Dashboards

---
title: "Report on Diversity in Oregon Schools"
author: "David Keyes"
date: "4/28/2020"
params:
  race_ethnicity_category: "Hispanic/Latino"
output:
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    orientation: rows
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      cache = TRUE)
```

```{r}
library(tidyverse)
library(scales)
library(ggtext)
library(extrafont)
library(ggalt)
library(janitor)
library(gt)

loadfonts()
```

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![](https://github.com/rfortherestofus/going-deeper/raw/master/slides/images/ode-logo.jpg)

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# Introduction

This is a report for the [Oregon Department of Education](https://www.oregon.gov/ode/pages/default.aspx) on diversity in Oregon school districts. Here is the mission statement for ODE:

> The Oregon Department of Education fosters equity and excellence for every learner through collaboration with educators, partners, and communities.^[Source: [ODE Website](https://www.oregon.gov/ode/about-us/Pages/default.aspx)]



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The table below shows the districts with the highest growth of their `r params$race_ethnicity_category` student population from 2017-2018 to 2018-2019. 


```{r}
enrollment_by_race_ethnicity <- read_rds("data/enrollment-by-race-ethnicity.rds")
```


```{r}
options(scipen=999)

enrollment_by_race_ethnicity %>% 
  filter(race_ethnicity == params$race_ethnicity_category) %>% 
  pivot_wider(id_cols = c(district, district_id),
              names_from = year,
              values_from = percent_of_total_at_school) %>% 
  mutate(growth = `2018-2019` - `2017-2018`) %>% 
  arrange(desc(growth)) %>% 
  select(-district_id) %>% 
  slice_max(growth, n = 10) %>% 
  gt() %>% 
  cols_label(
    district = "District",
    growth = "Growth"
  ) %>% 
  fmt_percent(
    columns = vars(`2017-2018`, `2018-2019`, growth),
    decimals = 1
  )


```


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-------------------------------------
```{r}
highlight_district <- enrollment_by_race_ethnicity %>% 
  filter(race_ethnicity == "Hispanic/Latino") %>% 
  filter(district == "Douglas ESD") %>% 
  mutate(percent_display = percent(percent_of_total_at_school, accuracy = 1)) %>% 
  mutate(percent_display = case_when(
    year == "2018-2019" ~ str_glue("{percent_display} Latino students"),
    TRUE ~ percent_display
  ))
```


```{r fig.cap="Test Caption", fig.width = 10}
theme_student <- function() {
  theme_minimal(base_family = "Courier New") +
    theme(axis.title = element_blank(),
          panel.grid.minor.y = element_blank(),
          plot.title = element_markdown(face = "bold"))
}

enrollment_by_race_ethnicity %>% 
  filter(race_ethnicity == "Hispanic/Latino") %>% 
  ggplot(aes(x = year,
             y = percent_of_total_at_school,
             group = district)) +
  geom_line(color = "lightgray",
            alpha = 0.5) +
  geom_line(data = highlight_district,
            inherit.aes = TRUE,
            color = "blue") +
  geom_text(data = highlight_district,
            inherit.aes = TRUE,
            aes(label = percent_display),
            hjust = c(1, 0),
            family = "Courier New",
            color = "blue") +
  scale_y_continuous(labels = percent_format(),
                     limits = c(0, 1)) +
  labs(title = "<span style = 'color: blue'>Douglas ESD</span> saw a large growth in its Latino student population from 2017-2018 to 2018-2019") +
  annotate("text",
           x = 2.02,
           y = .825,
           hjust = 0,
           color = "gray",
           family = "Courier New",
           label = "Woodburn has the highest\npercentage of Latino students\nof any district") +
  scale_x_discrete(expand = expansion(add = c(0.1, 0.5))) +
  theme_student()
```

Row {data-height=500}
-------------------------------------
```{r}
enrollment_by_race_ethnicity %>% 
  filter(race_ethnicity == "Hispanic/Latino") %>% 
  select(-c(race_ethnicity, number_of_students, district_id)) %>% 
  pivot_wider(id_cols = district,
              names_from = year,
              values_from = percent_of_total_at_school) %>% 
  clean_names() %>% 
  slice(1:10) %>% 
  ggplot(aes(x = x2017_2018,
             xend = x2018_2019,
             y = district,
             yend = district)) +
  geom_dumbbell(colour_x = "gray",
             colour = "gray",
             colour_xend = "blue",
             size_x = 3,
             size_xend = 1.5) +
  theme_student() +
  scale_x_continuous(label = percent_format())
```