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R-Studio Assignment Exploring Factors That Make R Language The Most Preferred Machine Learning Software


Task: What are important points that should be covered on R studio assignments and why is R studio growing in popularity?


Introduction to R/ RStudio
This R-Studio assignment explored reasons R is the preferred machine learning language due to the applications vast flexibility. In addition to being widely flexibly, this is a free open source application which allows users to further modify the application and develop Packages specializing in specific function resulting in a very powerful machine learning tool that is capable of analysing all forms of data (Wickham & Grolemund, 2016). R refers to the primary machine learning application but it also has a user-friendly suite better known as R-Studio. On this R-Studio assignment we investigate why R delivers a friendly user interface thus making it considerably easier for the user to analyse data and review the results in the same dashboard. R does not come with this dashboard feature thus requiring for additional commands to be used to generate the windows displaying data, codes, analysis and results.

Important Features That Make R the Preferred Analysis Tool
R comes with powerful features making it the preferred machine learning language for professional Data mining analysts. Some important features covered on this R-Studio assignment include:

Open-source applications/ software
R is an open-source software environment thus any person can freely download and use the application (Dayal, 2020). The open-source licence also allows for the application to be modified and Packages developed catering to deference data mining needs.

Strong Graphical Capabilities
R delivers among the strongest graphics among all machine learning and data analysis tools. This makes R the preferred machine learning tool as it can be used to generate high definition graphical illustrations of data.

Large Active Community
While preparing this R-Studio assignment it was identified that R also has a huge community of followers, users and developers which results in the company have developed a huge community who continue to remain active towards the development and expansion of the program. This results in R constantly registering improvements and development thus ensuring the programs meets each user’s needs. Due to an increasing number of users, R has gradually developed a large community of active users who help new users resolve their concern as well as contribute towards the development of even more powerful packages to drive the program.

Wide Verity of Packages and Plugins
R has increased in popularity resulting in more people using the application which has also resulted in more users developing specific packages and plugins which target on developing specific packages to analyse different aspects of data and generate different results. As the number of R individual and corporate users increases, so is the requirement for the new packages to handle different operations and tasks. Each company or individuals have their machine learning and statistical analysis requirement resulting in an increasing number of packages being designed by developers each one catering to a specific analysis requirement.

Comprehensive R-Studio assignment Environments
This R-Studio assignment investigates how R has grown in popularity over the years thanks to its comprehensive environment whereby the application can be developed to cater to various aspects and data research. With the introduction and use of digital technologies, a wider variety and larger amounts of information are being collected from every business thus generating adequate information based on which further analysis and research can be performed (Vaidya et al., 2017). The working environment for business plays an important role in any businesses development as it encourages users to develop new software and make further modifications which can be used to further improve the businesses operations and performance.

Multi Operating system Support
R has been developed to support all major computer operating systems. This is important as it allows users having different operating system platforms to develop universal algorithm R codes which can be recognized by shared with users across different operating system platforms. This is important as it allows users to have access to the same code irrespective of their operating system being used.

Online R file review and support
R also has an important resource user can access online to review, verify and counter check the R codes and files. This is an important resource available at on several websites like for free where the R Codes can be uploaded and linked to the database to assess and verify the accuracy and areas of the code may require to be improved. The resources also help report errors on the algorithm codes thus allowing for amendments to be made on the code. It is important to understand that this R-Studio assignment explains how an R notebook uploaded to online R browsers render the code and data public thus there is a risk of data loss or misuse. Online browser R code test must only be performed during emergencies.

Machine Learning Usage Solutions
The corporate world is considered as being among the biggest beneficiaries of machine learning making it important to explore some of the ways the corporate world utilizes machine learning (Kassambara, 2017). Below are important uses of machine learning within the corporate industry?

Predictive Analysis
Planning for the future is a basic requirement for every business thus making it important to have tools that can help review historical data based on which future movements can be predicted.

Data Diagnostics
Digitalization has resulted in large amounts of data being collected and sorted but these large data sets are also growing too large to manage and understand user manual data analysis techniques. This has made it important for businesses to utilize Data mining tools like R to upload and analyse data before reporting trends identified in the data. R allows the data analyst to sort and analyse large data sets which using algorithm codes which makes it easier to understand data. Data analysis has become a basic requirement for understanding historical trends based on which future movements and predictions can be made.

Customer Satisfaction analysis
Customers are the foundation pillar for every business making them a primary target while assessing product performance as their satisfaction levels are a direct indication of the businesses performance and projected growth. R data analysis algorithms can be developed to collect customer feedback from certain review websites which would be analysing and feedback communicated to the consumer. This delivers instant customer feedback and satisfaction rates which can be used to modify product designs and marketing strategies based on consumer feedback and satisfaction rates discussed on the R-Studio assignment.

Financial Analytics
All businesses are heavily dependent on the sale and the amount of sales also helps influence investor decisions. R helps businesses product instant sales statistics which can be used to guide investors on investment opportunities as well as guide the business to making more informed market expansion and sales decisions. Financial analytics allows all stakeholders to determine important financial trends based on which businesses can design new marketing strategies and products to increase their market exposure.

