rcv

rcv helps you work directly with raw ballot image and cast vote record data to tabulate and visualize ranked choice election results.

Features

• Read in ballot image and master lookup files
• Merge these files to get a "readable" ballot layout
• Conduct elections, and view a round-by-round table of results
• Visualize the flow of voters with an interactive Sankey diagram
• Compatible with dplyr/magrittr pipe syntax (%>%)

Installation

We're on CRAN now! You can install our latest release (0.2.2):

install.packages("rcv")

Or, you can install the development version of rcv here:

devtools::install_github("ds-elections/rcv")

Created by:

• Jay Lee, jaylee@reed.edu
• Matthew Yancheff, yanchefm@reed.edu

The style of this README is inspired by the googlesheets R package.

Basic Workflow Demo

sf_bos_ballot is included as an example raw ballot image, and sf_bos_lookup as an example raw master lookup. Both are included as .rdas, and they are in the "WinEDS" format. This data comes from the 2016 San Francisco Board of Supervisors elections (San Francisco Department of Elections).

head(sf_bos_ballot)

## # A tibble: 6 x 1
##                                              X1
##                                           <chr>
## 1 000000900000660300000010020000406001000012800
## 2 000000900000660300000010020000406002000000001
## 3 000000900000660300000010020000406003000000001
## 4 000000900000660400000010020000406001000012300
## 5 000000900000660400000010020000406002000012500
## 6 000000900000660400000010020000406003000012100

head(sf_bos_lookup)

## # A tibble: 6 x 1
##                                                                            X1
##                                                                         <chr>
## 1 Candidate 0000121SAMUEL KWONG                                      00000010
## 2 Candidate 0000131TIM E. DONNELLY                                   00000010
## 3 Candidate 0000133DEAN PRESTON                                      00000010
## 4 Candidate 0000135JOEL ENGARDIO                                     00000010
## 5 Candidate 0000140MELISSA SAN MIGUEL                                00000010
## 6 Candidate 0000144AHSHA SAFAI                                       00000010

Cleaning Data

The streamlined version of this process is done with the clean_ballot() function. b_header and l_header are logical values, based on whether the ballot and lookup file respectively have a header for the first row.

cleaned <- clean_ballot(ballot = sf_bos_ballot, b_header = T, 
                        lookup = sf_bos_lookup, l_header = T, 
                        format = "WinEDS")
knitr::kable(head(readable(cleaned)))

contest	pref_voter_id	1	2	3
Board of Supervisors, District 1	000006603	SANDRA LEE FEWER	NA	NA
Board of Supervisors, District 1	000006604	MARJAN PHILHOUR	DAVID LEE	SAMUEL KWONG
Board of Supervisors, District 1	000006605	DAVID LEE	RICHIE GREENBERG	BRIAN J. LARKIN
Board of Supervisors, District 1	000006606	MARJAN PHILHOUR	DAVID LEE	SANDRA LEE FEWER
Board of Supervisors, District 1	000006607	BRIAN J. LARKIN	ANDY THORNLEY	JASON JUNGREIS
Board of Supervisors, District 1	000006608	MARJAN PHILHOUR	NA	NA

To access intermediate steps, the following process can be used.

# Import and label ballot image
a <- import_data(data = sf_bos_ballot, header = T) %>%
    label(image = "ballot", format = "WinEDS")

# Import and label master lookup
b <- import_data(data = sf_bos_lookup, header = T) %>%
    label(image = "lookup", format = "WinEDS")

# Merge these two tables
c <- characterize(ballot = a, lookup = b, format = "WinEDS")

knitr::kable(head(readable(c)))

contest	pref_voter_id	1	2	3
Board of Supervisors, District 1	000006603	SANDRA LEE FEWER	NA	NA
Board of Supervisors, District 1	000006604	MARJAN PHILHOUR	DAVID LEE	SAMUEL KWONG
Board of Supervisors, District 1	000006605	DAVID LEE	RICHIE GREENBERG	BRIAN J. LARKIN
Board of Supervisors, District 1	000006606	MARJAN PHILHOUR	DAVID LEE	SANDRA LEE FEWER
Board of Supervisors, District 1	000006607	BRIAN J. LARKIN	ANDY THORNLEY	JASON JUNGREIS
Board of Supervisors, District 1	000006608	MARJAN PHILHOUR	NA	NA

The readable() function takes the clean image, which is formatted for ease in computation, and formats it to be easily read manually.

Running Elections

This is done with the rcv_tally() function. sf_bos_clean is included as an example of a pre-cleaned ballot using the functions above. We will run the District 1 election from this ballot image.

results <- rcv_tally(sf_bos_clean, "Board of Supervisors, District 1")
knitr::kable(results)

candidate	round1	round2	round3	round4	round5	round6	round7	round8	round9
SANDRA LEE FEWER	12550	12689	12777	12840	13029	13093	13225	13354	14705
MARJAN PHILHOUR	11067	11135	11247	11348	11487	11680	11837	12086	13126
DAVID LEE	3396	3408	3488	3551	3622	3857	3961	4093	NA
RICHIE GREENBERG	974	984	1042	1220	1272	1386	1508	NA	NA
BRIAN J. LARKIN	747	773	832	896	956	997	NA	NA	NA
SAMUEL KWONG	740	744	760	785	814	NA	NA	NA	NA
JONATHAN LYENS	609	652	679	726	NA	NA	NA	NA	NA
JASON JUNGREIS	611	626	654	NA	NA	NA	NA	NA	NA
SHERMAN R. D'SILVA	557	566	NA	NA	NA	NA	NA	NA	NA
ANDY THORNLEY	359	NA	NA	NA	NA	NA	NA	NA	NA
NA	3499	3532	3630	3743	3929	4096	4578	5576	7278

Sandra Lee Fewer wins in Round 9, with 14,705 votes to Marjan Philhour's 13,126. 3,499 ballots were left blank for this election, and 3,779 were exhausted or marked invalid in this election.

Visualizing Data

We have two recommended methods of visualizing RCV data. Both utilize a flowchart called a "Sankey diagram" to show the transfer of voters between rounds. We will use each method to visualize the transfer of voters in the San Francisco District 7 Board of Supervisors election, because District 1 has too many crossings to be readable.

Method 1 (preferred because it is interactive, quicker, and more readable) uses the networkD3 package:

d3_7 <- rcv::make_d3list(results = sf_7_results)
networkD3::sankeyNetwork(Links = d3_7$values, Nodes = d3_7$names,
                         Source = "source", Target = "target",
                         Value = "value", NodeID = "candidate", units = "voters",
                         fontSize = 12, nodeWidth = 20)

Method 2 uses the alluvial package (this type of graphic is also called an alluvial diagram):

alluvial_7 <- rcv::make_alluvialdf(image = sf_bos_clean,
                                   rcvcontest = "Board of Supervisors, District 7")
alluvial::alluvial(
  alluvial_7[,1:4], freq = alluvial_7$frequency,
  col = ifelse(alluvial_7$round4 == "NORMAN YEE", "lightgreen", "gray"),
  border = "gray", alpha = 0.7, blocks = TRUE
)