Amy_Lei_Homework_1
Amy Lei
2024-01-20
R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
copy paste of task 1-5
#Provide code and answer.
#Prompt and question: calculate the average for the variable ‘happy’ for the country of Norway. On average, based on the ESS data, who reports higher levels of happiness: Norway or Belgium?
#Note: we already did it for Belgium. You just need to compare to Norway’s average, making sure to provide the code for both.
Task 2
#Provide code and answer.
#Prompt and question: what is the most common category selected, for Irish respondents, for frequency of binge drinking? The variable of interest is: alcbnge.
#More info here: https://ess-search.nsd.no/en/variable/0c65116e-7481-4ca6-b1d9-f237db99a694.
#Hint: need to convert numeric value entries to categories as specified in the variable information link. We did similar steps for Estonia and the climate change attitude variable.
Task 3
#Provide code and answer.
#Prompt and question: when you use the summary() function for the variable plnftr (about planning for future or taking every each day as it comes from 0-10) for both the countries of Portugal and Serbia, what do you notice? What stands out as different when you compare the two countries (note: look up the variable information on the ESS website to help with interpretation)? Explain while referring to the output generated.
Task 4
#Provide code and answer.
#Prompt and question: using the variables stfdem and gndr, answer the following: on average, who is more dissastified with democracy in Italy, men or women? Explain while referring to the output generated.
#Info on variable here: https://ess.sikt.no/en/variable/query/stfdem/page/1
Task 5
#Provide code and answer.
#Prompt: Interpret the boxplot graph of stfedu and stfhlth that we generated already: according to ESS data, would we say that the median French person is more satisfied with the education system or health services? Explain.
#Change the boxplot graph: provide the code to change some of the key labels: (1) Change the title to: Boxplot of satisfaction with the state of education vs. health services; (2) Remove the x-axis label; (3) Change the y-axis label to: Satisfaction (0-10).
#Hint: copy the boxplot code above and just replace or cut what is asked.
DOWNLOAD NECESSARY DATA FIRST
# Load necessary packages
# Install and load required packages
if (!requireNamespace("tidyverse", quietly = TRUE)) {
install.packages("tidyverse")
}
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ 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# List of packages
packages <- c("tidyverse", "fst", "modelsummary") # 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)## [[1]]
## [1] "lubridate" "forcats" "stringr" "dplyr" "purrr" "readr"
## [7] "tidyr" "tibble" "ggplot2" "tidyverse" "stats" "graphics"
## [13] "grDevices" "utils" "datasets" "methods" "base"
##
## [[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"ess <- read_fst("All-ESS-Data.fst")#belgium_data <- read.fst("belgium_data.fst")#estonia_data <- read.fst("estonia_data.fst")#france_data <- read.fst("france_data.fst")#norway_data <- read.fst("norway_data.fst")#ireland_data <- read.fst("ireland_data.fst")#portugal_data <- read.fst("portugal_data.fst")#serbia_data <- read.fst("serbia_data.fst")#italy_data <- read.fst("italy_data.fst")# Code for Belgium
belgium_happy <- ess %>%
filter(cntry == "BE") %>%
select(happy)
belgium_happy$y <- belgium_happy$happy
# Recode values 77 through 99 to NA
belgium_happy$y[belgium_happy$y %in% 77:99] <- NA##TASK 1
# Calculate the average for the variable 'happy' for Belgium
mean_belgium_happy <- mean(belgium_happy$y, na.rm = TRUE)
# Calculate the average for the variable 'happy' for Norway
norway_happy <- ess %>%
filter(cntry == "NO") %>%
select(happy)
norway_happy$y <- norway_happy$happy
# Recode values 77 through 99 to NA
norway_happy$y[norway_happy$y %in% 77:99] <- NA
mean_norway_happy <- mean(norway_happy$y, na.rm = TRUE)
# Compare averages
cat("Average happiness in Belgium:", mean_belgium_happy, "\n")## Average happiness in Belgium: 7.737334cat("Average happiness in Norway:", mean_norway_happy, "\n")## Average happiness in Norway: 7.975005# Compare countries
if (mean_belgium_happy > mean_norway_happy) {
cat("On average, Belgium reports higher levels of happiness than Norway.")
} else if (mean_norway_happy > mean_belgium_happy) {
cat("On average, Norway reports higher levels of happiness than Belgium.")
