Package 'crimedatasets'

Description

This dataset contains information on reported crimes in Abilene, Texas, including the type of crime, year of the incident, and the number of reported cases. It provides a snapshot of crime patterns in the city for the years 1992 and 1999.

Usage

data(Abilene_tbl_df)

Format

A tibble with 16 observations and 3 variables:

crimetype

Type of crime (character).

year

Year of the reported crime (factor).

number

Number of reported crimes (integer).

Details

The dataset name has been changed to 'Abilene_tbl_df' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble in R. The original content has not been modified in any way.

Source

Uniform Crime Reports, U.S. Department of Justice.

Description

This dataset contains information on the number of convictions reported by U.S. attorney's offices, along with the number of staff members, normalized per 1 million population. The dataset also includes the district names for each observation.

Usage

data(Attorney_tbl_df)

Format

A tibble with 88 observations and 3 variables:

staff

Number of U.S. attorneys' office staff per 1 million population (integer).

convict

Number of convictions reported by U.S. attorneys' offices per 1 million population (integer).

district

Name of the district (character). Possible values include major U.S. cities such as Albuquerque, Atlanta, Boston, Chicago, Houston, Miami, San Francisco, and others.

Details

The dataset name has been changed to 'Attorney_tbl_df' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble in R. The original content has not been modified in any way.

Source

Data from U.S. Attorney's Office Reports.

Description

This dataset contains information on housing values and various factors influencing those values in 506 suburbs of Boston. It provides detailed insights into the factors such as crime rates, proximity to highways, and the quality of the local environment that may affect housing prices.

Usage

data(Boston_df)

Format

A data frame with 506 observations and 14 variables:

crim

Per capita crime rate by town (numeric).

zn

Proportion of residential land zoned for lots over 25,000 sq.ft. (numeric).

indus

Proportion of non-retail business acres per town (numeric).

chas

Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) (integer).

nox

Nitrogen oxides concentration (parts per 10 million) (numeric).

rm

Average number of rooms per dwelling (numeric).

age

Proportion of owner-occupied units built prior to 1940 (numeric).

dis

Weighted mean of distances to five Boston employment centres (numeric).

rad

Index of accessibility to radial highways (integer).

tax

Full-value property-tax rate per $10,000 (numeric).

ptratio

Pupil-teacher ratio by town (numeric).

black

1000(Bk - 0.63)^2 where Bk is the proportion of Black population by town (numeric).

lstat

Lower status of the population (percent) (numeric).

medv

Median value of owner-occupied homes in $1000s (numeric).

Details

The dataset name has been changed to 'Boston_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

This dataset was obtained from the Boston dataset, which is part of the MASS package, with slight modifications.

Description

This dataset contains information on reported crimes in Camden, including spatial coordinates, dates of the incidents, and crime types. It provides a detailed view of crime patterns within the region.

Usage

data(camden_crimes_df)

Format

A data frame with 4,578 observations and 4 variables:

x

X-coordinate (numeric).

y

Y-coordinate (numeric).

date

Date of the reported crime (Date).

type

Type of crime (character).

Details

The dataset name has been changed to 'camden_crimes_df' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way

Source

Data comprising 'Theft' and 'Criminal Damage' records of Camden Borough of London, UK, 2021. (Source: https://data.police.uk/data/)

Description

This dataset contains information on nearly 20,000 officials who were investigated during Xi Jinping's anti-corruption campaign. It provides data on the province, prefecture, and county where the investigations occurred, as well as unique identifiers for each administrative level.

Usage

data(corruption_tbl_df)

Format

A tibble with 10 observations and 6 variables:

province

2-digit province number (numeric).

prefecture

Prefecture name in Chinese (character).

county

County name in Chinese (character).

province_id

6-digit province identifier (numeric).

prefecture_id

6-digit prefecture identifier (numeric).

county_id

6-digit county identifier (numeric).

Details

The dataset name has been changed to 'corruption_tbl_df' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble in R. The original content has not been modified in any way

Source

Data from China's anti-corruption campaign investigations.

Description

A comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, criminology, and socio-economic studies related to criminal behavior.

Details

crimedatasets: A Comprehensive Collection of Crime-Related Datasets

A Comprehensive Collection of Crime-Related Datasets.

Author(s)

Maintainer: Renzo Cáceres Rossi [email protected]

See Also

Useful links:

• https://github.com/lightbluetitan/crimedatasets

Description

This dataset examines the relationship between crime rates and the percentage of the population without a high school degree in various U.S. states. The dataset contains crime data (violent crimes) along with educational attainment (percentage of people without a high school degree).

Usage

data(crimeHSdegree_tbl_df)

Format

A tibble with 51 observations and 3 variables:

state

State name (character).

nodegree

Percent of the population without a high school degree (numeric).

crime

Violent crimes per 100,000 population (numeric).

Details

The dataset name has been changed to 'crimeHSdegree_tbl_df' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble in R. The original content has not been modified in any way.

Source

U.S. Crime Data and Education Statistics.

Description

This dataset contains annual crime-related statistics for US counties, including violent crime rates, murder rates, and socio-economic indicators such as poverty, education, and unemployment. It provides a comprehensive overview of crime and its potential correlates across the United States.

