In this article, we will study what is R programming and why working with R programming is so important in today’s era. Also, we will learn steps to carry out while practicing R projects. And at last, we will learn top R programming project ideas in 2022. So, let’s get started!
What is R Programming?
R is a programing language and free software developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an in-depth catalog of applied mathematics and graphical strategies. It includes machine learning algorithms, simple and linear regression, statistics, applied mathematics. Most of the R libraries are written in R, except for serious machine tasks, C, C++, and algebraic language codes are most well-liked.
R isn't solely entrusted by academics, however many massive firms and MNC’s additionally use R programing language, including Uber, Google, Airbnb, Facebook, and then on. Data analysis with R is finished in an exceedingly series of steps: programming, transforming, discovering, modeling, and communicating the results. Moreover, if you need any help with R programming homework or assignment, we have tutors available 24/7.
Why use R programming?
R language is in much demand in real-world applications because of the following reasons:
- Important for data science: As R is an interpreted language, we can run code without any compiler which is most important in data science. R is a vector language and hence powerful and faster than another language. R is used in biology, genetics as well as in statistics. Hence, it can perform any type of task.
- Open-Source: R language is an open-source language. It is also maintained by a large number of the programmer as a community across the world. Since R is issued under the General Public License(GNU), and hence there is no restriction on its usage.
- Popularity: R programming language has become the most popular programming language in the technological world. R language is not given importance in the academic world but with the emergence of data science, the requirement for R in industries has increased.
- Robust visualization library: R language consist of libraries like ggplot2, plotly that provides graphical plots to the user. R is mostly recognized for its amazing visualizations which is very important in data science programming language.
- Used to develop web apps: R provides the ability to build web applications. Using the R package, we can create develop interactive applications using the console of your R IDE.
- Platform independent: R language is a platform-independent language. It can work on any system irrespective of whether it is Windows, Linux, and Mac.
- Used in machine learning: Most important advantage of R programming is that it helps to carry out machine learning operations like classification, regression and also provides features for artificial intelligence and neural networks.
R programming project ideas
Below we have mentioned 13 R programming projects that will help you to master your skills in the R language and boost your knowledge.
1. Sentiment Analysis
Customer satisfaction is the most important achievement for every industry/company. It is the best way to increase the sales of the product and start a brand. We can make changes in our product according to the likes and dislikes of the customers and identify their preference patterns and their needs.
The sentiment analysis tool can be used for the same attitude to target the audience towards the product/service. The name itself suggests that the tool tries to analyze the words to identify the emotions of the people expressing them. Also, the sentiments which have different polarities like positive, negative, neutral, name polarity detection, and opinion mining is been identified. The data is categorized into different classes like binary, neutral, or multiple as happy, sad, angry, and so on.
2. Uber Data Analysis
Data Visualization provides companies with the understanding of complex datasets, which help them to make decisions.
In this project, we’ll design data analysis using the R libraries like ggplot2. We get the insights from the user data and create a precise prediction of customers who will avail Uber trips and rides. Here the project will analyze different parameters like the number of trips made in a day, the number of trips during a particular month, etc. Therefore by this project, we can figure out the average passenger that uber can have in a day, the peak hours where more customers are available, the number of trips found maximum on which day of the month, etc.
3. Movie Recommendation System
Ever wondered how YouTube, Netflix, etc suggest movies and other web series that you are most interested in? This is because they use something like a movie recommendation system that filter your previous search result, use your preferences and also browser history to form your watching pattern, and suggest you the movie and videos. Here the data will be the user browsing history on which the project is dependent. The most important benefit of building a movie recommendation system from scratch is that it will help you to know and understand the inner functionality of the recommendation engine. To build successful movie recommendation engine we will use R language with the package like ggplot2, recommenderlab, data.table and reshape2.
Using R programming we can create an application to detect fraudulent credit card transactions. Here, we will use different Machine Learning algorithms to differentiate the genuine transaction from the fraud transaction. This project uses algorithms like Decision Tree, Regressions, Artificial Neural Networks, etc.
The “card transaction” dataset is used in this fraud detection system as this dataset consists of both fraud and authentic transactions. The project follows the steps like importing the dataset containing the transaction, exploring data, manipulating and structuring data, modeling, fitting, and lastly implementing the algorithm.
