Cars93 Dataset Download: A Guide to Accessing and Working with the MASS Package of R
Cars93 Dataset Download: A Comprehensive Guide
If you are looking for a dataset that contains information about 93 cars on sale in the USA in 1993, you might be interested in the Cars93 dataset. This dataset is widely used for data analysis and visualization, as well as for teaching and learning purposes. In this article, we will show you what the Cars93 dataset is, how to download it from various sources, and how to use it in R. By the end of this article, you will have a better understanding of the Cars93 dataset and its applications.
cars93 dataset download
What is the Cars93 Dataset?
The Cars93 dataset is a data frame that has 93 rows and 27 columns. Each row represents a car model, and each column represents a feature or attribute of the car, such as manufacturer, price, type, fuel efficiency, engine size, horsepower, air bags, etc. The dataset was created by Lock (1993) from two sources: the Consumer Reports issue and the PACE Buying Guide. The dataset was intended to illustrate various statistical techniques and methods, such as regression, classification, clustering, etc.
The origin and purpose of the dataset
The Cars93 dataset was originally published in Lock (1993), a textbook that introduces statistical concepts and methods using real-world examples. The author selected 93 car models at random from among 1993 passenger car models that were listed in both the Consumer Reports issue and the PACE Buying Guide. He excluded pickup trucks and sport/utility vehicles due to incomplete information in the Consumer Reports source. He also eliminated duplicate models (e.g., Dodge Shadow and Plymouth Sundance) that were listed more than once.
The purpose of creating the Cars93 dataset was to provide a realistic and relevant example for teaching and learning statistics. The dataset covers a wide range of variables that can be used to explore various aspects of data analysis, such as descriptive statistics, graphical displays, correlation, regression, classification, clustering, etc. The dataset also allows students to compare different car models based on their features and preferences.
The structure and features of the dataset
The Cars93 dataset is a data frame that has 93 rows and 27 columns. The columns are as follows:
Manufacturer: Manufacturer name.
Model: Model name.
Type: Type of car: a factor with levels "Small", "Sporty" , "Compact", "Midsize", "Large" and "Van".
Min.Price: Minimum price (in $1,000): price for a basic version.
Price: Midrange price (in $1,000): average of Min.Price and Max.Price.
Max.Price: Maximum price (in $1,000): price for a premium version.
MPG.city: City MPG (miles per US gallon by EPA rating).
MPG.highway: Highway MPG.
AirBags: Air bags standard. Factor: none, driver only, or driver & passenger.
DriveTrain: Drive train type: rear wheel, front wheel or 4WD; (factor).
Cylinders: Number of cylinders (missing for Mazda RX-7, which has a rotary engine).
EngineSize: Engine size (litres).
Horsepower: Horsepower (maximum).
RPM: RPM (revs per minute at maximum horsepower).
Rev.per.mile: Engine revolutions per mile (in highest gear).
Man.trans.avail: Is a manual transmission version available? (yes or no ). Factor.
Fuel.tank.capacity: Fuel tank capacity (US gallons).
Passengers: Passenger capacity (persons).
Length: Length (inches).
Wheelbase: Wheelbase (inches).
Width: Width (inches).
Turn.circle: U-turn space (feet).
Rear.seat.room: Rear seat room (inches; missing for 2-seater cars).
Luggage.room: Luggage capacity (cubic feet; missing for some models).
Weight: Weight (pounds).
Origin: Origin of car (non-USA or USA). Factor.
Make: Combination of Manufacturer and Model.
The dataset also has some missing values, indicated by ".". For example, the Mazda RX-7 has a missing value for the Cylinders column, because it has a rotary engine instead of a piston engine. The dataset also has some outliers, such as the Mercedes-Benz 300E, which has a very high price and horsepower compared to other cars in the dataset.
How to Download and Use the Cars93 Dataset?
The Cars93 dataset is available from various sources online, such as Kaggle, RDocumentation, Picostat, and GitHub. You can download the dataset in different formats, such as CSV, RData, or TXT. In this section, we will show you how to download the dataset from each source and how to load and explore it in R.
Downloading the dataset from various sources
Kaggle
Kaggle is a popular platform for data science and machine learning enthusiasts. It hosts many datasets, competitions, notebooks, and courses for users to learn and practice their skills. You can find the Cars93 dataset on Kaggle by following this link: [Cars93 Dataset on Kaggle]. You can download the dataset as a CSV file by clicking on the "Download" button on the right side of the page. You will need to sign in or create an account on Kaggle to download the dataset.
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RDocumentation
RDocumentation is a website that provides documentation and examples for R packages and functions. It also hosts some datasets that are included in R packages, such as the Cars93 dataset. You can find the Cars93 dataset on RDocumentation by following this link: [Cars93 Dataset on RDocumentation]. You can download the dataset as an RData file by clicking on the "Download Dataset" button on the right side of the page. You will need to have R installed on your computer to open the RData file.
Picostat
Picostat is a website that provides statistical analysis and visualization tools for various datasets. It also hosts some datasets that are publicly available, such as the Cars93 dataset. You can find the Cars93 dataset on Picostat by following this link: [Cars93 Dataset on Picostat]. You can download the dataset as a TXT file by clicking on the "Download Data" button on the top right corner of the page. You can also view and edit the dataset online using Picostat's tools.
GitHub
GitHub is a website that provides hosting and collaboration services for software development projects. It also hosts some datasets that are uploaded by users or organizations, such as the Cars93 dataset. You can find the Cars93 dataset on GitHub by following this link: [Cars93 Dataset on GitHub]. You can download the dataset as a CSV file by clicking on the "Raw" button on the top right corner of the page. You can also view and edit the dataset online using GitHub's tools.
Loading and exploring the dataset in R
Using the MASS package
The easiest way to load and use the Cars93 dataset in R is to use the MASS package, which contains many functions and datasets for statistical analysis. The Cars93 dataset is one of the datasets included in this package. To use the MASS package, you need to install it first by running this command in R:
install.packages("MASS")
Then, you need to load it by running this command:
library(MASS)
After loading the package, you can access the Cars93 dataset by simply typing its name:
Cars93
This will display the first few rows and columns of the dataset in your console. You can also assign it to a variable for further manipulation:
cars
Using the read.csv function
Another way to load and use the Cars93 dataset in R is to use the read.csv function, which can read data from a CSV file. To use this function, you need to have the CSV file of the Cars93 dataset on your computer or online. You can download the CSV file from any of the sources mentioned above, such as Kaggle or GitHub. Then, you need to specify the path or the URL of the CSV file as an argument to the read.csv function. For example, if you have downloaded the CSV file from Kaggle and saved it in your working directory, you can run this command in R:
cars
This will create a dat