n-parameters

library(brms)

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library(tidybayes)

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library(gtsummary)

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#Uighur

library(tidyverse)

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library(primer.data)

\[voted ~ treatment + sex\]

data <- shaming |> 
mutate(p_00 = (primary_00 == "Yes"), p_02 = (primary_02 == "Yes"),
       p_04 = (primary_04 == "Yes"), g_00 = (general_00 == "Yes"),
       g_02 = (general_02 == "Yes"), g_04 = (general_04 == "Yes"),
civ_engage = p_00 + p_02 + p_04 + g_00 + g_02 + g_04,
voter_class = case_when(civ_engage %in% c(5, 6) ~ "Always Vote",
                        civ_engage %in% c(3, 4) ~ "Sometimes Vote",
                        civ_engage %in% c(1, 2) ~ "Rarely Vote"),
voter_class = factor(voter_class, levels = c("Rarely Vote", "Sometimes Vote", "Always Vote")), age_z = as.numeric(scale(age))) |>
rename(voted = primary_06) |>
select(voted, treatment, sex, age_z, civ_engage, voter_class) 


data |> sample_frac(0.5) |>
ggplot(aes(x = civ_engage, y = voted)) +
geom_jitter(alpha = 0.03, height = 0.1)+
scale_x_continuous(breaks = 1:6)+
scale_y_continuous(breaks = c(0,1), labels = c("No", "Yes")) +
labs(title = "Civic Engagement and Voting Behavior in Michigan",
subtitle = "Past voting preficts future voting",
x = "Civic Engagement", y = "Voted in 2006 Primary Election")

Made github repo n parameters, connected to R. Made graph explaning that past voting predicts future voting habits. Found out that stability may not hold due to the fact that Michigan and Texas have very different voting patterns. Piped fit_postcard_vote to tbl_regression to find the data of fit_postcard_vote. The quality of interest is how the texas votes are going to go, but comparing it from michigan data with different voters and time may not work out.