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at having community-driven discussions to find new ways in which to utilize the program
as well as to debug potential coding errors. This community support largely stems from the fact
that R, like Python, is an open-source and free-to-use software. However, it shares the same
issue that Python does; it does not necessarily present data in an easy-to-interpret way for the
layperson.
The analytical power of R is virtually unmatched; one of the strongest competitors, SAS,
requires three programming languages to accomplish the same tasks that R can do with one
(Ooi, 2016).. This makes it easier to teach as well as learn, as one need not commit as much
time to the intricate details of each language. R is a highly flexible software with many
additional features that can be downloaded in coding packages (Ooi, 2016).. These packages
come as updates both from R itself and as user-created code packages. The feedback process is,
therefore, reinforced as one is able to network with others in the same or different fields in
order to get software that optimally interacts with data. The user-contributed content can aid in
finding the appropriate package. R is also becoming more and more able to handle the strains of
large datasets; therefore it will be even more useful in the future than it is now (Ooi, 2016).
This is paramount as we move further into the digital age, where one consumer alone can
produce vast amounts of data that are usable to many businesses in several different models.
Therefore, the ability of R to continue to meet the demands that growing data trends place on it,
makes this a highly appealing software for those who want to either acquire or maintain a
presence in the large-scale analytics field.
R as a software, and as a program utilized by businesses, focuses on the analytics-side of
the equation, rather than on the readability of the data (Ooi, 2016). While this is not necessarily
an advantage (if one compares to MatLab), this may work in R’s favor as a popular software for
analysts. The focus on running the analyses and on keeping the data integrity at the forefront,
means that one is able to produce better-quality results and forecasts with R than one might get
from other software. Additionally, this also means that those who are less concerned with
coding and more concerned with strictly producing data models may find this an easier
software with which to work.
11. Comparison of R to Python
When beginning to use R the programmer reads their data into a data frame, used a built-in
model by using R’s formula language, and then can later look back at the model summary
output. When getting started with Python, the programmer has many more choices to make.
These can include choosing how they would like to read their data, what kind of structure they
should use to store their data in, what machine learning package they should use, and what type
of objects does the package even allow to be in the input. Other concerns for the programmer
when starting Python could include what shape should the previous talked about objects be in,
how does the programmer include categorical variables, and how does the user even access the
model’s output? There are many beginning questions for Python because it is a general purpose
programming language, On the other hand, R specializes in a smaller subset of statistical data
and tasks so it is much easier for a programmer to get started. There have been many