Research Report Final
Ruth Johns
2024-04-14
packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "MASS", "aod", "interactions", "kableExtra", "flextable", "scales", "ggeffects", "marginaleffects", "viridis", "knitr", "rmarkdown", "ggridges", "questionr", "performance", "broom")
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## [41] "methods" "base"ess <- read.fst("All-ESS-Data.fst")spain_data <- read.fst("spain_data.fst")spain_data <- ess %>%
filter(cntry == "HR")spain_data$weight <- spain_data$dweight * spain_data$pweight
survey_design <- svydesign(ids = ~1, data = spain_data, weights = ~weight)Table of Descriptive Statistics
spain_descriptive <- spain_data %>%
mutate(
imptrad = case_when (
imptrad == 1 ~ "Very Much Like Me",
imptrad == 2 ~ "Like Me",
imptrad == 3 ~ "Somewhat Like Me",
imptrad == 4 ~ "A Little Like Me",
imptrad == 5 ~ "Not Like Me",
imptrad == 6 ~ "Not Like Me At All",
imptrad %in% c(7,8,9) ~ NA_character_,
TRUE ~ as.character(imptrad)
)) %>%
filter (!is.na(imptrad))spain_descriptive <- spain_data %>%
mutate(
imptrad = ifelse(imptrad %in% c(7, 8, 9), NA, imptrad)
) %>%
filter(!is.na(imptrad))spain_descriptive <- spain_descriptive %>%
mutate(
lrscale = ifelse(lrscale %in% c(77,88,99), NA, lrscale)) %>%
filter(!is.na(lrscale))spain_descriptive <- spain_descriptive %>%
mutate(
gndr = ifelse(gndr %in% c(9), NA, gndr)
) %>%
filter(!is.na(gndr))spain_descriptive <- spain_descriptive %>%
mutate(
Traditions = imptrad,
PoliticalID = lrscale,
Gender = gndr
)spain_table1 <- datasummary_skim(spain_descriptive %>% dplyr::select(Traditions, PoliticalID, Gender), title = "Table 1. Descriptive statistics of view on traditions and customs, 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.spain_table1
| Unique (#) | Missing (%) | Mean | SD | Min | Median | Max |
|---|---|---|---|---|---|---|---|
Traditions | 6 | 0 | 2.3 | 1.3 | 1.0 | 2.0 | 6.0 |
PoliticalID | 11 | 0 | 5.0 | 2.5 | 0.0 | 5.0 | 10.0 |
Gender | 2 | 0 | 1.6 | 0.5 | 1.0 | 2.0 | 2.0 |
flextable::save_as_docx(spain_table1, path = "Spain_Table1.docx", width = 7.0, height = 9.0)#Null Distribution with Confidence Intervals
spain_null <- spain_data %>%
mutate(
imptrad = case_when (
imptrad == 1 ~ "Very Much Like Me",
imptrad == 2 ~ "Like Me",
imptrad == 3 ~ "Somewhat Like Me",
imptrad == 4 ~ "A Little Like Me",
imptrad == 5 ~ "Not Like Me",
imptrad == 6 ~ "Not Like Me At All",
imptrad %in% c(7,8,9) ~ NA_character_,
TRUE ~ as.character(imptrad)
)) %>%
filter (!is.na(imptrad))spain_null <- spain_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))spain_null <- spain_null %>%
mutate (gndr = case_when (
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
gndr %in% c(9) ~ NA_character_,
)) %>%
filter(!is.na(gndr))test_stat1 <- spain_null %>%
specify(explanatory = lrscale,
response = imptrad) %>%
hypothesize (null = "independence") %>%
calculate (stat = "Chisq")
print(test_stat1$stat)## X-squared
## 188.1765spain_tab <- spain_null
HRtab <- table(spain_tab$lrscale, spain_tab$imptrad)
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,] 0.04574059 0.1817626 0.02924398 0.01484695 0.07963362 0.1499691
## [2,] 0.04552213 0.1808945 0.02910431 0.01477603 0.07925328 0.1492529ftab <- N*ptab
cat("Table of Expected Frequencies:\n")## Table of Expected Frequencies:print(ftab)## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 152.8651 607.4506 97.73339 49.61849 266.1355 501.1969
## [2,] 152.1349 604.5494 97.26661 49.38151 264.8645 498.8031alpha <- 0.05
c_val <- qchisq(alpha, df = 1, lower.tail = FALSE)
cat("Critical Value:", round(c_val, 3), "\n")## Critical Value: 3.841null_distribution <- spain_null %>%
specify(explanatory = lrscale,
response = imptrad) %>%
hypothesize(null = "independence") %>%
generate(reps = 1000, type = "permute") %>%
calculate(stat="Chisq")p_val <- null_distribution %>%
get_pvalue(obs_stat = test_stat1, direction = "two-sided")## Warning: Please be cautious in reporting a p-value of 0. This result is an approximation
