AnalysisNCES.qmd

---
title: "AnalysisNCES"
format: html
---


## Setup

```{r}
#| label: setup
#| message: false
#| warning: false

library(tidyverse)
library(janitor)
library(scales)
library(sf)
library(mapview)
library(RColorBrewer)

```

```{r}
teaching_staff_totals <- read_rds("data-processed/teaching_staff_totals.rds")
salary_col_comparison <- read_rds("data-processed/salary_col_comparison.rds")
```


## NCES District Types 

We decided not to use this so I moved the analysis I already did with NCES district types down here and didn't continue to use it.

### Count and Average Pay for NCES

```{r}
teaching_staff_totals |> group_by(nces_description) |> count()
```

Finding average pay per school year for each NCES district type.

```{r}
district_types_nces_chart <- teaching_staff_totals |> group_by(nces_description, school_year) |> 
  summarize(avg_pay = mean(average_base_pay))

district_types_nces_chart
```

Graphing it. 

```{r}
ggplot(district_types_nces_chart, aes(x = school_year, y = avg_pay, color = nces_description, group = nces_description)) +
  geom_point() +
  geom_line() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(nrow = 4, byrow = TRUE))
```

### Average and Median of difference between salary and ALICE for each NCES district type

```{r}
salary_col_comparison |> group_by(nces_description) |> 
  summarize(avg_difference = mean(difference),
            median_difference = median(difference),
            number_of_districts = n()) |> 
  arrange(avg_difference)
```

```{r}
avg_difference_over_time_nces <- salary_col_comparison |> group_by(nces_description, end_year) |> 
  summarize(avg_difference = mean(difference))

avg_difference_over_time_nces
```

```{r}
ggplot(avg_difference_over_time_nces, 
       aes(x = end_year, y = avg_difference, color = nces_description, group = nces_description)) + 
  geom_line() +
  scale_y_continuous(limits = c(-10000,5000), n.breaks = 10)
```