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Pro Life Atheists or Pro Life Liberals
## Distribution of Abortion Opinion by Ideology ####
abg <- cces16 %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
mutate(ideo3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal",
ideo5 == 3 ~ "Moderate",
ideo5 == 4 | ideo5 == 5 ~ "Conservative",
TRUE ~ "REMOVE")) %>%
filter(abort != "NA") %>%
group_by(ideo3) %>%
ct(abort)
abg %>%
filter(ideo3 != "REMOVE") %>%
ggplot(.,aes(x = abort, y = pct, fill = ideo3)) +
geom_col(color = "black") +
facet_wrap(~ ideo3, ncol= 3) +
scale_fill_manual(values = c("dodgerblue3", "azure3", "firebrick3")) +
theme_gg("Abel") +
scale_y_continuous(labels = percent) +
labs(x = "<- More Pro-Choice:More Pro-Life -->", y = "", title = "Distribution of Abortion Opinion by Political Ideology", caption = "Data: CCES 2016") +
geom_text(aes(y = pct + .015, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 3, family = "font") +
ggsave("D://cces/images/abort_ideo3.png")
## More than Two Quesstions Yes ####
graph <- cces16 %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
mutate(prolife = recode(abort, "2:5=1; else =0")) %>%
mutate(nones = frcode(religpew == 1 ~ "Protestant",
religpew == 2 ~ "Catholic",
religpew == 3 ~ "Mormon",
religpew == 5 ~ "Jewish",
religpew == 6 ~ "Muslim",
religpew == 7 ~ "Buddhist",
religpew == 8 ~ "Hindu",
religpew == 9 ~ "Atheist",
religpew == 10 ~ "Agnostic",
religpew == 11 ~ "Nothing\nin\nParticular",
TRUE ~ "REMOVE")) %>%
filter(abort != "NA") %>%
group_by(nones) %>%
mean_ci(prolife)
graph1 <- cces16 %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
mutate(prolife = recode(abort, "2:5=1; else =0")) %>%
filter(evangelical == 1) %>%
filter(abort != "NA") %>%
mean_ci(prolife) %>%
mutate(nones = "Evangelical")
graph2 <- cces16 %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
mutate(prolife = recode(abort, "2:5=1; else =0")) %>%
filter(mainline == 1) %>%
filter(abort != "NA") %>%
mean_ci(prolife) %>%
mutate(nones = "Mainline")
graph <-bind_rows(graph, graph1, graph2)
showtext_opts(dpi = 300)
graph %>%
mutate(mean = round(mean, 3)) %>%
filter(nones != "REMOVE") %>%
filter(nones != "Protestant") %>%
ggplot(., aes(x = reorder(nones, -mean), y = mean, fill = mean)) +
geom_col(color = "black") +
scale_fill_gradient(low = "#1565C0", high = "#b92b27") +
geom_errorbar(aes(ymin=lower, ymax=upper), width=.2, position=position_dodge(.9)) +
theme_gg("Abel") +
scale_y_continuous(labels = percent) +
geom_text(aes(y = .08 , label = paste0(mean*100, '%')), position = position_dodge(width = .9), size = 4, family = "font") +
labs(x = "", y = "", title = "Percent Who Agree With At Least Two Pro-Life Questions", caption = "Data: CCES 2016") +
ggsave("D://cces/images/abortion_rel.png", width = 8)
## Individual Question Means ####
ab_fun <- function(df, var, var1){
var <- enquo(var)
df1 <- df %>%
filter(religpew == 9) %>%
mutate(ab = recode(!! var, "1=1; 2=0; else = NA")) %>%
mean_ci(ab, ci = .84) %>%
mutate(group = " Atheist ")
df2 <- df %>%
mutate(ab = recode(!! var, "1=1; 2=0; else = NA")) %>%
filter(religpew == 10) %>%
mean_ci(ab, ci = .84) %>%
mutate(group = " Agnostic ")
bind_rows(df1, df2) %>%
mutate(ques = var1)
}
fff <- cces16 %>% ab_fun(CC16_332a, "Matter of\nChoice")
fff1 <- cces16 %>% ab_fun(CC16_332b, "Only in\nRape or Incest")
fff2 <- cces16 %>% ab_fun(CC16_332c, "Prohibit After\n20 Weeks")
fff3 <- cces16 %>% ab_fun(CC16_332d, "Employers Decline\nAbortion Coverage")
fff4 <- cces16 %>% ab_fun(CC16_332e, "Prohibit Federal\nFunds")
fff5 <- cces16 %>% ab_fun(CC16_332f, "Completely\nIllegal")
graph <- bind_df("fff")
graph %>%
mutate(mean = round(mean, 3)) %>%
ggplot(., aes(x = ques, y = mean, fill = group)) +
geom_col(color = "black", position = "dodge")+
geom_errorbar(aes(ymin=lower, ymax=upper), width=.2, position=position_dodge(.9)) +
theme_gg("Abel") +
scale_y_continuous(labels = percent) +
scale_fill_manual(values = c("#A7414A", "#A37C27")) +
geom_text(aes(y = .08 , label = paste0(mean*100, '%')), position = position_dodge(width = .9), size = 4, family = "font") +
theme(legend.position = "bottom") +
labs(x= "", y = "Percent that Support", title = "Where Do Atheists and Agnostics Differ?", caption = "Data: CCES 2016") +
ggsave("D://cces/images/abort_ag_ath.png", width = 7)
## Abortion Importance ####
graph <- cces16 %>%
