Last week, I shared a semi-lengthy write-up of one of my favorite spatial packages in R. In part, I’d been inspired by an experience I had at RStudio/Posit Conference where I realized part of why I don’t share is because I thought I didn’t have anything worth sharing. For fear of rehashing the entire post, here’s the TLDR from last post: I shared about why rdeck is great and got a lot of great feedback.
Part of that feedback was an interesting request that I’d actually been toying around with the day before. Jon Harmon asked if I’d share what I’d learned by deciding to share. Well, let’s get into that.
A follow-up blog about the things you learned by posting this would be both meta and cool 😁
— Jon Harmon (he/him) (@JonTheGeek) October 1, 2022
The first thing I learned was really a reminder, which is how supportive and inclusive the #rstats community is. Not kidding. I think, at times, I came to believe I didn’t have a place in contributing to the greater R community simply because I didn’t grow up as a quant-person. In fact, I grew up really poor and, even though I went to college, I majored in English1.
So, part of the thing that has kept me from sharing is thinking I didn’t qualify. It’s not entirely crazy to feel that way, TBH. Just within the #rstats community, there’s people like Hadley Wickham, Yihui Xie, Julia Silge, Jenny Bryan, Thomas Lin Pedersen, Alison Presmanes Hill, Mine Çetinkaya-Rundel, Edzer Pebesma, Kyle Walker, Bob Rudis, David Robinson, Danielle Navarro, Gabriela de Queiroz, and Tom Mock.
So, if you don’t come from a quant background like me, it’s not entirely crazy to feel like a fish out of water when you’re learning from these folks and, let’s be clear, I’ve learned a ton from these folks. They are all unique talents and most have got some kind of formal education or training that’s quant-oriented.
And, yet… part of the incredible success of R as a language is how ridiculously inclusive everyone is (folks above included). And that act of inclusion, IMO, is sharing. It’s all these people with incredible knowledge figuring out how to share it and make it accessible. It’s doing the work of translating knowledge to anyone that wants to learn.
Anyway, I was reminded of just how awesome the R community is online. And I learned that sharing is part of the way you join what makes R awesome, even if you don’t think you’d make the cut in a game of pick-up.
{tidycensus}
I didn’t actually learn about tidycensus through sharing the last post, but it’s another incredible asset in my own spatial workflow. Part of that is because I went to grad school for public policy in the US and working with data from the US Census Bureau was…
Something I wanted to be good at
A slight nightmare to work with. Especially if it involved making maps or doing any kind of spatial analysis.
It’s hard to overstate how much Kyle Walker’s tidycensus addressed both of those in the best way possible. Let me show you how. First things first, let’s load the packages.
```{r load-packages}
#| code-summary: "**The Code** | Package Setup"
library(tidyverse) # Loads core tidyverse tools
library(tidycensus) # Loads tools to pull census data
library(viridis) # Loads color palettes I use w/ {rdeck}
library(rdeck) # Loads rdeck library
library(sf) # Loads core spatial tools
options(tigris_use_cache = TRUE) # Caches data we pull from tidycensus to speed up recalls
```Now that tidycensus is loaded, we can start pulling census data as long as you’ve already got your API Key from the US Census. Fortunately, Kyle’s got a great tutorial for this on his tidycensus website. Using the spatial example on his website, I’m going to download some tract-level census data from the latest 2020 ACS Estimates and then get the spatial data to go along with it. The exception here is that I’m going to focus on Travis County (Austin), where I live, instead of Harris County (Houston).
```{r get-acs-spatial}
#| code-fold: show
#| code-summary: "**The Code** | {tidycensus} Call"
travis <- get_acs(
state = "TX",
county = "Travis",
geography = "tract",
variables = "B19013_001",
geometry = TRUE,
year = 2020
) |>
st_transform(crs = st_crs(4326))
```Alright, so what happened? Well, we got data for every single census tract’s Median Household Income in Travis County. A snapshot of that data is shown in the output below, but wait… what’s that st_transform() thing about in the chunk above?
Well, if you take a look at the bold/highlighted text below, you notice that our CRS reads “WGS 84”. I’ve done that because we’re going to map polygons and rdeck requires polygons to have a CRS of “WGS 84” and not “NAD 83” (which is what comes out of tidycensus). You don’t need to know the technical details to use it, but you’re more than welcome to learn more about it.
