findviews

The findviews package helps exploring wide data sets, by detecting, ranking and plotting groups of statistically dependent columns. It relies heavily on ggplot2 and shiny.

findviews is expecially useful to get quickly familiar with a new dataset. Load your data in a data frame, call findviews, and you are ready to go.

Installation

You may download findviews' latest release as follows:

install.packages("findviews")

Alternatively, you may install the latest development version:

devtools::install_github("tsellam/findviews")

How to use it

Overview

The findviews package is based on three functions:

• findviews detects and plots groups of dependent variables. This function is useful to explore new datasets.
• findviews_to_compare calls findviews and ranks the views by how well they separate two arbitrary subsets of the data. This function is useful to compare groups - for instance "Young people" vs. "Old people" in a survey dataset, or "Winners" vs. "Losers" for a sports use case.
• findviews_to_predict calls findviews and ranks the views by how well they predict an arbitrary variable. This function is useful to understand how one particular column is influenced by the other variables in the database - for instance, "Salary" in a census database.

The following sections describe these 3 functions in more detail.

The main function: findviews

findviews is the most important function in the package. It takes a data frame or a matrix as input, as well as a few optional parameters described in its R documentation. It then performs the following operations:

1. It detects columns types and removes unpractical columns (e.g., primary keys or constants values).
2. It computes the statistical dependency between all the pairs of colums.
3. It detects clusters of dependent columns - that is, views.
4. It plots the views with ggplot2 and loads them in a Shiny app.

You may call findviews as follows:

findviews(mtcars)

You can pick a view on the left panel and visualize it in the main panel.

Ranking the views: findviews_to_predict and findviews_to_compare

The function findviews can generate views, but it cannot tell which ones to look at. This where findviews_to_predict and findviews_to_compare come in. Those two functions generate views, exactly as findviews does (in fact, they call findviews internally) but they also rank the results.

The function findviews_to_compare ranks views which highlight how two groups of row differ from each other. Suppose for intance that we wish to compare the rows for which mpg > 20 and those for which mpg < 20. We call the function as follows:

findviews_to_compare(mtcars$mpg >= 20 , mtcars$mpg < 20 , mtcars)

The aim of findviews_to_predict is to help users understand how a specific column is influenced by the other columns in the database. For instance, suppose that we wish to understand what influences the variable mpg in the mtcars data set. We would call findviews_to_predict as follows:

findviews_to_predict('mpg', mtcars)

_core functions

The functions findviews, findviews_to_predict and findviews_to_compare present their results with Shiny. At times, this method can be heavy and we may prefer to obtain the results directly as R objects (possibly to use them in a more complex workflow). This is possible, with the _core functions. The functions findviews_core, findviews_to_predict_core and findviews_to_compare_core operate exactly as their counterparts, but they return their results as lists and data frames.

WARNING

Beware: the recommendations of findviews must be taken with a huge grain of salt. Some of its views are absurd. They are artifacts of the algorithms, or the system just "got lucky" and made totally spurious findings. Inversely, findviews will almost surely miss important aspects of the data.

In summary, findviews is designed to help you get started with a data set and give some inspiration. But it cannot replace critical judgement. In fact, the best way to use it is to understand what it does. To this end, I encourage you to read the functions' R documentation.

Papers and Acknowledgements

findviews was inspired by the following paper: > Semi-Automated Exploration of Data Warehouses
> T. Sellam, E. Müller and M. Kersten
> CIKM 2015

This work is carried out at the Dutch center for mathematics and computer science (CWI). It is funded by the national project COMMIT.