Create a gist now

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Making Dumbbell Plots
#### Racial Groups ####
a1 <- cces16 %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Entire Sample")) %>%
mutate(attend = c("All Levels"))
a2 <-cces16 %>%
filter(CC16_410a < 3) %>%
mutate(pres2 = recode(CC16_410a, "1=1; else=0")) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Entire Sample")) %>%
mutate(attend = c("High Attenders"))
a3 <-cces16 %>%
filter(CC16_410a < 3) %>%
filter(race ==2) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Black")) %>%
mutate(attend = c("All Levels"))
a4 <-cces16 %>%
filter(race ==2) %>%
filter(CC16_410a < 3) %>%
mutate(pres2 = recode(CC16_410a, "1=1; else=0")) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Entire Black Sample")) %>%
mutate(attend = c("High Attenders"))
a5 <- cces16 %>%
filter(race ==3) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Hispanic")) %>%
mutate(attend = c("All Levels"))
a6 <- cces16 %>%
filter(CC16_410a < 3) %>%
filter(race ==3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Entire Hispanic Sample")) %>%
mutate(attend = c("High Attenders"))
a7 <- cces16 %>%
filter(race ==4) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Asian")) %>%
mutate(attend = c("All Levels"))
a8 <- cces16 %>%
filter(CC16_410a < 3) %>%
filter(race ==4) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Entire Asian Sample")) %>%
mutate(attend = c("High Attenders"))
a9 <- cces16 %>%
filter(race ==1) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("White")) %>%
mutate(attend = c("All Levels"))
a10 <- cces16 %>%
filter(CC16_410a < 3) %>%
filter(race ==1) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Entire White Sample")) %>%
mutate(attend = c("High Attenders"))
race1 <- bind_rows(a1, a3, a5, a7, a9) %>%
select(pct, group) %>%
rename(all_pct = pct)
race2 <- bind_rows(a2, a4, a6, a8, a10) %>%
select(pct, group) %>%
rename(high_pct = pct)
race <- bind_cols(race1, race2)
race$group <- factor(race$group, levels = c("Asian", "Hispanic", "Black", "White", "Entire Sample"))
race %>%
ggplot(., aes(x=all_pct, xend=high_pct, y=group, group=group)) +
geom_dumbbell(size_x = 4, size_xend = 4, size = 1, color="azure3",
colour_x = "darkorchid", colour_xend = "tomato",
dot_guide=FALSE, dot_guide_size=0.05) + mean_rb() +
theme(plot.title = element_text(size=24)) +
scale_x_continuous(labels = scales::percent) +
geom_text(data=filter(race, group == "Entire Sample"), aes(x=all_pct, y=group, label="All Levels"), color="darkorchid", size=4, vjust=-1, fontface="bold", family="Product Sans") +
geom_text(data=filter(race, group == "Entire Sample"), aes(x=high_pct, y=group, label="Weekly+ Attenders"), color="tomato", size=4, vjust=-1, fontface="bold", family="Product Sans") +
labs(x = "Percent of Two Party Vote for Trump", y = "Racial Group", subtitle = "", caption = "Data: CCES 2016", title = "The Impact of Frequent Attendance on Vote Choice by Race")
ggsave(file="D://cces/high_att_dumbbell_race.png", type = "cairo-png", width = 12, height = 6)
#### Denoms ####
b1 <- cces16 %>%
filter(religpew ==2) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Catholics")) %>%
mutate(attend = c("All Levels"))
b2 <- cces16 %>%
filter(CC16_410a < 3) %>%
filter(religpew == 2) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Catholics")) %>%
mutate(attend = c("High Attenders"))
b3 <- cces16 %>%
filter(religpew_baptist ==1) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Southern Baptists")) %>%
mutate(attend = c("All Levels"))
b4 <- cces16 %>%
filter(religpew_baptist == 1) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Southern Baptists")) %>%
mutate(attend = c("High Attenders"))
b5 <- cces16 %>%
filter(religpew_protestant ==3) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Nondenominational")) %>%
mutate(attend = c("All Levels"))
