So far, you have learned basic usages of R AnalyticFlow. Now you can create and utilize analysis flows for various types of analyses.
R AnalyticFlow has many other functions which is not covered in this tutorial. The following sources are available to learn about them.
Self-learning example files are available to learn various functions of R AnalyticFlow.
These samples are placed at "Tutorial
" directory under the default
working directory, or "sample/Tutorial
" directory under the installation directory.
There are some nodes which have comments. To see the comments, mouse-over and stop on these nodes.
In this flow you can learn two basic types of nodes, and the relationships between nodes and icons.
In this flow you can learn about the box functions. A box is a special node that can contain subflow (part of flow). With box functions complex flows can be organized and simplified.
In this flow you can learn about the cache function. If cache is set on a node, computational results are automatically saved on first run, and the results are loaded instead of re-computation on next run or later. Once you run a time-consuming part of the analysis, you can smoothly continue the remaining analysis.
The following sample analyses are available.
These samples are placed at "SampleAnalysis
" directory under the default
working directory, or "sample/SampleAnalysis
" directory under the installation directory.
An analysis of iris data which we used in this tutorial. This sample contains more detailed analyses; it includes creating a decision tree model to predict iris species, and validating the prediction error of the model.
An R script for the same analysis is placed at "script
" directory
under the sample directory, as "IrisAnalysis.R
"
This is a sample analysis of Boston housing data(Harrison and Rubinfeld (1978)).
This is a more practical example with a data mining framework. It includes the following analyses:
Explorative data analysis
Transformation of variables
Dividing data for training and testing
Training and validating a preditive model
Writing the result of prediction to a file
This example includes functions which could not be explained in this tutorial, for example; using "complex node" (see the "Transform" node in the flow), writing data into a file, or leaving comments on a node (you can see it by stopping the mouse pointer on the "Sampling" node).