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What are Some Good Resources for Learning R Programming?

R is a popular statistical language version that offers higher-level graphics for debugging facilities, visualizations, and interfaces to other languages. As per the study report on data science skills, languages such as R and Python dominated the scale of preference, with a combined worth of over 81.9% utilization for statistical modeling.

 Here is the list of resources to learn R

Data Science: R Basics

It assists in understanding the basics of R programming in data science. This program will assist you in learning the functions and types of data of R language, comprehending how to function on vectors and at what time, and using the advanced version of the functions such as sorting, etc. One may also learn how one can apply the general programming attributes such as “for loop” and “if else” commands. And also, how to wrangle, visualize and analyze data. With this course’s help, students will grow a set of skills comprising data wrangling, R programming, organization of files, version control with GitHub and git and reproducible file preparation with R Studio.

Learn Data Science with R part 1 of 10

This resource will assist you in learning the fundamentals of data types, data science, factors, vectors, data frames, and list matrices, among others. Further, one may also comprehend how to read the information or data from the Oracle database with the help of Oracle, RODBC and JDBC.

Machine learning, data science, data analysis, R and Python

In this course, students can learn new skills and expertise and comprehend the difficult yet lucrative sub-areas of data science comprising probability, stats, analysis of data, etc. This program comprises practical exercises based on practical instances. Also, it consists of both R and Python code templates that one can download and use for their projects. Moreover, the topics listed in this are data analysis using Pandas and NumPy and data visualization with the R programming assignment help.

R for Data Science

Garrett Grolemund and Hadley Wickham introduced this book. It will assist students in understanding how to perform data science with R. Also, one can learn how to acquire the data in R language, transform it, visualize it and model it. Therefore, they will learn how to use the grammar of graphics, literate programming, etc.

R program design for data science

Roger D. Peng introduced this manuscript. It will teach students the basics of the R programming language. One can also learn the fundamentals of R language and the phases of manipulating data sets. This e-book will also aid you in comprehending how to inscribe several R operations, optimize and debug the code, etc. With the basics offered in this book, one will have a thorough foundation to establish the data science toolbox.

Hands-on machine learning with R

It offers modules for several common machine learning techniques like generalized lower ranking models, regularized models, clustering algorithms, and gradient boosting machines, among others. Students will be capable of learning how to develop and tune several models with the help of R packages. This book is meant for users who want to learn how to practice the machine learning stack inside R. It also consists of examples using several R packages like XGBoost, glmnet, and lime to competently model and obtain insights from the data.

Data Science: foundations with the aid of R specialization

It covers the introductory data science techniques and tools, including cleaning, receiving and discovering the data, programming in R and carrying out the reproducible study. In addition, the students will finish a plan at the finishing point of every course in such a specialty. Projects comprise installation tools, R programming, analysis, and peer review projects and tasks.

Statistics with R specialization

Students will learn to visualize and analyze data in R languages, create reproducible data analysis reports, and depict a conceptual comprehension of the unified scope of statistical inferences, among others. This program will also aid you in learning modeling to apprehend natural phenomena, create data-oriented decisions, and communicate the statistical outcomes correctly. In addition, it is in context without depending on the statistical jargon, critiques the data-oriented claims, assesses data-focused decisions, and many more.

Overview to Data Science: Facts prediction and analysis processes with R

Rafael A. Irizarry introduced this free e-book. This book lists down skills and concepts that can aid in tackling practical data analysis issues. It covers several concepts from stats inference, probability, linear regression and machine learning. It also aids you in progressing skills like data wrangling with dplyr, algorithm establishing with the caret, visualization of data with ggplot2, and version regulation with GitHub and Git, among others.

Dashboarding and data visualization with R specialization

This specialization intends for people looking to grow the capability to visualize the data with the help of R. It comprises five courses that will assist you in learning R to frame interactive and static data visualizations and publish them on the internet. Also, this course will aid you in preparing to extract the highlights into several forms of audiences.

What is the role of RStudio Cheat Sheets?

RStudio and R have been around the corner for a longer period. As an outcome, the resources available are phenomenal. RStudio offers short cheat sheets that comprise plenty of information.

Shortcuts and auto-completion in the R are immensely powerful. It is worth familiarizing yourself with such tools as they substantially raise productivity and output. Such cheat sheets assist with this as they comprise all the shortcut menus you require in one place.

Cheat sheets are also accessible for several packages. Also, it covers the installation and use of such packages, enabling you to acquire the most out of whatever package you select. These sheets are constantly updated, so keep checking for new ones.

Wrap Up

The courses listed above will help you study how to plug-in in R and practice R for competent data scrutiny. Students will be able to learn how to configure and install software essential for the statistical programming atmosphere and detail the basic programming language ideas as they are executed in the higher-level arithmetical language. Therefore, these courses cover applied issues in stats figuring that comprises program design in R.

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