Practice transforming and visualizing factors
library(tidyverse)
library(rcis)
theme_set(theme_minimal())
Run the code below in your console to download this exercise as a set of R scripts.
usethis::use_course("cis-ds/data-wrangling-relational-data-and-factors")
# load the data
data("gun_deaths")
gun_deaths
## # A tibble: 100,798 × 10
## id year month intent police sex age race place education
## <dbl> <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <fct>
## 1 1 2012 Jan Suicide 0 M 34 Asian/Paci… Home BA+
## 2 2 2012 Jan Suicide 0 F 21 White Stre… Some col…
## 3 3 2012 Jan Suicide 0 M 60 White Othe… BA+
## 4 4 2012 Feb Suicide 0 M 64 White Home BA+
## 5 5 2012 Feb Suicide 0 M 31 White Othe… HS/GED
## 6 6 2012 Feb Suicide 0 M 17 Native Ame… Home Less tha…
## 7 7 2012 Feb Undetermined 0 M 48 White Home HS/GED
## 8 8 2012 Mar Suicide 0 M 41 Native Ame… Home HS/GED
## 9 9 2012 Feb Accidental 0 M 50 White Othe… Some col…
## 10 10 2012 Feb Suicide 0 M NA Black Home <NA>
## # … with 100,788 more rows
Convert month into a factor column
Click for the solution
# create a character vector with all month values
month_levels <- c(
"Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)
# or use the built-in constant
month.abb
## [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
# use mutate() and factor() to convert the column and store the result
(gun_deaths <- gun_deaths %>%
mutate(month = factor(month,
levels = month_levels
)))
## # A tibble: 100,798 × 10
## id year month intent police sex age race place education
## <dbl> <dbl> <fct> <chr> <dbl> <chr> <dbl> <chr> <chr> <fct>
## 1 1 2012 Jan Suicide 0 M 34 Asian/Paci… Home BA+
## 2 2 2012 Jan Suicide 0 F 21 White Stre… Some col…
## 3 3 2012 Jan Suicide 0 M 60 White Othe… BA+
## 4 4 2012 Feb Suicide 0 M 64 White Home BA+
## 5 5 2012 Feb Suicide 0 M 31 White Othe… HS/GED
## 6 6 2012 Feb Suicide 0 M 17 Native Ame… Home Less tha…
## 7 7 2012 Feb Undetermined 0 M 48 White Home HS/GED
## 8 8 2012 Mar Suicide 0 M 41 Native Ame… Home HS/GED
## 9 9 2012 Feb Accidental 0 M 50 White Othe… Some col…
## 10 10 2012 Feb Suicide 0 M NA Black Home <NA>
## # … with 100,788 more rows
Visualize the total gun deaths per month, in chronological order
Click for the solution
ggplot(
data = gun_deaths,
mapping = aes(x = month)
) +
geom_bar() +
labs(
title = "Gun Deaths in the United States (2012-2014)",
x = "Month",
y = "Number of gun deaths"
)
Visualize the total gun deaths per month, sorted from lowest to highest
Click for the solution
# with geom_col() and fct_reorder()
gun_deaths %>%
count(month) %>%
mutate(month = fct_reorder(.f = month, .x = n)) %>%
ggplot(mapping = aes(x = month, y = n)) +
geom_col() +
labs(
title = "Gun Deaths in the United States (2012-2014)",
x = "Month",
y = "Number of gun deaths"
)
# with geom_bar() and fct_infreq()
gun_deaths %>%
mutate(month = fct_infreq(f = month) %>%
fct_rev()) %>%
ggplot(mapping = aes(x = month)) +
geom_bar() +
labs(
title = "Gun Deaths in the United States (2012-2014)",
x = "Month",
y = "Number of gun deaths"
)
Visualize the frequency of intent of gun deaths using a bar chart, sorted from most to least frequent
Click for the solution
# identify all possible types of intent
intent_levels <- c("Accidental", "Homicide", "Suicide", "Undetermined")
gun_deaths %>%
# remove rows with missing intent values
drop_na(intent) %>%
# parse_factor() is a tidyverse friendly form of factor()
# ensure values are properly ordered from highest to lowest frequency
mutate(intent = parse_factor(intent, levels = intent_levels) %>%
fct_infreq() %>%
fct_rev()) %>%
ggplot(mapping = aes(x = intent)) +
geom_bar() +
labs(
title = "Gun Deaths in the United States (2012-2014)",
x = "Intent of death",
y = "Number of gun deaths"
) +
coord_flip()
Visualize total gun deaths by season of the year using a bar chart.
Hint: do not use cut() to create the season column.
Click for the solution
gun_deaths %>%
# use fct_collapse() to condense into 4 categories
mutate(season = fct_collapse(month,
"Winter" = c("Jan", "Feb", "Mar"),
"Spring" = c("Apr", "May", "Jun"),
"Summer" = c("Jul", "Aug", "Sep"),
"Fall" = c("Oct", "Nov", "Dec")
)) %>%
ggplot(mapping = aes(x = season)) +
geom_bar() +
labs(
title = "Gun Deaths in the United States (2012-2014)",
x = "Season",
y = "Number of gun deaths"
)
Session Info
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
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## os macOS Monterey 12.2.1
## system aarch64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2022-08-08
## pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
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