| Type: | Package |
| Title: | A View Generator for Multidimensional Data |
| Version: | 0.1.3 |
| Author: | Thibault Sellam |
| Maintainer: | Thibault Sellam <thibault.sellam@gmail.com> |
| Description: | A tool to explore wide data sets, by detecting, ranking and plotting groups of statistically dependent columns. |
| License: | MIT + file LICENSE |
| LazyData: | TRUE |
| Imports: | shiny, ggplot2 (≥ 2.0.0), scales, grDevices, gridExtra, stats, grid |
| Suggests: | testthat |
| RoxygenNote: | 5.0.1 |
| URL: | https://github.com/tsellam/findviews |
| NeedsCompilation: | no |
| Packaged: | 2016-12-24 17:47:49 UTC; thib |
| Repository: | CRAN |
| Date/Publication: | 2016-12-24 20:04:40 |
Views of a multidimensional dataset.
Description
findviews detects and plots groups of mutually dependent columns.
It is based on Shiny and ggplot.
Usage
findviews(data, view_size_max = NULL, clust_method = "complete", ...)
Arguments
data |
Data frame or matrix to be processed |
view_size_max |
Maximum number of columns in the views. If set to
|
clust_method |
Character describing a clustering method, used internally
by |
... |
Optional Shiny parameters, used in Shiny's
|
Details
The function findviews takes a data frame or a matrix as input. It
computes the pairwise dependency between the columns, detects clusters in the
resulting structure and displays the results with a Shiny app.
findviews processes numerical and categorical data separately. It excludes
the columns with only one value, the columns in which all the values are
distinct (e.g., primary keys), and the columns with more than 75% missing values.
findviews computes the dependency between the columns differently
depending on their type. It uses Pearson's coefficient of correlation for
numerical data, and Cramer's V for categorical data.
To cluster the columns, findviews uses the function
hclust, R's implementation of agglomerative hierarchical
clustering. The parameter clust_method specifies which flavor of
agglomerative clustering to use. The number of clusters is determined by the
parameter view_size_max.
Examples
## Not run:
findviews(mtcars)
findviews(mtcars, view_size_max = 4, port = 7000)
## End(Not run)
Views of a multidimensional dataset, non-Shiny version
Description
findviews_core generates views of a multidimensional data set. It
produces the same results as findviews, but does
not present them with a Shiny app.
Usage
findviews_core(data, view_size_max = NULL, clust_method = "complete")
Arguments
data |
Data frame or matrix to be processed |
view_size_max |
Maximum number of columns in the views. If set to
|
clust_method |
Character describing a clustering method, used internally
by |
Details
findviews_core takes a data frame or a matrix as input. It computes the
pairwise dependency between the columns and detects clusters in the resulting
structure. See the documentation of findviews for more
details.
The difference between findviews and
findviews_core is that the former presents its results
with a Shiny app, while the latter simply outputs them as R stuctures.
Examples
findviews_core(mtcars)
findviews_core(mtcars, view_size_max = 4)
Views of a multidimensional dataset, ranked by their differentiation power.
Description
findviews_to_compare detects views on which two arbitrary sets
of rows differ. It plots the results with ggplot and Shiny.
Usage
findviews_to_compare(group1, group2, data, view_size_max = NULL,
clust_method = "complete", ...)
Arguments
group1 |
Logical vector of size |
group2 |
Logical vector, which describes the second group to compare.
The value |
data |
Data frame or matrix to be processed |
view_size_max |
Maximum number of columns in the views. If set to
|
clust_method |
Character describing a clustering method, used internally
by |
... |
Optional Shiny parameters, used in Shiny's
|
Details
The function findviews_to_compare takes two groups of rows as input
and detects views on which the statistical distribution of those two groups
differ.
To detect the set of views, findviews_to_compare eliminates
the rows which are present in neither group and applies findviews.
To evaluate the differentiation power of the views, findviews computes the histograms of the two groups to be compared, and computes their dissimilarity them with the Euclidean distance.
