Comparison Between R And Python Programming Languages Essay
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Comparison Between R And Python Programming Languages Essay
Description
Overview
In data sciences, data analysts use various types of analytics tools, programming languages, and various other technologies. These help them in the process of extracting insights and required values from the data.
Some of the most commonly used programming languages today include Python, R, SQL, and others. A senior data professional might use both Python and R from time to time as per convenience.
Comparison between R and Python
Both R and Python are available in the open-source and are used for data analytics. R is used more for statistical analysis, while python has applications that are more general.
However, both languages can be used for various tasks, which include data analytics, exploring datasets, and others. R is considered slower than python while python is much faster than R. R takes up some time to learn, whereas python is easier to learn.
Therefore, Python is more suitable for beginners who have a background in computer sciences. The user interface, which is available in R, is known as an integrated development environment (IDE).
It supports all the plotting functions, graphics, and debugging. Workspace management is also possible in the interface. Python hosts multiple IDEs at the same time, due to which it allows the user to select as one as per their preference (Kohn & Manaris, 2020).
Pros and cons of R
For statistical analysis, R is a great programming language. Data visualization is usually the best by using the R language since visualized data is easier to understand.
Data manipulation on R is easier as it contains a huge number of data packages. The statistical modeling of R is one of the most powerful used for data modeling purposes.
However, R contains a physical memory, where the objects are loaded, due to which it is slower and needs fast processors. R might be widely used in fields like finance, healthcare, academics, and others, but its use is not that widespread in other fields.
Pros and cons of Python
Python is one of the easiest languages to learn if the user is familiar with programming. However, for new users with little or no programming background, python might not be easy to understand. It is usually faster than R, due to which it can be used for large datasets.
It has a wide range of applications in the field of automation, robotics, or software development programs (Bartley et al., 2018). However, it has limited reach in other fields apart from these.
Use of R and python for data visualization
While R is good for creating plots for exploratory data analysis, it is not very useful for the data visualization of the final product. The base graphics modules in R allow the user to make basic charts and plots from the given data matrices.
These files can then be saved into image formats, like jpg or in PDF format. However, it is advisable to use tools like ggplot2 for more advanced plots (Gould, 2019).
Python comes in with many tools for data visualization. With the advances in Matplotlib, python has now caught up with many aspects of data visualization.
With the help of the data, embedded, basic maps and charts can be drawn with the help of python. This can be done by using the aforementioned Matplotlib library.
Comparison Between R And Python Programming Languages Essay
RUBRIC
Excellent Quality
95-100%
Introduction 45-41 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Literature Support
91-84 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Methodology
58-53 points
Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score
50-85%
40-38 points
More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.
83-76 points
Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.
52-49 points
Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality
0-45%
37-1 points
The background and/or significance are missing. No search history information is provided.
75-1 points
Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.
48-1 points
There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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Comparison Between R And Python Programming Languages Essay