} else {
cat("Belgium and Norway have the same average happiness levels.")
}## On average, Norway reports higher levels of happiness than Belgium.##TASK 2
ireland_alcbnge <- ess %>%
filter(cntry == "IE") %>%
select(alcbnge)
# Determine the mode of the alcbnge category
table_alcbnge <- table(ireland_alcbnge$alcbnge_category)
mode_alcbnge <- names(table_alcbnge)[which.max(table_alcbnge)]
cat("Most common category for frequency of binge drinking in Ireland:", mode_alcbnge, "\n")## Most common category for frequency of binge drinking in Ireland:# Code for Ireland
ireland_alcbnge <- ess %>%
filter(cntry == "IE") %>%
select(alcbnge)
ireland_alcbnge$alcbnge_category <- case_when(
ireland_alcbnge$alcbnge == 0 ~ "Never",
ireland_alcbnge$alcbnge == 1 ~ "Less than monthly",
ireland_alcbnge$alcbnge == 2 ~ "Monthly",
ireland_alcbnge$alcbnge == 3 ~ "Weekly",
ireland_alcbnge$alcbnge == 4 ~ "Daily or almost daily",
TRUE ~ NA_character_
)
# To confirm the conversion:
table(ireland_alcbnge$alcbnge_category)##
## Daily or almost daily Less than monthly Monthly
## 417 65 650
## Weekly
## 346##TASK 3
# Code for Portugal
portugal_plnftr <- ess %>%
filter(cntry == "PT") %>%
select(plnftr)
# Code for Serbia
serbia_plnftr <- ess %>%
filter(cntry == "RS") %>%
select(plnftr)
# Summary for Portugal
summary(portugal_plnftr)## plnftr
## Min. : 0.000
## 1st Qu.: 3.000
## Median : 5.000
## Mean : 6.426
## 3rd Qu.: 8.000
## Max. :88.000
## NA's :14604# Summary for Serbia
summary(serbia_plnftr)## plnftr
## Min. : 0.000
## 1st Qu.: 0.000
## Median : 4.000
## Mean : 4.983
## 3rd Qu.: 8.000
## Max. :88.000
## NA's :1505##TASK 4
# Code for Italy
italy_stfdem <- ess %>%
filter(cntry == "IT") %>%
select(stfdem, gndr)
# Recode values 77, 88, 99 to NA for 'stfdem'
italy_stfdem$stfdem[italy_stfdem$stfdem %in% c(77, 88, 99)] <- NA
# Group by gender and calculate mean dissatisfaction
mean_dissatisfaction <- italy_stfdem %>%
group_by(gndr) %>%
summarize(mean_stfdem = mean(stfdem, na.rm = TRUE))
# Print the result
print(mean_dissatisfaction)## # A tibble: 3 × 2
## gndr mean_stfdem
## <dbl> <dbl>
## 1 1 4.78
## 2 2 4.66
## 3 9 3.25##TASK 5
france_data <- ess %>%
filter(cntry == "FR")
ggplot(france_data %>%
mutate(stfedu = ifelse(stfedu %in% c(77, 88, 99), NA, stfedu),
stfhlth = ifelse(stfhlth %in% c(77, 88, 99), NA, stfhlth)) %>%
select(stfedu, stfhlth) %>%
gather(variable, value, c(stfedu, stfhlth)),
aes(x = variable, y = value)) +
geom_boxplot() +
labs(title = "Boxplot of satisfaction with the state of education vs. health services",
x = "", # Remove x-axis label
y = "Satisfaction (0-10)") # Change y-axis label## Warning: Removed 364 rows containing non-finite values (`stat_boxplot()`).
# Modified boxplot code
ggplot(france_data %>%
gather(variable, value, c(stfedu, stfhlth)),
aes(x = variable, y = value)) +
geom_boxplot() +
labs(title = "Boxplot of satisfaction with the state of education vs. health services",
x = "", # Remove x-axis label
y = "Satisfaction (0-10)")
# Add a code chunk to print the explanation
cat("\nBased on the boxplot, the median satisfaction level for health services (stfhlth) is higher than the median satisfaction level for education (stfedu) in France.")##
## Based on the boxplot, the median satisfaction level for health services (stfhlth) is higher than the median satisfaction level for education (stfedu) in France.