Usage

data(crimestatewide_tbl_df)

Format

A tibble with 51 observations and 9 variables:

State

State name (character).

violent crime rate

Violent crime rate per 100,000 people (numeric).

murder rate

Murder rate per 100,000 people (numeric).

poverty

Poverty rate as a percentage (numeric).

high school

Percentage of high school graduates (numeric).

college

Percentage of college graduates (numeric).

single parent

Percentage of single-parent households (numeric).

unemployed

Unemployment rate as a percentage (numeric).

metropolitan

Percentage of the population living in metropolitan areas (numeric).

Details

The dataset name has been changed to 'crimestatewide_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble, a modern and more readable alternative to traditional data frames in R. The original content has not been modified in any way.

Source

Annual crime data of US counties.

Description

This dataset contains information on criminal offenders who were screened in Florida during 2013-2014.

Usage

data(crimOffenders_df)

Format

A data frame with 5,855 observations and 16 variables:

age

Age of the offender (numeric).

juv_fel_count

Number of juvenile felonies committed (numeric).

decile_score

COMPAS score decile (numeric).

juv_misd_count

Number of juvenile misdemeanors committed (numeric).

juv_other_count

Number of other juvenile convictions (numeric).

v_decile_score

Predicted decile score of the offender (numeric).

priors_count

Number of prior crimes committed (numeric).

sex

Gender of the offender (factor with levels 'Female' and 'Male').

two_year_recid

Recidivism within two years (factor with levels 'Yes' and 'No').

race

Race of the offender (factor with levels 'White', 'Black', 'Hispanic', 'Asian', 'Other', 'Native').

c_jail_in

Date of entry into jail (normalized between 0 and 1, numeric).

c_jail_out

Date of release from jail (normalized between 0 and 1, numeric).

c_offense_date

Date the offense was committed (numeric).

screening_date

Date the offender was screened (numeric).

in_custody

Date the offender was placed in custody (numeric, normalized between 0 and 1).

out_custody

Date the offender was released from custody (numeric, normalized between 0 and 1).

Details

The dataset name has been changed to 'crimOffenders_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

Data collected from criminal offenders screened in Florida during 2013-2014.

Description

Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

Usage

data(crimtab_table)

Format

A table with 42 rows and 22 columns:

Var1

Factor or categorical variable representing different crime categories.

Var2

A second factor or categorical variable, potentially representing different classifications such as location, time, or crime severity.

Freq

Frequency of occurrences within each combination of categories, representing the number of reported incidents for each combination.

Details

The dataset name has been changed to 'crimtab_table' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'table' indicates that the dataset is stored as a contingency table, rather than a traditional data frame. The original content has not been modified in any way.

Source

Public crime data.

Description

This dataset contains records of cybersecurity breaches reported to the US Department of Health and Human Services (HHS). Since October 2009, organizations in the United States that store data on human health are required to report incidents compromising the confidentiality of 500 or more patients or human subjects (45 C.F.R. 164.408). These reports are publicly available and provide detailed information about the affected entities, breach types, and impacted individuals.

Usage

data(CyberSecurityBreaches_df)

Format

A data frame with 1,151 observations and 9 variables:

Name.of.Covered.Entity

Name of the covered entity involved in the breach (character).

State

US state where the entity is located (factor with 52 levels).

Covered.Entity.Type

Type of the covered entity (factor with 4 levels).

Individuals.Affected

Number of individuals affected by the breach (integer).

Breach.Submission.Date

Date the breach was reported (Date).

Type.of.Breach

Type of breach (factor with 29 levels).

Location.of.Breached.Information

Location of the breached information (factor with 47 levels).

Business.Associate.Present

Indicates whether a business associate was involved (logical).

Web.Description

Description of the breach provided online (character).

Details

The dataset name has been changed to 'CyberSecurityBreaches_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

Cybersecurity breach data downloaded from the Office for Civil Rights of the US Department of Health and Human Services (HHS) on 2015-02-26.

Description

This dataset contains data collected by lawyers on convicted Black murderers in the state of Georgia. The goal was to examine whether convicted Black murderers whose victim was white were more likely to receive the death penalty compared to those whose victim was Black, accounting for the level of aggravation of the crime.

Usage

data(DeathPenaltyRace_df)

Format

A data frame with 12 observations and 4 variables:

Aggravation

Level of aggravation of the murder (integer). Categories range from 1 (least serious) to 6 (most serious).

Victim

Race of the victim (factor with 2 levels: "White" and "Black").

Death

Number of cases where the death penalty was given (integer).

NoDeath

Number of cases where the death penalty was not given (integer).

Details

The dataset name has been changed to 'DeathPenaltyRace_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

Data collected on death penalty cases in Georgia.

Description

This dataset contains data on fatalities and casualties in the U.S. for drunk-driving incidents, suicides, and acts of terrorism. The dataset spans the years 1970 to 2018 and provides insights into the impact of these causes of death and injury over time.

Usage

data(DrunkDST_tbl_df)

Format

A tibble with 49 observations and 5 variables:

year

Year of the observation (numeric).

nkill

Number of people killed in acts of terrorism (numeric).

terrtotal

Total number of casualties (injuries and fatalities) caused by terrorism (numeric).

suicides

Number of suicides (numeric).

ddfat

Number of fatalities caused by drunk-driving incidents (numeric).

