Next steps

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

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.

NodeExample.rflow

In this flow you can learn two basic types of nodes, and the relationships between nodes and icons.

BoxExample.rflow

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.

CacheExample.rflow

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.

Sample analyses

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.

IrisAnalysis.rflow

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"

BostonAnalysis.rflow

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).