b6 <- cces16 %>%
filter(religpew_protestant == 3) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Nondenominational")) %>%
mutate(attend = c("High Attenders"))
b7 <- cces16 %>%
filter(religpew_methodist ==1) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("United Methodist")) %>%
mutate(attend = c("All Levels"))
b8 <- cces16 %>%
filter(religpew_methodist ==1) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("United Methodist")) %>%
mutate(attend = c("High Attenders"))
b9 <- cces16 %>%
filter(religpew_lutheran ==1) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("ELCA")) %>%
mutate(attend = c("All Levels"))
b10 <- cces16 %>%
filter(religpew_lutheran ==1) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("ELCA")) %>%
mutate(attend = c("High Attenders"))
b11 <- cces16 %>%
filter(religpew ==3) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Mormon")) %>%
mutate(attend = c("All Levels"))
b12 <- cces16 %>%
filter(religpew ==3) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Mormon")) %>%
mutate(attend = c("High Attenders"))
denom1 <- bind_rows(b1, b3, b5, b7, b9, b11) %>%
select(pct, group) %>%
rename(all_pct = pct)
denom2 <- bind_rows(b2, b4, b6, b8, b10, b12) %>%
select(pct, group) %>%
rename(high_pct = pct)
denom <- bind_cols(denom1, denom2)
denom$group <- factor(denom$group, levels = c("Mormon", "ELCA", "United Methodist", "Nondenominational", "Southern Baptists", "Catholics"))
denom %>%
ggplot(., aes(x=all_pct, xend=high_pct, y=group, group=group)) +
geom_dumbbell(size_x = 4, size_xend = 4, size = 1, color="azure3",
colour_x = "darkorchid", colour_xend = "tomato",
dot_guide=FALSE, dot_guide_size=0.05) + mean_rb() +
theme(plot.title = element_text(size=24)) +
scale_x_continuous(limits = c(.45,.85), labels = scales::percent) +
geom_text(data=filter(denom, group == "Catholics"), aes(x=all_pct, y=group, label="All Levels"), color="darkorchid", size=4, vjust=-1, fontface="bold", family="Product Sans") +
geom_text(data=filter(denom, group == "Catholics"), aes(x=high_pct, y=group, label="Weekly+ Attenders"), color="tomato", size=4, vjust=-1, fontface="bold", family="Product Sans") +
labs(x = "Percent of Two Party Vote for Trump", y = "Religious Group", subtitle = "", caption = "Data: CCES 2016", title = "The Impact of Frequent Attendance on Vote Choice by Denomination")+
theme(plot.title = element_text(size =18))
ggsave(file="D://cces/high_att_dumbbell_denoms1.png", type = "cairo-png", width = 12, height = 6)
#### Education ####
c1 <- cces16 %>%
filter(educ ==5) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("College Degree")) %>%
mutate(attend = c("All Levels"))
c2 <- cces16 %>%
filter(educ ==5) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("College Degree")) %>%
mutate(attend = c("High Attenders"))
c3 <- cces16 %>%
filter(educ ==1 | educ ==2) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("High School or Less")) %>%
mutate(attend = c("All Levels"))
c4 <- cces16 %>%
filter(educ ==1 | educ ==2) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("High School or Less")) %>%
mutate(attend = c("High Attenders"))
c5 <- cces16 %>%
filter(educ ==6) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Post Grad")) %>%
mutate(attend = c("All Levels"))
c6 <- cces16 %>%
filter(educ ==6) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Post Grad")) %>%
mutate(attend = c("High Attenders"))
ed1 <- bind_rows(c1, c3, c5) %>%
select(pct, group) %>%
rename(all_pct = pct)
ed2 <- bind_rows(c2, c4, c6) %>%
select(pct, group) %>%
rename(high_pct = pct)
ed <- bind_cols(ed1, ed2)
ed$group <- factor(ed$group, levels = c("Post Grad", "College Degree", "High School or Less"))
ed %>%
ggplot(., aes(x=all_pct, xend=high_pct, y=group, group=group)) +
geom_dumbbell(size_x = 4, size_xend = 4, size = 1, color="azure3",