Details

The dataset name has been changed to 'DrunkDST_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble. The original content has not been modified in any way.

Source

Data on casualties and fatalities from drunk-driving, suicide, and terrorism in the U.S., 1970–2018.

Description

This dataset contains data on traffic fatalities and laws related to drunk driving across U.S. states. It includes information on beer taxes, minimum legal drinking age (MLDA), and other socioeconomic factors observed between 1982 and 1988.

Usage

data(Fatality_df)

Format

A data frame with 336 observations and 10 variables:

state

State identifier (integer).

year

Year of the observation (integer).

mrall

Motor vehicle fatality rate per 100,000 population (numeric).

beertax

Beer tax in dollars per gallon (numeric).

mlda

Minimum legal drinking age (MLDA) (numeric).

jaild

Indicator for mandatory jail sentence for drunk-driving (Factor: Yes/No).

comserd

Indicator for mandatory community service for drunk-driving (Factor: Yes/No).

vmiles

Vehicle miles traveled in billions (numeric).

unrate

Unemployment rate (numeric).

perinc

Per capita income in dollars (numeric).

Details

The dataset name has been changed to 'Fatality_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is stored as a traditional data frame in R. The original content has not been modified in any way.

Source

Panel data on drunk driving laws and traffic deaths in the U.S. for 48 states, 1982–1988.

Description

This dataset contains detailed data from the FBI's National Instant Criminal Background Check System (NICS) on firearm background checks across U.S. states. It includes monthly data on gun sales, population statistics, and various firearm-related activities from multiple categories.

Usage

data(FBICriminal_tbl_df)

Format

A tibble with 11,648 observations and 35 variables:

state

State where the data was recorded (character).

year

Year of the observation (integer).

month

Month of the observation (character).

month.num

Numeric representation of the month (integer).

population

Population of the state (integer).

guns_per_thousand

Number of guns per 1,000 people (numeric).

guns_sold

Total guns sold (integer).

multiplier

Adjustments for sales data (numeric).

instore_purchases

Number of in-store purchases (integer).

permit

Number of gun permits issued (integer).

permit_recheck

Flag for permit recheck status (character).

handgun

Number of handguns sold (integer).

longgun

Number of long guns sold (integer).

other

Number of other types of firearms sold (integer).

multiple

Number of multiple gun purchases (integer).

multiple_corrected

Corrected count of multiple purchases (integer).

admin

Administrative checks conducted (integer).

prepawn_handgun

Number of prepawned handguns (integer).

prepawn_longgun

Number of prepawned long guns (integer).

prepawn_other

Number of prepawned other firearms (integer).

redemption_handgun

Number of redeemed handguns (integer).

redemption_longgun

Number of redeemed long guns (integer).

redemption_other

Number of redeemed other firearms (integer).

returned_handgun

Number of returned handguns (integer).

returned_longgun

Number of returned long guns (integer).

returned_other

Number of returned other firearms (integer).

rental_handgun

Number of handguns rented (integer).

rental_longgun

Number of long guns rented (integer).

private_handgun

Number of privately sold handguns (integer).

private_longgun

Number of privately sold long guns (integer).

private_other

Number of privately sold other firearms (integer).

privatereturn_handgun

Number of privately returned handguns (integer).

privatereturn_longgun

Number of privately returned long guns (integer).

privatereturn_other

Number of privately returned other firearms (integer).

totals

Total checks conducted (integer).

Details

The dataset name has been changed to 'FBICriminal_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble, which is a modern form of a data frame in R. The original content has not been modified in any way.

Source

FBI's National Instant Criminal Background Check System (NICS).

Description

This dataset contains information on 127 automobile insurance claims arising from accidents in Massachusetts, USA, in 1989. Each claim was classified as either fraudulent or legitimate by consensus among four independent claims adjusters who thoroughly examined each case file.

Usage

data(fraudulent_df)

Format

A data frame with 42 observations and 12 variables:

r1

Numeric score or rating 1 (numeric).

r2

Numeric score or rating 2 (numeric).

AC1

Indicator for a specific automobile claim condition (factor with 2 levels).

AC9

Indicator for a second specific automobile claim condition (factor with 2 levels).

AC16

Indicator for a third specific automobile claim condition (factor with 2 levels).

CL7

Claim-level indicator for condition 7 (factor with 2 levels).

CL11

Claim-level indicator for condition 11 (factor with 2 levels).

IJ2

Insurance adjuster’s information indicator for condition 2 (factor with 2 levels).

IJ3

Insurance adjuster’s information indicator for condition 3 (factor with 2 levels).

IJ4

Insurance adjuster’s information indicator for condition 4 (factor with 2 levels).

IJ6

Insurance adjuster’s information indicator for condition 6 (factor with 2 levels).

IJ12

Insurance adjuster’s information indicator for condition 12 (factor with 2 levels).

Details

The dataset name has been changed to 'fraudulent_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

Fraudulent automobile insurance claims data from Massachusetts, 1989.

