knitr::opts_chunk$set(cache = FALSE)

Introduction

This notebook illustrates data access through both tigris and tidycensus as well as joins using dplyr.

Dependencies

This notebook requires the following packages:

# tidyverse packages
library(dplyr)       # data wrangling

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
# spatial packages
library(mapview)     # preview geometric data
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
library(sf)          # spatial tools
Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1
library(tidycensus)  # demographic data
library(tigris)      # tiger/line data
To enable 
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

Attaching package: ‘tigris’

The following object is masked from ‘package:tidycensus’:

    fips_codes
# other packages
library(here)        # file path management
here() starts at /Users/prenercg/GitHub/slu-soc5650/module-2-combine-sources

tidycensus Set-up

Before using tidycensus, you need to install a census API key. Use the syntax below, copied into your console, to install the key you received via email.

census_api_key("KEY", install = TRUE)

This is not a code chunk you will need in each notebook. As long as install = TRUE, you will only have to do this once!

Decennial Census Data

Get List of Variables

To get a preview of variables available in the get_decennial() function, we can use the load_variables() function:

census <- load_variables(year = 2000, dataset = "sf1") 

I find it useful to assign the output of this function to an object so that I can search through it. Try searching for the variable P0010001, the total population of a geographic unit, in the census object.

Download a Single Variable

To download data, we can use use the get_decennial() function to access, for example, population by state in 2000:

popStates <- get_decennial(geography = "state", year = 2000, variable = "P001001")

A full list of the geographies available in tidycensus can be found here.

Download a Full Table

Most variables in the decennial census are actually a part of a table. There are individual variables, for example, for race:

census %>%
  filter(concept == "P3. RACE [8]")

We rarely want to download these one at a time. Instead, we want to download them at one time into a single data frame. The table number for these data is P003 - we take the first four characters from the name variable.

cityRace00 <- get_decennial(geography = "tract", year = 2000, state = 29,
                            county = "510", table = "P003", output = "wide")

We’ve used the FIPS codes for both Missouri (29) and St. Louis City (29510) here - you can find a full list of Missouri counties here.

Add Geometry

The tidycensus package also includes tools for downloading the geometries for these data as well. For instance, we can add geometric data to our previous call for City of St. Louis tract-level data on race by adding the geometry = TRUE argument:

## download
cityRace00 <- get_decennial(geography = "tract", year = 2000, state = 29,
                            county = "510", table = "P003", output = "wide",
                            geometry = TRUE)
Getting data from the 2000 decennial Census
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Loading SF1 variables for 2000 from table P003. To cache this dataset for faster access to Census tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per Census dataset.
Using Census Summary File 1
Using Census Summary File 1

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## preview
mapview(cityRace00, zcol = "P003005")

Notice how I used the zcol argument for mapview() to preview a specific set of data as a thematic layer on the map! These data are not normalized, but we do get a quick preview of the distribution of Asian residents in St. Louis City.

Decennial Census Data

Get List of Variables

To get a preview of variables available in the get_acs() function, we can use the load_variables() function again. We’ll use "acs5" for our dataset and, for this example, we’ll pull from the most recent 2019 ACS year:

census <- load_variables(year = 2019, dataset = "acs5") 

Try searching for the table B19013, the median household income table.

Get and Interpret ACS Data

We’ll illustrate get_acs() by using the data in table B19019. First, we’ll download these data as a full table for all counties in Missouri:

## download
countyIncome <- get_acs(geography = "county", year = 2019, state = 29,
                        table = "B19019", output = "wide", geometry = TRUE)
Getting data from the 2015-2019 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Loading ACS5 variables for 2019 from table B19019. To cache this dataset for faster access to ACS tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per ACS dataset.

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## preview
mapview(countyIncome, zcol = "B19019_001E")

Notice how we needed to specify _001E for zcol. That references the specific variable we want to map - variable 1 in the table’s estimate (or E). The M values refer to the margin of the error - we expect this estimate to be off by some amount within +/- this value.

