Skip to contents

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")