Description

This dataset contains the results of a Gallup poll conducted in 1980 regarding public opinion on whether possession of marijuana should be considered a criminal offense. The dataset includes demographic information and the corresponding opinions of the respondents.

Usage

data(Gallup_tbl_df)

Format

A tibble with 1,200 observations and 2 variables:

demographics

Demographic category of the respondent (factor with 12 levels).

opinion

Respondent's opinion on marijuana possession as a criminal offense (factor with 3 levels).

Details

The dataset name has been changed to 'Gallup_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble in R. The original content has not been modified in any way.

Source

Results of a Gallup poll conducted in 1980.

Description

This dataset contains information on reported crimes across Georgia State, including spatial coordinates, dates of incidents, and crime types. It provides valuable insights into crime patterns within the region.

Usage

data(georgia_sf)

Format

An sf object (spatial data frame) with 10,523 observations and 5 variables:

geometry

Spatial geometry of each crime record (sf object).

date

Date of the reported crime (Date).

type

Type of crime (character).

city

City where the crime occurred (character).

county

County where the crime occurred (character).

Details

The dataset name has been changed to 'georgia_sf' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'sf' indicates that the dataset is a spatial data frame in R. The original content has not been modified in any way.

Source

Public crime data for Georgia State.

Description

This dataset, known as the Hartnagel dataset, contains an annual time series of crime rates and related socio-economic data in Canada from 1931 to 1968. It includes variables such as total fertility rates, labor force participation rates, and crime statistics disaggregated by gender. Note that some data points are missing.

Usage

data(Hartnagel_df)

Format

A data frame with 38 observations and 8 variables:

year

Year of observation (integer).

tfr

Total fertility rate per 1,000 women (integer).

partic

Labor force participation rate per 1,000 people (integer).

degrees

Number of university degrees conferred per 1,000 people (numeric).

fconvict

Convictions of females per 100,000 people (numeric).

ftheft

Thefts by females per 100,000 people (numeric).

mconvict

Convictions of males per 100,000 people (numeric).

mtheft

Thefts by males per 100,000 people (numeric).

Details

The dataset name has been changed to 'Hartnagel_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

The data is an annual time-series from 1931 to 1968. Some observations contain missing data.

Source

Hartnagel dataset, providing insights into Canadian crime rates and socio-economic factors.

Description

This dataset contains data on hate crimes across the United States and associated socioeconomic factors. It provides insights into potential relationships between income inequality, socioeconomic characteristics, and the frequency of hate crimes.

Usage

data(hate_crimes_tbl_df)

Format

A tibble with 51 observations and 13 variables:

state

Full name of the state (character).

state_abbrev

Abbreviation of the state (character).

median_house_inc

Median household income (integer).

share_unemp_seas

Share of unemployed people (seasonally adjusted) (numeric).

share_pop_metro

Share of the population living in metropolitan areas (numeric).

share_pop_hs

Share of the population with at least a high school education (numeric).

share_non_citizen

Share of the population who are non-citizens (numeric).

share_white_poverty

Share of the white population living in poverty (numeric).

gini_index

Gini index of income inequality (numeric).

share_non_white

Share of the population who are non-white (numeric).

share_vote_trump

Share of votes for Donald Trump in the 2016 presidential election (numeric).

hate_crimes_per_100k_splc

Hate crimes per 100,000 people as reported by the SPLC (numeric).

avg_hatecrimes_per_100k_fbi

Average hate crimes per 100,000 people as reported by the FBI (numeric).

Details

The dataset name has been changed to 'hate_crimes_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble, a modern version of data frames in R. The original content has not been modified in any way.

Source

The raw data behind the story "Higher Rates Of Hate Crimes Are Tied To Income Inequality" by FiveThirtyEight.

Description

This dataset contains detailed records of homicides that occurred in nine large US cities during the year 2015. The data includes geographic locations, offense codes, and additional metadata, making it valuable for analyzing patterns and trends in urban crime.

Usage

data(homicides15_tbl_df)

Format

A tibble with 1,922 observations and 15 variables:

uid

Unique identifier for the record (integer).

city_name

Name of the city where the homicide occurred (character).

offense_code

Offense code for the homicide (character).

offense_type

Type of offense (character).

date_single

Date and time of the homicide (POSIXct).

address

Address where the homicide occurred (character).

longitude

Longitude of the location (numeric).

latitude

Latitude of the location (numeric).

location_type

Type of location (character).

location_category

Category of location (character).

fips_state

FIPS code for the state (integer).

fips_county

FIPS code for the county (character).

tract

Census tract identifier (character).

block_group

Census block group identifier (integer).

block

Census block identifier (integer).

Details

The dataset name has been changed to 'homicides15_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble, offering better printing and subsetting capabilities in R. The original content has not been modified in any way.

This dataset provides insights into homicides in urban areas, offering geographic and temporal information for each case.

Source

Crime Open Database, 2015.

Description

This dataset provides information on the type of drug offenses categorized by race. It contains records that can be used to analyze racial patterns in drug-related offenses. The data is sourced from a comparative study of federal and state prison inmates.

Usage

data(Inmate_tbl_df)

Format

A tibble with 28,047 observations and 2 variables:

race

Race of the individual (factor with 3 levels).

drug

Type of drug offense (factor with 4 levels).

Details

The dataset name has been changed to 'Inmate_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble. The original content has not been modified in any way.

This dataset provides insights into racial disparities and trends in drug offenses.

Source

C. Wolf Harlow (1994), *Comparing Federal and State Prison Inmates*, NCJ-145864, U.S. Department of Justice, Bureau of Justice Statistics.

Description

This dataset contains pre-treatment covariates, a binary treatment (Z), an ordinal decision (D), and an outcome variable (Y). It is used to study new criminal activity (NCA).

Usage

data(NCAdata_tbl_df)

Format

A tibble with 1,891 observations and 19 variables:

Sex

Numeric variable representing the individual's sex.

White

Numeric variable indicating whether the individual is White.

SexWhite

Numeric interaction term between Sex and White.

Age

Numeric variable indicating the individual's age.

PendingChargeAtTimeOfOffense

Numeric variable indicating if there was a pending charge at the time of offense.

NCorNonViolentMisdemeanorCharge

Numeric variable indicating a non-violent misdemeanor charge.

ViolentMisdemeanorCharge

Numeric variable indicating a violent misdemeanor charge.

ViolentFelonyCharge

Numeric variable indicating a violent felony charge.

NonViolentFelonyCharge

Numeric variable indicating a non-violent felony charge.

PriorMisdemeanorConviction

Numeric variable indicating prior misdemeanor convictions.

PriorFelonyConviction

Numeric variable indicating prior felony convictions.

PriorViolentConviction

Numeric variable indicating prior violent convictions.

PriorSentenceToIncarceration

Numeric variable indicating prior sentences to incarceration.

PriorFTAInPastTwoYears

Numeric variable indicating prior failures to appear (FTA) in the past two years.

PriorFTAOlderThanTwoYears

Numeric variable indicating prior failures to appear (FTA) older than two years.

Staff_ReleaseRecommendation

Numeric variable indicating the staff release recommendation.

Z

Binary treatment variable.

D

Ordinal decision variable.

Y

Outcome variable measuring new criminal activity (NCA).

Details

The dataset name has been changed to 'NCAdata_tbl_df' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble in R. The original content has not been modified in any way.

Source

Interim Dane data with new criminal activity (NCA) as an outcome.

Description

This dataset contains a network of co-attendance occurrences of suspected members of the Ndrangheta criminal organization at summits held between 2007 and 2009. These summits were meetings aimed at making important decisions, resolving internal issues, and establishing roles and powers.

Usage

data(Ndrangheta_list)

Format

A list with 2 elements:

X

A numeric matrix of dimensions 146 x 146 representing the co-attendance occurrences between members of the Ndrangheta organization at summits. The matrix includes member pairs and their respective co-attendance frequency.

node_meta

A data frame with 146 observations and 3 variables:

Role

Character vector indicating the role of each member in the organization.

Locale

Character vector indicating the geographic locale of each member.

Id

Integer vector representing a unique identifier for each member.

Details

The dataset name has been changed to 'Ndrangheta_list' to avoid confusion with other data sets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'list' indicates that the dataset is a list object in R. The original content has not been modified in any way.

Source

Ndrangheta mafia covert network dataset, containing data from summits between 2007 and 2009.

Description

This dataset contains information on terrorist attacks by Fulani Extremists and Boko Haram in Nigeria, starting from the year 2014. The attack data is sourced from the Global Terrorism Database, while other variables are derived from the UCDP PRIO-Grid data. The dataset includes geographic coordinates, population data, and information about mountainous areas relevant to the attacks.

Usage

data(NigeriaTerrorism_df)

Format

A data frame with 312 observations and 6 variables:

att.ful

Number of attacks by Fulani Extremists (numeric).

att.bok

Number of attacks by Boko Haram (numeric).

xcoord

X-coordinate of the attack location (numeric).

ycoord

Y-coordinate of the attack location (numeric).

pop

Population of the area (numeric).

mtns

Indicator of whether the location is mountainous (numeric).

Details

The dataset name has been changed to 'NigeriaTerrorism_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

Global Terrorism Database and UCDP PRIO-Grid data.

Description

This dataset contains information related to new violent criminal activity (NVCA) as an outcome. It includes pre-treatment covariates, a binary treatment variable (Z), an ordinal decision variable (D), and an outcome variable (Y). The dataset provides a rich set of variables that can be used to analyze factors influencing violent crime recidivism, with a focus on various demographic and criminal history attributes.

Usage

data(NVCAdata_tbl_df)

Format

A tibble with 1,891 observations and 19 variables:

Sex

Sex of the individual (numeric).

White

Indicates if the individual is White (numeric).

SexWhite

Indicates if the individual is both White and male (numeric).

Age

Age of the individual (numeric).

PendingChargeAtTimeOfOffense

Pending charge at the time of offense (numeric).

NCorNonViolentMisdemeanorCharge

Non-violent misdemeanor charge (numeric).

ViolentMisdemeanorCharge

Violent misdemeanor charge (numeric).

ViolentFelonyCharge

Violent felony charge (numeric).

NonViolentFelonyCharge

Non-violent felony charge (numeric).

PriorMisdemeanorConviction

Prior misdemeanor conviction (numeric).

