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Any data but iris
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DESCRIPTION initial content Jun 30, 2019
noiris.Rproj initial content Jun 30, 2019


This package is primarily to provide data that is more similar to what many people would typically come across in the wild, or is simply more interesting or accessible (in my opinion), and more useful for instruction and workshops. Far too often examples use iris, mtcars, etc. for convenience, but these are actually inconvenient for demonstrating common data and modeling problems, or are too small to even be realistic.

This package will provide larger and at some point messier data. The bias is towards data that could be understood regardless of discipline/background. In addition, it should have minimally several hundred observations, and often much larger, but not so large that analysis or data processing demonstration would take an inordinate amount of time. However, it should have relatively few columns (unless for demonstration of a ‘large p’ type of problem/analysis, e.g. penalized regression.).


In most cases the data has been cleaned up to make it easier to use and understand.

Right now it has:

  • gapminder_2019: a 2019 pull from
    • A nice longitudinal/time-series data set suitable for a wide range of standard and more complex mixed models, spatial visualization and analyses, etc.
  • star_wars: several data sets based on the Star Wars API.
    • Mostly just for fun, but it demonstrates usage of list columns and otherwise could be good for demonstrating joins.
  • instructor_evaluations: a nice-sized data set for mixed/multi-level modeling taken from the lme4 package.
    • Good for mixed models and similar.
  • fish: Number of fish caught on camping trips.
    • An accessible data set useful for demonstrating count models including zero-inflated/hurdle models.
  • pisa: OECD’s Programme for International Student Assessment with international scores for math, science, and reading, covering years 2000-2015.
    • Potentially good for demonstrating nonlinear relationships (e.g. GAM), imputing missing data, longitudinal/spatial analyses.
  • world_happiness: Multiyear data set with country level scores of ‘happiness’. From 2019 World Happiness Report, and includes data from 2005-2018.
    • Similar to Gapminder and PISA, this could be used for a variety of standard statistical models.
  • sp500: Daily S & P 500 data for a 10 year period covering +- 5 years before and after the Great Recession low.
    • Good for time series and related analyses. Includes industry classifications.
  • wine_reviews, wine_quality: Two data sets regarding wine reviews that can be used for a wide range of standard statistical and machine learning.
    • Can be used for standard regression and classification, ordinal regression, text analysis, sentiment analysis.
  • google_apps: Ratings and other information for Google Play Store apps.
    • Text & sentiment analysis, standard regression, etc.
  • fashion_train, fasion_test: The ‘Fashion MNIST’. Image data for clothing items.
    • Image classification, cluster analysis


This package is not on CRAN. To install:



To do:

  • Data for basic classification
  • Data for basic machine learning (reg and class)
  • Data for text analysis (more to come)
  • Data for image classification
  • Data for survival analysis
  • Data for factor analysis/SEM (PCA?)
  • Data Non-obvious cluster analysis (no iris! and no old faithful either!)
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