Integrating open source solutions into Statistica

Using Statistica, you can integrate a variety of other analytical platforms into your analyses. The most popular candidates for integration are the programming languages R and Python. Both are available under open source licenses and are developing rapidly.

 

R Integration

R integration has been available in Statistica for many years and offers you a wide range of applications:

  • Execute R scripts directly in Statistica and automatically work with the table opened in Statistica
  • Integration of R scripts in Statistica Visual Basic
  • Providing R functions in nodes in the Statistica workspace
  • Centralized management of R-based analyses with user rights and version management using Statistica server
  • Server-based execution of R

Thus, you can use R in different ways in Statistica: On the desktop, centrally managed and executed using the server as a script and integrated into workspace nodes.

R and Statistica in validated pharmaceutical environment

A pharmaceutical customer is using the Statistica server and R integration to monitor drug production. The customer benefits from the fact that data sources can be easily managed, and validated systems can be built in Statistica. And, the customer combines this with the advanced capabilities of R to build complex forecasting models. With this system many thousands of parameters are compared and monitored. The results are summarized in reports for the specialist departments and authorities.

R and Statistica on credit risk forecasting

In the financial sector, a customer uses the Statistica server and R integration to quickly and easily obtain analytical work-flows for credit risk forecasting. Statistica's simple user interface and the ability to use the Statistica server to manage and provide R components facilitate the customer's work. At the same time, the effort required to manage the R components is reduced.

Other integration options in Statistica

Python

R integration of Statistica is a success story. But there are other possibilities of integration. The Python programming language, for example, can be used in Statistica nodes and thus can be integrated into analytical processes. Python is supported in versions 2 and 3, and Statistica also comes with its own IronPython engine which is included with every installation of Statistica. Python has evolved from an elegant programming language to an analytical platform with many functions, especially in the area of deep learning, and thus offers a useful supplement to Statistica.

 

C#

Similar to Python a C# integration is offered (C# is not open source, however). C# is a powerful programming language especially used in application development, but its analytical possibilities are not as extensive as those of R and Python. Instead C# offers access to the many .Net libraries and strong possibilities for interaction with the Windows environment.

Spark Scala

Statistica offers Spark Scala integration. This differs from other integrations in that it is rather a remote control of Spark. Other integrations, on the other hand, are executed locally by the Statistica system. This is a sensible approach since Spark is an environment to perform big data analysis in a cluster. The amount of data that is converted is usually too large for individual computers. Therefore, the calculations are stored in a cluster. The integration of Spark Scala looks the same as the Python and C# integration, but the calculations are done by another system.