Here is a list of the top ten free resources to learn R: Data Science: R Basics; Learn Data Science With R Part 1 of 10 ; Data Science, Machine Learning, Data Analysis, Python & R; R for Data Science; R Programming for Data Science; Hands-On Machine Learning With R; Data Science: Foundations Using R Specialisation; Statistics with R Specialisation; Introduction to Data Science: Data Analysis and Prediction Algorithms with R; Data Visualisation & Dashboarding with R Specialisation

Great Learning brings you this comparative video discussing the differences between Python and R on a variety of grounds such as speed, libraries, visualization, deep learning and so on. We look at how to differentiate between the two in this interactive video. This video explains these concepts in detail with the help of demonstration, examples and thorough explanation to give you a clear idea of the differences between Python and R, as well as the relevant similarities.

Understanding Point Process Model with R. A hands-on introduction to point process models with R. A point pattern represents the distribution of a set of points in time, space or higher dimensions. A Spatial point processes is a description of the point pattern. We can think of it as the model which generated the point pattern.

Interface to use R from Python. rpy2 is an interface to R running embedded in a Python process.

Learn R and Become a Data Scientist. The popularity of the R programming language is increasing, especially in data science and analytics. I will talk about some good resources to learn the R programming language that will give you the right approach to making things easier.

We would be having a look at one of the most in-demand topics in the domain of Data Science and Automation — Machine Learning in R. Machine Learning is an important aspect of data science wherein we make use of various algorithms to work on real life scenarios and make predictions on the cases used to put the work at ease.

Powerful R Markdown for Python Users. Learn how to use R Markdown to integrate Python and Bash commands with Latex Doc generation. R Markdown is a powerful tool that lets you generate rich PDF using TeX engine. When it comes to writing your code along with explanations and descriptions, R Markdown is very similar to Jupyter Notebook, but there are several things that R Markdown can do that Jupyter Notebook lacks

We are going to learn the introduction of machine learning and linear regression in R 4.0 programming. We will start with the introduction of machine learning then we will discuss the introduction of linear regression. I will also discuss types of linear regression and use cases of linear regression. there are two types of linear regression; simple linear regression and multiple linear regression. Use cases of linear regression are in house price prediction, stock price prediction, Twitter popularity prediction. I will thereafter show you how to analyze the Boston housing dataset. We will analyze dataset variables to understand the variable dependency for the linear regression model. I will show you the linear and non-linear regression models. Thereafter, I will show how you can improve the accuracy of a linear regression model.

Before getting into data visualization with R, you need to make sure you have a fair understanding of how R programming language works. I will be using R Studio. Data visualization is a method for converting abstract data into concrete information and possibly knowledge.

This video on Data Manipulation in R will help you learn how to transform and summarize your data using different packages and functions. You will use the dplyr package to select, filter, arrange, and mutate data. You will use the tidyr library to create tidy data. You will look at functions such as gather, spread, separate, and unite. Let's begin!

You will learn complete data visualization using ggplot2. I will show you how to plot line plot in ggplot2, how to plot bar plot in ggplot2, how to plot hist plot in ggplot and so much more.

A Complete Beginners Guide to Regular Expressions in R. The regular expression is nothing but a sequence of characters that matches a pattern in a piece of text or a text file. In this article, I will use R and RStudio for all the exercises . But you can learn how to use the regular expression from this article even if you wish to use some other language. It may look too complicated when you do not know it

We will see how basic operations are done in Pandas (Python) and Data.table (R). I’m using R-studio IDE for R and VSCode for Python. We will compare popular data manipulation libraries in arguably the two most commonly used programming languages in data science domain: Pandas (Python) vs Data.table (R)

We will focus on how to read an XML file in the R language. The XML file is known as (Extensible Markup language). The XML files look more like an HTML document that uses tags to define the objects and you can also use it as a text-based database.

An example of how to consume data files in R using a data pipeline approach. trouble due to it’s size or complexity is high.

In this article, we will be focusing on an important algorithm in the domain of Machine Learning -- Decision Tree in R, in a stepwise approach.

I will discuss an overview on how to use Logistic Regression in R with an example dataset. Learn how to do Logistic Regression in R. Logistic regression is one of the most popular forms of the generalized linear model. Interpret, Predict, and Evaluate Logistic Regression with infidelity data example

You will learn complete plotly analysis with the Covid-19 dataset. Plotly for R.

This Edureka "R Power BI integration" video will help you to understand the value brought by the integration of the R language into Power BI Desktop and how it provides a powerful tool for transforming and presenting business intelligence data.

This video on GGplot Tutorial will help you understand one of R's most widely used data visualization libraries. You will learn how to create different charts such as bar graphs, stacked bar charts, histograms, scatter plots, box plots, and lots more. You will get to know the different functions that ggplot library provides, such as geom_bar, geom_point, geom_line, geom_violin, and others. Let's get started.