Showing Page:
1/2
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
Showing Page:
2/2
Since Python is a general-purpose language that can virtually do anything, which makes it is one
of the most popular programming languages (ZiniosEdge, 2021). New libraries and packages are
developed for Python at a more rapid pace as compared to R. Python is also great support for Web
Integration. Some of the downsides of Python are it can throw runtime errors due to its interpreted
nature which also makes it slow, and it has high memory consumption (Golchert, 2019).
R works great for statistical analysis, is open source, and has tons of packages to perform data
analysis through visualization (IBM, 2021). Some articles also tout R as the language of choice if data
visualization is important. Downsides of “R” are that it is hard to learn and does not have great web
integration support (ZiniosEdge, 2021).
References
Golchert, M. (2019, December 23). Data Visualization in R vs. Python. Data Science Solutions: INWT
Statistics. Retrieved January 13, 2022, from https://www.inwt-statistics.com/read-blog/data-
visualization-R-versus-python.html
IBM. (2021, March 23). Python vs. R: What's the difference? Retrieved January 13, 2022, from
https://www.ibm.com/cloud/blog/python-vs-r
ZiniosEdge. (2021, August 5). R vs python: Which is the best data visualization language? Retrieved
January 13, 2022, from https://ziniosedge.com/r-vs-python-which-is-the-best-data-visualization-
language/

Unformatted Attachment Preview

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 Since Python is a general-purpose language that can virtually do anything, which makes it is one of the most popular programming languages (ZiniosEdge, 2021). New libraries and packages are developed for Python at a more rapid pace as compared to R. Python is also great support for Web Integration. Some of the downsides of Python are it can throw runtime errors due to its interpreted nature which also makes it slow, and it has high memory consumption (Golchert, 2019). R works great for statistical analysis, is open source, and has tons of packages to perform data analysis through visualization (IBM, 2021). Some articles also tout R as the language of choice if data visualization is important. Downsides of “R” are that it is hard to learn and does not have great web integration support (ZiniosEdge, 2021). References Golchert, M. (2019, December 23). Data Visualization in R vs. Python. Data Science Solutions: INWT Statistics. Retrieved January 13, 2022, from https://www.inwt-statistics.com/read-blog/datavisualization-R-versus-python.html IBM. (2021, March 23). Python vs. R: What's the difference? Retrieved January 13, 2022, from https://www.ibm.com/cloud/blog/python-vs-r ZiniosEdge. (2021, August 5). R vs python: Which is the best data visualization language? Retrieved January 13, 2022, from https://ziniosedge.com/r-vs-python-which-is-the-best-data-visualizationlanguage/ Name: Description: ...
User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.
Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4