Virk_MuhammadQasim_Homework_2
2024-01-28
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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
lapply(packages, library, character.only = TRUE)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: viridisLite## [[1]]
## [1] "lubridate" "forcats" "stringr" "dplyr" "purrr" "readr"
## [7] "tidyr" "tibble" "ggplot2" "tidyverse" "stats" "graphics"
## [13] "grDevices" "utils" "datasets" "methods" "base"
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## [[2]]
## [1] "fst" "lubridate" "forcats" "stringr" "dplyr" "purrr"
## [7] "readr" "tidyr" "tibble" "ggplot2" "tidyverse" "stats"
## [13] "graphics" "grDevices" "utils" "datasets" "methods" "base"
##
## [[3]]
## [1] "modelsummary" "fst" "lubridate" "forcats" "stringr"
## [6] "dplyr" "purrr" "readr" "tidyr" "tibble"
## [11] "ggplot2" "tidyverse" "stats" "graphics" "grDevices"
## [16] "utils" "datasets" "methods" "base"
##
## [[4]]
## [1] "viridis" "viridisLite" "modelsummary" "fst" "lubridate"
## [6] "forcats" "stringr" "dplyr" "purrr" "readr"
## [11] "tidyr" "tibble" "ggplot2" "tidyverse" "stats"
## [16] "graphics" "grDevices" "utils" "datasets" "methods"
## [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)?
Loading data for Spain:
spain_data <- read.fst("spain_data.fst")Cleaning data for Spain:
spain_data <- spain_data %>%
mutate(
trstplt = ifelse(trstplt %in% c(77, 88, 99), NA, trstplt), # set values 77, 88, and 99 to NA.
)Double checking data cleaning: values that are not supposed to be here (e.g., 77, 88, 99 in this case)
table(spain_data$trstplt) ##
## 0 1 2 3 4 5 6 7 8 9 10
## 5165 1830 2329 2441 2085 2890 1154 639 355 80 71Creating a “year” variable -> will use it to visualize later
spain_data$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]
}Checking to see if it worked:
table(spain_data$year)##
## 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
## 1729 1663 1876 2576 1885 1889 1925 1958 1668 2283We will calculate the average by year to then visualize:
trust_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 1.94Visualization -> reducing the Y-axis limit from 0-10 to 1-5
ggplot(trust_by_year, aes(x = year, y = mean_trust)) +
geom_line(color = "green", size = 1) + # Line shows the trend
geom_point(color = "purple", size = 3) + # Points highlight each year's value
labs(title = "Trust in Parliment in Spain (2002-2020)",
x = "Survey Year",
y = "Average Trust (0-10 scale)") +
ylim(1, 5) + # Setting the y-axis limits from 1 to 5
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 y-axis shows average trust in parliament. There is a slight
increase in trust in the parliament prior to 2005. This is followed by a
drop from 2005 to roughly 2012. 2012 on wards to 2016 once against saw a
rise in trust in the parliament in Spain. 2016 to 2020, however faced a
stark decline. Overall, the trust in parliament has seen constant
fluctuation with the lowest in roughly 2012 and in the highest in
2004.
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 general trend shows a decline for all 3 countries in the proportion of people that feel close to a political party over time. This highlights that citizens for all 3 are becoming less closely affiliated to political parties.
Looking at the individual movement,from the years 1920-1940, Italy had a very slow rate of decrease in proportion. Norway saw a decline as well, while France saw an increase. Additionally, in the years 1980 on wards, France and Italy saw a continuous decline, while Norway had a slower decline, coming to a near stagnant proportion value in the years 1995 to 2000.
Task 3
Provide code and answer. Question: What is the marginal percentage of Italian men who feel close to a particular political party?
Loading data for Italy:
italy_data <- read.fst("italy_data.fst")Creating values and cleaning not applicable characters (7,8,9))
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
),
clsprty = case_when(
clsprty %in% 1:1 ~ "Yes", # 1= yes
clsprty %in% 2:2 ~ "No", # 2= No
TRUE ~ NA_character_ # Moderate (7, 8, 9) set to NA
)
) Filtering out rows ‘clsprty’ or ‘gender’ is not applicable in data set ‘italy_data’-> grouping data -> summarising each group -> get percentage for each group by dividing count by total count and multiplying by 100
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 # The resulting data frame## # 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.
Loading Sweden Data
sweden_data <- read.fst("sweden_data.fst")Setting values and removing not applicable characters from the data set
sweden_data <- sweden_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% 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
)
) Filter out data where ‘lrscale’ or ‘gender’ is not applicable -> group data into categories -> summarise the categories -> percentage through dividing count by total count and multiplying by 100
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 # 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 31.1Creaint a ggplot object for horizontal bar chart
lrscale_plot_v2 <- ggplot(lrscale_percentages,
aes(x = percentage,
y = reorder(gndr, -percentage),
fill = gndr)) +
geom_col() +
coord_flip() +
guides(fill = "none") +
facet_wrap(~ lrscale, nrow = 1) +
labs(x = "Percentage of Respondents",
y = NULL,
title = "Political Orientation by Gender for Sweden",
subtitle = "Comparing the percentage distribution of left vs. right for Sweden (2002-2020)") +
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?
Loading Hungary data
hungary_data <- read.fst("hungary_data.fst")Setting variables and removing NAs
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)) # Removing rows with NA in clsprty or geohungary_data <- hungary_data %>%
mutate(
clsprty = case_when (
clsprty %in% 1:1 ~ "Yes",
clsprty %in% 2:2 ~ "No",
TRUE ~ NA_character_ # Moderate (7, 8, 9) and special codes set to NA
)
) Calculate conditional probabilities, excluding NAs
cond <- hungary_data %>%
count(clsprty, geo) %>%
group_by(geo) %>%
mutate(prob = n / sum(n))
cond## # A tibble: 6 × 4
## # Groups: geo [2]
## clsprty geo n prob
## <chr> <chr> <int> <dbl>
## 1 No Rural 6275 0.532
## 2 No Urban 2395 0.495
## 3 Yes Rural 5055 0.429
## 4 Yes Urban 2283 0.472
## 5 <NA> Rural 457 0.0388
## 6 <NA> Urban 162 0.0335Answer:
In Hungary, the conditional probability of NOT feeling close to any particular party given that the person lives in a rural area is 53.24%