Machine Learning Datasets in R (10 datasets you can use right now)
You need standard datasets to practice machine learning.
In this short post you will discover how you can load standard classification and regression datasets in R.
This post will show you 3 R libraries that you can use to load standard datasets and 10 specific datasets that you can use for machine learning in R.
It is invaluable to load standard datasets in R so that you can test, practice and experiment with machine learning techniques and improve your skill with the platform.
Let’s get started.
Practice On Small Well-Understood Datasets
There are hundreds of standard test datasets that you can use to practice and get better at machine learning.
Most of them are hosted for free on the UCI Machine Learning Repository. These datasets are useful because they are well understood, they are well behaved and they are small.
This last point is critical when practicing machine learning because:
- You can download them fast.
- You can fit them into memory easily.
- You can run algorithms on them quickly.
Learn more about practicing machine learning using datasets from the UCI Machine Learning Repository in the post:
Access Standard Datasets in R
You can load the standard datasets into R as CSV files.
There is a more convenient approach to loading the standard dataset. They have been packaged and are available in third party R libraries that you can download from the Comprehensive R Archive Network (CRAN).
Which libraries should you use and what datasets are good to start with.
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How To Load Standard Datasets in R
In this section you will discover the libraries that you can use to get access to standard machine learning datasets.
You will also discover specific classification and regression that you can load and use to practice machine learning in R.
Library: datasets
Iris Flowers Dataset
Photo by Rick Ligthelm, some rights reserved.
The datasets library comes with base R which means you do not need to explicitly load the library. It includes a large number of datasets that you can use.
You can load a dataset from this library by typing:
| 1 | data(DataSetName) |
For example, to load the very commonly used iris dataset:
| 1 | data(iris) |
To see a list of the datasets available in this library, you can type:
| 12 | # list all datasets in the packagelibrary(help="datasets") |
Some highlights datasets from this package that you could use are below.
Iris Flowers Dataset
- Description: Predict iris flower species from flower measurements.
- Type: Multi-class classification
- Dimensions: 150 instances, 5 attributes
- Inputs: Numeric
- Output: Categorical, 3 class labels
- UCI Machine Learning Repository: Description
- Published accuracy results: Summary
| 12345 | # iris flowers datasetsdata(iris)dim(iris)levels(iris$Species)head(iris) |
You will see:
| 1234567 | Sepal.Length Sepal.Width Petal.Length Petal.Width Species1 5.1 3.5 1.4 0.2 setosa2 4.9 3.0 1.4 0.2 setosa3 4.7 3.2 1.3 0.2 setosa4 4.6 3.1 1.5 0.2 setosa5 5.0 3.6 1.4 0.2 setosa6 5.4 3.9 1.7 0.4 setosa |
Longley’s Economic Regression Data
- Description: Predict number of people employed from economic variables
- Type: Regression
- Dimensions: 16 instances, 7 attributes
- Inputs: Numeric
- Output: Numeric
| 1234 | # Longley's Economic Regression Datadata(longley)dim(longley)head(longley) |
You will see:
| 1234567 | GNP.deflator GNP Unemployed Armed.Forces Population Year Employed1947 83.0 234.289 235.6 159.0 107.608 1947 60.3231948 88.5 259.426 232.5 145.6 108.632 1948 61.1221949 88.2 258.054 368.2 161.6 109.773 1949 60.1711950 89.5 284.599 335.1 165.0 110.929 1950 61.1871951 96.2 328.975 209.9 309.9 112.075 1951 63.2211952 98.1 346.999 193.2 359.4 113.270 1952 63.639 |
Library: mlbench
Soybean Dataset
Photo by United Soybean Board, some rights reserved.
Direct from the manual for the library:
A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
You can learn more about the mlbench library on the mlbench CRAN page.
If not installed, you can install this library as follows:
| 1 | install.packages("mlbench") |
You can load the library as follows:
| 12 | # load the librarylibrary(mlbench) |
To see a list of the datasets available in this library, you can type:
| 12 | # list the contents of the librarylibrary(help="mlbench") |
Some highlights datasets from this library that you could use are:
Boston Housing Data
- Description: Predict the house price in Boston from house details
- Type: Regression
- Dimensions: 506 instances, 14 attributes
- Inputs: Numeric
- Output: Numeric
- UCI Machine Learning Repository: Description
| 1234 | # Boston Housing Datadata(BostonHousing)dim(BostonHousing)head(BostonHousing) |
You will see:
| 1234567 | crim zn indus chas nox rm age dis rad tax ptratio b lstat medv1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.02 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.63 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.74 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.45 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.26 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7 |
Wisconsin Breast Cancer Database
- Description: Predict whether a cancer is malignant or benign from biopsy details.
