Map3.qmd

---
title: "Map3"
format: html
---

## Setup

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

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

```

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

```{r}
school_districts <- st_read("data-raw/Current_Districts_2025/Current_Districts_2025.shp") 

school_districts |> glimpse()
```

```{r}
above_below_alice |> filter(district_number == "123913")

simple_alice <- above_below_alice |> mutate(
   tea_description = factor(tea_description, levels = c("Rural", "Non-metropolitan Stable", "Non-metropolitan Fast Growing", "Independent Town", "Other Central City Suburban", "Other Central City", "Major Suburban", "Major Urban"))
 ) |> 
  ungroup() |> 
  filter(end_year == 2024) |> 
  select(district_number, tea_description, nces_description, bill_category, difference) 

simple_alice
```

Now I will join that simplified dataframe with the shape file information.

```{r}
joined_districts <- school_districts |> left_join(simple_alice, by = c("DISTRICT_C" = "district_number"))

joined_districts |> glimpse()
```

Now I want to group the difference column into categories so we can see it better.

```{r}
difference_categories <- joined_districts |> mutate(
  difference_category = case_when(
    difference < -10000 ~ "less than -10,000",
    difference <= -5000 ~ "-10,000 to -5,000",
    difference <= 0 ~ "-5,000 to 0",
    difference <= 5000 ~"0 to 5,000",
    difference <= 10000 ~ "5,000 to 10,000",
    difference > 10000 ~ "greater than 10,000"
  )
) |> mutate(
  difference_category = difference_category |> factor(levels = c("less than -10,000","-10,000 to -5,000","-5,000 to 0","0 to 5,000","5,000 to 10,000","greater than 10,000"))
)

difference_categories |> glimpse()
```

Now, I can map the differences based on those categories. 

```{r}
mapview(difference_categories, zcol = "difference_category", col.regions=brewer.pal(6, "RdYlBu"))
```