Socrata is amazingly handy for open data

R
tidyverse
Socrata
dataviz
Author

Robert W. Walker

Published

November 25, 2020

Code 
library(RSocrata)
options(scipen=7)
library(tidyverse)
library(kableExtra)

The Socrata package makes it easy to access API calls built around SODA for open data access. If you try to skip the Socrata part, you usually only get a fraction of the available data. Socrata is intended to make open access data easier to manage and many government entities in the US use it as the portal to public data access. The R package makes interfacing with it much easier. First, how can we install it? It is on CRAN.

install.packages("RSocrata")
library(RSocrata)
SchoolSpend <- read.socrata("https://data.oregon.gov/resource/c7av-ntdz.csv")

The first bit of data that I found details various bits about spending and students in Oregon school districts. I want to look at a few basics of this. There is a lot more to plot but this is enough for now.

The Data

I found this on Oregon’s open data portal. In the spirit of transparency, when I tried to update this post and transition it, the data are no longer available. I have followed up, but for now, I can no longer access the data.

Code 
SchoolSpend <- read.socrata("https://data.oregon.gov/resource/c7av-ntdz.csv")
library(skimr)
skim(SchoolSpend) %>% kable() %>% scroll_box(width="100%")

How many school districts per county?

Code 
library(magrittr); library(hrbrthemes)
SchoolSpend %>% group_by(county_name, school_year) %>% tally() %>% mutate(school_year = as.Date(school_year, format = "%m/%d/%Y")) %>% filter(school_year == max(school_year)) %>% ggplot() + aes(x=fct_reorder(county_name, n), y=n, fill=county_name) + geom_col() + coord_flip() + guides(fill=FALSE) + labs(x= "County", y="Number of School Districts") + theme_minimal()

By Students?

Code 
SchoolSpend %>% group_by(county_name) %>% mutate(school_year = as.Date(school_year, format = "%m/%d/%Y")) %>% filter(school_year == max(school_year)) %>% summarise(Students = sum(student_count), Year = mean(school_year), County = as.factor(county_name)) %>% unique() -> Dat
ggplot(Dat) + aes(x=fct_reorder(County, -Students), y=Students, fill=county_name) + geom_col() + coord_flip() + guides(fill=FALSE) + labs(x= "County", y="Students") + theme_minimal()

There are a number of other bits of data organized by year and district. There is certainly more to examine, but then I found this.

Voter Registration Data

The database of Voter Registrations in Oregon is also available and easily accessible.

Code 
VoterReg <- read.socrata("https://data.oregon.gov/resource/6a4f-ecbi.csv")
VoterReg %>% 
  filter(sysdate == "2020-11-03") %>% 
  group_by(county) %>% 
  summarise(Voters = sum(count_v_id)) %>% 
  ggplot() + 
  aes(x=fct_reorder(county, Voters), y=Voters, label=Voters) +
  geom_col(fill="white", color="skyblue") + 
  geom_text(size=2.2) + 
  coord_flip() + 
  labs(x="County", y="Registered Voters") + 
  theme_minimal() -> Plot1
Plot1

Code 
library(plotly)
ggp1 <- ggplotly(Plot1)
ggp1

The Balance of Registrations

Code 
CurrVR <- VoterReg %>% filter(sysdate == "2020-11-03")
CurrVR$DRE <- "Other"
CurrVR$DRE[CurrVR$party=="Democrat"] <- "Democrat"
CurrVR$DRE[CurrVR$party=="Republican"] <- "Republican"
CurrVR %>% group_by(county) %>% mutate(Voters = sum(count_v_id)) %>% ggplot(., aes(x=fct_reorder(county, Voters), y=Voters, label=Voters)) + geom_col() + geom_text(size=2.2) + coord_flip() + labs(x="County", y="Registered Voters") + theme_minimal()

The Plot by Party

Now let me split these up by grouping and plot them.

Code 
CurrVR %>% group_by(county, DRE) %>% summarise(Voters = sum(count_v_id)) %>%
ggplot(.) +
 aes(x = fct_reorder(county, Voters), y=Voters, fill = DRE) +
 geom_col() + scale_fill_viridis_d() +
 coord_flip() +
 theme_minimal() + labs(x="County")

References

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

References

Devlin, Hugh, Ph. D., Tom Schenk, Jr., Gene Leynes, Nick Lucius, John Malc, Mark Silverberg, and Peter Schmeideskamp. 2022. RSocrata: Download or Upload Socrata Data Sets. https://github.com/Chicago/RSocrata.
Karambelkar, Bhaskar. 2017. Widgetframe: Htmlwidgets in Responsive Iframes. https://CRAN.R-project.org/package=widgetframe.
Müller, Kirill, and Hadley Wickham. 2022. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
Sievert, Carson. 2020. Interactive Web-Based Data Visualization with r, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.
Sievert, Carson, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec, and Pedro Despouy. 2022. Plotly: Create Interactive Web Graphics via Plotly.js. https://CRAN.R-project.org/package=plotly.
Vaidyanathan, Ramnath, Yihui Xie, JJ Allaire, Joe Cheng, Carson Sievert, and Kenton Russell. 2023. Htmlwidgets: HTML Widgets for r. https://github.com/ramnathv/htmlwidgets.
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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.
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Zhu, Hao. 2021. kableExtra: Construct Complex Table with Kable and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.