Skip to contents

Generates a plot of the aggregated poll results over time.

Usage

grafico_agregador(
  bd,
  salvar = FALSE,
  config_grafico = configurar_grafico(),
  dir_saida = NULL,
  ...
)

Arguments

bd

The results object returned by rodar_agregador().

salvar

Logical. If TRUE, saves the plot to disk.

config_grafico

A list of graphic parameters created by configurar_grafico().

dir_saida

Output directory for the saved plot if salvar = TRUE.

...

Additional arguments.

Value

A ggplot2 object.

Examples

if (instantiate::stan_cmdstan_exists()) {
  result <- rodar_agregador(
    data_inicio = "01/01/2025",
    turno = 2,
    cenario = "Lula vs Bolsonaro"
  )

  # Standard plot
  std_plot <- grafico_agregador(result)

  # Altering candidate colors
  custom_plot <- grafico_agregador(
    result,
    config_grafico = configurar_grafico(
      cores_candidaturas = c(Lula = "yellow")
    )
  )
}
#> 
#> ── Simulações do Segundo Turno ─────────────────────────────────────────────────
#>  Base carregada e filtrada com sucesso!
#>  Iniciando 4 cadeias de 1000 iterações por candidatura.
#> 30 pesquisas na base entre 01/01/25 e 03/03/26.
#>  Se esses números parecerem incorretos, revise os argumentos e configurações da função.
#> 
#> ── Estimando intenção de votos para: "Bolsonaro" ──
#> 
#> Running MCMC with 4 parallel chains...
#> 
#> Chain 1 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 2 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 3 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 4 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 1 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 4 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 3 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 2 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 1 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 4 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 3 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 2 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 4 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 3 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 2 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 4 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 2 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 3 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 1 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 1 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 2 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 4 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 4 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 3 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 3 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 4 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 3 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 2 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 4 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 3 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 2 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 4 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 3 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 2 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 4 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 3 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 4 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 2 finished in 16.9 seconds.
#> Chain 4 finished in 16.9 seconds.
#> Chain 1 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 3 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 3 finished in 17.2 seconds.
#> Chain 1 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 1 finished in 19.4 seconds.
#> 
#> All 4 chains finished successfully.
#> Mean chain execution time: 17.6 seconds.
#> Total execution time: 19.5 seconds.
#> 
#> ── Estimando intenção de votos para: "Lula" ──
#> 
#> Running MCMC with 4 parallel chains...
#> 
#> Chain 1 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 2 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 3 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 4 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 4 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 3 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 1 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 2 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 3 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 4 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 2 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 3 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 1 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 4 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 1 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 2 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 3 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 4 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 2 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 1 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 1 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 4 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 4 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 3 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 3 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 2 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 4 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 3 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 2 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 4 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 1 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 3 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 2 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 4 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 1 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 3 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 2 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 4 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 1 finished in 14.1 seconds.
#> Chain 3 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 4 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 4 finished in 14.7 seconds.
#> Chain 2 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 3 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 2 finished in 15.0 seconds.
#> Chain 3 finished in 15.0 seconds.
#> 
#> All 4 chains finished successfully.
#> Mean chain execution time: 14.7 seconds.
#> Total execution time: 15.0 seconds.
#>