Homework 2
Mingdi Zhou
2024-01-25
Setting up environment
# List of packages
packages <- c("tidyverse", "fst", "modelsummary", "viridis") # 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)
# Load the packages
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Task 1
Provide code and answer.
Prompt: in the tutorial, we calculated the average trust in others for France and visualized it. Using instead the variable ‘Trust in Parliament’ (trstplt) and the country of Spain (country file provided on course website), visualize the average trust by survey year. You can truncate the y-axis if you wish. Provide appropriate titles and labels given the changes. What are your main takeaways based on the visual (e.g., signs of increase, decrease, or stall)?
Task 2
Provide answer only.
Prompt and question: Based on the figure we produced above called task2_plot, tell us: what are your main takeaways regarding France relative to Italy and Norway? Make sure to be concrete and highlight at least two important comparative trends visualized in the graph.
Task 3
Provide code and answer.
Question: What is the marginal percentage of Italian men who feel close to a particular political party?
Task 4
Provide code and output only.
Prompt: In the tutorial, we calculated then visualized the percentage distribution for left vs. right by gender for France. Your task is to replicate the second version of the visualization but for the country of Sweden instead.
Task 5
Provide code and answer: In Hungary, what is the conditional probability of NOT feeling close to any particular party given that the person lives in a rural area?
Loading Data into R
spain_data <- read.fst("spain_data.fst")italy_data <- read.fst("italy_data.fst")sweden_data <- read.fst("sweden_data.fst")hungary_data <- read.fst("hungary_data.fst")Task 1
Provide code and answer.
Prompt: in the tutorial, we calculated the average trust in others for France and visualized it. Using instead the variable ‘Trust in Parliament’ (trstplt) and the country of Spain (country file provided on course website), visualize the average trust by survey year. You can truncate the y-axis if you wish. Provide appropriate titles and labels given the changes. What are your main takeaways based on the visual (e.g., signs of increase, decrease, or stall)?
!! WHY “Trust in Parliament” IS “TRSTPLT”? SHOULDN’T IT BE “TRSTPR”?
I will use trstprl here, because in the dataset website “trstprl - Trust in country’s parliament” while “trstplt - Trust in politicians”.
spain_data <- spain_data %>%
mutate(
trstprl = ifelse(trstprl%in% c(77, 88, 99), NA, trstprl),
)table(spain_data$trstprl) ##
## 0 1 2 3 4 5 6 7 8 9 10
## 2345 797 1365 2023 1855 4085 2272 1864 1178 304 297spain_data$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
spain_data$year[spain_data$essround == i] <- replacements[i]
}table(spain_data$year)##
## 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
## 1729 1663 1876 2576 1885 1889 1925 1958 1668 2283trust_by_year <- spain_data %>%
group_by(year) %>%
summarize(mean_trust = mean(trstprl, na.rm = TRUE))
trust_by_year## # A tibble: 10 × 2
## year mean_trust
## <dbl> <dbl>
## 1 2002 4.85
## 2 2004 5.07
## 3 2006 4.99
## 4 2008 4.97
## 5 2010 4.30
## 6 2012 3.43
## 7 2014 3.67
## 8 2016 3.92
## 9 2018 4.08
## 10 2020 3.89ggplot(trust_by_year, aes(x = year, y = mean_trust)) +
geom_line(color = "blue", size = 1) +
geom_point(color = "red", size = 3) +
labs(title = "Trust in Parliament in Spain (2002-2020)",
x = "Survey Year",
y = "Average Trust (0-10 scale)") +
ylim(0, 10) +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggplot(trust_by_year, aes(x = year, y = mean_trust)) +
geom_line(aes(group = 1), color = "blue", size = 1, linetype = "longdash") +
labs(title = "Trust in Parliament in Spain (2002-2020)",
x = "Survey Year",
y = "Average Trust (3-5.5 scale)") +
ylim(3, 5.5) 
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The line graph shows a general decrease in trust towards the Spanish Parliament from 2002 to 2020. After a period of relative stability until 2007, there’s a sharp decline until 2012. A slight recovery occurs until 2015, followed by a continued downward trend. The trust levels fluctuate between 5.0 to just above 3.5, indicating significant variation and overall reduced trust over the years.
Task 2
Provide answer only.
