# List of packages
packages <- c("tidyverse", "modelsummary", "forcats", "RColorBrewer",
"fst", "viridis", "knitr", "rmarkdown", "ggridges", "viridis", "questionr", "flextable", "infer") # 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 ──
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## [26] "datasets" "methods" "base"# read data
ess <- read_fst("All-ESS-Data.fst")Mission 1
Launch a new R project and R markdown file. Name it “Lastname_Firstname_Project_202”. Set up your environment with packages you will use.
Mission 2
Filter:
uk_data <- ess %>%
filter(cntry == "GR") Saving the dataset
write_fst(uk_data, "~/Desktop/School/Courses/soc202/uk_data.fst")
getwd()## [1] "/Users/rileyyang/Desktop/School/Courses/soc202/riley_y_soc202project"Mission 3
rm(list=ls()); gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 1262580 67.5 2128808 113.7 NA 2128808 113.7
## Vcells 2151950 16.5 1257022127 9590.4 16384 1338827798 10214.5df <- read_fst("~/Desktop/School/Courses/soc202/uk_data.fst")Mission 4
Produce and save a data summary output (using data summary skim) for potential outcomes of interest on a similar scale (e.g., 0-10, or 1 to 6, or binary). Add a title. You can do so while coding (explore package information for flextable and/or modelsummary) or add it directly in the word file. Title should be something like: Table 1: Descriptive Statistics for outcome variables. You can alter the title as you see fit.
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]
}
uk_data <- df
# make sure you know your current folder path. you can do getwd() to check
uk_data_table_subset <- uk_data %>%
mutate(
polintr = ifelse(polintr %in% c(7, 8, 9), NA, polintr),
pplfair = ifelse(pplfair %in% c(77, 88, 99), NA, pplfair),
ppltrst = ifelse(ppltrst %in% c(77, 88, 99), NA, ppltrst)
)
summary_table <- datasummary_skim(uk_data_table_subset %>% select(polintr, pplfair, ppltrst), output = "flextable")## Warning: The histogram argument is only supported for (a) output types "default",
## "html", "kableExtra", or "gt"; (b) writing to file paths with extensions
## ".html", ".jpg", or ".png"; and (c) Rmarkdown, knitr or Quarto documents
## compiled to PDF (via kableExtra) or HTML (via kableExtra or gt). Use
## `histogram=FALSE` to silence this warning.summary_table
| Unique (#) | Missing (%) | Mean | SD | Min | Median | Max |
|---|---|---|---|---|---|---|---|
polintr | 5 | 0 | 3.0 | 0.9 | 1.0 | 3.0 | 4.0 |
pplfair | 12 | 0 | 4.0 | 2.2 | 0.0 | 4.0 | 10.0 |
ppltrst | 12 | 0 | 4.0 | 2.3 | 0.0 | 4.0 | 10.0 |
Note: you well get a warning, just ignore it.
Now let’s change the variable labels to be clearer. Here’s how to do it tidyverse style.
uk_data_v2 <- uk_data_table_subset %>%
mutate(
`How interested in politics` = polintr,
`Trust in most people` = ppltrst,
`Fairness in most people` = pplfair
)Check our table again.
summary_table_v2 <- datasummary_skim(uk_data_v2 %>% select(c("How interested in politics","Trust in most people", "Fairness in most people")), output = "flextable")## Warning: The histogram argument is only supported for (a) output types "default",
## "html", "kableExtra", or "gt"; (b) writing to file paths with extensions
## ".html", ".jpg", or ".png"; and (c) Rmarkdown, knitr or Quarto documents
## compiled to PDF (via kableExtra) or HTML (via kableExtra or gt). Use
## `histogram=FALSE` to silence this warning.summary_table_v2
| Unique (#) | Missing (%) | Mean | SD | Min | Median | Max |
|---|---|---|---|---|---|---|---|
How interested in politics | 5 | 0 | 3.0 | 0.9 | 1.0 | 3.0 | 4.0 |
Trust in most people | 12 | 0 | 4.0 | 2.3 | 0.0 | 4.0 | 10.0 |
Fairness in most people | 12 | 0 | 4.0 | 2.2 | 0.0 | 4.0 | 10.0 |
Mission 5
Produce and save a data summary output (using data summary skim) for socio-demographic variables. Add a title. You can do so while coding (explore package information for flextable and/or modelsummary) or add it directly in the word file. Title should be something like: Table 1: Descriptive Statistics for socio-demographic variables. You can alter the title as you see fit.
Mission 6
Produce and save a plot as a PDF for your outcome of interest (any will do here).
# Calculate the mean of trstprl by year and clsprty
avg_ppltrst_by_year <- aggregate(ppltrst ~ year + polintr, data=uk_data_table_subset, mean)
# Storing plot as p1
p1 <- ggplot(avg_ppltrst_by_year, aes(x=year, y=ppltrst, color=as.factor(polintr))) +
geom_line(aes(group=polintr)) +
labs(title="Mean of Trust in People by Survey Year",
subtitle = "in relations to how interested",
x="Survey Year",
y="Average Trust in People (trust increases from 0 to 10)") +
scale_color_discrete(name="How interested in politics", labels=c("Not at all interested", "Hardly interested", "Quite interested", "Very interested")) +
theme_minimal()
p1
ggsave(filename = "Mean of Trust in People by Survey Year.pdf", plot = p1, device = "pdf", width = 6, height = 4)