Minneapolis police use of force data. — mn_police_use_of

From Minneapolis, data from 2016 through August 2021

Usage

mn_police_use_of_force

Format

A data frame with 12925 rows and 13 variables.

response_datetime: DateTime of police response.
problem: Problem that required police response.
is_911_call: Whether response was iniated by call to 911.
primary_offense: Offense of subject.
subject_injury: Whether subject was injured Yes/No/null.
force_type: Type of police force used.
force_type_action: Detail of police force used.
race: Race of subject.
sex: Gender of subject.
age: Age of subject.
type_resistance: Resistance to police by subject.
precinct: Precinct where response occurred.
neighborhood: Neighborhood where response occurred.

Source

Minneapolis

Examples

library(dplyr)
library(ggplot2)

# List percent of total for each race
mn_police_use_of_force |>
  count(race) |>
  mutate(percent = round(n / sum(n) * 100, 2)) |>
  arrange(desc(percent))
#>                 race    n percent
#> 1              Black 7648   59.17
#> 2              White 3129   24.21
#> 3               <NA> 1024    7.92
#> 4    Native American  784    6.07
#> 5 Other / Mixed Race  205    1.59
#> 6              Asian  129    1.00
#> 7   Pacific Islander    6    0.05

# Display use of force count by three races
race_sub <- c("Asian", "White", "Black")
ggplot(
  mn_police_use_of_force |> filter(race %in% race_sub),
  aes(force_type, ..count..)
) +
  geom_point(stat = "count", size = 4) +
  coord_flip() +
  facet_grid(race ~ .) +
  labs(
    x = "Force Type",
    y = "Number of Incidents"
  )
#> Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
#> ℹ Please use `after_stat(count)` instead.