ct_selection_untitled-7192A714.R

df <- read.fst("E://data/anes22.fst")

df <- df %>%
  mutate(
    relig = VCF0129,
    attend = case_when(
      VCF0130 %in% 1:3 ~ 2,
      VCF0130 == 4 ~ 1,
      TRUE ~ 0
    ),
    race = VCF0106,
    black = if_else(race == 2, 1, 0),
    prot1 = if_else(relig == 100, 1, 0),
    prot2 = if_else(relig == 101, 1, 0),
    evan = case_when(
      black != 1 & relig %in% c(123, 114, 122, 136, 131, 135, 139) ~ 1,
      prot1 == 1 & attend == 2 ~ 1,
      prot2 == 1 & attend == 2 ~ 1,
      TRUE ~ 0
    ),
    mline = case_when(
      relig %in% c(110, 115, 116, 111, 124, 112, 155) ~ 1,
      black != 1 & relig == 120 ~ 1,
      prot1 == 1 & attend == 1 ~ 1,
      prot2 == 1 & attend == 1 ~ 1,
      TRUE ~ 0
    ),
    bprot = if_else(
      black == 1 & relig %in% c(123, 120, 136, 131, 135, 139), 1, 0
    ),
    cath = if_else(relig %in% c(719, 200), 1, 0),
    jew = if_else(relig == 300, 1, 0),
    other = if_else(relig %in% c(152, 154, 156, 720, 721, 722, 790), 1, 0),
    none = if_else(relig %in% c(800, 998), 1, 0),
    reltrad = case_when(
      none == 1 ~ 7,
      other == 1 ~ 6,
      jew == 1 ~ 5,
      cath == 1 ~ 4,
      bprot == 1 ~ 3,
      mline == 1 ~ 2,
      evan == 1 ~ 1,
      TRUE ~ NA_real_ # Assigns NA to reltrad for cases that do not match any condition
    )
  )

# Re-evaluate prot1 and prot2 conditions considering 'black'
df <- df %>%
  mutate(
    prot1 = if_else(black == 1, 0, prot1),
    prot2 = if_else(black == 1, 0, prot2)
  )


graph1 <- df %>% 
  cces_pid3(VCF0301) %>% 
  filter(reltrad == 1 & race == 1) %>%
  group_by(VCF0004) %>% 
  ct(pid3, wt = VCF0009z, show_na = FALSE)




df <- read.fst("E://data/anes22.fst")

# Copying variables
df$relig <- df$VCF0152
df$attend <- df$VCF0130
df$race <- df$VCF0106

# Recoding attend
df$attend <- case_when(
  df$attend %in% 1:3 ~ 2,
  df$attend == 4 ~ 1,
  TRUE ~ 0
)

# Recoding race to black
df$black <- ifelse(df$race == 2, 1, 0)

# Creating prot variables and recoding
prot_vars <- c("prot1", "prot2", "prot3", "prot4", "prot5")
relig_codes <- c("010", "020", "030", "040", "99")

for (i in 1:length(prot_vars)) {
  df[[prot_vars[i]]] <- ifelse(df$relig == relig_codes[i], 1, 0)
  df[[prot_vars[i]]] <- ifelse(df$black == 1, 0, df[[prot_vars[i]]])
}

# Creating and recoding evan
evan_codes <- c(100, 102, 109, 120, 122, 123, 124, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135,
                147, 148, 149, 160, 161, 162, 164, 166, 167, 168, 170, 180, 181, 182, 183, 184, 185, 
                186, 199, 200, 201, 219, 221, 222, 223, 233, 234, 250, 251, 252, 253, 254, 255, 256, 
                260, 261, 262, 263, 264, 268, 269, 271, 272, 275, 276, 280, 291, 292)

df$evan <- ifelse(df$relig %in% evan_codes & df$black != 1, 1, 0)
df$evan <- ifelse(!df$relig %in% evan_codes, 0, df$evan)

# Recoding for attendance and prot variables
for (prot in prot_vars) {
  df$evan <- ifelse(df[[prot]] == 1 & df$attend == 2, 1, df$evan)
}

# Creating and recoding mline
mline_codes <- c(121, 249, 110, 111, 150, 155, 163, 165, 220, 229, 230, 270, 279, 281, 282, 289, 290, 293)
df$mline <- ifelse(df$relig %in% mline_codes & df$black != 1, 1, 0)
df$mline <- ifelse(!df$relig %in% mline_codes, 0, df$mline)

# Recoding for attendance and prot variables
for (prot in prot_vars) {
  df$mline <- ifelse(df[[prot]] == 1 & df$attend == 1, 1, df$mline)
}

