Mormons and the Environment

mormon_env.R

## Intro Stuff ####


## Load packages
library(ggsci)
library(socsci)
library(showtext)
library(car)
library(haven)

cces16 <- read_dta("D://cces/data/cces16.dta")

## This a quick template function I use for my plots 
theme_gg <- function(fff, base_size = 44, base_family = "font") 
{
  
  font_add_google(fff, "font")
  showtext_auto()
  
  theme_minimal() +
    theme(legend.position = "none") +
    theme(legend.title = element_blank()) +
    theme(text=element_text(size=44, family="font"))
  
}


## Load your CCES Data call it cces16
## Then run the evangelical RELTRAD coding that can be found here: https://github.com/ryanburge/reltrad/blob/master/CCES/reltrad16.R


## Concern for the Environment Bar Graph ####


aa1 <- cces16 %>% 
  filter(religpew == 3) %>% 
  filter(CC16_301h < 6) %>% 
  ct(CC16_301h, wt = commonweight_vv_post) %>% 
  mutate(group = "Mormons")


aa2 <- cces16 %>% 
  filter(evangelical ==1) %>% 
  filter(CC16_301h < 6) %>% 
  ct(CC16_301h, wt = commonweight_vv_post) %>% 
  mutate(group = "Evangelicals")

aa3 <- cces16 %>% 
  filter(pid7 == 5 | pid7 == 6 | pid7 ==7) %>% 
  filter(CC16_301h < 6) %>% 
  ct(CC16_301h, wt = commonweight_vv_post) %>% 
  mutate(group = "Republicans")


graph <- bind_df("aa")

graph <- graph %>% 
  mutate(env = frcode(CC16_301h == 5 ~ "No Importance",
                      CC16_301h == 4 ~ "Very Low", 
                      CC16_301h == 3 ~ "Somewhat Low",
                      CC16_301h == 2 ~ "Somewhat High", 
                      CC16_301h == 1 ~ "Very High", 
                      TRUE ~ "REMOVE"))

graph %>% 
  ggplot(., aes(x= env, y = pct, fill = group)) +
  geom_col(color = "black") +
  theme_gg("Abel") +
  facet_grid(~ group) +
  scale_fill_jama() +
  scale_y_continuous(labels = percent) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  geom_text(aes(y = pct + .015, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 10, family = "font") +
  labs(x = "", y = "", title = "How Important is the Environment to You?" , caption = "Data: CCES 2016") +
  ggsave("D://mormons_imp_env.png", width = 7)

## I just wanted to check the means for all three

aa1 <- cces16 %>% 
  filter(religpew == 3) %>% 
  filter(CC16_301h < 6) %>% 
  mean_ci(CC16_301h, wt = commonweight_vv_post) %>% 
  mutate(group = "Mormons")


aa2 <- cces16 %>% 
  filter(evangelical ==1) %>% 
  filter(CC16_301h < 6) %>% 
  mean_ci(CC16_301h, wt = commonweight_vv_post) %>% 
  mutate(group = "Evangelicals")

aa3 <- cces16 %>% 
  filter(pid7 == 5 | pid7 == 6 | pid7 ==7) %>% 
  filter(CC16_301h < 6) %>% 
  mean_ci(CC16_301h, wt = commonweight_vv_post) %>% 
  mutate(group = "Republicans")

bind_df("aa")

## 1 is Very Concerned, 5 is No Concern at All 
#
# mean       sd    n         se    lower    upper        group
# 1 2.569200 1.255490  182 0.09306311 2.385572 2.752828      Mormons
# 2 2.606419 1.214000 2797 0.02295475 2.561409 2.651428 Evangelicals
# 3 2.939406 1.173163 4756 0.01701130 2.906056 2.972756  Republicans


### Making the Env Protections Dots Plots ####


## Making a quick Republican dichotomous variable 
cces16 <- cces16 %>% 
  mutate(repub = recode(pid7, "5:7=1; else =0"))


