct_formatted = percent(pct, 
                                 accuracy = 1))


# Theme -------------------------------------------------------------------

theme_student <- function() {
  
  theme_minimal() +
    theme(axis.title = element_blank(),
          plot.title = element_markdown(family = "Inter Tight"),
          plot.title.position = "plot",
          panel.grid = element_blank(),
          axis.text = element_text(color = "grey60",
                                   family = "Inter Tight",
                                   size = 10),
          legend.position = "none")
  
}

# Plot --------------------------------------------------------------------

top_growth_district <- 
  enrollment_by_race_ethnicity |> 
  filter(race_ethnicity == "Hispanic/Latino") |> 
  group_by(district) |> 
  mutate(growth_from_previous_year = pct - lag(pct)) |> 
  ungroup() |> 
  drop_na(growth_from_previous_year) |>
  slice_max(order_by = growth_from_previous_year,
            n = 1) |> 
  pull(district)

enrollment_by_race_ethnicity |> 
  filter(race_ethnicity == "Hispanic/Latino") |> 
  mutate(highlight_district = case_when(
    district == top_growth_district ~ "Y",
    .default = "N"
  )) |> 
  mutate(pct_formatted = case_when(
    highlight_district == "Y" & year == "2022-2023" ~ str_glue("{pct_formatted} of students
                                                               were Hispanic/Latino
                                                               in {year}"),
    highlight_district == "Y" & year == "2021-2022" ~ pct_formatted,
    .default = NA
  )) |>
  mutate(district = fct_relevel(district, top_growth_district, after = Inf)) |>
  ggplot(aes(x = year, 
             y = pct,
             group = district,
             color = highlight_district,
             label = pct_formatted)) +
  geom_line() +
  geom_text_repel(hjust = 0,
                  lineheight = 0.9,
                  family = "Inter Tight",
                  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 = 0.4,
           hjust = 0,
           lineheight = 0.9,
           family = "Inter Tight",
           color = "grey70",
           label = str_glue("Each grey line
                            represents one district")) +
  labs(title = str_glue("<b style='color: orange;'>{top_growth_district}</b> saw a large growth in its Hispanic/Latino student population over the last two years")) +
  theme_student()

Set you system up to use custom fonts using the ragg and systemfonts packages.
Apply a custom font to your plot.
Save your plot using ggsave()and add your plot to a Quarto document to make sure that it renders correctly there.

Learn More

Cara Thompson has a great article on getting fonts to work in R.

If you want to go really deep on custom fonts in R, check out this article by June Choe.

Have any questions? Put them below and we will help you out!

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Karina Wheeler

November 16, 2023

When I downloaded the systemfonts package, I do not have the IBM Plex Mono or Inter Tight font. I do have a variety of other fonts. Do you know the reason why the fonts are different for me?

David Keyes Founder

November 16, 2023

I should have clarified. The systemfonts package does not install any fonts for you. It just gives you access to fonts that are on your system. If you wanted to work with the fonts I show in the video you could download them (click the Download Family button in the top right corner for each):

After that, you need to install the fonts on your system. After all of this, you can work with them in ggplot. Let me know if that works for you!

Going Deeper with R

Advanced Data Wrangling

Advanced Data Visualization

Quarto