The Best YouTube Courses for Learning R Language

The Best YouTube Courses for Learning R Language

Description: Understanding the R language, what it is used for, its pros and cons, and the best free videos to learn how to program with R.

Top 10 YouTube Courses for Learning R language

The popularity of the R language is on the rise, as numerous digital companies need data collection and management tools. Because of the growing demand for R developers, more people are interested in learning the language. However, many people find learning R challenging due to its complexity, and most online R courses are paid—fortunately, platforms like YouTube offer free courses for learning R visually.

Before we get started with the list of the top 10 YouTube courses for learning R, let us take a moment to review the R language in detail.

R Language Overview

R is a computer programming language that was created by the R Core Team and was first used in August 1993. The R version, which enables expert programmers and academic statisticians to conduct intricate statistical analysis on large amounts of data and present the findings in various visual formats, was first made available in 1995. The name R is said to have come from the first letters of the names of the key developers (Ross Ihaka and Robert Gentleman).

Following the success of the statistical analysis application of this language, it was adopted as a programming language for data visualization projects. This makes it the ideal programming language for data mining, machine learning, statistical computing, bioinformatics, and data analysis.

Every programming language has its weaknesses; the R language is no exception. It has been faulted for generating slow analyses when applied to large data sets. One CPU can only be used at a time in the basic open-source version of the language due to its use of single-threaded processing. These limitations have decreased the R language's applicability in big data applications. Rather than utilizing R for production tasks, numerous enterprise users employ it for exploratory and investigative purposes. After performing complex analyses on sample data using R and finding a significant correlation or cluster in the data, data scientists will use enterprise-scale tools to implement their findings.

There is some level of integration between analytics software and the R language offered by vendors such as IBM, Microsoft, Oracle, SAS Institute, TIBCO, and Tableau. Popular open-source big data platforms like Spark and Hadoop also have R packages available for them

R language: Features and Environment

When coding with the R language, developers can effortlessly implement new functionalities when they define new functions. Users can extract a single variable, join multiple data files into a single document, and run a regression on the resultant data set. Discrete operations can be combined into a single, reusable function within the environment.

The R programming language supports functions for a variety of applications, including classical statistics, linear and non-linear modeling, classifications, and clustering. It also incorporates a wide range of libraries, a sizable and vibrant user base, and integration capabilities with both C and C++. This programming language offers a large number of operators and tools for data analysis, effective data handling, and storage.

One of R's best qualities is that it can be used to create neural network packages for artificial intelligence (AI), and it is a platform-independent language that runs on all computers, including Windows, Mac, UNIX, and Linux. Developers can read data, add it to the workspace, issue commands, and see the outcomes through the conventional command-line interface.

Another common feature in the R programming environment is looping functions. These features enable users to carry out specific tasks, like repeatedly extracting samples from a larger data set. Its extensive feature set and free download from the Free Software Foundation under the GNU general public license have contributed to its enduring appeal in academic environments. The R language is robust and can be run on Windows, Linux, MacOS, and UNIX operating systems.

R language pros and cons

Below are some of the notable advantages and disadvantages of the R language.

Pros

• The R language is an efficient tool for programmers working on projects that demand higher-level analysis.
• It is an excellent programming language for machine learning, data science, data visualization, and data analysis.
• It offers a broad range of statistical methods, including data reduction, statistical testing, clustering, and classification.
• R makes it simple to create a variety of graphs, including scatter plots, box plots, histograms, and pie charts.
• It operates across several operating systems (Windows, Mac, Linux)
• Open-source and cost-free
• It has a lot of community backing.
• It offers advanced data analytics capabilities, a plethora of packages (function libraries), and a vibrant online user community where users can seek assistance.
• R integrates with add-on programs that can improve the fundamental features of data visualization, external database connections, geographic data mapping, and sophisticated statistical operations.

Cons

• R is an in-memory programming environment and a single-threaded processing language. During a particular session, every data object is kept in the RAM of a computer. Because of this, R may be able to handle fewer data sets at once.
• Because R is interpretive, it might not be the best option for high-performance computing applications. It is very possible that R users will experience slow performance and memory allocation errors.
• The R language operates noticeably more slowly than other programming languages like Python and MATLAB. R packages load far more slowly than those for other languages.
• The language R is complex, and learning it may be challenging for those without any prior programming knowledge or experience.
• Considering that it was derived from the much older programming language "S," it does not support dynamic or 3D graphics.

R language application and use cases

The R language finds applications in various industries, particularly those that handle large volumes of data. Here are examples of industries where the R language is used:

• Financial Technology (FinTech): R is widely used in the financial services industry for tasks such as credit scoring, risk management, and statistical analysis for investment predictions.
• Manufacturing: R is used in manufacturing for quality control, supply chain optimization, and predictive maintenance to improve operational efficiency.
• Healthcare: In epidemiology, R is used to track and predict the spread of diseases. It is also used in drug discovery to analyze data collected during pre-clinical trials.
• Market Research: R is commonly used in market research for data analysis and visualization to understand consumer behavior and market trends.
• Retail: Retail companies use R for inventory management, sales forecasting, and customer analytics to optimize pricing and promotions.
• Social Media Companies: Social media platforms gather vast amounts of data daily. This data is analyzed using R for insights, including targeted advertising and user behavior analysis.
• Telecommunications: In telecommunications, R is used for network analysis, customer segmentation, and churn prediction to improve service offerings and customer retention.

