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.
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!
#> ℹ Iniciando 4 cadeias de 1000 iterações por candidatura.
#> ℹ Há 30 pesquisas na base entre 01/01/25 e 03/03/26.
#> ℹ 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.
#>
