library(tidyverse)
library(lubridate)
library(patchwork)
library(ggthemes)12-factors
Warm Up
gss_cat gets loaded with the forcats package in the tidyverse. Use gss_cat to answer the following questions (I’d use a graph, but do whatever you’d like!)
gss_cat # A tibble: 21,483 × 9
year marital age race rincome partyid relig denom tvhours
<int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int>
1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… 12
2 2000 Divorced 48 White $8000 to 9999 Not str r… Prot… Bapt… NA
3 2000 Widowed 67 White Not applicable Independe… Prot… No d… 2
4 2000 Never married 39 White Not applicable Ind,near … Orth… Not … 4
5 2000 Divorced 25 White Not applicable Not str d… None Not … 1
6 2000 Married 25 White $20000 - 24999 Strong de… Prot… Sout… NA
7 2000 Never married 36 White $25000 or more Not str r… Chri… Not … 3
8 2000 Divorced 44 White $7000 to 7999 Ind,near … Prot… Luth… NA
9 2000 Married 44 White $25000 or more Not str d… Prot… Other 0
10 2000 Married 47 White $25000 or more Strong re… Prot… Sout… 3
# ℹ 21,473 more rowsWhich religions watch the least TV?
Do married people watch more or less TV than single people?
Try it: fct_reorder
Use rincome_summary to construct a dotplot of rincome against age.
Reorder rincome by age
rincome_summary <- gss_cat %>%
group_by(rincome) %>%
summarize(
age = mean(age, na.rm = TRUE),
tvhours = mean(tvhours, na.rm = TRUE),
n = n()
)Try it fct_collapse
Collapse the marital variable to have levels Married, not_married, and No answer
Include "Never married", "Divorced", and “Widowed" in not_married
Additional examples
Hotel bookings by month
# From TidyTuesday: https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md
hotels <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv", show_col_types = FALSE)- Run the code and examine the plot. How are the months ordered? What would be a better order?
hotels %>%
group_by(hotel, arrival_date_month) %>% # group by hotel type and arrival month
summarize(mean_adr = mean(adr)) %>% # calculate mean adr for each group
ggplot(aes(
x = arrival_date_month, # x-axis = arrival_date_month
y = mean_adr, # y-axis = mean_adr calculated above
group = hotel, # group lines by hotel type
color = hotel) # and color by hotel type
) +
geom_line() + # use lines to represent data
theme_minimal() + # use a minimal theme
labs(
x = "Arrival month", # customize labels
y = "Mean ADR (average daily rate)",
title = "Comparison of resort and city hotel prices across months",
subtitle = "Resort hotel prices soar in the summer while ciry hotel prices remain relatively constant throughout the year",
color = "Hotel type"
)
- Reorder the months on the x-axis (levels of
arrival_date_month) in a way that makes more sense. Use a function from theforcatspackage.
More hotel bookings by room type
Create a bar chart of the
reserved_room_type.Create another bar chart where you have bars for the top three room types and an “other” category lumping the other room types together.
More hotel bookings by room type
- Run the code and examine the plot. How are the months ordered? What would be a better order?
hotels %>%
group_by(meal, arrival_date_month) %>% # group by meal type and arrival month
summarize(mean_adr = mean(adr)) %>% # calculate mean adr for each group
ggplot(aes(
x = arrival_date_month, # x-axis = arrival_date_month
y = mean_adr, # y-axis = mean_adr calculated above
group = meal, # group lines by meal type
color = meal) # and color by meal type
) +
geom_line() + # use lines to represent data
theme_minimal() + # use a minimal theme
labs(
x = "Arrival month", # customize labels
y = "Mean ADR (average daily rate)",
color = "Meal type"
) +
ggthemes::scale_color_colorblind()
- Reorder the levels of meal type in a way that makes more sense. Use a function from the
forcatspackage to do this.