colour_x = "darkorchid", colour_xend = "tomato",
dot_guide=FALSE, dot_guide_size=0.05) + mean_rb() +
theme(plot.title = element_text(size=24)) +
scale_x_continuous(limits = c(.35,.85), labels = scales::percent) +
geom_text(data=filter(ed, group == "High School or Less"), aes(x=all_pct, y=group, label="All Levels"), color="darkorchid", size=4, vjust=-1, fontface="bold", family="Product Sans") +
geom_text(data=filter(ed, group == "High School or Less"), aes(x=high_pct, y=group, label="Weekly+ Attenders"), color="tomato", size=4, vjust=-1, fontface="bold", family="Product Sans") +
labs(x = "Percent of Two Party Vote for Trump", y = "", subtitle = "", caption = "Data: CCES 2016", title = "The Impact of Frequent Attendance on Vote Choice by Education") +
theme(plot.title = element_text(size =18))
ggsave(file="D://cces/high_att_dumbbell_educ1.png", type = "cairo-png", width = 12, height = 6)
#### Ideology ####
d1 <- cces16 %>%
filter(ideo5 == 1 | ideo5 ==2) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Liberals")) %>%
mutate(attend = c("All Levels"))
d2 <- cces16 %>%
filter(ideo5 == 1 | ideo5 ==2) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Liberals")) %>%
mutate(attend = c("High Attenders"))
d3 <- cces16 %>%
filter(ideo5 == 3) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Moderates")) %>%
mutate(attend = c("All Levels"))
d4 <- cces16 %>%
filter(ideo5 == 3) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Moderates")) %>%
mutate(attend = c("High Attenders"))
d5 <- cces16 %>%
filter(ideo5 == 4 | ideo5 ==5) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
mutate(group = c("Conservatives")) %>%
mutate(attend = c("All Levels"))
d6 <- cces16 %>%
filter(ideo5 == 4 | ideo5 ==5) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
mutate(group = c("Conservatives")) %>%
mutate(attend = c("High Attenders"))
ideo1 <- bind_rows(d1, d3, d5) %>%
select(pct, group) %>%
rename(all_pct = pct)
ideo2 <- bind_rows(d2, d4, d6) %>%
select(pct, group) %>%
rename(high_pct = pct)
ideo <- bind_cols(ideo1, ideo2)
ideo$group <- factor(ideo$group, levels = c("Liberals", "Moderates", "Conservatives"))
ideo %>%
ggplot(., aes(x=all_pct, xend=high_pct, y=group, group=group)) +
geom_dumbbell(size_x = 4, size_xend = 4, size = 1, color="azure3",
colour_x = "darkorchid", colour_xend = "tomato",
dot_guide=FALSE, dot_guide_size=0.05) + mean_rb() +
theme(plot.title = element_text(size=24)) +
scale_x_continuous(limits = c(.05,.95), labels = scales::percent) +
geom_text(data=filter(ideo, group == "Liberals"), aes(x=all_pct, y=group, label="All Levels"), color="darkorchid", size=4, vjust=-1, fontface="bold", family="Product Sans") +
geom_text(data=filter(ideo, group == "Liberals"), aes(x=high_pct, y=group, label="Weekly+ Attenders"), color="tomato", size=4, vjust=-1, fontface="bold", family="Product Sans") +
labs(x = "Percent of Two Party Vote for Trump", y = "", subtitle = "", caption = "Data: CCES 2016", title = "The Impact of Frequent Attendance on Vote Choice by Ideology") +
theme(plot.title = element_text(size = 22))
ggsave(file="D://cces/high_att_dumbbell_ideo1.png", type = "cairo-png", width = 12, height = 6)
#### Testing an Idea ####
e1 <- cces16 %>%
filter(ideo5 == 1 | ideo5 == 2) %>%
filter(mainline ==1) %>%
filter(CC16_410a < 3) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
# filter(CC16_410a ==1) %>%
# mutate(group = c("Conservatives")) %>%
mutate(attend = c("All Levels"))
e2 <- cces16 %>%
filter(ideo5 == 1 | ideo5 == 2) %>%
filter(mainline ==1) %>%
filter(CC16_410a < 3) %>%
group_by(pew_churatd) %>%
count(CC16_410a, wt = commonweight_vv_post) %>%
mutate(pct = prop.table(n)) %>%
filter(CC16_410a ==1) %>%
filter(pew_churatd ==1) %>%
ungroup(pew_churatd) %>%
# mutate(group = c("Conservatives")) %>%
mutate(attend = c("High Attenders"))
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment