answers-2_tidycensus.Rmd

---
title: "Exercise 2 Answers: Choropleths with tidycensus"
output: html_notebook
---

## Load Libraries
```{r loadlibraries}
library(tidyverse)
library(tidycensus)
library(sf)
library(leaflet)
library(mapview)
```

## Set API key

Insert your own API Key.  You may request a free key from the Census API tool https://api.census.gov/data/key_signup.html

```{r}
#census_api_key("did you enter your API key here?")
```

## Choose a census variable

I recommend using one of the following variables.  However, you can use the `tidycensus::load_variables()` and follow the "Searching for variables" [instructions](https://walkerke.github.io/tidycensus/articles/basic-usage.html#searching-for-variables). 

- `B08103_001` - MEDIAN AGE BY MEANS OF TRANSPORTATION TO WORK: Total: Taxicab, motorcycle, bicycle, or other means: Workers 16 years and over -- (Estimate)
- `B08131_001`	- AGGREGATE TRAVEL TIME TO WORK (IN MINUTES) OF WORKERS BY PLACE OF WORK--STATE AND COUNTY LEVEL: Worked in State of residence: Workers 16 years and over who did not work at home -- (Estimate)
- `B19013_001` - median household income




## Median Age by Means of Transportation to Work

- B08103_001E

### get_acs()

Load the variable and assign and object name for some USA county using the `get_acs` function.

```{r get_hh_inc}
MedianAge_Commuter <- 
  get_acs(geography = "county",
          variables = c("Median Age of Commuter" = "B08103_001"),
          state = "NC",
          geometry = TRUE)

MedianAge_Commuter
```


### Make choropleth 

Make choropleth via `mapview` by filling county polygons (census geography) with correlated value (from the ACS)


```{r make_choropleth}
mapview(MedianAge_Commuter, zcol = "estimate")
```

## Travel Time

- B08131_001E

AGGREGATE TRAVEL TIME TO WORK (IN MINUTES)

```{r travel_variable}
travel_time <- 
  get_acs(geography = "county",
          variables = "B08131_001",
          state = "NC",
          geometry = TRUE)

travel_time
```

AGGREGATE TRAVEL TIME TO WORK (IN MINUTES)

Convert Minutes to hours, hours to months.

```{r convert_time}
travel_time <-  travel_time %>% 
  mutate(Months = estimate / 60 / 730.0008)

```

```{r}
mapview(travel_time, zcol = "Months")
```

## Median Household Income in the Past 12 Months

- B19013_001E

```{r get_hh_inco}
census_variable <- 
  get_acs(geography = "county",
          variables = "B19013_001",
          state = "NC",
          geometry = TRUE)

census_variable
```


```{r}
mapview(census_variable, zcol = "estimate")
```