Is R language difficult to learn?

R's vast command set and distinct syntax from other languages like Python make it challenging to learn. While those with a background in statistics may find R easy to learn, most students without any coding experience need four to six weeks to get up to speed. However, the resources and approach used to learn this language determine how hard or easy it is to learn.

What is the best way to learn R programming languages?

Having a personal tutor who is physically or visually present with you is the best way to learn R. It is easier to ask questions and receive direct answers when your teacher is in person. But as the world grows more digital, R can now be learned quite effectively through online video courses. The remaining part of this article will show readers 10 YouTube courses for learning the R language.

Note: To install R, download the most recent version for Windows, Mac, or Linux from https://cloud.r-project.org/.

1. R programming full course

• Author: Simplilearn
• Video Duration: 10:10:55
• Level: Beginners
• Course outline:
  • What is R Programming
  • Variables and Data Types in R
  • Logical Operators
  • Vectors
  • List
  • Matrix
  • Data Frame
  • Flow Control
  • Functions in R
  • Data Manipulation in R- dplyr
  • Data Manipulation in R- tidy
  • Data Visualization In R
  • Time Series Analysis in R

2. R Programming course

• Author: Tutorialspoint
• Video Duration: 2 hours
• Course outline
• Course Overview
• R programming introduction
• Environment set up
• Basic syntax
• Variables
• Datatypes
• Operators
  • Arithmetic operator
  • Relational operator
  • Logical operator
• Decision-making: If statements
  • If else statement
  • Switch statement
• Loops
  • For loops
  • While loop
  • Repeat loop
• Array
• Functions
• Strings
• Vectors
• List
• Matrices
• Factors
• Data frames
• Packages
• Data Reshaping
• Opning files
• Web data
• Database
• Plotting
  • Pie chart
  • Bar chart
  • Box Plots
  • Histogram
  • Lime graphs
  • Scatterplots
• Mean, Median & Mode
• Linear Regression

3. Introduction to the R Programming Language and Data Science

• Author – DataDaft
• Video Duration: 9 hours
• Level: Beginners
• Course outline:
  • Getting started
  • R arithmetic
  • Atomic data types
  • Variables
  • Vectors
  • Matrices
  • Lists
  • Data frames
  • Factors
  • Reading and writing data
  • Control flow
  • Functions
  • Exploring and preparing data
  • Working with text data
  • Preparing numeric data
  • Dates
  • Merging data
  • Frequency table
  • Plotting in Base R
  • Plotting with ggplot2
  • Descriptive statistics
  • Probability distribution
  • Confidence intervals
  • T-Tests(Hypothesis)
  • Chi-squared tests
  • Anova
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests

4. R for Data Science: R Programming for Beginners

• Author: Softlect
• Video Duration:6 hours
• Level: Beginners and Intermediate
• Course outline:
  • What is R ? Why R?
  • Installations
  • Basic Mathematical Operations
  • Variables
  • Basic Data Types
  • Comments
  • Relational and Logical Operators
  • Sequences
  • Replicate Function
  • Control Structures
  • Functions
  • Vectors
  • Factors
  • Mathematical Functions in R
  • Random Numbers in R
  • Matrix
  • Lists
  • Dataframes
  • Missing data
  • dplyr Package
  • Bar Graphs
  • Histogram
  • Scatter Plots
  • Line Graphs
  • Box Plots
  • Multiple Plots in Layout

5. R Programming Course

• Author : Intellipaat
• Video Duration: 7 hours
• Level: Basic and Advanced
• Course outline:
  • Introduction to R programming
  • Variables and objects
  • Flow control statements
  • Anova concepts
  • Data mining using R
  • Hands on projects on book recommendation system
  • R programming vs python
  • R interview questions

6. R programming for beginners-full course

• Author: Great Learning
• Video Duration: 3 hours 29 minutes
• Level: Beginners
• Course outline:
  • Introduction
  • Agenda
  • Installing R and R-studio
  • Variables, Datatypes and Operators
  • Vector in R
  • List, Matrix and Array in R
  • Inbuilt Functions in R
  • Flow Control Statements in R
  • Data Manipulation with dplyr
  • Data Visualization with ggplot2
  • Pokemon Case Study
  • Intro to Neural Networks
  • Demo: Neural Networks using

7. R programming, full course

• Author: SkillCurb
• Video Duration: 4 hours
• Level: Beginners and intermediate
• Course outline:
  • R introduction
  • R Environment
  • Data Types
  • Basics of R
  • Control Structures
  • Functions
  • Data Structures
  • Vectors
  • Array
  • Lists
  • Data Frame
  • Factors
  • Data Visualization

