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:

# 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()

# 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()

# 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()