A Collection of Sample Datasets.

sampledatasets

The sampledatasets package provides a collection of sample datasets on various fields such as automotive performance, safety data, historical demographics, socioeconomic indicators, and recreational data. These datasets serve as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

Overview

The sampledatasets package includes datasets from various domains, all of which have been sourced from existing datasets in R and are clearly identified with suffixes to denote their structure as either a data frame (_df) or a tibble (_tbl). This clear naming convention helps users quickly identify the structure of each dataset.

Datasets included:

• mtcars_df: A data frame version of the classic mtcars dataset, containing automotive performance and specifications.
• swiss_df: A data frame version of the classic swiss dataset, focused on socio-economic indicators in Switzerland.
• cars_df: A data frame version of the cars dataset, which contains speed and stopping distance data for cars.
• arbuthnot_tbl: A tibble version of the arbuthnot dataset, with historical birth data from John Arbuthnot’s 1710 study.
• cards_tbl: A tibble version of the cards dataset, containing data about playing cards.

Installation

You can install the sampledatasets package from CRAN using the following command:

install.packages("sampledatasets")

Usage

To use the datasets provided by the sampledatasets package, simply load the package and call the desired dataset using the data() function.

Example:

# Load the sampledatasets package
library(sampledatasets)

# Load a dataset
data("mtcars_df")

# View the first few rows of the dataset
head(mtcars_df)

# Load another dataset
data("arbuthnot_tbl")

# View the dataset structure
str(arbuthnot_tbl)

Each dataset is structured either as a data frame (_df) or a tibble (_tbl), as indicated by the suffix in their names. You can use them just like any other dataset in R for your analysis, visualizations, or statistical modeling.