SETTING UP

packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "MASS", "aod", "interactions", "kableExtra", "flextable", "scales", "ggeffects", "marginaleffects", "viridis", "knitr", "rmarkdown", "ggridges", "questionr") 

new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

lapply(packages, library, character.only = TRUE)
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ess <- read.fst("All-ESS-Data.fst")
croatia_data <- ess %>%
  filter(cntry == "HR")
croatia_data$weight <- croatia_data$dweight * croatia_data$pweight
survey_design <- svydesign(ids = ~1, data = croatia_data, weights = ~weight)

DESCRIPTIVE STATISTICS

croatia_descriptive <- croatia_data %>%
  mutate(
    impenv = case_when (
      impenv == 1 ~ "Very Much Like Me",
      impenv == 2 ~ "Like Me",
      impenv == 3 ~ "Somewhat Like Me",
      impenv == 4 ~ "A Little Like Me",
      impenv == 5 ~ "Not Like Me",
      impenv == 6 ~ "Not Like Me At All",
      impenv %in% c(7,8,9) ~ NA_character_,
      TRUE ~ as.character(impenv)
  )) %>%
  filter (!is.na(impenv))
croatia_descriptive <- croatia_data %>%
  mutate(
       impenv = ifelse(impenv %in% c(7, 8, 9), NA, impenv)
  ) %>%
    filter(!is.na(impenv))
croatia_descriptive <- croatia_descriptive %>%
 mutate(
   lrscale = ifelse(lrscale %in% c(77,88,99), NA, lrscale)) %>%
  filter(!is.na(lrscale))
croatia_descriptive <- croatia_descriptive %>%
  mutate(
    gndr = ifelse(gndr %in% c(9), NA, gndr)
  ) %>%
  filter(!is.na(gndr))
croatia_descriptive <- croatia_descriptive %>%
  mutate(
    Environment = impenv,
    PoliticalID = lrscale,
    Gender = gndr
  )
croat_table1 <- datasummary_skim(croatia_descriptive %>% dplyr::select(Environment, PoliticalID, Gender), title = "Table 1. Descriptive statistics of environmental attitudes, political ideology, and gender", output = "flextable")
## Warning: The histogram argument is only supported for (a) output types "default",
##   "html", "kableExtra", or "gt"; (b) writing to file paths with extensions
##   ".html", ".jpg", or ".png"; and (c) Rmarkdown, knitr or Quarto documents
##   compiled to PDF (via kableExtra)  or HTML (via kableExtra or gt). Use
##   `histogram=FALSE` to silence this warning.
croat_table1
Table 1. Descriptive statistics of environmental attitudes, political ideology, and gender

Unique (#)

Missing (%)

Mean

SD

Min

Median

Max

Environment

6

0

2.1

1.1

1.0

2.0

6.0

PoliticalID

11

0

5.0

2.5

0.0

5.0

10.0

Gender

2

0

1.5

0.5

1.0

2.0

2.0

flextable::save_as_docx(croat_table1, path = "Croat_Table1.docx", width = 7.0, height = 9.0)

NULL DISTRIBUTION

croatia_null <- croatia_data %>%
  mutate(
    impenv = case_when (
      impenv == 1 ~ "Very Much Like Me",
      impenv == 2 ~ "Like Me",
      impenv == 3 ~ "Somewhat Like Me",
      impenv == 4 ~ "A Little Like Me",
      impenv == 5 ~ "Not Like Me",
      impenv == 6 ~ "Not Like Me At All",
      impenv %in% c(7,8,9) ~ NA_character_,
      TRUE ~ as.character(impenv)
  )) %>%
  filter (!is.na(impenv))
croatia_null <- croatia_null %>%
  mutate(
    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",
      lrscale %in% 6:10 ~ "Right",
      lrscale %in% c(5, 77, 88, 99) ~ NA_character_)
    ) %>%
  filter(!is.na(lrscale))
croatia_null <- croatia_null %>%
  mutate (gndr = case_when (
    gndr == 1 ~ "Male",
    gndr == 2 ~ "Female",
    gndr %in% c(9) ~ NA_character_,
  )) %>%
  filter(!is.na(gndr))
test_stat1 <- croatia_null %>%
  specify(explanatory = lrscale,
          response = impenv) %>%
  hypothesize (null = "independence") %>%
  calculate (stat = "Chisq")

print(test_stat1$stat)
## X-squared 
##  13.63082
croat_tab <- croatia_null

HRtab <- table(croat_tab$lrscale, croat_tab$impenv)

rsum <- rowSums(HRtab)

csums <- colSums(HRtab)

