If you are looking into expanding your knowledge scope in Data Science and Data Analytics, chances are you will come across the R programming language sooner than later. It is an open-source software environment for statistical modeling and data analysis, which has tremendously grown in popularity with its salient benchmark features. Data miners and statisticians widely use it for data analysis and computing. Various job profiles in this domain cite data science with R training as a prerequisite. 

Let us look into the features which have set the R programming language apart from its counterparts.

Why learn R programming?

• Open Source: R is an open-source project, which is continuously evolving to overcome any downsides it may have. Using the language free of cost and without a license has encouraged the community and the industry to adopt the language into their tech stack due to its robust applications. 

1. Platform Independent: It supports cross-platform usage allowing execution on Windows, macOS, or Linux.
2. Highly Compatible and Integrable: R can be easily paired with other popular languages like C, C++, Java, and Python. It can also be integrated with big data technologies and database management systems.

• Data Wrangling: One of R’s unique selling points is its ability to reform unstructured data to a structured form. Hence, its data-wrangling support is a class apart. 
• Visualization: With libraries like ggplot2, R provides powerful plotting and graphing, which are equally aesthetically pleasing. The visualization prowess of R has contributed immensely to its popularity. It also allows interactive plots to up the ante of data reports.

1. Packages and ML support: With over 10,000 libraries, it facilitates a lot of functionality for any user. It also provides for machine learning algorithms like classification, regression, and neural nets.
2. Statistics: R is the go-to language for statisticians when it comes to computing and modeling. Because of its statistical competency, R has also found a lot of applications in the research domain. 

Top R programming projects for beginners

Having known the R programming language’s unique points, it is time to get hands-on and look into projects that effectively utilize its functionality. 

• Music Recommendation System

Finding music to suit a person’s taste has been an application of machine learning for a while now. Apps that provide music suggestions to their users spend considerable funds in churning out an effective recommendation system to keep their consumer base satisfied. To get a sneak peek into this technology, use the KKBOX's Music Recommendation System database to build a recommendation engine using R. R has a recommended lab package specifically designed to develop and model recommendation algorithms. The visualization can be done using community favorite, ggplot. Appropriate data transformations can be used for feature modeling. 

• AdTracking Fraud Detection

Estimates of recent annual losses to digital ad fraud range from $6.5 billion to $19 billion. Numerous companies pay premium amounts to prevent ad fraud. The TalkingData AdTracking Fraud Detection dataset gives a comprehensive collection of data with the objective of detecting fraudulent click traffic for mobile apps ads. Moreover, the aim can be extended to predict if the user will download an app after clicking a mobile app advertisement. An approach to tackle this problem statement will be to analyze the Click Time variable, hyper tune other features, perform data balancing using SMOTE, and create predictive models using a decision tree or a random forest.

https://www.youtube.com/embed/KlsYCECWEWE

• Customer Segmentation 

Businesses’ effective growth tactic is to perform segmentation on a heterogeneous customer base to divide into smaller homogenous groups to identify requirements and marketing approach constructively. Clustering techniques like K-means clustering algorithm is used to perform such segmentation and determine the best customer. This is one of the most essential applications of R in grouping unlabelled data. E-Commerce Dataset can be used to execute this project. 

• Sales Forecasting

Making predictions is the core concept behind machine learning. This project uses the power of the sales data from 45 different stores from all over to predict the sales. The main objectives or “learning points” from this project would be EDA (or Exploratory Data Analysis), Handling missing values and outliers, merging different tables, using bivariate analysis, and making graphs, plots, and charts create powerful machine learning models, etc. To understand the impact of this project, we would have to dive deeper into the way the store functions. So, any given store would have a set amount of inventory space. Storing beyond would impose a physical restriction. If this project is scaled up, it can quickly determine which product would be the bestseller and predict the amount of each product sold with a certain degree of accuracy. Thus, the data obtained can be used in re-stocking and, in turn, improving the overall profits of the store.

• Sentiment Analysis

Sentiment analysis, or otherwise known as understanding human emotions, has been a long-standing issue in the paradigm of Artificial Intelligence. However, the advent of potent deep learning algorithms, precisely the whole “Natural Language Processing'' paradigm, has allowed machines to understand emotional cues from basic speech and written forms of media. To build such a model would be the goal of this project. There are many ways to tackle this issue. The easiest one (which should serve as a fantastic entry point to the beautiful world of Natural Language Processing for beginners) would be to use the NTLK library. NTLK allows the NLP models to be created with very few lines of code. Twitter data would be a good dataset to perform NLP on. Mining the data manually by tapping into various APIs will take this project to the next level.

Conclusion: 

The list of projects introduced here is only the tip of the iceberg of applications. It can also be applied to credit card fraud detection, loan application classification, turnover prediction of a company, and so on. The scope of implementation is endless. The R programming language has immense potential in data analysis and modeling. In coming times, the range of its functionality will only increase with its evolving development.