The goal of pollspain is to …
You can install the development version of R package {pollspain} from
GitHub through the following code:
install.packages("devtools") # only if not already installed
devtools::install_github("dadosdelaplace/pollspain")#> rlang (1.1.0 -> 1.1.1) [CRAN]
#> xml2 (1.3.3 -> 1.3.4) [CRAN]
#> httr (1.4.5 -> 1.4.6) [CRAN]
#> vroom (1.6.1 -> 1.6.3) [CRAN]
#>
#> There is a binary version available but the source version is later:
#> binary source needs_compilation
#> httr 1.4.5 1.4.6 FALSE
#>
#>
#> The downloaded binary packages are in
#> /var/folders/v8/rwlp504x0_s2b89pn8yrb6km0000gn/T//RtmpPgF5Rp/downloaded_packages
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> * checking for file ‘/private/var/folders/v8/rwlp504x0_s2b89pn8yrb6km0000gn/T/RtmpPgF5Rp/remotes6b3e4a4bfd32/dadosdelaplace-pollspain-ec39fb7/DESCRIPTION’ ... OK
#> * preparing ‘pollspain’:
#> * checking DESCRIPTION meta-information ... OK
#> * checking for LF line-endings in source and make files and shell scripts
#> * checking for empty or unneeded directories
#> Removed empty directory ‘pollspain/data/csv/candidacies’
#> Removed empty directory ‘pollspain/data/csv/candidacies_pollstation’
#> Removed empty directory ‘pollspain/data/csv/candidates’
#> Removed empty directory ‘pollspain/data/csv/cod_INE’
#> Removed empty directory ‘pollspain/data/csv/mun_data’
#> Removed empty directory ‘pollspain/data/csv/pollstation’
#> Removed empty directory ‘pollspain/data/csv/summary_data’
#> Removed empty directory ‘pollspain/data/csv’
#> * building ‘pollspain_0.1.0.9000.tar.gz’
library(pollspain)
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.2 ✔ readr 2.1.4
#> ✔ forcats 1.0.0 ✔ stringr 1.5.0
#> ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
#> ✔ purrr 1.0.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsVariables relativas a población (pop_res_mun, census_INE_mun,
census_counting_mun, census_CERE_mun)
mun_census_data <- get_mun_census_data("congress", 2019, 4)
mun_census_data
#> # A tibble: 16,262 × 20
#> cod_elec type_elec date_elec id_INE_mun id_MIR_mun cod_INE_ccaa cod_MIR_ccaa
#> <chr> <chr> <date> <glue> <glue> <chr> <chr>
#> 1 02 congress 2019-04-28 01-04-001 01-04-001 01 01
#> 2 02 congress 2019-04-28 01-04-002 01-04-002 01 01
#> 3 02 congress 2019-04-28 01-04-003 01-04-003 01 01
#> 4 02 congress 2019-04-28 01-04-004 01-04-004 01 01
#> 5 02 congress 2019-04-28 01-04-005 01-04-005 01 01
#> 6 02 congress 2019-04-28 01-04-006 01-04-006 01 01
#> 7 02 congress 2019-04-28 01-04-007 01-04-007 01 01
#> 8 02 congress 2019-04-28 01-04-008 01-04-008 01 01
#> 9 02 congress 2019-04-28 01-04-009 01-04-009 01 01
#> 10 02 congress 2019-04-28 01-04-010 01-04-010 01 01
#> # ℹ 16,252 more rows
#> # ℹ 13 more variables: ccaa <chr>, cod_INE_prov <chr>, prov <chr>,
#> # cod_INE_mun <chr>, cd_INE_mun <chr>, mun <chr>, cod_mun_jud_district <chr>,
#> # cod_mun_prov_council <chr>, n_poll_stations <dbl>, pop_res_mun <dbl>,
#> # census_INE_mun <dbl>, census_counting_mun <dbl>, census_CERE_mun <dbl>Datos generales de participación, votos en blanco, votos a candidaturas, etc.