## Need to make a function to do this analysis quickly. 
compare <- function(df, var1, des){
  var1 <- enquo(var1)
  
  df1 <- df %>% 
    mutate(env = recode(!! var1, "1=1; 2=0; else = NA")) %>% 
    filter(religpew ==3) %>% 
    mean_ci(env) %>% 
    mutate(group = "Mormons")
  
  df2 <- df %>% 
    mutate(env = recode(!! var1, "1=1; 2=0; else = NA")) %>% 
    filter(evangelical ==1) %>% 
    mean_ci(env) %>% 
    mutate(group = "Evangelicals")
  
  df3 <- df %>% 
    mutate(env = recode(!! var1, "1=1; 2=0; else = NA")) %>% 
    filter(repub ==1) %>% 
    mean_ci(env) %>% 
    mutate(group = "Republicans")
  
  df4 <- bind_rows(df1, df2, df3) %>% 
    mutate(ques = des)
}

## Running the function for the four questions
bb1 <- cces16 %>% compare(CC16_333a, "Give EPA Power over CO2")
bb2 <- cces16 %>% compare(CC16_333b, "Raise Fuel Efficiency")
bb3 <- cces16 %>% compare(CC16_333c, "Require Green Fuels")
bb4 <- cces16 %>% compare(CC16_333d, "Strengthen Clear Air/Water Acts")


## Binding
graph <- bind_df("bb") %>% as.tibble()

## Graphing 
graph %>% 
  ggplot(., aes(y=mean, x= fct_reorder(ques, mean), color = group)) +
  geom_point(position=position_dodge(width=0.5), size =3) +
  geom_errorbar(aes(ymin = lower, ymax=upper), position=position_dodge(0.5), size = 1) +
  coord_flip() +
  theme_gg("Abel") +
  labs(title = "Mormons' Views on Environmental Issues", x = "Issue Area", y = "Percent in Favor", caption = "Data: CCES 2016") +
  scale_y_continuous(labels = percent, limits = c(.25, .7)) +
  scale_color_jama() + 
  theme(legend.position = "bottom") +  
  theme(legend.title=element_blank()) +
  ggsave("D://mormon_env_dots.png", width = 8, height = 4)



## Comparing Mormons to Everyone Else ####


## Writing a function to compute the mean for all four questions 

fun <- function(df, relig, fam){
  relig <- enquo(relig)
  fam <- enquo(fam)
  
  df %>% 
    group_by(!! relig) %>% 
    mutate(env1 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>%
    mutate(env2 = recode(CC16_333b, "1=1; 2=0; else = NA")) %>%
    mutate(env3 = recode(CC16_333c, "1=1; 2=0; else = NA")) %>%
    mutate(env4 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>% 
    mutate(envir = env1 + env2 + env3 + env4) %>% 
    mean_ci(envir) %>% 
    filter(n > 100) %>% 
    mutate(relig = to_factor(!! relig)) %>% 
    filter(relig != "Skipped") %>% 
    select(relig, mean, sd, n, level, se, lower, upper) %>% 
    mutate(family = !! fam)
  
}

## Running this for all the Protestant Groups
aa <- fun(cces16, religpew_methodist, "Methodist")
aa1 <- fun(cces16, religpew_baptist, "Baptist")
aa2 <- fun(cces16, religpew_nondenom, "Non-Denom")
aa3 <- fun(cces16, religpew_lutheran, "Lutheran")
aa4 <- fun(cces16, religpew_presby, "Presbyterian")
aa5 <- fun(cces16, religpew_pentecost, "Pentecostal")
aa6 <- fun(cces16, religpew_episcop, "Episcopal")
aa7 <- fun(cces16, religpew_congreg, "Congregational")
aa8 <- fun(cces16, religpew_holiness, "Holiness")
aa9 <- fun(cces16, religpew_reformed, "Reformed")

## Now running it for all the larger traditions

aa10 <- cces16 %>% 
  group_by(religpew) %>% 
  mutate(env1 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>%
  mutate(env2 = recode(CC16_333b, "1=1; 2=0; else = NA")) %>%
  mutate(env3 = recode(CC16_333c, "1=1; 2=0; else = NA")) %>%
  mutate(env4 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>% 
  mutate(envir = env1 + env2 + env3 + env4) %>% 
  mean_ci(envir) %>% 
  filter(n > 100) %>% 
  mutate(relig = to_factor(religpew)) %>% 
  filter(relig != "Protestant") %>% 
  select(-religpew)


## Adding a family column by hand
aa10$family <- c("Catholic", "Mormon", "Orthodox", "Jewish", "Other Religion", "Other Religion", "Other Religion", "None", "None", "None", "Other Religion") 