8. R programming with John Hopkins University

• Author: Coursera
• Video Duration: 2 hours
• Course outline:
• Level: Intermediate
  • Introduction to R
  • Overview and History of R
  • Getting help with R
  • R console input
  • R Data types: Objects and attributes
    • Vectors and lists
    • Matrices
    • Factors
    • Missing values
    • Data frames
    • Names attributes
  • Reading tabular data
  • Reading large tables
  • Textual data formats
  • Connections: Interfaces to the outside world
  • Subsetting R objects: Basics
    • Lists
    • Matrices
    • Partial matching
    • Removing missing values
  • Vectorized operations

9. R Programming From scratch

• Author: Fahad Hussain
• Video Duration: 2 hours
• Level: Beginners
• Course outline:
• Introduction to R programming
• Data types in R programming
• Taking inputs|ReadLine
• Operator
• Vector data structure
• List data structure
• Array data structure
• Matrix data structure
• Factor Data structure
• Data frame
• Condition statements: if else family
  • Switch case
  • Loops
• Function
• Data visualization using plot methods
  • Plot()
  • Scatter plot()
  • Barplot()
  • Hist()
  • Boxplot()
  • Pie()
• Summary

10. R programming Tutorial

• Author: Barton Paulson (Freecodecamp.Org)
• Video Duration: 2 hours
• Level: Basic
• Course outline:
  • Welcome
  • Installing R
  • RStudio
  • Packages
  • plot()
  • Bar Charts
  • Histograms
  • Scatterplots
  • Overlaying Plots
  • summary()
  • describe()
  • Selecting Cases
  • Data Formats
  • Factors
  • Entering Data
  • Importing Data
  • Hierarchical Clustering
  • Principal Components
  • Regression
  • Next Steps

Conclusion

R is a robust and flexible programming language that is mostly employed in machine learning, data visualization, and statistical analysis. Numerous packages and libraries that enable sophisticated data manipulation, exploration, and modeling are available in R, a programming language created by statisticians and data analysts. With an active and vibrant community, R continues to evolve, remaining at the forefront of data science and statistical computing.

A diverse range of learners with varying learning preferences and styles can easily acquire knowledge of R thanks to the abundance of free video tutorials that can be found on YouTube. YouTube offers a flexible, self-directed learning environment and an interactive format that lets users pause, rewind, and interact with content at their own pace.

FAQ

1. What is the R language used for?

The three main applications of the R programming language are machine learning, data visualization, and statistical analysis. It is utilized in a variety of fields, including academic research, finance, data science, bioinformatics, and the social sciences. R offers a vast array of packages and libraries that facilitate the efficient manipulation, analysis, and visualization of data, rendering it an essential instrument for research and decision-making based on data.

2. Which other programming languages can be integrated with the R language?

The capabilities of the R programming language can be expanded by integrating it with several other programming languages. Some of the commonly integrated languages include:

• Python
• C/C++
• Java
• SQL

Integration with Python facilitates web scripting by allowing users to call functions and modules in Python from within R and vice versa. Java users can increase performance by using R's ability to interface with C or C++ code. Users can integrate R to access Java classes and methods, and vice versa, with packages such as rJava. Packages like RMySQL, RSQLite, and RODBC can be used to connect R to relational databases, enabling users to query and manipulate data in databases straight from R.

3. What is the main difference between Python and R?

R is a language for statistical programming, whereas Python is a general-purpose language. This indicates that Python is more adaptable and suitable for more diverse tasks, including machine learning, web development, and data manipulation. R is designed specifically for manipulating, visualizing, and conducting statistical analyses on data. It performs exceptionally well in tasks involving statistical modeling, machine learning, exploratory data analysis (EDA), and data analysis.

Both R and Python are useful for data analysis and machine learning; R is better suited for tasks involving statistical computing, while Python is preferred for a wide range of applications outside of statistics.

4. Which should I learn first, R or Python?

Learning R first can be daunting for someone who has no programming knowledge. However, since Python is simpler, learning Python first helps the programmer get program knowledge faster, making it easier to understand R when you start learning it.

Because of its clear syntax and comprehensible nature, it is more approachable and less complicated to learn than R. Python may offer a more seamless learning experience if you are new to programming. R's syntax is more tailored for statistical computing, which could make learning more difficult for novices. R might be more intuitive for you if you have experience with statistics or data analysis.

5. How do I get started with R programming?

Typically, you can get started with R with these simple steps.

• Download and install R from its official website. Ensure the format you are downloading is the version of your operating system (Windows, macOS, or Linux); follow the installation instructions provided.
• Select an IDE (Integrated Development Environment), such as R code, which is a straightforward text editor. The R kernel in Jupyter Notebooks and the R extension in Visual Studio Code are alternative IDEs for R.
• Learn the fundamentals of R's data structures and syntax through video tutorials (YouTube). Begin with simple assignments that involve variables, arithmetic calculations, and different data types (numeric, character, logical, etc.).
• Participate in the R community by subscribing to mailing lists, forums, and online communities like RStudio Community, R-bloggers, and Stack Overflow. Engage in dialogue, pose inquiries, and gain knowledge from the experiences of others.