# crimedatasets: A Comprehensive Collection of Crime Datasets

    library(crimedatasets)

    library(ggplot2)

    library(dplyr)
    #>
    #> Adjuntando el paquete: '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.