library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)
# get data from rcfss package
# install latest version if not already installed
# devtools::install_github("uc-cfss/rcfss")
library(rcfss)
# load the data
data("mass_shootings")
mass_shootings
#> # A tibble: 114 x 14
#> case year month day location summary fatalities injured total_victims
#> <chr> <dbl> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Dayt… 2019 Aug 4 Dayton,… "PENDI… 9 27 36
#> 2 El P… 2019 Aug 3 El Paso… "PENDI… 20 26 46
#> 3 Gilr… 2019 Jul 28 Gilroy,… "Santi… 3 12 15
#> 4 Virg… 2019 May 31 Virgini… "DeWay… 12 4 16
#> 5 Harr… 2019 Feb 15 Aurora,… "Gary … 5 6 11
#> 6 Penn… 2019 Jan 24 State C… "Jorda… 3 1 4
#> 7 SunT… 2019 Jan 23 Sebring… "Zephe… 5 0 5
#> 8 Merc… 2018 Nov 19 Chicago… "Juan … 3 0 3
#> 9 Thou… 2018 Nov 7 Thousan… "Ian D… 12 22 34
#> 10 Tree… 2018 Oct 27 Pittsbu… "Rober… 11 6 17
#> # … with 104 more rows, and 5 more variables: location_type <chr>, male <lgl>,
#> # age_of_shooter <dbl>, race <chr>, prior_mental_illness <chr>
# Generate a bar chart that identifies the number of mass shooters
# associated with each race category. The bars should be sorted
# from highest to lowest.
# using reorder() and aggregating the data before plotting
mass_shootings %>%
count(race) %>%
drop_na(race) %>%
ggplot(mapping = aes(x = reorder(race, -n), y = n)) +
geom_col() +
labs(
title = "Mass shootings in the United States (1982-2019)",
x = "Race of perpetrator",
y = "Number of incidents"
)
#> Error in drop_na(., race): could not find function "drop_na"
Created on 2020-11-05 by the reprex package (v0.3.0)