## based on the number of `reps` chosen in the `generate()` step.
## ℹ See `get_p_value()` (`?infer::get_p_value()`) for more information.p_val## # A tibble: 1 × 1
## p_value
## <dbl>
## 1 0conf_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: imptrad (factor)
## Explanatory: lrscale (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
## replicate stat
## <int> <dbl>
## 1 1 2.70
## 2 2 4.86
## 3 3 3.68
## 4 4 5.76
## 5 5 2.79
## 6 6 4.09
## 7 7 3.41
## 8 8 10.4
## 9 9 7.88
## 10 10 2.71
## # ℹ 990 more rowsRegression Models (3 Models)
spain_figure1 <- spain_data %>%
mutate(imptrad = na_if(imptrad, 7) %>% na_if(8) %>% na_if(9)) %>%
mutate(imptrad = 7 - imptrad) %>%
filter(!is.na(imptrad))spain_figure1 <- spain_figure1 %>%
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))spain_figure1 <- spain_figure1 %>%
mutate(
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
gndr %in% c(9) ~ NA_character_,
TRUE ~ as.character(gndr)
)) %>%
filter(!is.na(gndr))figure1 <- lm(imptrad ~ lrscale, data = spain_figure1, weights = weight)modelsummary(
list(figure1),
fmt = 3,
estimate = c("{estimate} ({std.error}){stars}"),
statistic = NULL,
title = "Figure 1: Regression model for Traditions and Customs, Political Ideology, and Gender in Spain")| Â (1) | |
|---|---|
| (Intercept) | 4.327 (0.031)*** |
| lrscaleRight | 0.663 (0.044)*** |
| Num.Obs. | 3342 |
| R2 | 0.064 |
| R2 Adj. | 0.064 |
| AIC | 11404.7 |
| BIC | 11423.0 |
| Log.Lik. | −5699.326 |
| RMSE | 1.27 |
figure2 <- lm(imptrad ~ lrscale + gndr, data = spain_figure1, weights = weight)
figure3 <- lm(imptrad ~ lrscale + gndr + lrscale*gndr, data = spain_figure1, weights = weight)modelsummary(
list(figure1, figure2, figure3),
fmt = 3,
estimate = c("{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL,
title = "Figure 2: Regression model for Traditions and Customs, Political Ideology, and Gender in Spain")| Â (1) | Â Â (2) | Â Â (3) | |
|---|---|---|---|
| (Intercept) | 4.327 (0.031)*** | 4.373 (0.036)*** | 4.399 (0.041)*** |
| lrscaleRight | 0.663 (0.044)*** | 0.674 (0.044)*** | 0.616 (0.061)*** |
| gndrMale | −0.105 (0.044)* | −0.165 (0.062)** | |
| lrscaleRight × gndrMale | 0.119 (0.088) | ||
| Num.Obs. | 3342 | 3342 | 3342 |
| R2 | 0.064 | 0.066 | 0.066 |
| R2 Adj. | 0.064 | 0.065 | 0.066 |
| AIC | 11404.7 | 11400.9 | 11401.1 |
| BIC | 11423.0 | 11425.4 | 11431.7 |
| Log.Lik. | −5699.326 | −5696.459 | −5695.540 |
| RMSE | 1.27 | 1.27 | 1.27 |
figure1.2 <- glm(imptrad ~ lrscale, data = spain_figure1, family = poisson)
figure2.2 <- glm(imptrad ~ lrscale + gndr, data = spain_figure1, family = poisson)
figure3.2 <- glm(imptrad ~ lrscale + gndr + lrscale*gndr, data = spain_figure1, family = poisson)modelsummary(
list(figure1.2, figure2.2, figure3.2),
fmt = 3,
estimate = c("{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL,
title = "Figure 3: Regression model for Traditions and Customs, Political Ideology, and Gender in Spain")| Â (1) | Â Â (2) | Â Â (3) | |
|---|---|---|---|
| (Intercept) | 1.472 (0.012)*** | 1.482 (0.014)*** | 1.488 (0.015)*** |
| lrscaleRight | 0.131 (0.016)*** | 0.133 (0.016)*** | 0.121 (0.022)*** |
| gndrMale | −0.023 (0.016) | −0.037 (0.024) | |
| lrscaleRight × gndrMale | 0.027 (0.032) | ||
| Num.Obs. | 3342 | 3342 | 3342 |
| AIC | 12605.6 | 12605.7 | 12607.0 |
| BIC | 12617.9 | 12624.0 | 12631.4 |
| Log.Lik. | −6300.819 | −6299.834 | −6299.489 |
| RMSE | 1.27 | 1.27 | 1.27 |
#Visualization of Choice
spain_plot <- spain_data %>%
mutate(
imptrad = case_when(
imptrad %in% c(7,8,9) ~ NA_character_,
TRUE ~ as.character(imptrad))
) %>%
filter (!is.na(imptrad))spain_plot <- spain_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))spain_plot <- spain_plot %>%
mutate (gndr = case_when (
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
gndr %in% c(9) ~ NA_character_,
)) %>%
filter(!is.na(gndr))table(spain_plot$imptrad, spain_plot$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 following Tradtions and Values in Spain by Political Ideology",
y = "Conditional Proportion",
x = "Very much to Not like me at all",
fill = "Pol ID")
table(spain_plot$imptrad, spain_plot$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 following Tradtions and Values in Spain by Gender",
y = "Conditional Proportion",
x = "Very much to Not like me at all",
fill = "Gender")