N <- sum(HRtab)

ptab <- tcrossprod(rsum/N, csums/N)
cat("Table of Expected Proportions:\n")
## Table of Expected Proportions:
print(ptab)
##            [,1]      [,2]       [,3]        [,4]       [,5]      [,6]
## [1,] 0.04123718 0.1882107 0.01419888 0.003776299 0.08322963 0.1723503
## [2,] 0.04074480 0.1859634 0.01402934 0.003731209 0.08223584 0.1702924
ftab <- N*ptab
cat("Table of Expected Frequencies:\n")
## Table of Expected Frequencies:
print(ftab)
##          [,1]     [,2]     [,3]     [,4]     [,5]     [,6]
## [1,] 137.3198 626.7417 47.28228 12.57508 277.1547 573.9264
## [2,] 135.6802 619.2583 46.71772 12.42492 273.8453 567.0736
alpha <- 0.05
c_val <- qchisq(alpha, df = 1, lower.tail = FALSE)
cat("Critical Value:", round(c_val, 3), "\n")
## Critical Value: 3.841
null_distribution <- croatia_null %>%
  specify(explanatory = lrscale,
          response = impenv) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 1000, type = "permute") %>%
  calculate(stat="Chisq")
p_val <- null_distribution %>%
  get_pvalue(obs_stat = test_stat1, direction = "two-sided")
p_val
## # A tibble: 1 × 1
##   p_value
##     <dbl>
## 1   0.024
conf_int <- null_distribution %>%
  get_confidence_interval(level = 0.95, type = "percentile")

null_distribution %>%
  visualize() +
  shade_p_value(obs_stat = test_stat1, direction = "two-sided") +
  shade_confidence_interval(endpoints = conf_int)
## Warning: Chi-Square usually corresponds to right-tailed tests. Proceed with
## caution.

null_distribution
## Response: impenv (factor)
## Explanatory: lrscale (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
##    replicate  stat
##        <int> <dbl>
##  1         1  4.99
##  2         2  1.91
##  3         3  2.35
##  4         4  4.36
##  5         5  4.13
##  6         6  7.51
##  7         7  2.52
##  8         8  3.16
##  9         9  1.57
## 10        10  1.71
## # ℹ 990 more rows

