## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- # Introducing RStudio: Hands-On Learning and Career Readiness in the Social, Behavioral, and # Educational Sciences © 2025 by Njesa Totty is licensed under CC BY-NC 4.0. To view a copy # of this license, visit https://creativecommons.org/licenses/by-nc/4.0/. ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- # 3 Introduction to the Tidyverse and Core Data Skills ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- library(tidyverse) ## -------------------------------------------------------------------------------------- summarise(group_by(txhousing, year), avg_sales = mean(sales, na.rm = TRUE)) ## -------------------------------------------------------------------------------------- txhousing |> group_by(year) |> summarise(avg_sales = mean(sales, na.rm = TRUE)) ## -------------------------------------------------------------------------------------- # read from a downloaded file in your working directory gssdat <- read.csv("gssdat.csv") ## -------------------------------------------------------------------------------------- gssdat |> select(year, age, degree, income) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> select(year, age, degree, income) |> filter(year == 2022, degree %in% c("bachelor's", "graduate")) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- # length one vector 5 %in% c(1, 3, 5, 7, 9) # length three vector c(1, 3, 5) %in% c(1, 2, 4, 6, 3) # toy dataset toydat <- data.frame(letter = c("A", "B", "C", "D", "E", "F", "I", "A")) # length two vector against variable c("A", "F") %in% toydat$letter # variable against length two vector toydat$letter %in% c("A", "F") ## -------------------------------------------------------------------------------------- gssdat |> mutate(young_adult = if_else(age >= 18 & age <= 30, "Yes", "No")) |> select(year, young_adult, degree, income) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> group_by(degree) |> summarise(avg_age = mean(age, na.rm = TRUE)) ## -------------------------------------------------------------------------------------- gssdat |> group_by(degree) |> summarise(avg_income = mean(income, na.rm = TRUE)) |> arrange(avg_income) ## -------------------------------------------------------------------------------------- unique(gssdat$incomecat) ## -------------------------------------------------------------------------------------- gssdat |> group_by(degree) |> summarise(avg_income = mean(income, na.rm = TRUE)) |> arrange(desc(avg_income)) ## -------------------------------------------------------------------------------------- gssdat |> filter(str_detect(occ10, regex("teacher|nurse|emergency"))) |> select(age, incomecat, occ10) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> filter(str_detect(occ10, "teacher")) |> select(age, incomecat, occ10) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> filter(str_detect(occ10, "miscellaneous")) |> mutate(occ10 = str_replace(occ10, "miscellaneous", "misc")) |> select(occ10) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- # 4 Importing and Cleaning Data ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- # Number of rows and columns dim(gssdat) # First ten variable names names(gssdat) # Summary of one variable summary(gssdat$age) ## -------------------------------------------------------------------------------------- # Count number of missing values in each column gssdat |> is.na() |> colSums() # How many rows do not have a missing income bracket gssdat |> filter(!is.na(income)) |> nrow() ## -------------------------------------------------------------------------------------- # Example with two trust variables gssdat |> select(trustfam, trustdoc) |> pivot_longer(cols = c(trustfam, trustdoc), names_to = "trust_type", values_to = "trust_level") |> group_by(trust_type, trust_level) |> summarize(count = n()) ## -------------------------------------------------------------------------------------- # widen workstatus by year gssdat |> select(id, year, wrkstat) |> pivot_wider(names_from = year, values_from = wrkstat) |> select('2021', '2022', '2024') |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- # Combine 'separated' and 'divorced' into 'not married' gssdat |> mutate(marital = recode(marital, "separated" = "not married", "divorced" = "not married")) |> count(marital) # compare gssdat |> count(marital) ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- # 5 Visualizing and Summarizing Data ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = agewed)) + geom_histogram(binwidth = 2, fill = "steelblue", color = "white") + labs(title = "Distribution of Age at First Marriage", x = "Age at First Marriage", y = "Count") ## -------------------------------------------------------------------------------------- agewed_summary <- gssdat |> summarize(mean_age = mean(agewed, na.rm = TRUE), med_age = median(agewed, na.rm = TRUE)) agewed_summary ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = agewed)) + geom_histogram(binwidth = 2, fill = "steelblue", color = "white") + geom_vline(data = agewed_summary, aes(xintercept = mean_age), color = "red", linewidth = 1) + geom_vline(data = agewed_summary, aes(xintercept = med_age), color = "forestgreen", linewidth = 1) + labs(title = "Distribution of Age at First Marriage", x = "Age at First Marriage", y = "Count") ## -------------------------------------------------------------------------------------- gssdat |> group_by(year) |> mutate(conjudge = recode(conjudge, `1` = 3L, `3` = 1L, `2` = 2L)) |> summarize(mean_conf = mean(conjudge, na.rm = TRUE)) |> ggplot(aes(x = year, y = mean_conf)) + geom_line() + geom_point() + labs(title = "Average Confidence in the Supreme Court Over Time", x = "Year", y = "Mean Confidence") ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = age, y = realrinc)) + geom_point(alpha = 0.3) + labs(title = "Scatterplot of Age and Income", x = "Age", y = "Income (1986 US dollars)") ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = marital, y = hrs1)) + geom_boxplot(fill = "lightblue") + labs(title = "Hours Worked per Week by Marital Status", x = "Marital Status", y = "Hours Worked per Week") ## -------------------------------------------------------------------------------------- gssdat |> group_by(marital) |> summarize(avg_hrs = mean(hrs1, na.rm = TRUE)) |> arrange(avg_hrs) ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = conpress)) + geom_bar(fill = "forestgreen") + labs(title = "Confidence in the Press", x = "Response Category", y = "Count") ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = race, fill = conpress)) + geom_bar(position = "fill") + labs(title = "Confidence in the Press by Race", x = "Race", y = "Count") ## -------------------------------------------------------------------------------------- gssdat |> select(conpress, consci, race) |> pivot_longer(cols = c(conpress, consci), names_to = "confidence_type", values_to = "confidence_level") |> ggplot(aes(x = race, fill = confidence_level)) + geom_bar(position = "fill") + facet_wrap(~confidence_type) + labs(title = "Confidence in Institutions by Race", x = "Race", y = "Count") ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- # 7 Practice Solutions ## -------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------- ?mean ## -------------------------------------------------------------------------------------- plot(1:5) ## -------------------------------------------------------------------------------------- gssdat |> select(fepol, year) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> filter(year == 2018, happy == "very happy") |> select(year, happy) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> mutate(above_med_inc = income >= median(income, na.rm = TRUE)) |> select(above_med_inc, income) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> group_by(marital) |> summarize(mean_child = mean(childs, na.rm = TRUE)) ## -------------------------------------------------------------------------------------- gssdat |> group_by(marital) |> summarize(mean_age = mean(age, na.rm = TRUE)) |> arrange(desc(mean_age)) ## -------------------------------------------------------------------------------------- gssdat |> filter(str_detect(occ10, "nurse")) |> select(occ10, income, age) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> mutate(occ10 = str_replace(occ10, "telephone", "phone")) |> select(occ10, income) |> filter(str_detect(occ10, "phone")) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> filter((age <= 40 | age >= 25) & str_detect(occ10, regex("manager|teacher|engineer"))) |> select(year, occ10) |> group_by(year) |> summarize(count = n()) ## -------------------------------------------------------------------------------------- dim(gssdat) gssdat |> select(degree) |> glimpse() ## -------------------------------------------------------------------------------------- gssdat |> filter(!is.na(income)) |> select(income, age) |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> select(happy, health) |> pivot_longer(cols = c(happy, health), names_to = "response_type", values_to = "response") |> slice_sample(n = 10) ## -------------------------------------------------------------------------------------- gssdat |> mutate(happy = recode(happy, "not too happy" = "unhappy")) |> count(happy) ## -------------------------------------------------------------------------------------- gssdat |> filter((year <= 2020 | year >= 2010) & !is.na(age)) |> group_by(marital) |> summarize(mean_age = mean(age)) ## -------------------------------------------------------------------------------------- gssdat |> ggplot(aes(x = realinc)) + geom_histogram(fill = "darkorange", color = "black", binwidth = 10000) + labs(x = "Family income in 1986 US dollars", y = "Count", title = "Income Distribution") ## -------------------------------------------------------------------------------------- gssdat %>% mutate(coneduc = recode(coneduc, `1` = 3L, `3` = 1L, `2` = 2L)) |> group_by(year) %>% summarise(mean_coneduc = mean(coneduc, na.rm = TRUE)) %>% ggplot(aes(x = year, y = mean_coneduc)) + geom_line(color = "purple") + geom_point(color = "purple") + labs(title = "Average Confidence in the Education System Over Time", x = "Survey Year", y = "Mean Confidence in Education") ## -------------------------------------------------------------------------------------- gssdat %>% ggplot(aes(x = race, fill = conlegis)) + geom_bar(position = "fill") + facet_wrap(~sex) + labs(title = "Confidence in Congress by Gender", x = "Confidence in Congress", y = "Count") ## -------------------------------------------------------------------------------------- gssdat %>% ggplot(aes(x = race, y = educ, fill = race)) + geom_boxplot() + labs( title = "Distribution of Education by Race", x = "Race", y = "Years of Education" )