index.qmd

---
title: "School Corporation Opportunity Score Dashboard for Indiana"
author:
  - name: Jeremy Price 
    url: https://www.jeremyfprice.info/
    affiliation: CEISL / IU School of Education-Indianapolis
    affiliation_url: https://education.iupui.edu/
    orcid: 0000-0002-6506-3526
  - name: Akaash Arora
    affiliation: CEISL / IU School of Education-Indianapolis
    affiliation_url: https://education.iupui.edu/
  - name: Maxim Bulanov
    affiliation: CEISL / IU School of Education-Indianapolis
    affiliation_url: https://education.iupui.edu/
  - name: AJ Knoors
    affiliation: CEISL / IU School of Education-Indianapolis
    affiliation_url: https://education.iupui.edu/
license: "CC BY-SA"
toc: true
citation:
  type: report
  issued: 2023
  url: https://github.com/jeremyfprice/SCOscore-dashboard
  doi: 10.17605/OSF.IO/CHJ38
repo-url: https://github.com/jeremyfprice/SCOscore-dashboard
mainfont: spectral
sansfont: rubik
monofont: "JetBrains Mono"
format: html
highlight-style: a11y
code-overflow: wrap
google-scholar: true
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  comment = "#>",
  echo = FALSE,
  message = FALSE,
  warning = FALSE
)
```

```{r load-libraries}
library(readr)
library(tidyr)
library(dplyr)
library(rio)
library(DT)
library(ggplot2)
library(ggpubr)
library(plotly)
library(ggbeeswarm)
library(edbuildmapr)
library(maps)
library(mapdata)
library(ggmap)
library(ggrepel)
library(sf)
library(showtext)

ucl_palette <- c(
  "City large" = "#990000",
  "City midsize" = "#F23A3F",
  "City small" = "#FF636A",
  "Suburb large" = "#FFAA00",
  "Suburb midsize" = "#FFD563",
  "Suburb small" = "#FFE694",
  "Town fringe" = "#056E41",
  "Town distant" = "#63B363",
  "Town remote" = "#A7D094",
  "Rural fringe" = "#006298",
  "Rural distant" = "#63B1D3",
  "Rural remote" = "#94D2E7"
)
```

```{r load-fonts, echo = FALSE, include=FALSE}
font_add_google("Rubik", "rubik", regular.wt = 700)
font_add_google("JetBrains Mono", "jetbrains", regular.wt = 700)
font_add_google("Spectral", "spectral", regular.wt = 700)
```

```{r read-data}
SCO_frame <- import("https://osf.io/download/dkjn6/", format = "csv") |>
  na.omit()

SCO_frame$urm_pct <- round(SCO_frame$urm_pct, digits = 2)
SCO_frame$frl_pct = round(SCO_frame$frl_pct, digits = 2)
SCO_frame$academic = round(SCO_frame$academic, digits = 2)
SCO_frame$scoScore = round(SCO_frame$scoScore, digits = 2)
```

## Introduction

The School Corporation Opportunity Score (SCOscore) is a measure of potentiality for learners in a
school community. It is a composite score that combines multiple factors, particularly structural
factors such as race and SES, as well as performance factors such as test scores and graduation
pathway completion rates, to provide a more holistic view of school corporations.

For more information, see the [documentation](https://ceisl-stem.github.io/scoscore/).


```{r prep-map, message = FALSE, warning=FALSE, output = FALSE}
states <- sf::st_as_sf(map("state", region = "indiana", plot = FALSE, fill = TRUE))

in_sc_shapes <- sd_shapepull("2019", with_data = TRUE) |>
  filter(State == "Indiana") |>
  select(leaid = GEOID, geometry) |>
  mutate(leaid = as.numeric(leaid))

SCO_frame <- SCO_frame |>
  right_join(in_sc_shapes)

school_corp_map <- st_as_sf(SCO_frame)

sco_map <- ggplot(data = states) +
  geom_sf(data = states, fill = "#eeeeee") +
  geom_sf(data = school_corp_map, color = "#243142", aes(
    label = lea_name,
    fill = scoScore)) +
  scale_fill_steps2(
    name = "SCOscore",
    low = "#FFF4C6",
    high = "#990000",
    mid = "#FFAA00",
    midpoint = 1.1,
    breaks = c(0, 0.6, 0.8, 1, 1.2, 1.4)
  ) +
  coord_sf(xlim = c(-88.5, -84.5), ylim = c(37.5, 42), expand = FALSE) +
  theme_minimal()  +
  theme(panel.grid.major = element_blank(),
        axis.text = element_blank(),
        axis.title = element_blank())
```

## Table of School Corporations

The following is a table with all of the school corporations in Indiana. You can
sort and search to identify trends or a specific school corporation.

```{r}

SCO_display_frame <- SCO_frame |>
  select(lea_name, urban_centric_locale, enrollment, urm_pct, frl_pct, academic, scoScore) |>
  arrange(desc(scoScore), desc(enrollment), lea_name) |>
  na.omit()
#  select(-enrollment)

datatable(
  SCO_display_frame,
  rownames = FALSE,
  colnames = c(
    "Corporation",
    "Locale",
    "Enrollment",
    "%URM",
    "%FRL",
    "AcScore",
    "SCOscore"
    ),
  options = list(
  pageLength = 5)
  ) |>
  formatStyle(columns = c(2), fontSize = "90%")

```

## Map of Indiana School Corporations

The following is a map of the Indiana school corporations. You may tap or hover over
a school corporation to see the SCOscore for the corporation.

```{r display-map, results = "asis"}
ggplotly(sco_map)
```

## SCOscores by NCES Urban-Centric Locale Category

The following is a plot of the SCOscores for Indiana school corporations by
NCES defined urban-centric locale categories. You may hover or click on any point
to see the data.

```{r plot-beeswarm, results = "asis"}
SCO_frame$urban_centric_locale <- factor(
  SCO_frame$urban_centric_locale,
  levels = c(
    "City large",
    "City midsize",
    "City small",
    "Suburb large",
    "Suburb midsize",
    "Suburb small",
    "Town fringe",
    "Town distant",
    "Town remote",
    "Rural fringe",
    "Rural distant",
    "Rural remote"
  )
)
urcl_swarm <- ggplot(
  SCO_frame,
  aes(
    label = lea_name,
    x = urban_centric_locale,
    y = scoScore,
    color = scoScore
  )
) +
  geom_beeswarm(cex = 1.5, corral = "wrap", shape = 18, size = 1) + #, method = "center"
  annotate("segment", x = 0, xend = 12.5, y = 1, yend = 1, color = "#A7A9AB",
           linetype = "dotted") +
  scale_color_steps2(
    low = "#FFF4C6",
    high = "#990000",
    mid = "#FFAA00",
    midpoint = 1.1,
    breaks = c(0, 0.6, 0.8, 1, 1.2, 1.4)
  ) +
  ylim(0, 2) +
  ylab("School Corporation Opportunity Score") +
  xlab("NCES Urban-Centric Locale Category") +
  theme_pubr() +
  theme(
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
    legend.position = "none"
  )
ggplotly(urcl_swarm)
```

## Data Sources

Data for these calculations are drawn from the following sources:

* Indiana Department of Education's [Indiana Graduates Prepared to Succeed (GPS)](https://indianagps.doe.in.gov/)
* Urban Institute's [Education Data Explorer](https://educationdata.urban.org/data-explorer) via
the [educationdata](https://github.com/UrbanInstitute/education-data-package-r) R package

## Acknowledgments {.appendix}
This is a project of the Collaborative for Equitable and Inclusive STEM Learning (CEISL) at the IU School of Education-Indianapolis.