poll_data <- get_poll_station_data("congress", 2019, c(4, 11))
poll_data
#> # A tibble: 119,697 × 23
#> id_elec type_elec date_elec id_INE_poll_station ccaa prov mun
#> <glue> <chr> <date> <glue> <chr> <chr> <chr>
#> 1 02-2019-04-28 congress 2019-04-28 01-04-003-01-004-B Andalucía Almer… Adra
#> 2 02-2019-04-28 congress 2019-04-28 01-04-003-01-007-U Andalucía Almer… Adra
#> 3 02-2019-04-28 congress 2019-04-28 01-04-003-02-001-A Andalucía Almer… Adra
#> 4 02-2019-04-28 congress 2019-04-28 01-04-006-01-002-A Andalucía Almer… Albox
#> 5 02-2019-04-28 congress 2019-04-28 01-04-008-01-001-A Andalucía Almer… Alcó…
#> 6 02-2019-04-28 congress 2019-04-28 01-04-010-01-001-U Andalucía Almer… Alha…
#> 7 02-2019-04-28 congress 2019-04-28 01-04-013-02-005-A Andalucía Almer… Alme…
#> 8 02-2019-04-28 congress 2019-04-28 01-04-013-03-011-A Andalucía Almer… Alme…
#> 9 02-2019-04-28 congress 2019-04-28 01-04-013-06-013-A Andalucía Almer… Alme…
#> 10 02-2019-04-28 congress 2019-04-28 01-04-013-06-016-B Andalucía Almer… Alme…
#> # ℹ 119,687 more rows
#> # ℹ 16 more variables: census_counting <dbl>, ballots_1 <dbl>, turnout_1 <dbl>,
#> # ballots_2 <dbl>, turnout_2 <dbl>, blank_ballots <dbl>,
#> # invalid_ballots <dbl>, party_ballots <dbl>, valid_ballots <dbl>,
#> # total_ballots <dbl>, turnout <dbl>, porc_valid <dbl>, porc_invalid <dbl>,
#> # porc_parties <dbl>, porc_blank <dbl>, pop_res_mun <dbl>Datos de los/as candidatos que forman las listas (por circunscripción provincial en las generales)
candidates_data <- get_candidates_data("congress", 2019, c(4, 11))
candidates_data
#> # A tibble: 11,182 × 15
#> cod_elec type_elec date_elec turn cod_INE_prov cod_mun_district cod_INE_mun
#> <chr> <chr> <date> <dbl> <chr> <chr> <chr>
#> 1 02 congress 2019-04-28 1 11 <NA> <NA>
#> 2 02 congress 2019-04-28 1 11 <NA> <NA>
#> 3 02 congress 2019-04-28 1 11 <NA> <NA>
#> 4 02 congress 2019-04-28 1 11 <NA> <NA>
#> 5 02 congress 2019-04-28 1 11 <NA> <NA>
#> 6 02 congress 2019-04-28 1 11 <NA> <NA>
#> 7 02 congress 2019-04-28 1 11 <NA> <NA>
#> 8 02 congress 2019-04-28 1 11 <NA> <NA>
#> 9 02 congress 2019-04-28 1 11 <NA> <NA>
#> 10 02 congress 2019-04-28 1 11 <NA> <NA>
#> # ℹ 11,172 more rows
#> # ℹ 8 more variables: id_candidacies <chr>, order <dbl>, holder <lgl>,
#> # name <chr>, surname <chr>, sex <chr>, id_card <chr>, elected <lgl>Datos de las candidaturas por mesa electoral, con los votos obtenidos en cada mesa para cada candidatura, así como los electos por provincia obtenidos
candidacies_data <- get_candidacies_data("congress", 2019, c(4, 11))
candidacies_data
#> # A tibble: 1,482,129 × 13
#> id_elec type_elec date_elec id_INE_poll_station ccaa prov mun
#> <glue> <chr> <date> <glue> <chr> <chr> <chr>
#> 1 02-2019-04-28 congress 2019-04-28 01-04-001-01-001-B Andalucía Almer… Abla
#> 2 02-2019-04-28 congress 2019-04-28 01-04-001-01-001-B Andalucía Almer… Abla