- Type: Binary Classification
Dimensions: 699 instances, 11 attributes - Inputs: Integer (Nominal)
- Output: Categorical, 2 class labels
- UCI Machine Learning Repository: Description
- Published accuracy results: Summary
| 12345 | # Wisconsin Breast Cancer Databasedata(BreastCancer)dim(BreastCancer)levels(BreastCancer$Class)head(BreastCancer) |
You will see:
| 1234567 | Id Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses Class1 1000025 5 1 1 1 2 1 3 1 1 benign2 1002945 5 4 4 5 7 10 3 2 1 benign3 1015425 3 1 1 1 2 2 3 1 1 benign4 1016277 6 8 8 1 3 4 3 7 1 benign5 1017023 4 1 1 3 2 1 3 1 1 benign6 1017122 8 10 10 8 7 10 9 7 1 malignant |
Glass Identification Database
- Description: Predict the glass type from chemical properties.
- Type: Classification
- Dimensions: 214 instances, 10 attributes
- Inputs: Numeric
- Output: Categorical, 7 class labels
- UCI Machine Learning Repository: Description
- Published accuracy results: Summary
| 12345 | # Glass Identification Databasedata(Glass)dim(Glass)levels(Glass$Type)head(Glass) |
You will see:
| 1234567 | RI Na Mg Al Si K Ca Ba Fe Type1 1.52101 13.64 4.49 1.10 71.78 0.06 8.75 0 0.00 12 1.51761 13.89 3.60 1.36 72.73 0.48 7.83 0 0.00 13 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0 0.00 14 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0 0.00 15 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0 0.00 16 1.51596 12.79 3.61 1.62 72.97 0.64 8.07 0 0.26 1 |
Johns Hopkins University Ionosphere database
- Description: Predict high-energy structures in the atmosphere from antenna data.
- Type: Classification
- Dimensions: 351 instances, 35 attributes
- Inputs: Numeric
- Output: Categorical, 2 class labels
- UCI Machine Learning Repository: Description
- Published accuracy results: Summary
| 12345 | # Johns Hopkins University Ionosphere databasedata(Ionosphere)dim(Ionosphere)levels(Ionosphere$Class)head(Ionosphere) |
You will see:
| 1234567891011121314 | V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V191 1 0 0.99539 -0.05889 0.85243 0.02306 0.83398 -0.37708 1.00000 0.03760 0.85243 -0.17755 0.59755 -0.44945 0.60536 -0.38223 0.84356 -0.38542 0.582122 1 0 1.00000 -0.18829 0.93035 -0.36156 -0.10868 -0.93597 1.00000 -0.04549 0.50874 -0.67743 0.34432 -0.69707 -0.51685 -0.97515 0.05499 -0.62237 0.331093 1 0 1.00000 -0.03365 1.00000 0.00485 1.00000 -0.12062 0.88965 0.01198 0.73082 0.05346 0.85443 0.00827 0.54591 0.00299 0.83775 -0.13644 0.755354 1 0 1.00000 -0.45161 1.00000 1.00000 0.71216 -1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 -1.00000 0.14516 0.54094 -0.39330 -1.000005 1 0 1.00000 -0.02401 0.94140 0.06531 0.92106 -0.23255 0.77152 -0.16399 0.52798 -0.20275 0.56409 -0.00712 0.34395 -0.27457 0.52940 -0.21780 0.451076 1 0 0.02337 -0.00592 -0.09924 -0.11949 -0.00763 -0.11824 0.14706 0.06637 0.03786 -0.06302 0.00000 0.00000 -0.04572 -0.15540 -0.00343 -0.10196 -0.11575 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 Class1 -0.32192 0.56971 -0.29674 0.36946 -0.47357 0.56811 -0.51171 0.41078 -0.46168 0.21266 -0.34090 0.42267 -0.54487 0.18641 -0.45300 good2 -1.00000 -0.13151 -0.45300 -0.18056 -0.35734 -0.20332 -0.26569 -0.20468 -0.18401 -0.19040 -0.11593 -0.16626 -0.06288 -0.13738 -0.02447 bad3 -0.08540 0.70887 -0.27502 0.43385 -0.12062 0.57528 -0.40220 0.58984 -0.22145 0.43100 -0.17365 0.60436 -0.24180 0.56045 -0.38238 good4 -0.54467 -0.69975 1.00000 0.00000 0.00000 1.00000 0.90695 0.51613 1.00000 1.00000 -0.20099 0.25682 1.00000 -0.32382 1.00000 bad5 -0.17813 0.05982 -0.35575 0.02309 -0.52879 0.03286 -0.65158 0.13290 -0.53206 0.02431 -0.62197 -0.05707 -0.59573 -0.04608 -0.65697 good6 -0.05414 0.01838 0.03669 0.01519 0.00888 0.03513 -0.01535 -0.03240 0.09223 -0.07859 0.00732 0.00000 0.00000 -0.00039 0.12011 bad |
Pima Indians Diabetes Database
- Description: Predict the onset of diabetes in female Pima Indians from medical record data.
- Type: Binary Classification
- Dimensions: 768 instances, 9 attributes
- Inputs: Numeric
- Output: Categorical, 2 class labels
- UCI Machine Learning Repository: Description
- Published accuracy results: Summary
| 12345 | # Pima Indians Diabetes Databasedata(PimaIndiansDiabetes)dim(PimaIndiansDiabetes)levels(PimaIndiansDiabetes$diabetes)head(PimaIndiansDiabetes) |
You will see:
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