This method is loosely based on the following paper:
Fast, Explainable View Detection to Characterize Exploration Queries Thibault Sellam, Martin Kersten SSDBM, 2016
Examples
## Not run:
findviews_to_compare(mtcars$mpg >= 20 , mtcars$mpg < 20 , mtcars)
## End(Not run)
Views of a multidimensional dataset, ranked by their differentiation power, non-Shiny version
Description
findviews_to_compare_core detects views on which two arbitrary sets of
tuples are well separated. It produces the same
results as findviews_to_compare, but does not
present them with a Shiny app.
Usage
findviews_to_compare_core(group1, group2, data, view_size_max = NULL,
clust_method = "complete")
Arguments
group1 |
Logical vector of size |
group2 |
Logical vector, which describes the second group to compare.
The value |
data |
Data frame or matrix to be processed |
view_size_max |
Maximum number of columns in the views. If set to
|
clust_method |
Character describing a clustering method, used internally
by |
Details
The function findviews_to_compare_core takes two groups of tuples as
input, and detects views on which the statistical distribution of those two
groups is different. See the documentation of
findviews_to_compare for more details.
The difference between findviews_to_compare and
findviews_to_compare_core is that the former presents
its results with a Shiny app, while the latter simply outputs them as R
stuctures.
Examples
findviews_to_compare_core(mtcars$mpg >= 20 , mtcars$mpg < 20 , mtcars)
Views of a multidimensional dataset, ranked by their prediction power.
Description
findviews_to_predict detects groups of mutually dependent columns,
ranks them by predictive power, and plots them with Shiny and ggplot.
Usage
findviews_to_predict(target, data, view_size_max = NULL,
clust_method = "complete", ...)
Arguments
target |
Name of the variable to be predicted. |
data |
Data frame or matrix to be processed |
view_size_max |
Maximum number of columns in the views. If set to
|
clust_method |
Character describing a clustering method, used internally
by |
... |
Optional Shiny parameters, used in Shiny's
|
Details
The function findviews_to_predict takes a data set and a target
variable as input. It detects clusters of statistically dependent columns in
the data set - e.g., views - and ranks those groups according to how well
they predict the target variable.
To detect the views, findviews_to_predict relies on findviews.
To evaluate their predictive power, it uses the mutual information
between the joint distribution of the columns and that of the target
variable. Internally, findviews_to_predict discretizes all the
continuous variables with equi-width binning.
Note: findviews_to_predict removes the column to be predicted (the
target column) from the dataset before it creates the column groups. Hence,
the views it returns may be different from those return by calling by
findviews directly on the dataset.
Examples
## Not run:
findviews_to_predict('mpg', mtcars)
findviews_to_predict('mpg', mtcars, view_size_max = 4)
## End(Not run)
Views of a multidimensional dataset, ranked by their prediction power, non-Shiny version.
Description
findviews_to_predict_core detects groups of mutually dependent
columns, and ranks them by their predictive power. It produces the same
results as findviews_to_predict, but does not
present them with a Shiny app.
Usage
findviews_to_predict_core(target, data, view_size_max = NULL,
clust_method = "complete")
Arguments
target |
Name of the variable to be predicted. |
data |
Data frame or matrix to be processed |
view_size_max |
Maximum number of columns in the views. If set to
|
clust_method |
Character describing a clustering method, used internally
by |
Details
The function findviews_to_predict_core takes a data set and a target
variable as input. It detects clusters of statistically dependent columns in
the data set - e.g., views - and ranks those groups according to how well
they predict the target variable.
See the documentation of findviews_to_predict for more
details.
The difference between findviews_to_predict and
findviews_to_predict_core is that the former presents its results
with a Shiny app, while the latter simply outputs them as R stuctures.
Examples
findviews_to_predict_core('mpg', mtcars)
findviews_to_predict_core('mpg', mtcars, view_size_max = 4)