Plotting sped data for answers

Goals of this notebook

• Introduce datatables() from the DT package
• Practice pivots to prepare data for plotting
• Practice plots to reveal insights in data
• Wrap-up assignments that include writing, charts and this analysis

Questions to answer

• Has the percentage of special education students in Texas changed since the benchmarking policy was dropped?
• How many districts were above that arbitrary 8.5% benchmark before and after the changes?
• How have local districts changed?

Setup

Loading the libraries

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.5.0 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(scales)

## 
## Attaching package: 'scales'
## 
## The following object is masked from 'package:purrr':
## 
##     discard
## 
## The following object is masked from 'package:readr':
## 
##     col_factor

library(DT)

Importing cleaned data

Importing the cleaned data

sped <- read_rds("data-processed/01-sped.rds")

sped %>% head()

Creating a table of district percents

Pivoting wider so that each year is its own column

district_percents_data <- sped |> 
  select(distname, cntyname, year, sped_percent) |> 
  pivot_wider(names_from = year, values_from = sped_percent)

district_percents_data |> 
  datatable()

Finding yearly percentages

Has the percentage of special education students in Texas changed since the benchmarking policy was dropped?

yearly_percent <- sped |> 
  group_by(year) |> 
  summarise(
    total_students = sum(all_count),
    total_sped = sum(sped_count)
  ) |> 
  mutate(sped_percent = ((total_sped / total_students) * 100) |> round(1))

yearly_percent

yearly_percent |> 
  ggplot(aes(x = year, y = sped_percent)) + 
  geom_col() +
  geom_text(aes(label = sped_percent, vjust = -.5))

Counting districts by the audit flag

How many districts were above that arbitrary 8.5% benchmark before and after the changes?

flag_count_districts <- sped %>%
  group_by(year, audit_flag) %>%
  summarize("count_districts" = n())

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

flag_count_districts

Building the first exploratory chart

flag_count_districts |> 
  ggplot(aes(x = year, y = count_districts, fill = audit_flag)) +
  geom_col()

Building the grouped column version

flag_count_districts |> 
  ggplot(aes(x = year, y = count_districts, fill = audit_flag)) +
  geom_col(position = "dodge")

Building the line chart

flag_count_districts |> 
  ggplot(aes(x = year, y = count_districts, group = audit_flag)) +
  geom_line(aes(color = audit_flag)) +
  ylim(0,1000)

Looking at changes in local districts

How have local districts changed?

sped %>%
  filter(cntyname == "BASTROP") %>%
  ggplot(aes(x = year, y = sped_percent, group = distname)) +
  geom_line(aes(color = distname))

sped %>%
  filter(cntyname == "HAYS") %>%
  ggplot(aes(x = year, y = sped_percent, group = distname)) +
  geom_line(aes(color = distname))

sped %>%
  filter(cntyname == "TRAVIS") %>%
  ggplot(aes(x = year, y = sped_percent, group = distname)) +
  geom_line(aes(color = distname))

sped %>%
  filter(cntyname == "WILLIAMSON") %>%
  ggplot(aes(x = year, y = sped_percent, group = distname)) +
  geom_line(aes(color = distname))