# usdatasets: A Comprehensive Collection of U.S. Datasets

# Introduction

The `usdatasets` package provides a comprehensive collection of U.S. datasets, encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more. This package serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.

# Dataset Suffixes

To facilitate the identification of data types, a suffix is added to the end of the name of each dataset. These suffixes indicate the format and type of the datasets, such as:

- `tbl_df`: A tibble data frame
- `df`: A standard data frame
- `ts`: A time series object
- `matrix`: A matrix object
- `character`: A character vector
- `numeric`: A numeric vector
- `factor`: A factor variable

# Example Datasets

Here are some examples of datasets included in the `usdatasets` package:

- marathon_tbl_df: A tibble containing marathon race data, including runner statistics and performance metrics.

- mn_police_use_of_force_df: A data frame documenting incidents of police use of force in Minnesota.

- nba_players_19_tbl_df: A tibble that includes data on NBA players for the 2019 season.

- ncbirths_tbl_df: A tibble summarizing birth statistics across various demographics.

- nyc_marathon_tbl_df: A tibble containing results and statistics from the New York City Marathon.

- nycvehiclethefts_tbl_df: A data frame documenting vehicle theft incidents in New York City.

# Visualizing Data with ggplot2

To illustrate the data, we can use the `ggplot2` package to create some visualizations. Here are a few examples:

## 1. Visualization of Marathon Finish Times

    # Example: Visualizing finish times of the NYC Marathon
    # Ajustado para las columnas disponibles en 'marathon_tbl_df'
    marathon_tbl_df %>%
      ggplot(aes(x = year, y = time, color = gender)) +
      geom_point(alpha = 0.6) +
      labs(title = "Marathon Finish Times by Year and Gender",
           x = "Year",
           y = "Finish Time (minutes)",
           color = "Gender") +
      theme_minimal()

## 2. Visualization of NBA Player Heights

    # Example: Visualizing the distribution of NBA player heights
    nba_players_19_tbl_df %>%
      ggplot(aes(x = height)) +
      geom_histogram(binwidth = 2, alpha = 0.7, fill = "blue", color = "black") +
      labs(title = "Distribution of NBA Player Heights",
           x = "Height (inches)",
           y = "Count") +
      theme_minimal()

## 3. Visualization of Police Use of Force Incidents

    # Example: Visualizing police use of force incidents by race
    mn_police_use_of_force_df %>%
      group_by(race) %>%
      summarize(count = n()) %>%
      ggplot(aes(x = reorder(race, count), y = count, fill = race)) +
      geom_bar(stat = "identity") +
      labs(title = "Incidents of Police Use of Force by Race",
           x = "Race",
           y = "Number of Incidents") +
      theme_minimal() +
      coord_flip()

## Conclusion

The `usdatasets` package is an invaluable tool for those looking to analyze and derive insights from a variety of U.S.-specific datasets. The suffixes used in the dataset names help users quickly identify the type of data they are working with, facilitating a smoother analysis process.

For more information and to explore the datasets, please refer to the package documentation.