The Data

Robert Husseman (RHusseman?) on Twitter is running to represent Oregon District 21 in the Oregon House of Representatives. He has a platform that focuses on housing and livability in the state by making sensible allocations of public resources to public problems. He has an MBA; he was my student and his head and heart are very much in the right place to serve Oregon well.

His campaign has a voter list for the Democratic voters in Oregon 21. He shared access to this. Let me load them up from a saved Excel spreadsheet derived from a Google sheet.

Code

library(readxl)
RHCamp <- read_excel("data/RHCamp.xlsx", 
    col_types = c("numeric", "text", "text", 
        "text", "text", "text", "text", "date", 
        "text", "text", "text", "text", "text", 
        "text", "text", "text", "text", "text", 
        "text", "text", "text", "text", "text", 
        "text", "text", "text", "text", "text", 
        "text", "text", "numeric", "numeric", 
        "text", "text", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric"))

Geocoding

I ran across a package called tidygeocoder to do the heavy lifting. Here is what I have to work with.

Code

library(tidyverse)
library(tidygeocoder)
names(RHCamp)

 [1] "VOTER_ID"          "FIRST_NAME"        "MIDDLE_NAME"      
 [4] "LAST_NAME"         "NAME_SUFFIX"       "BIRTH_DATE"       
 [7] "CONFIDENTIAL"      "EFF_REGN_DATE"     "STATUS"           
[10] "PARTY_CODE"        "PHONE_NUM"         "UNLISTED"         
[13] "COUNTY"            "RES_ADDRESS_1"     "RES_ADDRESS_2"    
[16] "HOUSE_NUM"         "HOUSE_SUFFIX"      "PRE_DIRECTION"    
[19] "STREET_NAME"       "STREET_TYPE"       "POST_DIRECTION"   
[22] "UNIT_TYPE"         "UNIT_NUM"          "ADDR_NON_STD"     
[25] "CITY"              "STATE"             "ZIP_CODE"         
[28] "ZIP_PLUS_FOUR"     "EFF_ADDRESS_1"     "EFF_ADDRESS_2"    
[31] "EFF_ADDRESS_3"     "EFF_ADDRESS_4"     "EFF_CITY"         
[34] "EFF_STATE"         "EFF_ZIP_CODE"      "EFF_ZIP_PLUS_FOUR"
[37] "ABSENTEE_TYPE"     "PRECINCT_NAME"     "PRECINCT"         
[40] "SPLIT"

A Great Thing about R

There is a vignette explaining the usage.

My use case is pretty easy though there is a lot of data. I want to try this with just the addresses. I have loaded the tidyverse and now I want to peel off just the data that I need in order to geocode it, and then process it with the Census geocoder. This results in 9443 queries.

Code

GeoMe <- RHCamp %>% select(VOTER_ID, RES_ADDRESS_1, CITY, STATE) %>% geocode(street = RES_ADDRESS_1, city=CITY, state=STATE, method="census")
NewData <- GeoMe %>% left_join(., RHCamp)
save(NewData, file="~/Desktop/NewDataRH.RData")

This takes quite a long time to run – 667.1 seconds.

When it is complete, I will want to figure out how to map them in a way that is best fit for assisting a campaign. This seems like a good use for leaflet. Rather than taking 10 minutes or so to compile each time, I saved them off. I am loading them from a local file in the first line.

Leaflet Mapping

Code

load("data/NewDataRH.RData")

The leaflet package uses very powerful mapping tools that will be perfect for this use case because the maps have easy zoom features.

Code

library(leaflet)
NewData %>% mutate(labMy = paste0(FIRST_NAME,LAST_NAME,RES_ADDRESS_1,sep="\n")) %>%
  leaflet(data = .) %>% 
  addTiles() %>%
  addCircleMarkers(~long, ~lat, label = ~labMy, fillOpacity = 0.1, stroke=FALSE, radius=1.6)

That works but it isn’t quite right. Turns out that I will have to mess with html to get the labels quite right.

So, of course, I went to stackoverflow.

Building Better Labels

I found a post on how to do this.

The key piece of code seems to be:

Code

labs <- lapply(seq(nrow(NewData)), function(i) {
  paste0( '<p>', NewData[i,"RES_ADDRESS_1"],'</p>' )
})
NewData %>%
  leaflet(data = .) %>% 
  addTiles() %>%
  addCircleMarkers(~long, ~lat, label = lapply(labs, htmltools::HTML), fillOpacity = 0.15, stroke=FALSE, radius=1.6)

References

Code

knitr::write_bib(names(sessionInfo()$otherPkgs), file="bibliography.bib")

References

Cambon, Jesse, Diego Hernangómez, Christopher Belanger, and Daniel Possenriede. 2021. Tidygeocoder: Geocoding Made Easy. https://CRAN.R-project.org/package=tidygeocoder.

Cheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2022. Leaflet: Create Interactive Web Maps with the JavaScript Leaflet Library. https://rstudio.github.io/leaflet/.

Müller, Kirill, and Hadley Wickham. 2022. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

———. 2022a. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.

———. 2022b. Tidyverse: Easily Install and Load the Tidyverse. https://CRAN.R-project.org/package=tidyverse.

———. 2023. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.

Wickham, Hadley, and Jennifer Bryan. 2023. Readxl: Read Excel Files. https://CRAN.R-project.org/package=readxl.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2023. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.

Wickham, Hadley, Romain François, Lionel Henry, Kirill Müller, and Davis Vaughan. 2023. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.

Wickham, Hadley, and Lionel Henry. 2023. Purrr: Functional Programming Tools. https://CRAN.R-project.org/package=purrr.

Wickham, Hadley, Jim Hester, and Jennifer Bryan. 2023. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.

Wickham, Hadley, Davis Vaughan, and Maximilian Girlich. 2023. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.