We can also download a specific column, like the median income for one-person households (B19019_002):

## download
countyIncome <- get_acs(geography = "county", year = 2019, state = 29,
                        variables = "B19019_002", output = "wide", 
                        geometry = TRUE)
Getting data from the 2015-2019 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## preview
mapview(countyIncome, zcol = "B19019_002E")

Combining Data Sources

Perhaps we have a range of data that we want to include. For this example, we’ll download data on median income and the proportion of women in tracts in Boone County, Missouri. We’ll download the income data with geometry = TRUE and the sex data with geometry = FALSE:

## download
booneIncome <- get_acs(geography = "tract", year = 2019, state = 29,
                       county = "019", variables = "B19019_001", 
                       output = "wide", geometry = TRUE) %>%
  rename(median_income = B19019_001E) %>%
  select(GEOID, median_income)
Getting data from the 2015-2019 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

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## download
booneSex <- get_acs(geography = "tract", year = 2019, state = 29,
                       county = "019", variables = c("B01001_001", "B01001_026"),
                       output = "wide") %>%
  mutate(pct_women = B01001_026E/B01001_001E*100) %>%
  select(GEOID, pct_women)
Getting data from the 2015-2019 5-year ACS

To combine these data, we’ll use left_join() from dplyr. Our sf object should always be the first object in the join (the x data) and our non-sf data should be the second data (the y data):

boone <- left_join(booneIncome, booneSex, by = "GEOID")

Three common issues arise:

  1. The ID columns are named differently: by = c("GEOID" = "geoid")
  2. The ID columns are different type: booneIncome <- mutate(GEOID = as.numeric(GEOID))
  3. Both objects are sf objects: st_geometry(booneSEX) <- NULL

Using Tigris

To get data from the TIGER/line database, we can use the tigris package. You can see a full list of the data available here.

State Data

We can download a generalized version, which smooths out state boundaries so that the overall image is both smaller in disk size and (sometimes) easier to read. This is particularly helpful if you are making small scale maps of the entire United States. We’ll get these data at the “20m” resolution using the states() function:

states <- states(cb = TRUE, resolution = "20m")

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County Data

Now, we’ll get more detailed data - all of the county boundaries for Missouri. We’ll use the counties() function using a slightly less generalized resolution, “5m”:

moCounties <- counties(cb = TRUE, resolution = "5m")

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Tract Data

Now, we’ll get even more detailed data - all of the tract boundaries for St. Charles County, Missouri. We’ll use the tracts() function with cb = FALSE by default:

stCharlesTracts <- tracts(state = 29, county = 183)

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---
title: "Meeting Examples - Complete"
author: "Christopher Prener, PhD"
date: '(`r format(Sys.time(), "%B %d, %Y")`)'
output: 
  github_document: default
  html_notebook: default 
---

```{r setup}
knitr::opts_chunk$set(cache = FALSE)
```

## Introduction
This notebook illustrates data access through both `tigris` and `tidycensus` as well as joins using `dplyr`.

## Dependencies
This notebook requires the following packages:

```{r load-packages}
# tidyverse packages
library(dplyr)       # data wrangling

# spatial packages
library(mapview)     # preview geometric data
library(sf)          # spatial tools
library(tidycensus)  # demographic data
library(tigris)      # tiger/line data

# other packages
library(here)        # file path management
```

## tidycensus Set-up
Before using `tidycensus`, you need to install a census API key. Use the syntax below, copied into your console, to install the key you received via email.

```r
census_api_key("KEY", install = TRUE)
```

This is not a code chunk you will need in each notebook. As long as `install = TRUE`, you will only have to do this once!

## Decennial Census Data
### Get List of Variables
To get a preview of variables available in the `get_decennial()` function, we can use the `load_variables()` function:

```{r preview-census}
census <- load_variables(year = 2000, dataset = "sf1") 
```

I find it useful to assign the output of this function to an object so that I can search through it. Try searching for the variable `P0010001`, the total population of a geographic unit, in the `census` object.

### Download a Single Variable
To download data, we can use use the `get_decennial()` function to access, for example, population by state in 2000:

```{r census-state-pop, results = "hide"}
popStates <- get_decennial(geography = "state", year = 2000, variable = "P001001")
```

A full list of the geographies available in `tidycensus` can be found [here](https://walker-data.com/tidycensus/articles/basic-usage.html#geography-in-tidycensus-1).

### Download a Full Table
Most variables in the decennial census are actually a part of a table. There are individual variables, for example, for race:

```{r show-variables}
census %>%
  filter(concept == "P3. RACE [8]")
```

We rarely want to download these one at a time. Instead, we want to download them at one time into a single data frame. The table number for these data is `P003` - we take the first four characters from the `name` variable.

```{r census-stl-race, results = "hide"}
cityRace00 <- get_decennial(geography = "tract", year = 2000, state = 29,
                            county = "510", table = "P003", output = "wide")
```

We've used the FIPS codes for both Missouri (`29`) and St. Louis City (`29510`) here - you can find a full list of Missouri counties [here](https://www.msdis.missouri.edu/resources/fips.html).