REGRESSION MODEL

croatia_model1 <- croatia_data %>%
  mutate(impenv = na_if(impenv, 7) %>% na_if(8) %>% na_if(9)) %>%
  mutate(impenv = 7 - impenv) %>%
  filter(!is.na(impenv))
croatia_model1 <- croatia_model1 %>%
  mutate(
    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",
      lrscale %in% 6:10 ~ "Right",
      lrscale %in% c(5, 77, 88, 99) ~ NA_character_
    )) %>%
  filter(!is.na(lrscale))
croatia_model1 <- croatia_model1 %>%
  mutate(
    gndr = case_when(
      gndr == 1 ~ "Male",
      gndr == 2 ~ "Female",
      gndr %in% c(9) ~ NA_character_,
      TRUE ~ as.character(gndr)
  )) %>%
  filter(!is.na(gndr))
model1 <- lm(impenv ~ lrscale, data = croatia_model1, weights = weight)
modelsummary(
  list(model1),
  fmt = 3,
  estimate = c("{estimate} ({std.error}){stars}"),
  statistic = NULL
)
 (1)
(Intercept) 4.908 (0.027)***
lrscaleRight −0.047 (0.038)
Num.Obs. 3330
R2 0.000
R2 Adj. 0.000
AIC 10401.4
BIC 10419.8
Log.Lik. −5197.715
RMSE 1.10 
model2 <- lm(impenv ~ lrscale + gndr, data = croatia_model1, weights = weight)
model3 <- lm(impenv ~ lrscale + gndr + lrscale*gndr, data = croatia_model1, weights = weight)
modelsummary(
  list(model1, model2, model3),
  fmt = 3,
  estimate = c("{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}"),
  statistic = NULL
)
 (1)   (2)   (3)
(Intercept) 4.908 (0.027)*** 4.967 (0.032)*** 4.978 (0.036)***
lrscaleRight −0.047 (0.038) −0.033 (0.038) −0.057 (0.053)
gndrMale −0.135 (0.038)*** −0.160 (0.054)**
lrscaleRight × gndrMale 0.050 (0.076)
Num.Obs. 3330 3330 3330
R2 0.000 0.004 0.004
R2 Adj. 0.000 0.004 0.003
AIC 10401.4 10390.9 10392.5
BIC 10419.8 10415.4 10423.0
Log.Lik. −5197.715 −5191.458 −5191.240
RMSE 1.10 1.09 1.09 
model1.2 <- glm(impenv ~ lrscale, data = croatia_model1, family = poisson)
model2.2 <- glm(impenv ~ lrscale + gndr, data = croatia_model1, family = poisson)
model3.2 <- glm(impenv ~ lrscale + gndr + lrscale*gndr, data = croatia_model1, family = poisson)
modelsummary(
  list(model1.2, model2.2, model3.2),
  fmt = 3,
  estimate = c("{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}"),
  statistic = NULL
)
 (1)   (2)   (3)
(Intercept) 1.596 (0.011)*** 1.610 (0.013)*** 1.613 (0.015)***
lrscaleRight −0.013 (0.016) −0.011 (0.016) −0.017 (0.022)
gndrMale −0.033 (0.016)* −0.040 (0.022)+
lrscaleRight × gndrMale 0.013 (0.032)
Num.Obs. 3330 3330 3330
AIC 12345.2 12342.7 12344.5
BIC 12357.4 12361.0 12368.9
Log.Lik. −6170.578 −6168.336 −6168.247
RMSE 1.09 1.09 1.09

THIRD VISUAL

croatia_plot <- croatia_data %>%
   mutate(
    impenv = case_when(
      impenv %in% c(7,8,9) ~ NA_character_,
      TRUE ~ as.character(impenv))
  ) %>%
  filter (!is.na(impenv))
croatia_plot <- croatia_plot %>%
  mutate(
    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",
      lrscale %in% 6:10 ~ "Right",
      lrscale %in% c(5, 77, 88, 99) ~ NA_character_)
    ) %>%
  filter(!is.na(lrscale))
croatia_plot <- croatia_plot %>%
  mutate (gndr = case_when (
    gndr == 1 ~ "Male",
    gndr == 2 ~ "Female",
    gndr %in% c(9) ~ NA_character_,
  )) %>%
  filter(!is.na(gndr))
table(croatia_plot$impenv, croatia_plot$lrscale) %>%
  cprop() %>% 
  as.data.frame() %>%
  filter(Var1 != "Total",
         Var2 != "All") %>%
  ggplot(aes(x=Var1, y=Freq, fill=Var2)) +
  geom_col(position = "dodge") +
  labs(title="Importance of Caring for the Environment in Croatia by Political Ideology",
       y = "Conditional Percentage",
       x = "Very much - to - Not like me at all",
       fill = "Political Ideology")

table(croatia_plot$impenv, croatia_plot$gndr) %>%
  cprop() %>% 
  as.data.frame() %>%
  filter(Var1 != "Total",
         Var2 != "All") %>%
  ggplot(aes(x=Var1, y=Freq, fill=Var2)) +
  geom_col(position = "dodge") +
  labs(title="Importance of Caring for the Environment in Croatia by Gender",
       y = "Conditional Percentage",
       x = "Very much - to - Not like me at all",
       fill = "Gender")