Use Annotations to Explain

# 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()) +
  annotate(geom = "text",
           x = 2.02,
           y = 0.6,
           hjust = 0,
           lineheight = 0.9,
           color = "grey80",
           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_minimal() +
  theme(axis.title = element_blank(),
        legend.position = "none",
        plot.title = element_markdown(),
        plot.title.position = "plot",
        panel.grid = element_blank())