# Creating and recoding bprot
bprot_codes <- c(120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 147, 148, 149, 231, 232, 233, 234, 240, 249, 257, 258, 267)
df$bprot <- ifelse(df$relig %in% bprot_codes & df$black == 1, 1, 0)
df$bprot <- ifelse(!df$relig %in% bprot_codes, 0, df$bprot)



df <- df %>%
  mutate(
    cath = if_else(relig %in% c(400, 700:708, 719), 1, 0),
    jew = if_else(relig %in% c(500:503, 524), 1, 0),
    other = if_else(relig %in% c(300:306, 309, 720:727, 729, 750, 790, 795, 997, 998), 1, 0),
    none = if_else(relig %in% c(800, 801), 1, 0),
    reltrad = case_when(
      none == 1 ~ 7,
      other == 1 ~ 6,
      jew == 1 ~ 5,
      cath == 1 ~ 4,
      bprot == 1 ~ 3,
      mline == 1 ~ 2,
      evan == 1 ~ 1,
      TRUE ~ NA_real_ # This assigns NA to reltrad where none of the conditions are met
    )
  )


graph2 <- df %>% 
  cces_pid3(VCF0301) %>% 
  filter(reltrad == 1 & race == 1) %>%
  group_by(VCF0004) %>% 
  ct(pid3, wt = VCF0009z, show_na = FALSE)

both <- bind_rows(graph1, graph2)

both %>% 
  ggplot(., aes(x = VCF0004, y = pct, fill = pid3)) +
  geom_area(alpha = .6, color = "black") +
  pid3_fill() +
  theme_rb() +
  y_pct() +
  labs(x = "", y = "Survey Year", title = "Partisanship of White Evangelicals", caption = "@ryanburge\nData: American National Election Study, 1960-2020") +
  add_text(x = 1962, y = .10, word = " 30%", sz = 7) +
  add_text(x = 1962, y = .31, word = " 9%", sz = 7) +
  add_text(x = 1962, y = .68, word = " 61%", sz = 7) +
  add_text(x = 2018, y = .35, word = "78%", sz = 7) +
  add_text(x = 2018, y = .78, word = "8%", sz = 7) +
  add_text(x = 2018, y = .92, word = "16%", sz = 7) +
  add_text(x = 1990, y = .82, word = "Democrats", sz = 7) +
  add_text(x = 1990, y = .57, word = "Independents", sz = 7) +
  add_text(x = 1990, y = .28, word = "Republicans", sz = 7) 
save("wht_evan_pid3_new.png")


pid <- both %>% 
  filter(pid3 == "Republican") %>% 
  select(year = VCF0004, pct) %>% 
  mutate(type = "Rep.\nID")

pres <- all %>% 
  filter(vote == "Republican") %>% 
  select(year, pct) %>% 
  mutate(type = "Rep.\nVote")

final <- bind_rows(pid, pres) %>% 
  filter(year == 1960 | year == 1964 | year == 1968 | year == 1972 | year == 1976 | year == 1980 | year == 1984 | year == 1988 | year == 1992 | year == 1996 | year == 2000 | year == 2004 | year == 2008 | year == 2012 | year == 2016 | year == 2020)


final %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = type, y = pct, fill = type)) +
  geom_col(color = "black") +
  facet_wrap(~ year) +
  theme_rb() + 
  scale_fill_calc() +
  scale_y_continuous(labels = percent, limits = c(0, .95)) +
  geom_text(aes(y = pct + .08, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 8, family = "font") +
  labs(x = "", y = "", title = "Partisanship and Voting Behavior Among White Evangelicals", caption = "@ryanburge\nData: American National Election Study, 1960-2020")
save("wev_anes_vote_pid3.png", wd = 6, ht = 10)



pid <- both %>% 
  filter(pid3 == "Republican") %>% 
  select(year = VCF0004, pid = pct) 

pres <- all %>% 
  filter(vote == "Republican") %>% 
  select(year, vote = pct) 

graph <- left_join(pid, pres) %>% 
  filter(vote != "NA") %>% 
  mutate(diff = vote - pid)

graph %>% 
  ggplot(., aes(x = year, y = diff)) +
  geom_line() + 
  geom_point(stroke = 1, shape = 21, fill = "white") + 
  geom_smooth(se = FALSE, color = "firebrick3", linetype = "twodash") +
  theme_rb() + 
  scale_y_continuous(labels = percent, limits = c(0, .5)) +
  labs(x = "", y = "", title = "Republican Vote Share - Republican Partisanship Among White Evangelicals", caption = "@ryanburge\nData: American National Election Study, 1960-2020")
save("pid_vote_wev_trendline.png", wd = 10, ht = 6)