#> 3 02-2019-04-28 congress 2019-04-28 01-04-002-01-001-A Andalucía Almer… Abru…
#> 4 02-2019-04-28 congress 2019-04-28 01-04-002-01-001-A Andalucía Almer… Abru…
#> 5 02-2019-04-28 congress 2019-04-28 01-04-003-01-003-A Andalucía Almer… Adra
#> 6 02-2019-04-28 congress 2019-04-28 01-04-003-01-003-B Andalucía Almer… Adra
#> 7 02-2019-04-28 congress 2019-04-28 01-04-003-01-003-B Andalucía Almer… Adra
#> 8 02-2019-04-28 congress 2019-04-28 01-04-003-01-006-A Andalucía Almer… Adra
#> 9 02-2019-04-28 congress 2019-04-28 01-04-003-01-007-U Andalucía Almer… Adra
#> 10 02-2019-04-28 congress 2019-04-28 01-04-003-02-002-A Andalucía Almer… Adra
#> # ℹ 1,482,119 more rows
#> # ℹ 6 more variables: id_candidacies <chr>, id_candidacies_prov <chr>,
#> # abbrev_candidacies <chr>, name_candidacies <chr>, ballots <dbl>,
#> # elected_by_prov <dbl>Según el INE:
«El censo electoral contiene la inscripción de quienes reúnen los requisitos para ser elector y no se hallen privados, definitiva o temporalmente, del derecho de sufragio. El censo electoral está compuesto por:
- El censo electoral de españoles residentes en España (CER).
- El censo electoral de españoles residentes-ausentes que viven en el extranjero (CERA).
El censo electoral de residentes en España que sean nacionales de países con Acuerdos para las elecciones municipales (CERE Acuerdos), y el censo electoral de ciudadanos de la Unión Europea residentes en España para las elecciones municipales y al Parlamento Europeo (CERE UE).»
La función get_CERA_data() nos devuelve los datos relativos al CERA
ccaa_CERA_data <- get_CERA_data(election_data, level = "ccaa")La función get_elections_data() nos permite obtener los datos
electorales de las elecciones pedidas y con el nivel (level) de
agregación que queramos, pudiendo ser all, ccaa, prov, mun,
mun_district, sec (sección censal) y poll_station (a nivel de mesa
electoral)
prov_data <- get_elections_data("congress", year = 2019, month = c(4, 11), level = "prov")
#> 🔎 Check if parameters are allowed...
#> 🔔 Since include_candidacies = FALSE, aggregating by parties has not been implemented
#> 📦 Get poll station data...
#> - Download poll station data...
#> - Aggregating election data at prov level...
#> ✅ Last summaries and tasks...
prov_data
#> # A tibble: 104 × 27
#> id_elec type_elec date_elec pop_res cod_INE_ccaa cod_INE_prov ccaa prov
#> <glue> <chr> <date> <dbl> <chr> <chr> <chr> <chr>
#> 1 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 2 02-2019-0… congress 2019-04-28 1238714 01 11 Anda… Cádiz
#> 3 02-2019-0… congress 2019-04-28 785240 01 14 Anda… Córd…
#> 4 02-2019-0… congress 2019-04-28 912075 01 18 Anda… Gran…
#> 5 02-2019-0… congress 2019-04-28 519932 01 21 Anda… Huel…
#> 6 02-2019-0… congress 2019-04-28 638099 01 23 Anda… Jaén
#> 7 02-2019-0… congress 2019-04-28 1641121 01 29 Anda… Mála…