Simple feature collection with 6 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -97.77649 ymin: 30.18394 xmax: -97.644 ymax: 30.32954
Geodetic CRS: WGS 84
GEOID NAME variable estimate
1 48453002201 Census Tract 22.01, Travis County, Texas B19013_001 57803
2 48453000304 Census Tract 3.04, Travis County, Texas B19013_001 80000
3 48453001307 Census Tract 13.07, Travis County, Texas B19013_001 76009
4 48453002105 Census Tract 21.05, Travis County, Texas B19013_001 40811
5 48453002413 Census Tract 24.13, Travis County, Texas B19013_001 45462
6 48453000801 Census Tract 8.01, Travis County, Texas B19013_001 83958So, now, I’ve got my spatial data and want to map it. Well, one thing I thought would be great is trying to replicate a ggplot2-style map. I know I could use ggiraph (another amazing package I’ll write about at some point), but I wanted to make something that feels like a ggplot2 map made with geom_sf(), but interactive. I also learned that rdeck was partly inspired by ggplot2. To get that feel with rdeck, you can set the map_style argument to NULL and controller=FALSE get something similar to that ggplot2-style map.
```{r tidycensus-map}
#| column: page
#| fig-height: 7
#| code-summary: "**The Code** | {tidycensus} ggplot-style Map"
travis_map <- rdeck(map_style = NULL,
initial_bounds = travis,
controller = FALSE,
theme = "light") |>
add_polygon_layer(data = travis,
visibility_toggle = TRUE,
name = "Median Household Income (by Census Tract)",
opacity = 0.9,
get_polygon = geometry,
tooltip = estimate,
pickable = TRUE,
auto_highlight = TRUE,
get_fill_color = scale_color_linear(
col = estimate,
palette = magma(10)
))
travis_map
```In the last map, I talked about using a fairly complicated function to get a bounding box. One of the things Anthony clarified is that you can just pass a spatial object of st_bbox, sf, or sfc class. This means we can just pass the travis object we created with tidycensus and pass it to the initial_bounds argument (second line in the code below). Amazing.
travis_map <- rdeck(map_style = NULL,
initial_bounds = travis,
controller = FALSE,
theme = "light") |>
add_polygon_layer(data = travis,
visibility_toggle = TRUE,
name = "Median Household Income (by Census Tract)",
opacity = 0.9,
get_polygon = geometry,
tooltip = estimate,
pickable = TRUE,
auto_highlight = TRUE,
get_fill_color = scale_color_linear(
col = estimate,
palette = magma(10)
)){rdeck} with the {rdeck} editorOne really amazing thing I didn’t realize is that rdeck has an editor built-in. The editor can be turned on by setting editor=TRUE in the main rdeck() function. Miles McBain pointed out that you can use this with shiny for filtering spatial data and any linked data in-app. That sounds very useful, but I also think you can use it for creating custom annotations to spatial maps if you want to call out a particular area on maps you regularly make.
This sounds simple enough, but spatial annotations can be pretty difficult without a tool like this where you can see the area you’re trying to highlight on a map. Take a look at the GIF below to see how it works.
In short, you click on the tool you want (in my case a polygon), then you can draw with the tool, and once you’re done, rdeck saves it as a “.geojson” file which can be read back into R using read_sf() and now you can add that spatial object onto your map as an annotation. The code chunk below does just that. Pretty great, huh?
```{r annotated-map}
#| column: page
#| fig-height: 7
#| code-summary: "**The Code** | Annotated Map"
custom_shape <- read_sf("rdeck.geojson")
travis_map |>
add_polygon_layer(data = custom_shape,
name = "Highlighted Area",
opacity = 0.1,
get_polygon = geometry,
get_fill_color = "#5da5da")
```Anyway, enjoy digging more into rdeck (and tidycensus). Hope you find new stuff and write about it! Doesn’t even have to be a blog. Could just be twitter.
Photo by Devin Avery on Unsplash
Fun Fact. My brother was the quant person. He has an actual degree in mathematics.↩︎