Georgia elections

What it covers

The Georgia scraper returns election results from the Georgia Secretary of State at results.sos.ga.gov. It covers all elections available on the site — primaries, runoffs, generals, and special elections — from 2000 to the present.

Results are available at three levels: statewide candidate totals, county breakdowns, and precinct-level results. Precinct data is obtained by navigating each county page and following “View results by precinct” links for each contest.

Optionally, it can also return a vote-method breakdown (Advance in Person, Election Day, Absentee by Mail, Provisional) for each contest.

Note: The Georgia SOS site is a JavaScript-rendered Angular SPA. All scraping uses a headless Chromium browser via Playwright. County and precinct scraping is parallelised across up to max_workers browsers (default 4). Georgia has 159 counties; a full state + county scrape for one year typically takes 20–60 minutes. Adding precinct data significantly increases runtime.

Arguments

Argument	Default	Description
`state`	—	`"GA"`, `"georgia"`, or `"Georgia"`
`year_from`	`NULL`	Start year, inclusive; `NULL` = no lower bound
`year_to`	`NULL`	End year, inclusive; `NULL` = no upper bound
`level`	`"all"`	`"all"` — statewide + county + precinct; `"state"` — statewide only (fastest); `"county"` — county-level only; `"precinct"` — precinct-level only
`max_workers`	`4L`	Parallel Chromium browsers for county and precinct scraping
`include_vote_methods`	`FALSE`	If `TRUE`, also return vote-method breakdown tables

Examples

Statewide + county results for a single year

res <- scrape_elections(state = "GA", year_from = 2024, year_to = 2024)

# Statewide candidate totals
res$state %>%
  filter(office == "President of the United States") %>%
  select(election_name, candidate, party, votes, vote_pct)

# County-level breakdown
res$county %>%
  filter(office == "President of the United States") %>%
  select(county, candidate, party, votes, vote_pct) %>%
  arrange(county, desc(votes))

Statewide totals only (faster — skips county scraping)

state_df <- scrape_elections(
  state     = "GA",
  year_from = 2024,
  year_to   = 2024,
  level     = "state"
)

state_df %>%
  select(election_name, office, candidate, party, votes, vote_pct)

With vote-method breakdown

res <- scrape_elections(
  state                = "GA",
  year_from            = 2024,
  year_to              = 2024,
  include_vote_methods = TRUE
)

# Statewide vote-method totals
res$vote_method_state %>%
  filter(office == "United States Senator") %>%
  select(candidate, party, votes_advance_voting, votes_election_day,
         votes_absentee, votes_provisional, votes_total)

# County vote-method breakdown
res$vote_method_county %>%
  filter(office == "United States Senator", county == "Fulton") %>%
  select(candidate, votes_advance_voting, votes_election_day,
         votes_absentee, votes_provisional, votes_total)

Faster county scraping with more parallel browsers

Georgia has 159 counties. By default, county pages are scraped with 4 parallel Chromium browsers. Increasing max_workers can cut runtime significantly on machines with enough CPU/RAM.

res <- scrape_elections(
  state       = "GA",
  year_from   = 2024,
  year_to     = 2024,
  level       = "all",
  max_workers = 8L
)

Multi-year scrape

res <- scrape_elections(
  state       = "GA",
  year_from   = 2020,
  year_to     = 2024,
  level       = "all",
  max_workers = 4L
)

Precinct-level results

# level = "all" includes precinct data automatically
res <- scrape_elections(state = "GA", year_from = 2024, year_to = 2024)
res$precinct %>%
  filter(office == "President of the United States", county == "Fulton") %>%
  select(precinct, candidate, party, votes, vote_pct) %>%
  arrange(precinct, desc(votes))

# Precinct only (skips statewide + county scraping)
precinct_df <- scrape_elections(
  state     = "GA",
  year_from = 2024,
  year_to   = 2024,
  level     = "precinct"
)

Filter to a specific county

res <- scrape_elections(state = "GA", year_from = 2022, year_to = 2022)

res$county %>%
  filter(county == "Fulton") %>%
  select(election_name, office, candidate, party, votes, vote_pct) %>%
  arrange(office, desc(votes))

Output columns

`$state` (when `level = "all"` or `"state"`)

Column	Description
`state`	State abbreviation (`"GA"`)
`election_name`	Human-readable election name (e.g. `"November General Election"`)
`election_type`	Election type (e.g. `"General"`, `"Primary"`)
`election_year`	Calendar year of the election
`election_date`	Date string from the page header
`office_level`	`"Federal"`, `"State"`, or `"Local"`
`office`	Office name
`district`	District identifier; `NA` when not applicable
`candidate`	Candidate name
`party`	Party abbreviation
`votes`	Vote total
`vote_pct`	Percentage of votes within the contest
`winner`	`TRUE` if the candidate won the statewide contest
`url`	URL of the election results page scraped

`$county` (when `level = "all"` or `"county"`)

Column	Description
`state`	State abbreviation (`"GA"`)
`election_name`	Human-readable election name
`election_type`	Election type (e.g. `"General"`, `"Primary"`)
`election_year`	Calendar year of the election
`election_date`	Date string from the page header
`office_level`	`"Federal"`, `"State"`, or `"Local"`
`office`	Office name
`district`	District identifier; `NA` when not applicable
`county`	County name
`candidate`	Candidate name
`party`	Party abbreviation
`votes`	County-level vote total
`vote_pct`	Percentage of votes within the contest for this county
`county_winner`	`TRUE` if the candidate won within this county
`url`	URL of the election results page scraped

`$precinct` (when `level = "all"` or `"precinct"`)

Column	Description
`state`	State abbreviation (`"GA"`)
`election_name`	Human-readable election name
`election_type`	Election type (e.g. `"General"`, `"Primary"`)
`election_year`	Calendar year of the election
`election_date`	Date string from the page header
`office_level`	`"Federal"`, `"State"`, or `"Local"`
`office`	Office name
`district`	District identifier; `NA` when not applicable
`county`	County name
`precinct`	Precinct name
`candidate`	Candidate name
`party`	Party abbreviation
`votes`	Precinct-level vote total
`vote_pct`	Percentage of votes within the contest for this precinct
`precinct_winner`	`TRUE` if the candidate won within this precinct
`url`	URL of the precinct results page scraped

`$vote_method_state` / `$vote_method_county` (when `include_vote_methods = TRUE`)

Same identifying columns as above, with votes split by method instead of a single votes + pct:

Column	Description
`votes_advance_voting`	Advance in Person ballots
`votes_election_day`	Election Day ballots
`votes_absentee`	Absentee by Mail ballots
`votes_provisional`	Provisional ballots
`votes_total`	Sum of all methods

Performance notes

level = "state" is much faster. County scraping opens up to max_workers parallel Chromium browsers across Georgia’s 159 counties. A full state + county scrape for one year takes 20–60 minutes. Use level = "state" when county breakdowns are not needed.
Precinct scraping is significantly slower. It navigates each county page, fetches one ballot-item page per contest (in parallel), and then scrapes each individual precinct page. Expect a full state + county + precinct run to take several hours for a general election year. Use level = "county" or level = "state" when precinct data is not needed.
include_vote_methods is significantly slower. Each contest panel requires an extra Playwright click-and-re-render step. Budget an extra 5–15 minutes per election page.
Virtual scroll. The GA SOS site uses Angular virtual scrolling, rendering only panels visible in the viewport. The scraper automatically scrolls to load all panels before parsing — without this, pages with 100+ contests would silently return incomplete data.
Multiple elections per year. The GA SOS site lists all elections for a year (primaries, runoffs, generals, specials). All are scraped and combined in the output.