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Discussion 4

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University of the Cumberlands
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For this discussion, I will compare R and Python, as both these languages are popular amongst industry
professionals and academia for data visualization, largely due to them being open source and backed by
a large open-source community. Statistical Analytics System (SAS) is an expensive proprietary
application meant for statistical analysis, although they provide a free university edition it has limited
features, and SAS is not specifically known for data visualization. Structured Query language (SQL) is a
domain-specific language mainly used for handling data in Relational Databases.
R was developed as a statistical analysis tool, whereas python is a general-purpose programming
language. Both languages have extensive support for data visualization. Some of the libraries/packages
available on each platform for data visualization are as follows:
R
ggplot2: Allows creation of various charts and graphs
plotly: Used for creating scatterplot matrix.
Esquisse: Data exploration using ggplot2 package
Shiny: Helps translate analytics to web apps
Python
matplotlib: Used for plotting and visualization
Seaborne: Used to create charts and graphs
plotly: Used for creating scatterplot matrix.
Pros and Cons of Python and R

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For this discussion, I will compare R and Python, as both these languages are popular amongst industry professionals and academia for data visualization, largely due to them being open source and backed by a large open-source community. Statistical Analytics System (SAS) is an expensive proprietary application meant for statistical analysis, although they provide a free university edition it has limited features, and SAS is not specifically known for data visualization. Structured Query language (SQL) is a domain-specific language mainly used for handling data in Relational Databases. R was d ...
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