#> 8 02-2019-0… congress 2019-04-28 1939887 01 41 Anda… Sevi…
#> 9 02-2019-0… congress 2019-04-28 219345 02 22 Arag… Hues…
#> 10 02-2019-0… congress 2019-04-28 134572 02 44 Arag… Teru…
#> # ℹ 94 more rows
#> # ℹ 19 more variables: n_poll_stations <int>, census_counting <dbl>,
#> # ballots_1 <dbl>, ballots_2 <dbl>, blank_ballots <dbl>,
#> # invalid_ballots <dbl>, party_ballots <dbl>, valid_ballots <dbl>,
#> # total_ballots <dbl>, turnout_1 <dbl>, turnout_2 <dbl>, turnout <dbl>,
#> # porc_valid <dbl>, porc_invalid <dbl>, porc_parties <dbl>, porc_blank <dbl>,
#> # census_cera <dbl>, total_ballots_cera <dbl>, turnout_cera <dbl>Esta función combina get_poll_station_data() (el dato en bruto por
mesa electoral) con get_CERA_data() y aggregate_election_data(), que
nos proporciona la agregación pedida. El código anterior es equivalente
a:
# Raw data at poll station level
election_data <-
get_poll_station_data("congress", year = 2019, month = c(4, 11))
# and then aggregate at provided level
prov_data <-
election_data |>
aggregate_election_data(level = "prov")
prov_data
#> # A tibble: 104 × 27
#> id_elec type_elec date_elec pop_res cod_INE_ccaa cod_INE_prov ccaa prov
#> <glue> <chr> <date> <dbl> <chr> <chr> <chr> <chr>
#> 1 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 2 02-2019-0… congress 2019-04-28 1238714 01 11 Anda… Cádiz
#> 3 02-2019-0… congress 2019-04-28 785240 01 14 Anda… Córd…
#> 4 02-2019-0… congress 2019-04-28 912075 01 18 Anda… Gran…
#> 5 02-2019-0… congress 2019-04-28 519932 01 21 Anda… Huel…
#> 6 02-2019-0… congress 2019-04-28 638099 01 23 Anda… Jaén
#> 7 02-2019-0… congress 2019-04-28 1641121 01 29 Anda… Mála…
#> 8 02-2019-0… congress 2019-04-28 1939887 01 41 Anda… Sevi…
#> 9 02-2019-0… congress 2019-04-28 219345 02 22 Arag… Hues…
#> 10 02-2019-0… congress 2019-04-28 134572 02 44 Arag… Teru…
#> # ℹ 94 more rows
#> # ℹ 19 more variables: n_poll_stations <int>, census_counting <dbl>,
#> # ballots_1 <dbl>, ballots_2 <dbl>, blank_ballots <dbl>,
#> # invalid_ballots <dbl>, party_ballots <dbl>, valid_ballots <dbl>,
#> # total_ballots <dbl>, turnout_1 <dbl>, turnout_2 <dbl>, turnout <dbl>,
#> # porc_valid <dbl>, porc_invalid <dbl>, porc_parties <dbl>, porc_blank <dbl>,
#> # census_cera <dbl>, total_ballots_cera <dbl>, turnout_cera <dbl>Función principal, combinando la extracción y la agregación
national_data <-
get_elections_data("congress", 2019, c(4, 11),
include_candidacies = TRUE, level = "all")
#> 🔎 Check if parameters are allowed...
#> 📦 Get poll station data...
#> - Download poll station data...
#> - Aggregating election data at national level...
#> 📦 Get candidacies (parties) data...
#> - Download candidacies (parties) data... (please wait, intensive task)
#> - Aggregating candidacies data at national level...
#> 🖇 Join information...
#> ✅ Last summaries and tasks...
