Discussion and Conclusion

Discussion and Conclusion

DOI: 10.4018/978-1-68318-016-6.ch009
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After all of the material we covered throughout this book, this chapter ends the book with a discussion and conclusion about the document's purpose. Thus, in this chapter, we try to clearly state the reasons why we have used the tools we chosen for the statistical analysis tasks and finally conclude the comparison between them.
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As previously reported, besides focusing on the analytical tasks in this book, we provided practice procedures and examples in both R and Python languages. There are many information sources about these languages. We detailed a brief summary of both languages characteristics in the course of this book. However, the reader might be thinking what would be our preference if we had to choose between both languages. We present our considerations and points of view about this matter. We also present this in a way that appears to us as being more interesting to the reader, which is to foment a discussion about this subject, which we feel to be very, very subjective.

R vs. Python?

The discussion of choice between R and Python when choosing a language to do data analysis is not new in the academic world. It has been discussed if a direct approach favors R in detriment of Python or a more generic approach favors Python in detriment of R. What we think about this matter is that it is mainly a subjective case of choosing between languages. This is true in other fields of study, not only in the data analysis area. The experience of the programmer with languages frequently dictates his/her choice to proceed with programming tasks. Thus, we think an experienced Python developer would naturally choose Python to do data analysis tasks even though we might believe R language, which is a specifically created language, might be easier to work with if there is no previous contact with any of these two languages.

R is a powerful programming language, used in statistical tasks, data analysis, numerical analysis and others. The main characteristics of R we previously stated in this book, favor visualization of data and the results of statistical analysis. Nonetheless, Python, as a generic programming language also provides these advantages, although this might have to be done with a little more effort from the programmer. Throughout the writing of this book, we had to develop statistical algorithms in Python because, to our knowledge, there were no module functions available to proceed with the tasks. Additionally, not less than once, we had to use R within Python to finish certain statistical analysis tasks. This is something that might have two different readings, depending on the point of view. For the experienced Python programmer, this might be seen as added freedom degrees because Python is a generic programming language. The possibility of connection to other languages and the freedom to create an original piece of code to solve the problem is indeed a dominant characteristic of universal programming languages. On the other side, for less experienced programmers, this added effort of having to use other languages, to install added packages or even to have to program additional code might be a handicap. Consequently, on the downside, those added degrees of programming freedom might not be initially suitable for everyone. We dealt with that in this book, to make the reader’s life easier if this is the reader’s situation.

Due to the relative proliferation of both languages, there is a high amount of sources of information, beyond the manuals of the programming languages. Users of both languages have long been discussing the advantages and disadvantages in internet forums, blogs, Webpages, pseudo-manuals, scientific publications and others. Through the point of view of the inexperienced programmer, this might be good, and his/her confidence of getting help when in difficulty is high. Nonetheless, this information is scattered and spread in such an amount of sources we feel sometimes is difficult to connect everything in a consistent result. Again, in this book, we try to aggregate all the crucial available information to proceed with data analysis with any chosen language, R, Python or even both.



In this book, we presented data analysis procedures in Python and R languages. In the first chapters, we presented several subjects, to introduce the reader to Statistics. Additionally, the reader was introduced to simple programming tasks and to provide his/her comprehension of the use of features that would be applied elsewhere in the book. Although the book was organized with a crescent complexity of materials, the reader encountered an imminently practical book with examples throughout.

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