PriorFelonyConviction

Prior felony conviction (numeric).

PriorViolentConviction

Prior violent conviction (numeric).

PriorSentenceToIncarceration

Prior sentence to incarceration (numeric).

PriorFTAInPastTwoYears

Prior failure to appear in the past two years (numeric).

PriorFTAOlderThanTwoYears

Prior failure to appear older than two years (numeric).

Staff_ReleaseRecommendation

Staff release recommendation (numeric).

Z

Binary treatment variable (numeric).

D

Ordinal decision variable (numeric).

Y

Outcome variable indicating new violent criminal activity (numeric).

Details

The dataset name has been changed to 'NVCAdata_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble in R. The original content has not been modified in any way.

Source

Interim data on violent criminal activity (NVCA).

Description

This dataset contains information about recorded murder cases in New Zealand between 2004 and 2019. It includes details on the sex, age, and cause of death of the victims, as well as the identity of the alleged killer, the date of the crime, and the region where the crime occurred. The dataset is in the form of a simple features (sf) object, with geographic data represented as points.

Usage

data(nz_murders_sf)

Format

An sf data frame with 967 observations and 12 variables:

sex

Sex of the victim (character).

age

Age of the victim (integer).

date

Date of the murder (character).

year

Year the murder occurred (integer).

cause

Cause of death (character).

killer

Name of the alleged killer (character).

name

Name of the victim (character).

full_date

Full date and time of the murder (POSIXct).

month

Month of the murder (ordered factor with 12 levels).

cause_cat

Category of the cause of death (character).

region

Region where the murder occurred (character).

geometry

Geographic coordinates (sf POINT) representing the location of the murder (list of 967).

Details

The dataset name has been changed to 'nz_murders_sf' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix '_sf' indicates that the dataset is an sf object in R, used for storing and handling spatial data. The original content has not been modified in any way.

Source

Recorded murder data for New Zealand (2004 - 2019).

Description

This dataset contains records of every fatal police shooting by an on-duty officer since January 1, 2015. It includes information about the shooting incidents, the characteristics of the individuals involved, and details such as mental illness signs, body camera usage, and more. This dataset is valuable for analyzing trends and patterns in fatal police shootings in the United States.

Usage

data(police_shootings_tbl_df)

Format

A tibble with 6,421 observations and 12 variables:

date

Date of the shooting (Date).

manner_of_death

How the individual died (character).

armed

Indicates if the individual was armed (character).

age

Age of the individual (numeric).

gender

Gender of the individual (character).

race

Race of the individual (character).

city

City where the shooting occurred (character).

state

State where the shooting occurred (character).

signs_of_mental_illness

Whether the individual showed signs of mental illness (logical).

threat_level

Perceived threat level of the individual (character).

flee

Whether the individual was fleeing (character).

body_camera

Whether the officer was wearing a body camera (logical).

Details

The dataset name has been changed to 'police_shootings_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble, which is a modern version of a data frame in R. The original content has not been modified in any way.

Source

Washington Post Fatal Police Shootings database.

Description

This dataset contains information on rearrests of juvenile felons based on the type of court in which they were tried. The data originates from a sample of juveniles convicted of felony in Florida in 1987, with matched pairs formed using criteria such as age and the number of previous offenses. The dataset provides counts of rearrests for juveniles, categorized by adult and juvenile courts. This data is useful for analyzing rearrest rates and judicial outcomes for juveniles convicted of felonies.

Usage

data(rearrests_table)

Format

A table with 2 rows and 2 columns:

Adult court

Number of rearrests (numeric) and no rearrests (numeric) in adult court.

Juvenile court

Number of rearrests (numeric) and no rearrests (numeric) in juvenile court.

Details

The dataset name has been changed to 'rearrests_table' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'table' indicates that the dataset is a contingency table in R, representing the counts of rearrests by court type. The original content has not been modified in any way.

Source

Agresti, 1996. Data on rearrests of juvenile felons in Florida, 1987.

Description

This dataset contains information on the length of sentences served by 41 prisoners convicted of a homicide offense. The data was taken from a report by the U.S. Department of Justice, Bureau of Justice Statistics, which provides insight into the sentencing and time served for violent crimes, specifically homicides. The dataset includes the number of months each prisoner served in prison.

Usage

data(Sentence_tbl_df)

Format

A tibble with 41 observations and 1 variable:

months

The number of months served in prison by each prisoner (integer).

Details

The dataset name has been changed to 'Sentence_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified in any way.

Source

U.S. Department of Justice, Bureau of Justice Statistics, Prison Sentences and Time Served for Violence, NCJ-153858, April 1995.

Description

This dataset contains information about state prison sentencing counts by county in Florida for the years 1905-1910. The data includes various aggregated statistics such as the population of white and Black residents, the number of sentences, and other demographic and agricultural factors at the county level. The dataset also includes geographic information in the form of simple features (sf) representing county boundaries from the year 1910. The population data for each county has been interpolated linearly between the decennial censuses of 1900 and 1910.

Usage

data(sentencing_sf)

Format

A simple features (sf) object with 47 observations and 9 variables:

name

Name of the county (character).

wpop

White population (numeric).

bpop

Black population (numeric).

sents

Number of sentences in the county (numeric).

plantation_belt

Indicator of plantation belt counties (numeric).

pct_ag_1910

Percentage of agricultural land in 1910 (numeric).

expected_sents

Expected number of sentences based on population (numeric).

sir_raw

Index of racial disparities in sentencing (numeric).

geometry

Geometry column containing the spatial boundaries of the counties (list of simple features).

Details

The dataset name has been changed to 'sentencing_sf' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'sf' indicates that the dataset is a spatial object, using the Simple Features format. The original content has not been modified in any way.

Source

Data compiled from historical census and sentencing records of Florida, 1905-1910.

Description

This dataset contains information on suicide rates in West Germany, classified by age, sex, and method of suicide. The data was collected from Heuer (1979) and provides detailed insight into suicide rates across different demographic groups. It includes the frequency of suicides, categorized by sex, method of suicide, and age group.

Usage

data(Suicide_Germany_df)

Format

A data frame with 306 observations and 6 variables:

Freq

Numeric variable representing the frequency of suicides.

sex

Factor indicating the sex of the individual (2 levels: 'Male', 'Female').

method

Factor indicating the method of suicide (9 levels).

age

Numeric variable representing the age of the individual.

age.group

Factor indicating the age group (5 levels).

method2

Factor indicating a secondary categorization of the suicide method (8 levels).

Details

The dataset name has been changed to 'Suicide_Germany_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Heuer, 1979. Suicide Rates in West Germany.

Description

This dataset contains yearly summaries of global terrorism incidents from 1970 onward. The data includes information on over 209,000 incidents of terrorism, with details on the country, year, and other relevant variables related to each incident.

Usage

data(TerrorismGlobal_table)

Format

A table with 10,200 rows and 50 columns:

country_txt

Character vector representing the country where the terrorist incident occurred.

iyear

Character vector representing the year the incident took place.

Details

The dataset name has been changed to 'TerrorismGlobal_table' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'table' indicates that the dataset is represented as a table in R. The original content has not been modified in any way.

Source

Global Terrorism Database (GTD), 1970-2020.

Description

This dataset contains information about the serial killers in the UK, including their name, number of kills, years active, and the population during their time. It provides a historical view of some of the most infamous serial killers in the United Kingdom.

Usage

data(uk_serial_df)

Format

A data frame with 62 observations and 8 variables:

number_of_kills

Total number of murders committed by the serial killer (integer).

years

The years during which the serial killer was active (factor).

name

Name of the serial killer (character).

aka

Known aliases of the serial killer (character).

year_start

The first year the serial killer was active (integer).

year_end

The last year the serial killer was active (integer).

date_of_first_kill

The date when the serial killer committed their first murder (factor).

population_million

Population in millions at the time the serial killer was active (numeric).

Details

The dataset name has been changed to 'uk_serial_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

https://www.murderuk.com/

Description

This dataset contains statistics on violent crime rates in each of the 50 US states for the year 1973. The data includes arrests per 100,000 residents for assault, murder, and rape, as well as the percentage of the population living in urban areas.

Usage

data(USArrests_df)

Format

A data frame with 50 observations and 4 variables:

Murder

Murder arrests per 100,000 residents (numeric).

Assault

Assault arrests per 100,000 residents (integer).

UrbanPop

Percentage of the population living in urban areas (integer).

Rape

Rape arrests per 100,000 residents (numeric).

Details

The dataset name has been changed to 'USArrests_df' to maintain consistency with the naming conventions of the crimedatasets package. The suffix 'df' indicates that the dataset is stored as a data frame in R. The original content has not been modified in any way.

Source

1973 crime data, originally included in the USArrests dataset from R.

Description

This dataset provides a summary of terrorism incidents recorded in the United States during the period from January 1968 to April 1974. It is part of a larger chronology of international terrorism incidents compiled by Jenkins and Johnson (1975).

Usage

data(USATerror_data_df)

Format

A data frame with 6 observations and 2 variables:

Incidents

Number of recorded terrorism incidents (integer).

fre

Frequency of incidents (numeric).

Details

The dataset name has been changed to 'USATerror_data_df' to align with the naming conventions of the crimedatasets package. The suffix 'df' indicates that the dataset is a data frame in R. The original content has not been modified in any way.

Source

Jenkins, B. M., & Johnson, W. (1975). Chronology of International Terrorism (1968-1974). Extracted from: Li, X. H., Huang, Y. Y., & Zhao, X. Y. (2011). *The Kumaraswamy Binomial Distribution*. Chinese Journal of Applied Probability and Statistics, 27(5), 511-521.

Description

This dataset contains aggregate data on crime rates and socioeconomic indicators for 47 states in the USA for 1960. It explores the effect of punishment regimes on crime rates, with variables scaled to convenient numbers.

Usage

data(UScrime_df)

Format

A data frame with 47 observations and 16 variables:

M

Number of males aged 14–24 per 100,000 (integer).

So

Indicator for Southern states (1 = South, 0 = non-South) (integer).

Ed

Mean years of schooling (integer).

Po1

Police expenditure in 1960 per capita (integer).

Po2

Police expenditure in 1959 per capita (integer).

LF

Labor force participation rate per 100,000 (integer).

