This article will take you to the world of R Programming and its technicalities. It will provide you with the answers to question like What is R Programming all about? Whether learning it is difficult or easy, what type of projects can you do in R? And it will detail you on the best engineering projects based on R Language, that you can take up. It will briefly detail you on the applications of R programming language and what purpose does learning it serves in today’s industrial setup.
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Founded and created by Ross Ihaka and Robert Gentleman at the University of Auckland in 1993, R is a Language to carry out programming and you can run the software for any purpose like to study, change, and distribute it in any adapted versions, i.e. it is free software. It is a widely used powerful programming language- employed for statistical modeling, computing, Machine Learning algorithm, and data visualization. It also includes an extensive catalog of graphical representation methods and reporting. Under the GNU General Public License, R is freely available and pre-assembled binary versions are available for the various operating systems like Linux, Windows, and Mac.
As it is open-source, R savors the community support of fervent developers who continuously put in their efforts in releasing new packages, updating R and making it a reliable programming package in the domain of Data Science. You can download this software for free from this “https://www.r-project.org/” website. Due to its communicative syntax and user-friendly interface, R Programming has become famous amongst programmers, statisticians, knowledge analysts, researchers and marketers to retrieve, clean, analyze, compile and present data.
Many renowned tech-giants like Twitter, Ford, Google, Uber, Microsoft, and Facebook are using this software to make calculated decisions and even contributing to the R ecosystem.
Some calculated steps are performed in series to do data analysis with the help of R, namely: Programming, Transforming, Discovering, Modelling and Communicating the results at the end.
Programming: R is a brief, concise and accessible programming tool that is clear to understand.
Transform: A collection of graphical libraries that are designed specifically for data science is also present in R.
Discover: Allows the user to investigate the data, refine their hypothesis and analyze them thoroughly.
Model: R provides a wide range of tools to capture the right and accurate model for your data representation.
Communicate: You can integrate codes, graphs, and outputs to form a report with R Markdown or can build gripping apps to share with the people all across the globe. R Language possesses a variety of ways to present and share data, either through a markdown document or a shiny app. Everything can be put on in Rpub, GitHub or the business's websites.
R is an integrated software that facilitates data manipulation, calculation, and graphical representation. The following features are provided in the R software:
The job scenario is bubbling with opportunities as R is the primary tool for Data Science. The immense growth in Data Science has led to a rise in demand for skilled engineers in this sector.
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Well, for the starters it would be slightly difficult to get through with the conceptual terms associated with R programming. However, as you proceed further with the learning process, you will begin to enjoy coding in R Programming.
Initially, the language used to be quite confusing and not as structured as other programs available. Later on, to overcome this disadvantage, Hadley Wickham developed a collection of packages called tidyverse. The rule of the game changed for the best. Data manipulation becomes inconsequential and interesting. Graph creation and analyzation are not cumbersome anymore.
R can effectively communicate with other language like Python, Java, C++, etc. R Language has accessibility to the world of big data as well. You can also connect R with different databases like Spark or Hadoop. R Programming has evolved over time and has allowed the parallelizing operations to increase the computation process. The parallel package allows you to carry out tasks in different core sectors of the machine.
They are numerous tools available in the industry to perform data analysis and computations. It’s obvious that learning a new language requires you to invest some time. If you want to give the best insight from the data, then you must spend some time to learn the appropriate tool, which is R Programming. R is definitely a good trade-off between implementation and data analysis. As a beginner in the domain of data analysis, selecting a data science mini project in R Programming at an appropriate skill level will help you learn new data science skills as you proceed towards the completion of the project. Below mentioned are some good recommendations from the experts in this sector on some of the must-do projects in R Programming for engineering students:
1. Data Analytics using R
Data Analytics is used in almost all the fields like hospitality industry, public service agencies, healthcare companies, and retail businesses. In this project, you will learn about R programming and how to use it to perform data analysis. You will also learn the basics of data analysis and usage of packages like ggplot2 and dplyr in R.
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2. Predict Wine Preferences using Wine quality Dataset
Wine tasting is a unique and elite profession. It is usually difficult to guess what the customer would like based on the memory from their past preferences. In this machine learning project, before suggesting any particular variety of wine to the customer if we can identify their taste using data mining processing from the physical and chemical properties of the wines, it would be a cakewalk for the restaurant to recommend wines.
3. Predict Credit Default
Banks usually rely on credit score prediction models to either pass a loan approval request or deny it. A good prediction model is essential for a bank so that it can provide maximum credit without exceeding the risk threshold value. The predictive models will be build using various approaches like random forests, gradient boosting and logistic regression. By the time this project concludes, you will be able to build a predictive model that will automatically score each applicant with a credit score which is easy to interpret.
4. Predict Churn for a Telecom Company
Customer churn refers to a decision taken by the customer about ending a business relationship. It also implies the loss of clients or customers. Customer loyalty and customer churn perpetually add up to 100%. If a firm promises a loyalty rate of 70%, then its churn rate or the loss in the number of customers is 30%. As per the 80/20 client profitableness rule, 20% of customers are generating 80% of revenue. So, it is imperative to predict the users likely to churn from the business relationship and the factors influencing the customer decisions.
5. Classifying Loan Applications using German Credit Dataset
The German credit dataset consists of information on 1000 loan applicants. Each applicant is described by a set of 20 different characteristics. Of these 20 attributes, 17 attributes are discrete while the other 3 are continuous. The main plan is to use techniques from the sphere of data theory to pick a group of vital attributes that will be used to classify tuples.
Apart from these projects, you can also refer to the following:
Hope you got some good list of engineering projects on R programming. If you have any other good project ideas, let us know in the comments.
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