Inline R Code

---
title: "Portland Public Schools Math Proficiency Report"
format: 
  html:
    fig-height: 10
    fig-width: 5
    toc: true
    toc-location: left
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-height: 5
#| fig-alt: A line chart showing math proficiency rates among all PPS schools in 2018-2019 and 2021-2022

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()
```

```{r}
top_growth_school_percents <-
  third_grade_math_proficiency |> 
  filter(school == top_growth_school) |> 
  mutate(percent_proficient_formatted = percent(percent_proficient)) |> 
  pull(percent_proficient_formatted)
```


# Table

The following table shows math proficiency in the last two years for five key schools. If you want to see `r top_growth_school`, the school with the greatest growth (going from `r top_growth_school_percents[1]` to `r top_growth_school_percents[2]`), you can use the search bar to find it.

```{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
  )
```