## Binding everything together, but then I want to give the Mormon column a different color 
## So there is a column with 1 for the Mormon, and zero for everyon else
graph <- bind_df("aa") %>% 
   mutate(color = case_when(relig == "Mormon" ~ 1,
                           TRUE ~ 0 ))

graph %>% 
  ggplot(., aes(y=mean, x= fct_reorder(relig, mean), color = factor(color))) +
  geom_point(size = 2) +
  geom_errorbar(aes(ymin = lower, ymax=upper), size = 1) +
  coord_flip() +
  scale_color_manual(values = c("azure4", "firebrick3")) +
  theme_gg("Abel") +
  labs(title = "Support for Environmental Protection by Religious Tradition", y = "<--- Less Support for the Environment:More Support for the Environment --->", x = "Religious Tradition", caption = "Data: CCES 2016") +
  theme(legend.position = "none") +  
  theme(legend.title=element_blank()) +
  theme(plot.title = element_text(size = 64)) +
  geom_hline(yintercept = 2.70, linetype = "dashed") +
  annotate("text", x=48, y = 2.55, label = "Sample Avg.", size = 16, family = "font") +
  annotate("text", x=46, y = 2.62, label = "2.70", size = 16, family = "font") +
  ggsave("D://mormon_env_dots_all.png", width = 12, height = 8)

## I calculated the mean for the entire sample -- so I can shoot a vertical line in the graph to compare the groups to. 

cces16 %>% 
  mutate(env1 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>%
  mutate(env2 = recode(CC16_333b, "1=1; 2=0; else = NA")) %>%
  mutate(env3 = recode(CC16_333c, "1=1; 2=0; else = NA")) %>%
  mutate(env4 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>% 
  mutate(envir = env1 + env2 + env3 + env4) %>% 
  mean_ci(envir)

# 
# mean    sd     n level      se lower upper
# <dbl> <dbl> <int> <dbl>   <dbl> <dbl> <dbl>
#   1  2.70  1.53 64600  0.05 0.00603  2.69  2.71


## Making the Last Graph Comparing by PID ####

graph1 <- cces16 %>% 
  filter(religpew == 3) %>% 
  filter(pid3 <= 3) %>% 
  mutate(env1 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>%
  mutate(env2 = recode(CC16_333b, "1=1; 2=0; else = NA")) %>%
  mutate(env3 = recode(CC16_333c, "1=1; 2=0; else = NA")) %>%
  mutate(env4 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>% 
  mutate(envir = env1 + env2 + env3 + env4) %>% 
  group_by(pid3) %>% 
  mean_ci(envir) %>% 
  mutate(group = "Mormons")


graph2 <- cces16 %>% 
  filter(evangelical == 1) %>% 
  filter(pid3 <= 3) %>% 
  mutate(env1 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>%
  mutate(env2 = recode(CC16_333b, "1=1; 2=0; else = NA")) %>%
  mutate(env3 = recode(CC16_333c, "1=1; 2=0; else = NA")) %>%
  mutate(env4 = recode(CC16_333a, "1=1; 2=0; else = NA")) %>% 
  mutate(envir = env1 + env2 + env3 + env4) %>% 
  group_by(pid3) %>% 
  mean_ci(envir) %>% 
  mutate(group = "Evangelicals")

graph <- bind_rows(graph1, graph2)

graph <- graph %>% 
  mutate(pid3 = frcode(pid3 ==1 ~ "Democrat",
                       pid3 ==2 ~ "Independent",
                       pid3 ==3 ~ "Republican")) 


graph %>% 
  ggplot(., aes(y=mean, x= pid3, color = pid3)) +
  geom_point(size = 2) +
  geom_errorbar(aes(ymin = lower, ymax=upper), size = 1, width = .25) +
  coord_flip() +
  facet_wrap(~ group, ncol = 1) +
  scale_color_manual(values = c("dodgerblue3", "azure4", "firebrick3")) +
  theme_gg("Abel") +
  scale_y_continuous(limits = c(1,4)) +
  labs(title = "Support for Environmental Protection by Party ID", y = "<--- Less Support for the Environment:More Support for the Environment --->", x = "Religious Tradition", subtitle = "", caption = "Data: CCES 2016") +
  theme(legend.position = "none") +  
  theme(legend.title=element_blank()) +
  theme(plot.title = element_text(size = 64)) +
  ggsave("D://mormon_3pid.png", width = 12, height = 6)