Prompt and question: Based on the figure we produced above called task2_plot, tell us: what are your main takeaways regarding France relative to Italy and Norway? Make sure to be concrete and highlight at least two important comparative trends visualized in the graph.
Answer
The graph illustrates that France initially had a higher proportion of individuals feeling close to a political party than Italy and Norway. However, France shows a steeper decline in party affiliation over time, with Italy surpassing France around the 1960 cohort. Despite this decline, France maintains a higher level of political party closeness than Norway across all cohorts.
Task 3
Provide code and answer.
Question: What is the marginal percentage of Italian men who feel close to a particular political party?
italy_data <- italy_data %>%
mutate(
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
TRUE ~ NA_character_
),
clsprty = case_when(
clsprty %in% 1 ~ "Yes",
clsprty %in% 2 ~ "No",
TRUE ~ NA_character_
)
) clsprty_percentages <- italy_data %>%
filter(!is.na(clsprty), !is.na(gndr)) %>%
group_by(gndr, clsprty) %>%
summarise(count = n(), .groups = 'drop') %>%
mutate(percentage = count / sum(count) * 100)
clsprty_percentages ## # A tibble: 4 × 4
## gndr clsprty count percentage
## <chr> <chr> <int> <dbl>
## 1 Female No 3228 34.2
## 2 Female Yes 1686 17.9
## 3 Male No 2593 27.5
## 4 Male Yes 1936 20.5Answer
The marginal percentage of Italian men who feel close to a particular political party is 20.50196%.
Task 4
Provide code and output only.
Prompt: In the tutorial, we calculated then visualized the percentage distribution for left vs. right by gender for France. Your task is to replicate the second version of the visualization but for the country of Sweden instead.
Percentages
sweden_data <- sweden_data %>%
mutate(
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
TRUE ~ NA_character_
),
lrscale = case_when(
lrscale %in% 0:3 ~ "Left",
lrscale %in% 7:10 ~ "Right",
TRUE ~ NA_character_
)
) Calculations
lrscale_percentages <- sweden_data %>%
filter(!is.na(lrscale), !is.na(gndr)) %>%
group_by(gndr, lrscale) %>%
summarise(count = n(), .groups = 'drop') %>%
mutate(percentage = count / sum(count) * 100)
lrscale_percentages ## # A tibble: 4 × 4
## gndr lrscale count percentage
## <chr> <chr> <int> <dbl>
## 1 Female Left 2296 23.0
## 2 Female Right 2530 25.3
## 3 Male Left 2062 20.6
## 4 Male Right 3107 31.1Visualization
lrscale_plot <- ggplot(lrscale_percentages, aes(x = lrscale, y = percentage, fill = lrscale)) +
geom_bar(stat = "identity", position = position_dodge()) +
facet_wrap(~ gndr, scales = "fixed") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Political Orientation (Left vs. Right) by Gender in Sweden",
x = "Political Orientation",
y = "Percentage of Respondents",
fill = "Orientation"
) +
theme_minimal() +
theme(legend.position = "bottom")
lrscale_plot
Task 5
Provide code and answer: In Hungary, what is the conditional probability of NOT feeling close to any particular party given that the person lives in a rural area?
hungary_data <- hungary_data %>%
mutate(
geo = recode(as.character(domicil),
'1' = "Urban",
'2' = "Urban",
'3' = "Rural",
'4' = "Rural",
'5' = "Rural",
'7' = NA_character_,
'8' = NA_character_,
'9' = NA_character_)
) %>%
filter(!is.na(clsprty), !is.na(geo)) hungary_data <- hungary_data %>%
filter(!is.na(clsprty)) %>%
mutate(
clsprty = case_when(
clsprty == 1 ~ "Yes",
clsprty == 2 ~ "No"
)
) %>%
filter(!is.na(clsprty)) cond <- hungary_data %>%
count(clsprty, geo) %>%
group_by(geo) %>%
mutate(prob = n / sum(n))
cond## # A tibble: 4 × 4
## # Groups: geo [2]
## clsprty geo n prob
## <chr> <chr> <int> <dbl>
## 1 No Rural 6275 0.554
## 2 No Urban 2395 0.512
## 3 Yes Rural 5055 0.446
## 4 Yes Urban 2283 0.488Answer
In Hungary, the conditional probability of not feeling close to any particular party given that the person lives in a rural area is approximately 55.38%.