Abstract
This chapter first introduces the two most popular Open Source Statistical Software (OSSS), R and Python, along with their Integrated Development Environment (IDE) and Graphical User Interface (GUI). Secondly, additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, will also be introduced in this chapter with function descriptions and modeling examples. The chapter intends to create a reference for readers to make proper selection of the Open Source Software when a statistical analysis task is in demand. The chapter describes software explicitly in words. In addition, working platform and selective numerical, descriptive, and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.
TopBackground
Open Source Software (OSS) is a type of computer software that had its code released to the public. St. Laurent (2008) indicated that users have the right to study, change and redistribute the software under the copyright granted by the software license holder. Closed source or proprietary software can only be modified and maintained by the people, teams and organizations who own the software. Microsoft Office and Adobe Photoshop are well-known proprietary software.
Open Source Software is popular to statistical analysis practitioners, not only because it is free, but also because it is more adaptive to the current rapidly developing academic research advancement environment.
This chapter first introduces the two most popular Open Source Statistical Software (OSSS) R and Python along with its Integrated Development Environment (IDE) and Graphical User Interface (GUI). Then, additional OSSS, like JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME and Scilab, are introduced with description of their functions and modeling examples. Figure 1 lists all of the popular open source statistical software and IDEs that are introduced in this Chapter.
Figure 1. Logos of popular open source statistical software and its IDEs (Designed by Niu, 2019)
Figure 2 and 3 demonstrate the popularity development within last five years of the Open Source Statistical Software discussed in this chapter. The value represents the Google search interest. A value of 100 is the peak popularity which happens on the third week of 2019 for Python, a value of 50 represents the software is half as popular. The data is extracted on 12/19/2019 from trends.google.com under the category of “Science” and “Web Search”. Since R and Python dominate the popularity charts, two figures are created in order to better presents the relationship between all of the software. Figure 2 demonstrates R and Python popularity. Figure 3 shows other Open Source Statistical Software (OSSS).
Figure 2. Python and R popularity 2014-2019 (Designed by Niu, 2019)
Figure 3. Stacked Area Graph for Open Source Statistical Software Popularity (Designed by Niu, 2019)