---
title: "Mapping BLS Data"
author: "Kris Eberwein"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Mapping BLS Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Mapping County Data

County data mapped on a national scale can sometimes be difficult to see. The `get_bls_county()` and `bls_map_county()` functions both have arguments that will accommodate data and maps for specific states or groups of states.

The example below shows the most recent unemployment rates for Florida.

```{r eval=FALSE}
library(blscrapeR)
# Map the unemployment rate for the Southeastern United States.
df <- get_bls_county(stateName = "Florida")

bls_map_county(map_data=df, fill_rate = "unemployed_rate", 
               stateName = "Florida")
```

![](https://www.datascienceriot.com/wp-content/uploads/2016/07/bus_fl_map.png)

Also, the states can be called together to form larger districts. Below is an example of the most recent unemployment rates for the southeastern states of Florida, Georgia and Alabama.

```{r eval=FALSE}
# Map the unemployment rate for the Southeastern United States.
df <- get_bls_county(stateName = c("Florida", "Georgia", "Alabama"))

bls_map_county(map_data=df, fill_rate = "unemployed_rate", 
stateName = c("Florida", "Georgia", "Alabama"))
```

![](https://www.datascienceriot.com/wp-content/uploads/2016/07/bus_south_map.png)

## Build Your Own Maps

If you don't like any of the pre-fabricated maps, that's alright because `blscrapeR` provides the fortified map data, which includes `longitude`, `latitude` and `FIPS` codes. This data set is suitable for any kind of `ggplot2` map you can think of.

####First, call the internal map data and have a look:
```{r}
library(blscrapeR)
us_map <- county_map_data
head(us_map)
```

Notice the `id` column looks a lot like one of the FIPS codes returned by the `get_bls_county()` function? This is actually a concatenation of the state + county FIPS codes. The first two numbers are the state FIPS and the last four are the county FIPS. These boundaries currently represent 20015/2016 and will be updated accordingly so they always represent the current year.

####Next, produce your custom map:
```{r eval=FALSE}
library(blscrapeR)
library(ggplot2)
# Get the most recent unemployment rate for each county on a national level.
df <- get_bls_county()
# Get map data
us_map <- county_map_data

# Insert larger breaks in unemployment rates
df$rate_d <- cut(df$unemployed_rate, breaks = c(seq(0, 10, by = 2), 35))
# Plot
ggplot() +
    geom_map(data=us_map, map=us_map,
             aes(x=long, y=lat, map_id=id, group=group),
             fill="#ffffff", color="#0e0e0e", size=0.15) +
    geom_map(data=df, map=us_map, aes_string(map_id="fips", fill=df$rate_d),
             color="#0e0e0e", size=0.15) +
    scale_fill_brewer()+
    coord_equal() +
    theme(axis.line=element_blank(),
          axis.text.x=element_blank(),
          axis.text.y=element_blank(),
          axis.ticks=element_blank(),
          axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title=element_blank())

```

![](https://www.datascienceriot.com/wp-content/uploads/2016/07/bls_custom_map.png)


## More Advanced Mapping

What's R mapping without some interactivity? Below we’re using two additional packages, `leaflet`, which is popular for creating interactive maps and `tigris`, which allows us to download the exact shape files we need for these data and includes a few other nice tools.

```{r eval=FALSE}
# Leaflet map
library(blscrapeR)
library(tigris)
library(leaflet)
map.shape <- counties(cb = TRUE, year = 2015)
df <- get_bls_county()

# Slice the df down to only the variables we need and rename "fips" colunm
# so I can get a cleaner merge later.
df <- subset(df, select = c("unemployed_rate", "fips"))
colnames(df) <- c("unemployed_rate", "GEOID")

# Merge df with spatial object.
leafmap <- geo_join(map.shape, df, by="GEOID")

# Format popup data for leaflet map.
popup_dat <- paste0("<strong>County: </strong>", 
                    leafmap$NAME, 
                    "<br><strong>Value: </strong>", 
                    leafmap$unemployed_rate)

pal <- colorQuantile("YlOrRd", NULL, n = 20)
# Render final map in leaflet.
leaflet(data = leafmap) %>% addTiles() %>%
    addPolygons(fillColor = ~pal(unemployed_rate), 
                fillOpacity = 0.8, 
                color = "#BDBDC3", 
                weight = 1,
                popup = popup_dat)

```
**Note:** This is just a static image since the full map would not be as portable for the purpose of documentation.

![](https://www.datascienceriot.com/wp-content/uploads/2016/07/blscrape_docfig3-1.png)