### Add Geometry
The `tidycensus` package also includes tools for downloading the geometries for these data as well. For instance, we can add geometric data to our previous call for City of St. Louis tract-level data on race by adding the `geometry = TRUE` argument:

```{r}
## download
cityRace00 <- get_decennial(geography = "tract", year = 2000, state = 29,
                            county = "510", table = "P003", output = "wide",
                            geometry = TRUE)

## preview
mapview(cityRace00, zcol = "P003005")
```

Notice how I used the `zcol` argument for `mapview()` to preview a specific set of data as a thematic layer on the map! These data are not normalized, but we do get a quick preview of the distribution of Asian residents in St. Louis City.

## Decennial Census Data
### Get List of Variables
To get a preview of variables available in the `get_acs()` function, we can use the `load_variables()` function again. We'll use `"acs5"` for our dataset and, for this example, we'll pull from the most recent 2019 ACS year:

```{r preview-acs}
census <- load_variables(year = 2019, dataset = "acs5") 
```

Try searching for the table `B19013`, the median household income table.

### Get and Interpret ACS Data
We'll illustrate `get_acs()` by using the data in table `B19019`. First, we'll download these data as a full table for all counties in Missouri:

```{r median-income-1}
## download
countyIncome <- get_acs(geography = "county", year = 2019, state = 29,
                        table = "B19019", output = "wide", geometry = TRUE)

## preview
mapview(countyIncome, zcol = "B19019_001E")
```

Notice how we needed to specify `_001E` for `zcol`. That references the specific variable we want to map - variable 1 in the table's estimate (or `E`). The `M` values refer to the margin of the error - we expect this estimate to be off by some amount within +/- this value.

We can also download a specific column, like the median income for one-person households (`B19019_002`):

```{r median-income-2}
## download
countyIncome <- get_acs(geography = "county", year = 2019, state = 29,
                        variables = "B19019_002", output = "wide", 
                        geometry = TRUE)

## preview
mapview(countyIncome, zcol = "B19019_002E")
```

## Combining Data Sources
Perhaps we have a range of data that we want to include. For this example, we'll download data on median income and the proportion of women in tracts in Boone County, Missouri. We'll download the income data with `geometry = TRUE` and the sex data with `geometry = FALSE`:

```{r download-boone}
## download
booneIncome <- get_acs(geography = "tract", year = 2019, state = 29,
                       county = "019", variables = "B19019_001", 
                       output = "wide", geometry = TRUE) %>%
  rename(median_income = B19019_001E) %>%
  select(GEOID, median_income)

## download
booneSex <- get_acs(geography = "tract", year = 2019, state = 29,
                       county = "019", variables = c("B01001_001", "B01001_026"),
                       output = "wide") %>%
  mutate(pct_women = B01001_026E/B01001_001E*100) %>%
  select(GEOID, pct_women)
```

To combine these data, we'll use `left_join()` from `dplyr`. Our `sf` object should always be the first object in the join (the `x` data) and our non-sf data should be the second data (the `y` data):

```{r boone-join}
boone <- left_join(booneIncome, booneSex, by = "GEOID")
```

Three common issues arise:

  1. The ID columns are named differently: `by = c("GEOID" = "geoid")`
  2. The ID columns are different type: `booneIncome <- mutate(GEOID = as.numeric(GEOID))`
  3. Both objects are `sf` objects: `st_geometry(booneSEX) <- NULL`

## Using Tigris
To get data from the TIGER/line database, we can use the `tigris` package. You can see a full list of the data available [here](https://cran.r-project.org/web/packages/tigris/tigris.pdf).

### State Data
We can download a generalized version, which smooths out state boundaries so that the overall image is both smaller in disk size and (sometimes) easier to read. This is particularly helpful if you are making small scale maps of the entire United States. We'll get these data at the "20m" resolution using the `states()` function: 

```{r get-states}
states <- states(cb = TRUE, resolution = "20m")
```

### County Data
Now, we'll get more detailed data - all of the county boundaries for Missouri. We'll use the `counties()` function using a slightly less generalized resolution, "5m":

```{r get-counties}
moCounties <- counties(cb = TRUE, resolution = "5m")
```

### Tract Data
Now, we'll get even more detailed data - all of the tract boundaries for St. Charles County, Missouri. We'll use the `tracts()` function with `cb = FALSE` by default:

```{r get-tracts}
stCharlesTracts <- tracts(state = 29, county = 183)
```

```{r move-to-docs, include=FALSE}
# you do need to include this in any notebook you create for this class
fs::file_copy(here::here("examples", "meeting-2-2-examples-complete.nb.html"), 
              here::here("docs", "index.nb.html"), 
              overwrite = TRUE)
```