Hope Love Robes Research Report
Hope Love Robes (1009584790)
2024-04-13
packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "performance", "flextable", "broom", "scales", "ggeffects", "marginaleffects") # add any you need here
# Install packages if they aren't installed already
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
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## [29] "utils" "datasets" "methods" "base"ess <- read_fst("All-ESS-Data.fst")First, I will code the main variable of interest after filtering to our country of interest: Germany
germany_data <- ess %>%
filter(cntry == "FR")
germany_data$weight <- germany_data$dweight * germany_data$pweight
survey_design <- svydesign(ids = ~1, data = germany_data, weights = ~weight)
germany_data <- germany_data %>%
mutate(protest = case_when(
pbldmn == 1 ~ 1, # Yes to 1
pbldmn == 2 ~ 0, # No to 0
TRUE ~ NA_real_ # Everything else to NA
))
germany_data <- germany_data %>% filter(!is.na(protest))Now, I will focus on at Germany by itself:
df <- germany_data
df$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
df$year[df$essround == i] <- replacements[i]
}
protest_avg <- df %>%
group_by(year) %>%
summarize(protest_avg = mean(protest, na.rm = TRUE))
protest_plot_FR <- ggplot(protest_avg, aes(x = year, y = protest_avg)) +
geom_point(aes(size = protest_avg, color = protest_avg), alpha = 0.7) +
geom_line(aes(group = 1), color = "blue", linetype = "dashed") +
labs(
x = "Survey Year",
y = "Pr(Protest)") +
theme_minimal() +
theme(legend.position = "none")
print(protest_plot_FR)
df <- df %>%
mutate(
polID = case_when(
lrscale %in% 0:3 ~ "Left",
lrscale %in% 7:10 ~ "Right",
lrscale %in% 4:6 ~ "Moderate",
lrscale %in% c(77, 88, 99) ~ NA_character_
),
religious = case_when(
rlgdgr %in% c(77, 88, 99) ~ NA_real_,
TRUE ~ rlgdgr
)
)Loading Germany Dataset into R
germany_data <- read.fst("germany_data.fst")Clean and transforming the variable, imwbcnt, immigrants make country worse or better place to live:
germany_data <- germany_data %>%
mutate(
imwbcnt = ifelse(imwbcnt %in% c(77, 88, 99), NA, ppltrst), # set values 77, 88, and 99 to NA.
)