_Amaral_Alyssia_Homework2
Alyssia Amaral
2024-01-24
# 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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## [21] "base"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)?
spain_data <- read.fst("spain_data.fst")spain_data <- spain_data %>%
mutate(
trstplt = ifelse(trstplt %in% c(77, 88, 99), NA, trstplt), # set values 77, 88, and 99 to NA.
)table(spain_data$trstplt) # if there are values that are not supposed to be there (e.g., 77, 88, 99 in this case), then we need to deal with it##
## 0 1 2 3 4 5 6 7 8 9 10
## 5165 1830 2329 2441 2085 2890 1154 639 355 80 71spain_data$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(trstplt, na.rm = TRUE))
trust_by_year## # A tibble: 10 × 2
## year mean_trust
## <dbl> <dbl>
## 1 2002 3.41
## 2 2004 3.66
## 3 2006 3.49
## 4 2008 3.32
## 5 2010 2.72
## 6 2012 1.91
## 7 2014 2.23
## 8 2016 2.40
## 9 2018 2.55
## 10 2020 1.94ggplot(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 parliment 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.
Answer: The main takeaways from the graph is that people in Spain trust in parliment starts around almost 3.0 and then is somewhat stable with slight decreases in 2010 and then stays stable and then drops in 2020 again.
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: Based on the figure produced, the main takeaways regarding France relative to Italy and Norway is that France exhibits feeling ’close to a political party across cohorts greater than Norway and Italy. Additionally, the lines for all three countries show they all have a stable difference in the proportion of people feeling close to a party over time.
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 <- read.fst("italy_data.fst")italy_data <- italy_data %>%
mutate(
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
TRUE ~ NA_character_ # Set anything that is not 1 or 2 to NA
),
lrscale = case_when(
lrscale %in% 1 ~ "yes",
lrscale %in% 2 ~ "no",
TRUE ~ NA_character_ # Moderate (4, 5, 6) and special codes (77, 88, 99) set to NA
)
) lrscale_percentages <- italy_data %>% # Begin with the dataset 'france_data'
filter(!is.na(lrscale), !is.na(gndr)) %>% # Filter out rows where 'lrscale' or 'gender' is NA (missing data)
group_by(gndr, lrscale) %>% # Group the data by 'gender' and 'lrscale' categories
summarise(count = n(), .groups = 'drop') %>% # Summarise each group to get counts, and then drop groupings
mutate(percentage = count / sum(count) * 100) # Calculate percentage for each group by dividing count by total count and multiplying by 100
lrscale_percentages # The resulting dataframe## # A tibble: 4 × 4
## gndr lrscale count percentage
## <chr> <chr> <int> <dbl>
## 1 Female no 225 33.7
## 2 Female yes 107 16.0
## 3 Male no 248 37.1
## 4 Male yes 88 13.2Answer: The marginal percentage of Italian men who feel close to a particular political party is 13.2%.
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.
sweden_data <- read.fst("sweden_data.fst")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", # Left-wing (0 to 3)
lrscale %in% 7:10 ~ "Right", # Right-wing (7 to 10)
TRUE ~ NA_character_ # Moderate (4, 5, 6) and special codes (77, 88, 99) set to NA
)
) lrscale_percentages <- sweden_data %>%
filter(!is.na(lrscale), !is.na(gndr)) %>% # Filter out rows where 'lrscale' or 'gender' is NA (missing data)
group_by(gndr, lrscale) %>% # Group the data by 'gender' and 'lrscale' categories
summarise(count = n(), .groups = 'drop') %>% # Summarise each group to get counts, and then drop groupings
mutate(percentage = count / sum(count) * 100) # Calculate percentage for each group by dividing count by total count and multiplying by 100
lrscale_percentages # The resulting dataframe## # 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 31.1# Create a ggplot object for horizontal bar chart with the specified style
lrscale_plot_v2 <- ggplot(lrscale_percentages,
aes(x = percentage, # Use percentage directly
y = reorder(gndr, -percentage), # Order bars within each gender
fill = gndr)) + # Fill color based on Gender
# Create horizontal bar chart
geom_col() + # Draws the bars using the provided data
coord_flip() + # Flip coordinates to make bars horizontal
# Remove fill color legend
guides(fill = "none") + # Removes legend for the fill aesthetic
# Split the plot based on Political Orientation
facet_wrap(~ lrscale, nrow = 1) + # Separate plots for Left/Right
# Labels and titles for the plot
labs(x = "Percentage of Respondents", # X-axis label
y = NULL, # Remove Y-axis label
title = "Political Orientation by Gender", # Main title
subtitle = "Comparing the percentage distribution of left vs. right for Sweden ") + # Subtitle
# Adjust visual properties of the plot
theme(plot.title = element_text(size = 16, face = "bold"), # Format title
plot.subtitle = element_text(size = 12), # Format subtitle
axis.title.y = element_blank(), # Remove Y-axis title
legend.position = "bottom") # Position the legend at the bottom
# Display the ggplot object
lrscale_plot_v2
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 <- read.fst("hungary_data.fst")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(lrscale), !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: Therefore, the conditional probability of NOT feeling close to a particular party given that the person lives in a rural area is is 55.3%.