Making Your Reports Shine: PDF Edition

---
title: "Portland Public Schools Math Proficiency Report"
format: 
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    margin:
      top: 1in
execute: 
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---

```{r}
library(tidyverse)
library(here)
library(flextable)
library(gt)
library(scales)
library(marquee)
library(ggrepel)
```

# Introduction

![Portland Public Schools](portland-public-schools-logo.svg){width=300px fig-align="center" fig-alt="Portland Public Schools logo"}

This is a report on math proficiency results in [Portland Public Schools (PPS)](https://www.pps.net/). The PPS mission statement is as follows:

> We provide rigorous, high-quality academic learning experiences that are inclusive and joyful. We disrupt racial inequities to create vibrant environments for every student to demonstrate excellence.^[https://www.pps.net/about/portland-public-schools-information/overview]

Hello hello hello!

# Plot

```{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)

```

```{r}
theme_dk <- function() {
  theme_minimal(base_family = "Geist") +
    theme(
      axis.title = element_blank(),
      legend.position = "none",
      panel.grid = element_blank(),
      plot.title = element_marquee(width = 1),
      plot.title.position = "plot"
    )
}
```


```{r}
#| fig-alt: Chart showing growth in math proficiency for PPS schools from 2018-2019 to 2021-2022
#| fig-cap: Chart showing growth in math proficiency for PPS schools from 2018-2019 to 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() |>
  slice_max(
    order_by = growth_from_previous_year,
    n = 1
  ) |>
  pull(school)


plot_title <-
  marquee_glue(
    "{.orange **{top_growth_school}**} showed large growth 
        in math proficiency over the last two years"
  )

third_grade_math_proficiency |>
  filter(district == "Portland SD 1J") |>
  mutate(
    highlight_school = case_when(
      school == top_growth_school ~ "Y",
      .default = "N"
    )
  ) |>
  mutate(
    school = fct_relevel(
      school,
      top_growth_school,
      after = Inf
    )
  ) |>
  mutate(
    percent_proficient_formatted = 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
    )
  ) |>
  ggplot(
    aes(
      x = year,
      y = percent_proficient,
      color = highlight_school,
      group = school,
      label = percent_proficient_formatted
    )
  ) +
  geom_line() +
  geom_text_repel(
    hjust = 0,
    lineheight = 0.9,
    direction = "x",
    family = "Geist"
  ) +
  scale_color_manual(
    values = c(
      "Y" = "orange",
      "N" = "gray80"
    )
  ) +
  scale_x_discrete(
    expand = expansion(add = c(0, 0.5))
  ) +
  scale_y_continuous(
    labels = percent_format(),
    limits = c(0, 1)
    # expand = expansion(add = c(0.1, 0.2))
  ) +
  annotate(
    geom = "text",
    x = 2.02,
    y = 0.6,
    hjust = 0,
    lineheight = 0.9,
    color = "gray70",
    label = str_glue(
      "Each gray line
    represents one
    school"
    )
  ) +
  labs(
    title = plot_title
  ) +
  theme_dk()
```

```{r}
third_grade_math_proficiency_wide <-
  read_rds(here("data/third_grade_math_proficiency_dichotomous.rds")) |>
  filter(district == "Portland SD 1J") |>
  filter(
    school %in%
      c(
        "Abernethy Elementary School",
        "Ainsworth Elementary School",
        "Alameda Elementary School",
        "Arleta Elementary School",
        "Atkinson Elementary School"
      )
  ) |>
  select(year, school, percent_proficient) |>
  arrange(school) |>
  pivot_wider(
    id_cols = school,
    names_from = year,
    values_from = percent_proficient
  )
```

{{< pagebreak >}}

# Table

The following table shows math proficiency in 2018-2019 and 2021-2022 for all PPS schools. If you want to see `r top_growth_school`, you can use the search bar to find it.

```{r}
third_grade_math_proficiency_wide_full <-
  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
  )
```

```{r}
third_grade_math_proficiency_wide_full |>
  gt() |>
  cols_label(school = "School") |>
  fmt_percent(
    columns = 2:3,
    decimals = 0
  )
```

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