031_thematic_mapping.qmd

---
title: "Thematic Mapping with tmap"
---

```{r}
#| warning: false
#| message: false
library(tidyverse)       # Tidyverse for Tidy Data
library(readxl)
library(tmap)            # Thematic Mapping
library(tmaptools) 
library(tigris)          # Get Census Geography Poloygons
library(sf)
```

## Shapefiles as sf

Using the `tigris` package, get Census Tiger shapefiles for census geographies. Tigris will return the shapefile in the `sf`, or simple features, format.

```         
us_geo <- tigris::states(class = "sf") 
```

```{r}
#| message: false
#| warning: false
#| echo: true
#| results: false
us_geo <- tigris::states(class = "sf") 
```

## Data Structure

The "Simple Features" (`sf`) data structure can easily be viewed and manipulated as a rectangular data frame, before visualizing. As an historical note -- an `sf` predecessor -- the `sp` data structure uses `@data` slots to hold data. We'll focus on the `sf` package. Below are two methods of viewing the structure of the downloaded shapefiles.

```{r}
class(us_geo)
glimpse(us_geo)
```

## sf as data frame

And as noted, here's the data frame view. Notice the geometry (polygon shape) in the far-right column of the data frame.

```{r}
as_tibble(us_geo)
```

### Quick Plotting

If you want to see a very quick view of your mapping data, you can plot the geometry data with the `plot` function. In this case we use the `sf::st_geometry()` function to plot only the geometry. You can quickly generate a faceted map by excluding the `st_geometry` function: e.g. `plot(us_geo)` but that will consume computation cycles (i.e. wait time). Therefore, I recommend trying the smaller layer for now.

> **Note:** Census geography for the USA will span the globe in part becuase Region 9 includes a multitude of pacific islands. Later we will limit to simply the "lower 48" states.

```{r}
plot(st_geometry(us_geo))
```

## Get BLS data

I've already downloaded and stored some data from the Bureau of Labor Statistics. Those data are stored in an excel file in the `data` directory of the [repository](https://github.com/libjohn/mapping-with-R): `data/OES_Report.xlsx`. **The goal is to attach this data to the previously downloaded shapefiles.**

But you may be interested in how I gathered the data. below are some summary notes documenting my steps of gathering the data from the Bureau of Labor Statistics.

https://data.bls.gov/oes/#/occGeo/One%20occupation%20for%20multiple%20geographical%20areas

-   One occupation for multiple geographical areas

    -   Mental Health and Substance Abuse Social Workers

        -   State

        -   All States in this list

        -   Annual Mean wage

            -   Excel

-   Read the Data in with the RStudio "Import Dataset" wizard available in the *Environment* tab. This will generate the code below and ensure the import

    -   Skips the first 4 lines
    -   Coerces the 2nd column to numeric

```{r}
#| message: false
#| warning: false
#| echo: true
Salary4Helpers <- 
  read_excel("data/OES_Report.xlsx",
             col_types = c("text", "numeric"), 
             skip = 4)

Salary4Helpers
```

## Wrangle the data

Before we join the BLS data to the shapefile we need to transform the structure of the downloaded BLS data

```{r}
BlsWage_ToJoin <- Salary4Helpers %>% 
  rename(Area = "Area Name") %>% 
  rename(wages = "Annual mean wage(2)") %>% 
  mutate(State = gsub("\\(\\d{7}\\)", "", Area)) %>% 
  filter(wages != "NA_character_") %>% 
  select(State, wages)

#BlsWage_ToJoin
```

## Join data

Use the `dplyr::left_join()` function or the base-R `merge()` function to append BLS data to the previously loaded shape object

```{r}
HelperShapeObject <- left_join(us_geo, BlsWage_ToJoin, by = c("NAME" = "State"))

as_tibble(HelperShapeObject)
```

## Quick Thematic Map

```{r}
qtm(HelperShapeObject, fill = "wages")
```

## 50 states

Filter to only the contiguous 50 states + D.C. Note that `tigris::shift_geometry()` will shift and resize Alaska and Hawaii.

```{r}
contiguous_states <- HelperShapeObject %>% 
  filter(REGION != 9) %>% 
  shift_geometry()
```

## Make Choropleth

```{r}
tm_shape(contiguous_states) +
  tm_polygons("wages", id = "Name")
```

## Projection

Mark likes the USA_Contiguous_Albers_Equal_Area_Conic_USGS_version projection for the continental US. `EPSG:5070`

```{r}
contiguous_states %>% 
  st_transform(5070) %>% 
  tm_shape() +
  tm_polygons("wages", id = "Name") 
```

### Alternative Syntax

```{r}
tm_shape(contiguous_states, projection = 5070) +
  tm_polygons("wages", id = "Name")
```

### Explore `tmap` syntax and functions

```{r}
tm_shape(contiguous_states, projection = 5070) +
  tm_borders(col = "black", alpha = 0.4) +
  tm_fill(col = "REGION", alpha = 0.6) +
  tm_layout(title = "Regions of the USA", 
                  attr.color = "navy", 
                  title.position = c("center", "top"), 
                  title.bg.color = "lightblue")
```

## End Notes

This session inspired by https://www.computerworld.com/article/3175623/data-analytics/mapping-in-r-just-got-a-whole-lot-easier.html