R Level 1 – Data Analytics with R

R Level 1 – Data Analytics with R, available at $99.99, has an average rating of 4.5, with 91 lectures, based on 3443 reviews, and has 16954 subscribers.

You will learn about this course will show you how the most common types of graphs can be produced with R base you will get a good understanding of functions and loops in R which are very useful programming skills to have you will get the necessary theoretical background for R you will learn how to create and handle different types of objects you will get fluent in the R programming language to master your specific quantitative tasks This course is ideal for individuals who are scientists or data analysts or entrepreneurs or web developers or anybody interested in statistical programming It is particularly useful for scientists or data analysts or entrepreneurs or web developers or anybody interested in statistical programming.

Enroll now: R Level 1 – Data Analytics with R

Summary

Title: R Level 1 – Data Analytics with R

Price: $99.99

Average Rating: 4.5

Number of Lectures: 91

Number of Published Lectures: 87

Number of Curriculum Items: 91

Number of Published Curriculum Objects: 87

Original Price: $149.99

Quality Status: approved

Status: Live

What You Will Learn

this course will show you how the most common types of graphs can be produced with R base
you will get a good understanding of functions and loops in R which are very useful programming skills to have
you will get the necessary theoretical background for R
you will learn how to create and handle different types of objects
you will get fluent in the R programming language to master your specific quantitative tasks

Who Should Attend

scientists
data analysts
entrepreneurs
web developers
anybody interested in statistical programming

Target Audiences

scientists
data analysts
entrepreneurs
web developers
anybody interested in statistical programming

Are you new to R?

Do you want to learn more about statistical programming?

Are you in a quantitative field?

You just started learning R but you struggle with all the free but unorganized material available elsewhere?

Do you want to hack the learning curve and stay ahead of your competition?

If your answer is YES to some of those points – read on!

This Tutorial is the first step – your Level 1 – to R mastery.

All the important aspects of statistical programming ranging from handling different data types to loops and functions, even graphs are covered.

While planing this course I used the Pareto 80/20 principle. I filtered for the most useful items in the R language which will give you a quick and efficient learning experience.

Learning R will help you conduct your projects. On the long run it is an invaluable skill which will enhance your career.

Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the apply family of functions can be used for looping.

In the graphics section you will learn how to create and tailor your graphs. As an example we will create boxplots, histograms and piecharts. Since the graphs interface is quite the same for all types of graphs, this will give you a solid foundation.

With the R Commander you will also learn about an alternative to RStudio. Especially for classic hypothesis tests the R Commander GUI can save you some time.

According to the teaching principles of R Tutorials every section is enforced with exercisesfor a better learning experience.

You can download the code pdf of every section to try the presented code on your own.

This tutorial is your first step to benefit from this open source software.

What R you waiting for?

Martin

Course Curriculum

Chapter 1: Introduction – the very Basics of R

Lecture 1: R Level 1 Intro

Lecture 2: Download R and RStudio

Lecture 3: RStudio Orientation

Lecture 4: The Structure of the R Ecosystem

Lecture 5: R Help Features

Lecture 6: R Basics Script

Lecture 7: Using the R Functions

Lecture 8: R Exercises

Lecture 9: Three Common Mistakes of R Beginners

Lecture 10: Your first Lines of R

Lecture 11: Using some basic Functions

Lecture 12: Exercise and Solutions – Basic Coding

Lecture 13: R Datasets and Dataframes

Lecture 14: Basic Graphs in R 1

Lecture 15: Basic Graphs in R 2

Lecture 16: Exercise and Solutions – Basic Graphs

Lecture 17: Thoughts on Learning R

Chapter 2: Theoretical background

Lecture 1: Operators in R

Lecture 2: Operators script

Lecture 3: Object types

Lecture 4: Object types script

Lecture 5: Data types

Lecture 6: Data types script

Lecture 7: Random number generation

Lecture 8: Exercise random number generation

Lecture 9: Solution random number generation

Lecture 10: Random number generation script

Lecture 11: Export/Import of Excel files

Lecture 12: Data export/import script

Chapter 3: Creating objects

Lecture 1: Matrices part 1

Lecture 2: Matrices part 2

Lecture 3: Exercise matrices

Lecture 4: Solution matrices

Lecture 5: Matrices script

Lecture 6: Lists in R

Lecture 7: Exercise and solution Lists in R

Lecture 8: Lists script

Lecture 9: Data Frames part 1

Lecture 10: Data Frames part 2

Lecture 11: Factors

Lecture 12: Exercise and solution Data Frames

Lecture 13: Data frames script

Lecture 14: Factors script

Chapter 4: Functions in R

Lecture 1: Intro to functions in R

Lecture 2: Open ended functions

Lecture 3: Ifelse statement and summary

Lecture 4: Exercise and solution functions

Lecture 5: Functions script

Lecture 6: Functions Script txt

Chapter 5: Loops

Lecture 1: Introduction to loops and If statement

Lecture 2: For loop

Lecture 3: While loop

Lecture 4: Repeat loop and loop summary

Lecture 5: Exercise and solution loop part 1

Lecture 6: Exercise and solution loop part 2 – hard

Lecture 7: Loop script

Chapter 6: Apply family

Lecture 1: Introduction to the apply family

Lecture 2: Tapply and the by command

Lecture 3: Eapply, sapply, lapply

Lecture 4: Vapply, replicate, mapply

Lecture 5: Rapply and summary

Lecture 6: Apply family exercises

Lecture 7: Apply family solutions

Lecture 8: Apply family script

Chapter 7: Graphs in R

Lecture 1: Graphs in R

Lecture 2: Graphical parameters

Lecture 3: Boxplots

Lecture 4: Exporting graphs

Lecture 5: Exercise boxplots

Lecture 6: Solution boxplots

Lecture 7: Piecharts

Lecture 8: Histograms

Lecture 9: Exercise and solution histograms

Lecture 10: Advanced scatterplots and legends

Lecture 11: Exercise and solution scatterplots

Lecture 12: Graphs script

Chapter 8: Advanced topic: working with strings

Lecture 1: Working with Strings – Introduction

Lecture 2: Working with Strings – gsub

Lecture 3: Working with Strings – gsub advanced

Lecture 4: Regular Expressions Overview

Lecture 5: Working with Strings – Library Stringr

Lecture 6: Exercise and Solution: Strings in R

Lecture 7: Code Section: Strings

Chapter 9: Using the R Commander GUI

Lecture 1: R Commander Intro

Lecture 2: Installation Hints

Lecture 3: Getting Data into RCmdr

Lecture 4: Modeling with RCmdr

Instructors

R-Tutorials Training
Data Science Education

Rating Distribution

1 stars: 34 votes
2 stars: 61 votes
3 stars: 395 votes
4 stars: 1330 votes
5 stars: 1623 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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