Smooth Image Recognition
With the increased use of digital technology, image recognition is growing in demand globally. R allows for businesses to develop an image recognition algorithm which will help the system read and recognize products without the customer needing to punch in the product details. This is important towards reducing the time spent on the website thus improving the customer experience.

Streamlined Business Processes
R-Studio assignments can be used to business processes and the generation of decision trees which can be used to determine business processes and channels. R decision tree algorithms allow businesses to map their processes based on which further decisions can be made. Business decisions are plotted based on the number of occurrences they are used while making businesses decisions. Using the decision trees businesses can identify bottlenecks and process hindrances which need attention to improve operations.

Accident Prevention
Certain business operations, processes and locations tend to register a higher risk of accidents among customers and employees. R can be used to assess data related to accidents to project important statistics that can be used to determine accident occurrence rates based on which intervention and prevention strategies can be adopted to reduce the accident risks exposure.

Perform Complex Statistical Calculations
On this R-Studio assignment, R is identified as a machine learning and data analysis tool which is capable of performing highly complex statistical calculations in a fraction of the time it would take to perform the same calculations manually (Stiglic et al., 2019). The speed at which data is uploaded analysis and results reported are directly governed by the computer system specifications. The higher the computer specifications and processing speed, the faster results can be generated thus making machine learning software like R have an important feature to help businesses and individuals make an informed decision in a timely basis.

Distributed Computing
R is the most popular machine learning software used globally, resulting in there also is a large number of contributors to the software’s development and usage (Gesing et al., 2019). As the number of R users increases, the number of focus and other contributor communities is also rising thus making it easier for more people to continue towards the development and use of R as a machine learning tool.

R users Forums
R forum refers to special websites set up by dedicated R enthusiasts where users can register and participate in discussions and problem-solving agenda. The forum platforms play a normal role toward sharing users insights towards the programs, package and algorithms which helps build better insight among the users relating to R usage and approaches the business can adopt to improve it’s the softwares and package usage.

R user communities
R users have also set up several communities where information, advice and help are proved by novice users by advance and professional users. R is a code-based machine learning tool thus there are no specific rules which govern how an algorithm can be developed. Communities help bring together different ideas and strategies to write algorithm codes which are essential towards developing R algorithm codes which produce accurate results.

Free R support materials
R is among the most popular machine learning software despite it being code-based machine learning tool simply due to the wide verity of support materials available on the internet (Loy et al., 2019). There are a large number of tutorials videos; tutorials and sample code published on the internet which delivers adequate support for users to assist solve R developers produce code which delivers accurate solutions to problems.

Written content tutorials
There is a large number of written content published on the internet relating to different R programming topics which users can refer to and develop R algorithms and codes accordingly. Written tutorials offer guidance to R developers who better comprehend and learn from the written content.

Videos tutorials
There is also a wide verity of tutorials and R developer support materials available on YouTube and other video content websites. This is an important resource for visual learners who are unable to understand written instructions and require practical guidance to guide them towards preparing the assignment. Video tutorials are also short tutorials focusing on educating and individuals on a specific area of R as opposed to training them regarding the entire software. This is useful as it allows learners to focus on learning a specific function they require at that moment.

Free sample code
In addition to written and visual support content, there is a wide variety of sample R algorithm codes which can be downloaded from different websites on the internet. This is important as they deliver the basic scripts requires to analysis data and require minimal editing to extract the desired results. Free sample codes allow for fast algorithm development which speeds up data analysis and reporting.

Running Code without a Compiler
There are also various online R code testing and compilers identified on the internet on this R-Studio assignment. This allows the developer to develop small scripts of code and test them instantly on the websites. This allows for R code to be tested and modified on the fly without needing to install the R program to your computer. The compilers and editors are accessible directly from your browser which delivers a highly convenient R algorithm development environment thus further enhancing the program's popularity among users.

With the increasing number of businesses and the use of computers, the collection of data has increased considerably during the past decade. This sharp rise in data collection has also resulted in the requirement for more powerful data analysis software to handle the large data sets and ensure they produce accurate results. This R-Studio assignment, has identified R as being the most favourite among consumers as it allows businesses to collect information and analyse data and make accurate observations and predictions.

Reference List
Dayal, V., 2020. RStudio and R. Springer, Singapore; Quantitative Economics with R, pp.9-27. Gesing, S. et al., 2019. HUBzero®: Novel Concepts Applied to Established Computing Infrastructures to Address Communities' Needs. In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 1-7.

Kassambara, A., 2017. Practical guide to cluster analysis in R: Unsupervised machine learning. R-Studio assignment STHDA, 1.

Loy, A., Kuiper, S. & Chihara, L., 2019. Supporting data science in the statistics curriculum. Journal of Statistics Education, 27(1), pp.2-11.

Stiglic, G., Watson, R. & Cilar, L., 2019. R you ready? Using the R programme for statistical analysis and graphics. Research in nursing & health, 42(6), pp.494-99.

Vaidya, M., Vaghela, D., Patel, Y. & Solanki, H., 2017. R: An Open Source Software Environment for Statistical Analysis. R-Studio assignment

Wickham, H. & Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.


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