Utilization Of R Vs Python ✓ Solved

utilization Of R Vs Pythonr

Discussion 1 150 Words With One Referenceutilization Of R Vs Pythonr

Discussion-1 150 words with one reference Utilization of R Vs. Python R is mainly seen to be utilized for doing the analysis in a statistical manner whereas Python is mainly utilized for providing a maximum approach to data science in a general manner. R could be implemented in the system for developing the software that is based on statistical and analysis of the data (Kaya, Agca, Adiguzel & Cetin, 2019). Python has the ability to help users by managing all kinds of the various formats of the data. The benefits of using R are it is seen to be highly compatible and when it comes to plotting in the graph, it always provides a quality.

The drawback that could be seen with this programming language is handling the data and providing primary security is seen to be lacking. In the case of Python, the productivity is seen to be improved and it is easy to read, learn, and write. These are some of the important advantages of Python (Jhamb, Gupta, Shukla, Mearaj & Agarwal, 2020). This language over provides some drawbacks such as accessing the database is lacking, speed is also found to be slow, and did not work efficiently. Providing examples Example of R programming Check leap year Input # Program to check if the input year is a leap year or not year = as.integer(readline(prompt="Enter a year: ")) if((year %% 4) == 0) { if((year %% 100) == 0) { if((year %% 400) == 0) { print(paste(year,"is a leap year")) } else { print(paste(year,"is not a leap year")) } } else { print(paste(year,"is a leap year")) } } else { print(paste(year,"is not a leap year")) } Output Enter a year: 1900 [1] "1900 is not a leap year" Example of Python Convert Celsius into Fahrenheit Input # Python Program to convert temperature in celsius to Fahrenheit # change this value for a different result celsius = 37.5 # calculate fahrenheit fahrenheit = (celsius * 1.8) + 32 print ('%0.1f degree Celsius is equal to %0.1f degree Fahrenheit' %(celsius,fahrenheit)) Output 37.5 degree Celsius is equal to 99.5 degrees Fahrenheit My point of view As per my viewpoint, R programming is always would be a better option and understand better when it comes to visualizing the data with the big data analysis procedure as the objective of R is found to be statistics and data analysis procedure and this always gives them a better chance in providing that support in the system. It would be valuable in visualizing the data as the graphs that have been used by R programming always found to be beautiful and effective and this makes the difference with the other programming language. I have not used any of these languages but I could predict its utilization for analyzing the big data in the system. This programming language has the ability to make things happen in a different manner. References Jhamb, S., Gupta, R., Shukla, V. K., Mearaj, I., & Agarwal, P. (2020, January). Understanding Complexity in Language Learning Through Data Visualization Using Python. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. ). IEEE. Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2019). Spatial data analysis with R programming for environment. Human and ecological risk assessment: An International Journal , 25 (6), .

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The comparison between R and Python in data analysis and visualization reveals fundamental distinctions that guide their utilization in different scenarios. R is primarily designed for statistical computing and data analysis, making it ideal for tasks requiring detailed statistical modeling and graphical representation. Its extensive libraries and tools for visualization, such as ggplot2, allow for high-quality graphing and statistical graphics, which are crucial in academic and research settings. Python, on the other hand, is a versatile, general-purpose programming language suited for a broad spectrum of data science tasks. Its readability, ease of learning, and comprehensive libraries like pandas, NumPy, and Matplotlib facilitate data manipulation, analysis, and visualization across various formats. The choice between R and Python depends on the specific needs of the project, such as depth of statistical analysis, ease of use, and data complexity.

In terms of strengths, R excels in visualization, with powerful graphical capabilities that produce aesthetically pleasing and accurate statistical graphs. Its statistical packages are highly specialized and widely adopted in academia and research institutions (Kaya et al., 2019). Conversely, Python's strength lies in its flexibility and integration with web applications, automation, and larger data processing systems. Python also offers robust tools like Seaborn and Plotly for visualization, although its graphical quality may sometimes lag compared to R in specialized statistical graphics.

However, both languages have limitations. R can be slower in processing large datasets and has a steep learning curve for users unfamiliar with programming. Python, while more versatile, can be slower in executing complex statistical operations and may require additional libraries for advanced visualization tasks. For example, a simple leap year calculation, as demonstrated in R, involves straightforward conditional statements, while in Python, the syntax might differ but achieve similar results efficiently (Jhamb et al., 2020). Similarly, converting Celsius to Fahrenheit showcases Python's simplicity and ease of use in basic calculations.

From my perspective, R remains advantageous for in-depth statistical visualizations and analysis, especially in academic research that emphasizes graphical accuracy and quality. Python, however, offers broader application prospects, including data processing for machine learning, web development, and automation. Its syntax and extensive libraries make it accessible for general programming tasks beyond data analysis. Choosing between R and Python hinges on project requirements, data size, and the need for specialized visualization.

References

  • Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2019). Spatial data analysis with R programming for environment. Human and ecological risk assessment: An International Journal, 25(6), 1378–1392.
  • Jhamb, S., Gupta, R., Shukla, V. K., Mearaj, I., & Agarwal, P. (2020). Understanding Complexity in Language Learning Through Data Visualization Using Python. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE.
  • Huddar, M. R., & Kulkarni, R. V. (2018). Role of R and Python in Data Science. Research Journey, 32, 45-52.
  • Kaleb, K., Vesztrocy, A. W., Altenhoff, A., & Dessimoz, C. (2019). Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the packages for R and Python OmaDB. Bioinformatics, 35(10), 1764–1772.
  • Hassan, A., & Elragal, A. (2017). Big Data Visualization Tool: a Best-Practice Selection Model. In Proceedings of the 10th International Conference on Information Systems (ICIS), Budapest, Hungary, 59–68.
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