Are you going to hit the mark in the field of Data Analytics? You are definitely in the right place to explore and deepen your knowledge in the field of data science. In the current scenario, Data Analytics is used in a wide variety of industries, and identifying and analyzing data improves business productivity and profitability.

Moreover, it helps us to know the reliable customers in our industry and also the trends with the help of data visualization. If you are a beginner in this field then without any insight try the most demanding resource which is the R programming course.

This blog will consist of:

• Getting started with R programming
• Features of R Programming
• Lead R projects on data analysis
• Application of R programming
• Summary

Getting started with R programming

Let’s dive into R programming before discussing ideas for R projects. At the University of Auckland, Robert Gentleman and Ross Ihaka designed and developed the R programming language. It’s free software, so it can be used for a variety of tasks, including search and change, and it can be released in any modified version. Standard statistical testing, linear and nonlinear modeling, classification, clustering, time series analysis, and other statistical research can all be done with R. It is quite extensible and can be applied to both approaches graphs and data visualization. R provides an Open Source option for researching statistical methods. Also, if you want to have 100% faith and want to pass any data analyst exam with flying colors, definitely try our R programming tutorial which will give you detailed information about Data Analytics.

Features of R Programming

1. R programming is used as a weapon for machine learning, statistics, and data analysis. Recursion, objects and packages can easily be created by R.
2. It is a free open-source language. This implies that anyone can install it in any company without having to buy a license.
3. In addition to offering a statistical package, the R programming language also allows integration with C and C++. Therefore, interaction with a wide range of data sources and statistical software is simple.
4. The user base of the R programming language is huge and constantly growing.
5. R is currently one of the most sought-after programming languages ​​by employers, making it the biggest current trend in data science.

Lead R projects on data analysis

• Sentiment analysis on Twitter: Thoughts expressed in tweets posted by a Twitter account will now be attempted to be analyzed. We will build the R code step by step and explore how sentiment analysis is actually used in R.
• use the Twitter app to extract tweets
• organize tweets for further analysis
• calculate the mood of each tweet
• positive and negative tweets should be separated
• Movie recommendation system: The objective of this R project is to acquire knowledge on how recommender systems work. You’ll create a collaborative item-based filter for this project. You can gain hands-on experience using R, Data Science, and Machine Learning through this project concept in a real-world project. We used the Movie Lens dataset. The ratings.csv file, from which the data was extracted, contains 105339 ratings. More than 10329 movies in movies received ratings.csv documents.
• Detect credit card fraud: To distinguish valid transactions from fraudulent transactions, we will use a number of machine learning techniques. In this study, many techniques such as artificial neural networks, decision trees and regression are applied. The “card transaction” data set, which includes both legitimate and fraudulent transactions, is used by this fraud detection system. The phases of the project include importing the transaction dataset, analyzing the data, editing and organizing the data, modeling, tuning, and finally implementing the algorithm.
• Prediction of wine quality: Predictive modeling can provide us with the notion of wine quality improvement. The “red wine” dataset will be used in the study to assess the quality of wine. This study aims to investigate the chemical properties of red wine. We will first predict the quality of the wine using the input variables, then we will categorize the wines with the best qualities. Data from the dataset will be searched for the unique relationship and charts will be changed to reflect it. Working on this project will teach us about data mining, data visualization and regression models.
• Identification of the batch of products: These strategies are employed to convince customers to buy more of their products. Consider a Pizza Hut meal plan for illustration. In this R project, we use subjective segmentation and clustering approaches to help us group products with a discount. You can use “Weekly Sales Transactions” information, which includes the number of different items purchased in each unit. In the past, market basket analysis has been used to identify such bundles; however, in this research you will compare the relative usefulness of time series clustering to do this.

Application of R programming

The R programming language is very popular in real world applications for the reasons listed below:

• open-source: The R programming language is available for free download and use. The source code can also be optimized to contribute.
• Platform independent:R is capable of running on a variety of operating systems, including UNIX, Windows, and Mac.
• Data manipulation: By using packages like readr and dplyr, R can organize complex code.
• Plots and graphs:R uses ggplot and plotly to create visually appealing graphs using notations and formulas.

Summary

The importance of R programming that I highlighted at the beginning of the blog should now be clear to you. This field of analytics is in high demand and in the current scenario, those who choose this valuable field for their lives can excel to a greater extent beyond their imagination.