national_data |> arrange(desc(ballots))
#> # A tibble: 134 × 34
#> id_elec type_elec date_elec pop_res id_candidacies abbrev_candidacies
#> <glue> <chr> <date> <dbl> <chr> <chr>
#> 1 02-2019-04-28 congress 2019-04-28 46722980 000096 PSOE
#> 2 02-2019-11-10 congress 2019-11-10 46722980 000094 PSOE
#> 3 02-2019-11-10 congress 2019-11-10 46722980 000083 PP
#> 4 02-2019-04-28 congress 2019-04-28 46722980 000083 PP
#> 5 02-2019-04-28 congress 2019-04-28 46722980 000022 CS
#> 6 02-2019-11-10 congress 2019-11-10 46722980 000116 VOX
#> 7 02-2019-04-28 congress 2019-04-28 46722980 000077 UP
#> 8 02-2019-04-28 congress 2019-04-28 46722980 000117 VOX
#> 9 02-2019-11-10 congress 2019-11-10 46722980 000078 UP
#> 10 02-2019-11-10 congress 2019-11-10 46722980 000018 CS
#> # ℹ 124 more rows
#> # ℹ 28 more variables: name_candidacies <chr>, ballots <dbl>, elected <dbl>,
#> # ballots_by_elec <dbl>, porc_candidacies_parties <dbl>,
#> # porc_candidacies_valid <dbl>, porc_candidacies_census <dbl>,
#> # porc_elected <dbl>, anomaly_ballots_elected <dbl>, n_poll_stations <int>,
#> # census_counting <dbl>, ballots_1 <dbl>, ballots_2 <dbl>,
#> # blank_ballots <dbl>, invalid_ballots <dbl>, party_ballots <dbl>, …ccaa_data <-
get_elections_data("congress", 2019, c(4, 11),
include_candidacies = TRUE, level = "ccaa")
#> 🔎 Check if parameters are allowed...
#> 📦 Get poll station data...
#> - Download poll station data...
#> - Aggregating election data at ccaa level...
#> 📦 Get candidacies (parties) data...
#> - Download candidacies (parties) data... (please wait, intensive task)
#> - Aggregating candidacies data at ccaa level...
#> 🖇 Join information...
#> ✅ Last summaries and tasks...
ccaa_data
#> # A tibble: 503 × 36
#> id_elec type_elec date_elec pop_res cod_INE_ccaa ccaa id_candidacies
#> <glue> <chr> <date> <dbl> <chr> <chr> <chr>
#> 1 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000077
#> 2 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000117
#> 3 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000028
#> 4 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000054
#> 5 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000022
#> 6 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000104
#> 7 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000058
#> 8 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000096
#> 9 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000100
#> 10 02-2019-04-28 congress 2019-04-28 8384408 01 Andal… 000083
#> # ℹ 493 more rows
#> # ℹ 29 more variables: abbrev_candidacies <chr>, name_candidacies <chr>,
#> # ballots <dbl>, elected <dbl>, ballots_by_elec <dbl>,
#> # porc_candidacies_parties <dbl>, porc_candidacies_valid <dbl>,
#> # porc_candidacies_census <dbl>, porc_elected <dbl>,
#> # anomaly_ballots_elected <dbl>, n_poll_stations <int>,
#> # census_counting <dbl>, ballots_1 <dbl>, ballots_2 <dbl>, …
prov_data <-
get_elections_data("congress", 2019, c(4, 11),
include_candidacies = TRUE, level = "prov")
#> 🔎 Check if parameters are allowed...
#> 📦 Get poll station data...
#> - Download poll station data...
#> - Aggregating election data at prov level...
#> 📦 Get candidacies (parties) data...
#> - Download candidacies (parties) data... (please wait, intensive task)
#> - Aggregating candidacies data at prov level...
#> 🖇 Join information...
#> ✅ Last summaries and tasks...
prov_data
#> # A tibble: 1,210 × 38
#> id_elec type_elec date_elec pop_res cod_INE_ccaa cod_INE_prov ccaa prov
#> <glue> <chr> <date> <dbl> <chr> <chr> <chr> <chr>
#> 1 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 2 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 3 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 4 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 5 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 6 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 7 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 8 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 9 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> 10 02-2019-0… congress 2019-04-28 709340 01 04 Anda… Alme…
#> # ℹ 1,200 more rows
#> # ℹ 30 more variables: id_candidacies <chr>, abbrev_candidacies <chr>,
#> # name_candidacies <chr>, ballots <dbl>, elected <dbl>,
#> # ballots_by_elec <dbl>, porc_candidacies_parties <dbl>,
#> # porc_candidacies_valid <dbl>, porc_candidacies_census <dbl>,
#> # porc_elected <dbl>, anomaly_ballots_elected <dbl>, n_poll_stations <int>,
#> # census_counting <dbl>, ballots_1 <dbl>, ballots_2 <dbl>, …Es lo mismo extraer a nivel municipio que a un nivel más bajo y luego agrupar y sumarizar.
mun_data <-
get_elections_data("congress", 2019, c(4, 11),
include_candidacies = TRUE, level = "mun")
#> 🔎 Check if parameters are allowed...