5. Wine Quality Prediction
Using predictive modeling we can get the idea to improve the quality of the wine. Here, the project will access the “red wine” dataset to know the quality of the wine. The purpose of this project is to explore the chemical properties of red wine. To begin with this project first we will utilize the input variables to predict the wine quality and then we will classify the wines having excellent attributes. We will find the unique relationship in the data from the dataset and refine the plots to illustrate it. By working with this project we will learn data visualization, data exploration, and regression models.
6. Music Recommendation System
Ever thought that how the auto-play music system works? This is because such a system uses the music recommendation engine to know your interest in music and songs and play accordingly. This project is similar to a movie recommendation system but here, instead of movies and web series, the system will suggest the music and songs of your interest. The dataset of this project is from KKBOX, which is the leading music streaming service containing a library of 30 million music tracks. Here, we will build the machine learning system using python and R language which can predict the chances of the user listening to the song over and over again after the first listening event was initiated within a specific period.
7. Customer Segmentation Project
This is one of the important R project ideas. When the industries need to identify the most potential customer base, customer segmentation is used. Here the targeted audience is divided into different clusters, each cluster having some similar characteristics such as age, gender, habits, etc. Using this cluster, it becomes easy for industries and companies to develop and update the product according to the need and demand and minimize the chance of investment-related risks. Here we will use the unsupervised learning method of machine learning along with the K-means algorithm for clustering the unlabeled dataset. This also helps to analyze the relations and patterns in the dataset.
8. Speech Emotion Recognition
Among all the activities the human is enabled to do, most of that is governed by speech and emotions attached to it. This project will help you to identify the human emotions from the speech or sample voices. It mostly focuses on extracting the emotions from recording. Here the knowledge of the library Librosa is required as it is used to analyze music and audio. Along with R language, the algorithms of neural networks, convolution neural network, and support vector machine are used.
9. Product Bundle Identification
The product bundling approach is a marketing strategy to combine different products to be sold as one single product at usually discount price. These strategies are used to encourage the customer to buy more of their products. For example, Pizza Hut meal combo. In this R project, we use subjective segmentation and clustering technique that can help us to bundle the product together to make a great sale. We can use the “weekly sales transaction” dataset containing the purchase quantities of different products.
10. Time-series Analysis and Modeling
The series of data points listed, indexed, or graphed in timely order is called the Time series. It is the most used technique in data science with a wide range of applications for predicting sales, predicting tractions, weather forecasting, website traffic, etc using R programming. Business companies many times uses the time series data to analyze a number of the future. In this project, we can use the “statsmodels” library, which contains many statistical modeling functions, including time series.
11. Walmart Sales Forecasting
Departmental store chains such as Walmart use sales forecasting techniques to anticipate the number of shoppers coming to their stores. They do this in order to plan inventory and determine how many staff members are needed at a given time of year. In addition, sales forecasting allows companies to better understand their cash-flows and overall growth.
For inventory planning, you also need to know which products will be used up more quickly and which require less frequent replacement. Don't understock items that sell well, or your sales will suffer. Don't overstock perishables, because they will go bad before you can sell them all.
12. Predict Churn for Telecom Company using Logistic Regression
Every company wants to increase its revenue and profitability. The key to this is to acquire new customers while making sure that existing ones continue to use the services. Moreover, it’s important for a company to know beforehand if certain of its customers are planning to stop using its services (especially recurring ones like internet, cable, phone, etc.) in order to prevent any negative consequences. To enable these features, all you have to do is build a chur model which suggests the output indicating the warning that some customers are likely to churn. To develop this model successfully, make use of the Logistic Regression model in R programming and integrate it with the customer data set for timely relation.
13. Classification of Data Sets
Ensemble algorithms are a set of machine learning methods that construct a set of classifiers and then classify new data points by taking a vote of their predictions. Bayesian averaging is the most basic ensemble method, which has been updated by newer algorithms, such as error-correcting output coding, bagging, and boosting. In the age of artificial intelligence and machine learning, ensemble methods have become new norms in order to account for the dynamics of data variability. Using this ensemble method for data classification and prediction turns out to be the best beginner's project when dealing with R programming.
Hence, these are 13 amazing R programming projects that you can perform by yourself. Performing these projects will help you to get strong commands on concepts of R programming and also data science. Therefore, in the above article, we studied what is R language and its importance in the technological world. Along with that, we learned the steps to be executed while creating the project on R programming and various project ideas in detail.