Numbers of crimes by state and source

A dataset containing the state-wide counts of offenses for a selected number of crimes since 1979 as reported through the API for the FBI's Crime Data Explorer at https://cde.ucr.cjis.gov/LATEST/webapp/#/pages/docApi. Last updated: Sep 20 2023
The variables are as follows (more detail on the FBI website):

fbi.v2

Format

A tibble with 26520 rows and 12 columns

state: name of the state for which numbers are reported.
state_id: id for each state.
state_abbr: two letter state abbreviation.
year: year of the reporting.
population: state population.
type: type of crime.
count: number of reported offenses.
total_agency_count: total number of crime-solving agencies in the state.
agency_submitting: number of agencies who reported data.
population_covered: percent of the state's population covered by reporting agencies.
source: source of the estimate: SRS (standard reporting system) or NIBRS (national incidence based reporting system)

Examples

# example code
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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#> ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

# compliance to report to NIBRS varies drastically by state
fbi.v2 %>% 
  ggplot(aes(x = year, y = agency_submitting/total_agency_count*100)) + 
    geom_point(aes(colour = source)) + 
    facet_wrap(~state, scales="free_y")
#> Warning: Removed 9 rows containing missing values (`geom_point()`).


# population size is related to compliance
fbi.v2 %>% 
  filter(year==2021) %>%
  ggplot(aes(x = population, y = agency_submitting/total_agency_count*100)) + 
    geom_point() +
    geom_text(aes(label=state_abbr), 
              nudge_y = 3,
              data = fbi.v2 %>% 
                     filter(year==2021, 
                            agency_submitting/total_agency_count*100 < 50 | 
                            population > 2e+07) %>% unique())

    
# comparison of SRS and NIBRS counts in Iowa across all types of offenses
fbi.v2 %>% filter(state_abbr=="IA") %>%  
  ggplot(aes(x = year, y = count)) + 
    geom_point(aes(colour = source)) + 
    facet_wrap(~type, scales="free_y")
#> Warning: Removed 38 rows containing missing values (`geom_point()`).


# comparison of SRS and NIBRS counts in New York across all types of offenses
fbi.v2 %>% filter(state_abbr=="NY") %>%  
  ggplot(aes(x = year, y = count)) + 
    geom_point(aes(colour = source)) + 
    facet_wrap(~type, scales="free_y")
#> Warning: Removed 38 rows containing missing values (`geom_point()`).