Practice transforming and visualizing factors
library(tidyverse)
library(knitr)
library(rcfss)
theme_set(theme_minimal())
# load the data
data("gun_deaths")
gun_deaths## # A tibble: 100,798 x 10
## id year month intent police sex age race place education
## <int> <int> <dbl> <chr> <int> <chr> <int> <chr> <chr> <fct>
## 1 1 2012 1. Suicide 0 M 34 Asia… Home BA+
## 2 2 2012 1. Suicide 0 F 21 White Stre… Some col…
## 3 3 2012 1. Suicide 0 M 60 White Othe… BA+
## 4 4 2012 2. Suicide 0 M 64 White Home BA+
## 5 5 2012 2. Suicide 0 M 31 White Othe… HS/GED
## 6 6 2012 2. Suicide 0 M 17 Nati… Home Less tha…
## 7 7 2012 2. Undetermined 0 M 48 White Home HS/GED
## 8 8 2012 3. Suicide 0 M 41 Nati… Home HS/GED
## 9 9 2012 2. Accidental 0 M 50 White Othe… Some col…
## 10 10 2012 2. Suicide 0 M NA Black Home <NA>
## # ... with 100,788 more rowsConvert month into a factor column
Click for the solution
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"
## [12] "Dec"(gun_deaths <- gun_deaths %>%
mutate(month = factor(month, levels = 1:12, labels = month_levels)))## # A tibble: 100,798 x 10
## id year month intent police sex age
## <int> <int> <fctr> <chr> <int> <chr> <int>
## 1 1 2012 Jan Suicide 0 M 34
## 2 2 2012 Jan Suicide 0 F 21
## 3 3 2012 Jan Suicide 0 M 60
## 4 4 2012 Feb Suicide 0 M 64
## 5 5 2012 Feb Suicide 0 M 31
## 6 6 2012 Feb Suicide 0 M 17
## 7 7 2012 Feb Undetermined 0 M 48
## 8 8 2012 Mar Suicide 0 M 41
## 9 9 2012 Feb Accidental 0 M 50
## 10 10 2012 Feb Suicide 0 M NA
## # ... with 100,788 more rows, and 3 more variables: race <chr>,
## # place <chr>, education <fctr>Visualize the total gun deaths per month, in chronological order
Click for the solution
ggplot(gun_deaths, aes(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) %>%
ggplot(aes(fct_reorder(month, n), 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()
ggplot(gun_deaths, aes(month %>%
fct_infreq() %>%
fct_rev())) +
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
intent_levels <- c("Accidental", "Homicide", "Suicide", "Undetermined")
gun_deaths %>%
filter(!is.na(intent)) %>%
mutate(intent = parse_factor(intent, levels = intent_levels)) %>%
ggplot(aes(intent %>%
fct_infreq() %>%
fct_rev())) +
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 %>%
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(aes(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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## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
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## yaml 2.1.18 2018-03-08 CRAN (R 3.4.4)This work is licensed under the CC BY-NC 4.0 Creative Commons License.