Course Overview
So, you've decided that you want to learn R or you want to get familiar with it, but don't know where to start? Or are you a data/business analyst or data scientist that wants to have a smooth transition into R programming?
Then, this course was designed just for you!
Hear what other students have to say first:
"I came to this course after an online university course *cough coursera cough* left me crying. The entire curriculum that this class covers was what they covered in week 1, and from what I've heard, R has a very steep learning curve. The extra time taken to even walkthrough installation is clearer here. It is much appreciated. You get lots of opportunities to practice, follow along, and really build your knowledge step by step." 5 star review by a Udemy user
"A really really good course for beginners in R, highly recommended. Everything is well explained in detail and enough coding exercises to get you familiarize with concepts before moving to the next section.
Thanks for this Ivo, appreciated." 5 star review by a Udemy user
"Easy to understand, calm and clear explanation. Covered points which are ignored by other tutors. Great" 5 star review by a Udemy user
This course was designed to be your first step into the R programming world! We will delve deeper into the concepts of R objects, understand the R user interface and play around with several datasets. This course contains lectures around the following groups:
Introductory slides lectures with the most well-known commands for each type of R object.
Code along lectures where you will see how we can implement the stuff we will learn!
Test your knowledge with questions and practical exercises with different levels of difficulty!
Analyze real datasets and understand the thought process from question to R code solution!
This course was designed to be focused on the practical side of coding in R - instead of teaching you every function and method out there, I'll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects.
At the end of the course you should be able to use R to analyze your own datasets. Along the way you will also learn what R vectors, arrays, matrixes and lists are and how you can combine the knowledge of those objects to power up your analysis.
Here are some examples of things you will be able to do after finishing the course:
Load CSV and Excel files into R;
Do interesting line plots that enable you to draw conclusions from data.
Plot histograms of numerical data.
Create your own functions that will enable you to reutilize code.
Slice and dice Data Frames, subsetting data for specific domains.
Join thousands of professionals and students in this R journey and discover the amazing power of this statistical open-source language.
This course will be constantly updated based on students feedback.
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Course curriculum
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Creating Vectors and Knowing the Environment
492 -
Vector Indexing and Slicing
389 -
Calculations with Vectors
593 Xem trước -
More Functions and Dealing with Unexpected Values
658 -
Comparison Operators
286 -
Vectors Names Property
346 -
Modifying Vector Elements
350 -
Comparing R with Excel and SQL
277 -
R Vectors and the Environment
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[TUTORIAL] - Completing and Debugging Coding Exercises on Udemy Platform
500 -
Vectors Coding Exercise
0 -
Practical Exercises - Time to test your skills on Vectors!
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[Slides] - R Data Types
259 -
Underlying Data Types and Types at the Class Level
403 -
Checking Data Types of Objects
268 -
Converting Data Types
446 -
Introduction to Factors
490 -
Dealing with Dates
690 -
R Data Types
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R Data Types Coding Exercise
0 -
Practical Exercises - Time to test your skills on Data Types!
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Introduction
120 -
Inspecting Data Frames
732 -
Filtering Data Frames
852 -
Creating New Columns in a Data Frame
445 -
Apply Family
769 -
New Column with Sapply
689 -
Aggregating and Sorting
577 -
Merging Data Frames
740 -
Extra - Merging Data Frames using a SQL-Like Library
238 -
Plotting Overview (Base R)
732 -
GGPlot 2 Overview
429 -
Working with Data Frames Questions
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Working with Data Frames Coding Exercises
0 -
Practical Exercises - Time to test your skills on manipulating Data Frames!
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Introduction
166 -
Loading the Data
225 -
Extracting the Number of Rows
58 -
Checking the First Rows
49 -
Counting Rows by Store
87 -
Converting to a Data Frame
83 -
Extracting the Store with most Rows
143 -
Summing up the Total Sales by Store
127 -
Modifying Column Names
69 -
Plotting Total Sales per Store
153 -
Extracting the Mean of every Column
135 -
Creating a New Column
202 -
Plotting the Sales by Date for a Specific Store
424 -
Using Ggplot to Enhance the Sales Plot
420 -
Plotting the Sales By Date for Multiple Departments
769
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Reading the Crime Data Excel File
168 -
Setting Column Names
98 -
Building a Pipeline and Dropping Null Values
400 -
Preparing Row Names
159 -
Drop Columns
228 -
Convert Data to Numeric
159 -
Assigning Row Names
125 -
Automating Column Selection
310 -
Hardcoded Selection vs. Automated (Rule Based) Selection
147 -
Aggregating Data and Bar Plotting
643 -
Creating a Data Pipeline Exercise
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Functions - Intro
724 -
Functions - Arguments
322 -
Functions - Control Flow - Loops
661 -
Functions - Control Flow - If Statements
376 -
Functions - Control Flow - While Loops
414 -
Functions - Working with Data 1
583 -
Functions - Working with Data 2
241 -
Functions Questions
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Coding Exercise - Functions and Vectors
0 -
Coding Exercise - Functions 2
0 -
Coding Exercise - Functions and DataFrames
0
Requirment
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Computer with at least 4GB Ram
Outcomes
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Installing R and R Studio
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Manipulating R Vectors, Arrays and Matrixes
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Manipulating R Data Frames
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Plotting Data Using R
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Analyzing Real Datasets using R
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Organizing your Code in R
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Develop your own Functions in R
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Summarizing Data with R
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Load Excel Files with R
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Compute Basic Statistics about a Dataset
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Install External Libraries to Power up R
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Aggregate and Sort Data
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Subset Data
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