group_by(religpew) %>%
filter(CC16_301b <=5) %>%
ct(CC16_301b, wt = commonweight_vv) %>%
ungroup(religpew) %>%
mutate(religpew = frcode(religpew == 1 ~ "Protestant",
religpew == 2 ~ "Catholic",
religpew == 3 ~ "Mormon",
religpew == 5 ~ "Jewish",
religpew == 6 ~ "Muslim",
religpew == 7 ~ "Buddhist",
religpew == 8 ~ "Hindu",
religpew == 9 ~ "Atheist",
religpew == 10 ~ "Agnostic",
religpew == 11 ~ "Nothing\nin\nParticular",
TRUE ~ "REMOVE")) %>%
mutate(CC16_301b = frcode(CC16_301b == 5 ~ "No Importance",
CC16_301b == 4 ~ "Very Low",
CC16_301b == 3 ~ "Somewhat Low",
CC16_301b == 2 ~ "Somewhat High",
CC16_301b == 1 ~ "Very High"))
graph1 <- cces16 %>%
filter(evangelical ==1) %>%
filter(CC16_301b <=5) %>%
ct(CC16_301b, wt = commonweight_vv) %>%
ungroup(religpew) %>%
mutate(CC16_301b = frcode(CC16_301b == 5 ~ "No Importance",
CC16_301b == 4 ~ "Very Low",
CC16_301b == 3 ~ "Somewhat Low",
CC16_301b == 2 ~ "Somewhat High",
CC16_301b == 1 ~ "Very High")) %>%
mutate(religpew = "Evangelical")
graph2 <- cces16 %>%
filter(mainline ==1) %>%
filter(CC16_301b <=5) %>%
ct(CC16_301b, wt = commonweight_vv) %>%
ungroup(religpew) %>%
mutate(CC16_301b = frcode(CC16_301b == 5 ~ "No Importance",
CC16_301b == 4 ~ "Very Low",
CC16_301b == 3 ~ "Somewhat Low",
CC16_301b == 2 ~ "Somewhat High",
CC16_301b == 1 ~ "Very High")) %>%
mutate(religpew = "Mainline")
graph <- bind_rows(graph, graph1, graph2)
graph %>%
filter(religpew != "REMOVE") %>%
filter(religpew != "Protestant") %>%
ggplot(., aes(x = CC16_301b, y = pct, fill = religpew)) +
geom_col(color = "black") +
facet_wrap(~ religpew) +
theme_gg("Abel") +
scale_fill_d3(palette = "category20") +
scale_y_continuous(labels = percent) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_text(aes(y = pct + .025 , label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 3, family = "font") +
labs(x = "Importance of Abortion", y = "", title = "Importance of Abortion by Religious Tradition", caption = "Data: CCES 2016") +
ggsave("D://cces/images/facet_abort.png", height = 8)
## Link Between Importance and Policy ####
graph1 <- cces16 %>%
mutate(nones = recode(religpew, "9:10 =1; else = 0")) %>%
mutate(CC16_301b = frcode(CC16_301b == 5 ~ "No Importance",
CC16_301b == 4 ~ "Very Low",
CC16_301b == 3 ~ "Somewhat Low",
CC16_301b == 2 ~ "Somewhat High",
CC16_301b == 1 ~ "Very High")) %>%
group_by(nones, CC16_301b) %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
mean_ci(abort, ci = .84) %>%
na.omit() %>%
filter(nones ==1) %>%
mutate(group = "Atheist/Agnostic")
graph2 <- cces16 %>%
filter(evangelical ==1) %>%
mutate(CC16_301b = frcode(CC16_301b == 5 ~ "No Importance",
CC16_301b == 4 ~ "Very Low",
CC16_301b == 3 ~ "Somewhat Low",
CC16_301b == 2 ~ "Somewhat High",
CC16_301b == 1 ~ "Very High")) %>%
group_by(CC16_301b) %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
mean_ci(abort, ci = .84) %>%
na.omit() %>%
mutate(group = "Evangelicals")
graph <- bind_rows(graph1, graph2)
graph %>%
ggplot(., aes(x = mean, y = CC16_301b)) +
geom_point(shape=21, size =3, aes(fill = factor(group)), show.legend = TRUE) +
geom_errorbarh(aes(xmin = lower, xmax=upper, colour = factor(group)), height=0, size = 1, show.legend = FALSE) +
scale_color_manual(values = c("darkorchid", "forestgreen")) +
scale_fill_manual(values = c("darkorchid", "forestgreen")) +
theme_gg("Abel") +
theme(legend.position = c(0.5, 0.8)) +
scale_x_continuous(limits = c(0, 4)) +
labs(x = "<- More Pro-Choice:More Pro-Life -->", y ="Importance of Abortion", title = "Abortion Importance is Polarizing", caption = "Data: CCES 2016", subtitle = "84% Confidence Intervals") +
ggsave("D://cces/images/ab_imp.png", width = 7)
cces16 %>%
filter(religpew == 9 | religpew == 10) %>%
mutate(CC16_301b = frcode(CC16_301b == 5 ~ "No Importance",
CC16_301b == 4 ~ "Very Low",
CC16_301b == 3 ~ "Somewhat Low",
CC16_301b == 2 ~ "Somewhat High",
CC16_301b == 1 ~ "Very High")) %>%
mutate(ab1 = recode(CC16_332a, "2=1; 1=0; else = NA")) %>%
mutate(ab2 = recode(CC16_332c, "1=1; 2=0; else = NA")) %>%
mutate(ab3 = recode(CC16_332d, "1=1; 2=0; else = NA")) %>%
mutate(ab4 = recode(CC16_332e, "1=1; 2=0; else = NA")) %>%
mutate(ab5 = recode(CC16_332f, "1=1; 2=0; else = NA")) %>%
mutate(abort = ab1 + ab2 + ab3 + ab4 + ab5) %>%
filter(CC16_301b == "Very High") %>%
ct(abort)
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