#> 📦 Get poll station data...
#> - Download poll station data...
#> - Aggregating election data at mun level...
#> 📦 Get candidacies (parties) data...
#> - Download candidacies (parties) data... (please wait, intensive task)
#> - Aggregating candidacies data at mun level...
#> 🖇 Join information...
#> ✅ Last summaries and tasks...
mun_data |> filter(date_elec == "2019-04-28" & mun == "Dos Hermanas")
#> # A tibble: 13 × 40
#> id_elec type_elec date_elec pop_res cod_INE_ccaa cod_INE_prov cod_INE_mun
#> <glue> <chr> <date> <dbl> <chr> <chr> <chr>
#> 1 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 2 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 3 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 4 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 5 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 6 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 7 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 8 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 9 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 10 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 11 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 12 02-2019-0… congress 2019-04-28 133168 01 41 038
#> 13 02-2019-0… congress 2019-04-28 133168 01 41 038
#> # ℹ 33 more variables: ccaa <chr>, prov <chr>, mun <chr>, id_candidacies <chr>,
#> # abbrev_candidacies <chr>, name_candidacies <chr>, ballots <dbl>,
#> # elected <dbl>, ballots_by_elec <dbl>, porc_candidacies_parties <dbl>,
#> # porc_candidacies_valid <dbl>, porc_candidacies_census <dbl>,
#> # porc_elected <dbl>, anomaly_ballots_elected <dbl>, n_poll_stations <dbl>,
#> # census_counting <dbl>, ballots_1 <dbl>, ballots_2 <dbl>,
#> # blank_ballots <dbl>, invalid_ballots <dbl>, party_ballots <dbl>, …
mun_district_data <- get_elections_data("congress", 2019, c(4, 11),
include_candidacies = TRUE,
level = "mun_district")
#> 🔎 Check if parameters are allowed...
#> 📦 Get poll station data...
#> - Download poll station data...
#> - Aggregating election data at mun_district level...
#> 📦 Get candidacies (parties) data...
#> - Download candidacies (parties) data... (please wait, intensive task)
#> - Aggregating candidacies data at mun_district level...
#> 🖇 Join information...
#> ✅ Last summaries and tasks...
mun_district_data |>
group_by(id_elec, type_elec, date_elec, cod_INE_mun, mun, id_candidacies) |>
summarise(sum(ballots)) |>
ungroup() |>
filter(date_elec == "2019-04-28" & mun == "Dos Hermanas")
#> `summarise()` has grouped output by 'id_elec', 'type_elec', 'date_elec',
#> 'cod_INE_mun', 'mun'. You can override using the `.groups` argument.
#> # A tibble: 13 × 7
#> id_elec type_elec date_elec cod_INE_mun mun id_candidacies `sum(ballots)`
#> <glue> <chr> <date> <chr> <chr> <chr> <dbl>
#> 1 02-2019… congress 2019-04-28 038 Dos … 000011 195
#> 2 02-2019… congress 2019-04-28 038 Dos … 000022 13944
#> 3 02-2019… congress 2019-04-28 038 Dos … 000054 1436
#> 4 02-2019… congress 2019-04-28 038 Dos … 000056 279
#> 5 02-2019… congress 2019-04-28 038 Dos … 000057 118
#> 6 02-2019… congress 2019-04-28 038 Dos … 000058 73
#> 7 02-2019… congress 2019-04-28 038 Dos … 000066 69
#> 8 02-2019… congress 2019-04-28 038 Dos … 000077 13183
#> 9 02-2019… congress 2019-04-28 038 Dos … 000083 7915
#> 10 02-2019… congress 2019-04-28 038 Dos … 000096 26918
#> 11 02-2019… congress 2019-04-28 038 Dos … 000100 95
#> 12 02-2019… congress 2019-04-28 038 Dos … 000104 149
#> 13 02-2019… congress 2019-04-28 038 Dos … 000117 10232
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent