library(crimedatasets)

library(ggplot2)

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

Introduction

The crimedatasets package provides a comprehensive collection of datasets focusing exclusively on crimes, criminal activities, and related socio-economic factors. This package is an essential resource for researchers, analysts, and students working in criminology, socio-economic studies, and crime analysis. All datasets included in the crimedatasets package are sourced from various established crime and public data repositories, ensuring the authenticity and reliability of the data.

Dataset Suffixes

The datasets in the crimedatasets package are distinguished by suffixes that specify the type and format of the data. These suffixes include:

tbl_df: A tibble data frame df: A standard data frame ts: A time series object sf: A spatial object (simple features)

Example Datasets

Here are some examples of datasets included in the crimedatasets package:

Abilene_tbl_df: Crime records from Abilene, Texas, USA (Tabular Data).

Attorney_tbl_df: Convictions reported by U.S. Attorney’s Offices (Tabular Data).

wmurders_ts: Annual female murder rate in the USA from 1950-2004 (Time-series Data).

Visualizing Data with ggplot2

Below are some examples of how to create visualizations using the datasets from the crimedatasets package.

1. Visualizing Abilene (Texas) Crime Records


# Bar Chart with Abilene_tbl_df data set

Abilene_tbl_df %>%
  ggplot(aes(x = factor(year), y = number, fill = crimetype)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Number of Violent Crimes by Year in Abilene, Texas",
       x = "Year",
       y = "Number of Violent Crimes") +
  theme_minimal()

2. Visualizing Annual Female Murder Rates


# Convert the ts object into a data.frame
wmurders_df <- data.frame(
  year = as.numeric(time(wmurders_ts)), # Extract the time values as numeric
  murder_rate = as.numeric(wmurders_ts) # Convert ts values to numeric
)

# Plot using ggplot2
ggplot(wmurders_df, aes(x = year, y = murder_rate)) +
  geom_line(color = "red") +
  labs(
    title = "Annual Female Murder Rate in the USA (1950-2004)",
    x = "Year",
    y = "Murder Rate per 100,000 Women"
  ) +
  theme_minimal()

Conclusion

The crimedatasets package provides a valuable and extensive collection of crime-related datasets, empowering researchers, analysts, and students to explore and analyze various aspects of criminal behavior and socio-economic factors. By offering datasets in diverse formats (e.g., tbl_df, df, ts, sf), this package ensures compatibility with a wide range of analytical tools and methodologies.

Through examples and visualizations in this vignette, we have demonstrated how to explore and gain insights from these datasets using popular R packages like dplyr and ggplot2. Whether you are investigating historical trends, studying regional crime patterns, or analyzing socio-economic correlations, crimedatasets serves as a comprehensive resource for your analytical needs.

We encourage users to explore the full range of datasets provided in crimedatasets and leverage them to advance research in criminology, policy-making, and data-driven decision-making.

- Introduction