M.F

Ratio of males to females (integer).

Pop

Population size per 100,000 (integer).

NW

Percent non-white population (integer).

U1

Unemployment rate of urban males aged 14–24 (integer).

U2

Unemployment rate of urban males aged 35–39 (integer).

GDP

Gross domestic product per capita (integer).

Ineq

Income inequality indicator (integer).

Prob

Probability of imprisonment (numeric).

Time

Average time served in state prisons (in months) (numeric).

y

Crime rate: number of offenses per 100,000 population (integer).

Details

The dataset name has been changed to 'UScrime_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a traditional data frame in R. The original content has not been modified in any way.

Source

Aggregate data on crime and punishment regimes in the USA, 1960.

Description

This dataset contains national data on the number of crimes committed in the United States between 1960 and 2019. It provides annual statistics on total crimes, violent crimes, property crimes, and their subcategories.

Usage

data(UScrimerates_tbl_df)

Format

A tibble with 60 rows and 12 variables:

year

Year of the recorded data (numeric).

population

Total US population (numeric).

total

Total number of crimes (numeric).

violent

Total number of violent crimes (numeric).

property

Total number of property crimes (numeric).

murder

Number of murders (numeric).

forcible_rape

Number of reported cases of forcible rape (numeric).

robbery

Number of robberies (numeric).

aggravated_assault

Number of aggravated assaults (numeric).

burglary

Number of burglaries (numeric).

larceny_theft

Number of larceny-theft crimes (numeric).

vehicle_theft

Number of motor vehicle thefts (numeric).

Details

The dataset name has been changed to 'UScrimerates_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble. The original content has not been modified in any way.

Source

National crime data for the United States (1960–2019).

Description

This dataset contains counts of prisoners under the jurisdiction of state and federal correctional authorities in the United States from 1925 onward. The data excludes jail inmates and focuses on individuals in state and federal incarceration facilities.

Usage

data(USincarcerations_df)

Format

A data frame with 95 rows and 7 variables:

year

Year of the recorded data (numeric).

stateFedIncarcerees

Number of prisoners under state and federal jurisdiction (numeric).

stateFedIncarcerationRate

Incarceration rate per 100,000 population for state and federal facilities (numeric).

stateFedMales

Number of male prisoners in state and federal facilities (numeric).

stateFedMaleRate

Male incarceration rate per 100,000 male population (numeric).

stateFedFemales

Number of female prisoners in state and federal facilities (numeric).

stateFedFemaleRate

Female incarceration rate per 100,000 female population (numeric).

Details

The dataset name has been changed to 'USincarcerations_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

US incarceration data (1925 onward).

Description

This dataset contains ratings of U.S. state judges in the Superior Court as evaluated by lawyers. The ratings are based on various attributes of the judges, including integrity, diligence, and legal knowledge.

Usage

data(USJudgeRatings_df)

Format

A data frame with 43 rows and 12 variables:

CONT

Rating for judicial control over the court proceedings (numeric).

INTG

Rating for integrity (numeric).

DMNR

Rating for demeanor (numeric).

DILG

Rating for diligence (numeric).

CFMG

Rating for case management (numeric).

DECI

Rating for decision-making ability (numeric).

PREP

Rating for preparation (numeric).

FAMI

Rating for familiarity with the law (numeric).

ORAL

Rating for oral communication skills (numeric).

WRIT

Rating for written communication skills (numeric).

PHYS

Rating for physical appearance (numeric).

RTEN

Overall rating (numeric).

Details

The dataset name has been changed to 'USJudgeRatings_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Lawyers' ratings of U.S. state judges in the Superior Court.

Description

This dataset contains detailed records of motor vehicle thefts in New York City from 2014 to 2017. The dataset includes spatial coordinates, timestamps, and additional contextual information about each theft. It provides valuable insights into patterns and trends of vehicle thefts in NYC.

Usage

data(vehiclethefts_tbl_df)

Format

A tibble with 35,746 rows and 9 variables:

uid

Unique identifier for each record (integer).

date_single

Single date of the incident (character).

date_start

Start date of the incident (character).

date_end

End date of the incident (character).

longitude

Longitude of the theft location (numeric).

latitude

Latitude of the theft location (numeric).

location_type

Type of location where the theft occurred (character).

location_category

Category of the location (character).

census_block

Census block of the theft location (character).

Details

The dataset name has been changed to 'vehiclethefts_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is stored as a tibble in R. The original content has not been modified in any way.

Source

Crime Open Database: Motor Vehicle Theft Records.

Description

This dataset contains the annual female murder rate per 100,000 standard population in the United States from 1950 to 2004. The data represents the total number of murdered women per 100,000 population on an annual basis, providing insights into trends and patterns in female homicides over a period of 55 years.

Usage

data(wmurders_ts)

Format

A time series object with 55 observations and 1 variable:

wmurders_ts

Numeric vector representing the annual female murder rate per 100,000 population in the USA.

Details

The dataset name has been changed to 'wmurders_ts' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the crimedatasets package and assists users in identifying its specific characteristics. The suffix 'ts' indicates that the dataset is a time series object in R. The original content has not been modified in any way.

Source

U.S. crime statistics and historical records.