8. Candidate Explorer

Browse Individual LGBTQ+ Candidates

Show code 
source(here::here("code", "00_setup.R"))
library(DT)
df <- readRDS(paths$analysis_full_rds)
lgbtq <- df %>% filter(lgbtq_candidate)

1 Overview

This page provides an interactive, searchable table of all LGBTQ+ candidates identified in Brazil’s 2024 municipal elections. Rather than viewing aggregated statistics, you can browse individual records — searching by name, state, party, identity category, or any combination of attributes.

The table below is searchable (use the global search box or per-column filters), sortable (click any column header), filterable (use the dropdown/text filters at the top of each column), and supports CSV and Excel export of the filtered results.

2 Summary

Total Candidates

3,134

States Represented

26

Elected

227

Municipalities

1,429

3 Identity Breakdown

Show code 
lgbtq %>%
  filter(lgbt_category != "Non-LGBTQ+") %>%
  count(lgbt_category, .drop = FALSE) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  rename(Identity = lgbt_category, N = n, `%` = pct) %>%
  kable(align = c("l", "r", "r"))
Table 1: LGBTQ+ Candidates by Identity Category
Identity N %
Non-LGBTQ+ 0 0.0%
Gay 1077 34.4%
Lesbian 681 21.7%
Bisexual+ 564 18.0%
Trans 614 19.6%
Asexual 195 6.2%
Other LGBTQ+ 3 0.1%

4 Candidate Table

Show code 
lgbtq_display <- lgbtq %>%
  transmute(
    Name           = ballot_name,
    State          = state_abbrev,
    Municipality   = municipality_name,
    `Muni Pop.`    = populacao_2022,
    Capital        = if_else(capital_uf == 1, "Yes", "No", missing = "---"),
    Party          = party_abbrev,
    Position       = position_simple,
    Identity       = as.character(lgbt_category),
    Source         = disclosure_source,
    Age            = age,
    Gender         = translate_gender(gender),
    Race           = as.character(race_simple),
    Education      = as.character(education_simple),
    Ideology       = ideology_category,
    Elected        = case_when(
      elected ~ "Yes",
      !elected ~ "No",
      TRUE ~ "---"
    ),
    `Revenue (R$)` = round(total_revenue, 0),
    Donors         = n_unique_donors,
    Manifesto      = if_else(has_manifesto, "Yes", "No", missing = "No"),
    `Manifesto Words` = if_else(has_manifesto,
                                as.integer(manifesto_n_words), NA_integer_)
  ) %>%
  arrange(State, Municipality, Name)

DT::datatable(
  lgbtq_display,
  filter    = "top",
  rownames  = FALSE,
  extensions = c("Buttons", "Scroller"),
  options   = list(
    pageLength = 25,
    scrollX    = TRUE,
    scrollY    = "600px",
    scroller   = TRUE,
    dom        = "Bfrtip",
    buttons    = c("csv", "excel"),
    language   = list(
      search = "Search candidates:",
      info   = "Showing _START_ to _END_ of _TOTAL_ candidates"
    )
  )
) %>%
  DT::formatCurrency("Revenue (R$)", currency = "R$", digits = 0, mark = ",") %>%
  DT::formatRound("Muni Pop.", digits = 0, mark = ",")

5 Notes

  • The table includes all candidates identified as LGBTQ+ through either TSE self-disclosure or the VOTE LGBT project.
  • The Source column indicates how each candidate was identified: TSE (official self-declaration on the electoral registration form), VOTE (identified by the VOTE LGBT civil society project), or Both (confirmed by both sources).
  • Use the column filters (top row of the table) to narrow results by state, identity category, party, election outcome, or any other attribute.
  • Click csv or excel to download the currently filtered results.
  • For methodology on how candidates were identified, see Chapter 7.