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.
You may download findviews’ latest release as follows:
install.packages("findviews")Alternatively, you may install the latest development version:
devtools::install_github("tsellam/findviews")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.
findviewsfindviews 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:
You may call findviews as follows:
findviews(mtcars)
You can pick a view on the left panel and visualize it in the main panel.
findviews_to_predict and
findviews_to_compareThe 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 functionsThe 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.
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.
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.