R Programming - R Language for Absolute Beginners

Focused View

Ivo Bernardo

10:30:35

59 View

1 - Course Intro

1 - Course Material.txt

1 - DareData Website.txt

1 - Ivos Github.txt

1 - LinkedIn Ivo.txt

1 - Welcome.mp4

05:31

2 - Course Materials.html

2 - Installing R and R Studio

1 - Basic Questions about R.html

3 - Installing R.mp4

05:11

3 - Installing R on WindowsMacUbuntu.txt

3 - R Mac Os Download.txt

4 - Installing R Studio.mp4

06:20

4 - Installing R on Mac.txt

4 - R Studio Download Link.txt

3 - Introduction Basic Operations in R

2 - R as a Calculator Quiz.html

5 - Link to slides.txt

5 - Slides R as a Calculator and Vectors.mp4

14:14

6 - Course Scripts.txt

6 - Using R as a Calculator Simple Calculations.mp4

05:11

7 - Using R as a Calculator Functions.mp4

04:33

8 - Practical Exercises Time to test your skills.html

9 - Link to Exercise Solutions.html

4 - Vectors and the Environment

3 - R Vectors and the Environment.html

10 - Creating Vectors and Knowing the Environment.mp4

08:12

11 - Vector Indexing and Slicing.mp4

06:29

12 - Calculations with Vectors.mp4

09:53

13 - More Functions and Dealing with Unexpected Values.mp4

10:58

14 - Comparison Operators.mp4

04:46

15 - Vectors Names Property.mp4

05:46

16 - Modifying Vector Elements.mp4

05:50

17 - Comparing R with Excel and SQL.mp4

04:37

18 - Link to Solutions.txt

18 - TUTORIAL Completing and Debugging Coding Exercises on Udemy Platform.mp4

08:20

19 - Practical Exercises Time to test your skills on Vectors.html

5 - R Data Types

4 - R Data Types.html

20 - Link to slides.txt

20 - Slides R Data Types.mp4

04:19

21 - Underlying Data Types and Types at the Class Level.mp4

06:43

22 - Checking Data Types of Objects.mp4

04:28

23 - Converting Data Types.mp4

07:26

24 - Introduction to Factors.mp4

08:10

25 - Dealing with Dates.mp4

11:30

25 - R Date Formats.txt

26 - Practical Exercises Time to test your skills on Data Types.html

6 - R Arrays

5 - Arrays Quiz.html

27 - Link to slides.txt

27 - Slides Arrays and Matrices.mp4

06:17

28 - Creating Arrays.mp4

09:14

29 - Indexing and Modifying Arrays.mp4

07:41

30 - Array Operations.mp4

06:10

31 - Array Dimnames Property.mp4

09:24

32 - Combining Arrays.mp4

03:36

33 - Practical Exercises Time to test your skills on Arrays.html

7 - R Matrices

6 - Matrices Quiz.html

34 - Creating Matrices.mp4

06:19

35 - Matrix Operations.mp4

10:05

36 - Practical Exercises Time to test your skills on Matrices.html

8 - Data Frames Introduction

7 - Data Frames Questions.html

37 - Link to slides.txt

37 - Slides Data Frames & Lists.mp4

13:25

38 - Creating a Data Frame.mp4

09:19

39 - Indexing and Modifying Data Frames.mp4

02:55

40 - Expanding Data Frames.mp4

04:33

41 - Removing Elements from Data Frames.mp4

02:54

9 - R Lists

8 - Lists Questions.html

42 - Creating Lists.mp4

08:45

43 - List Indexing.mp4

07:32

44 - Changing and Adding elements to Lists.mp4

05:05

45 - Deleting List Elements.mp4

02:53

46 - Combining Lists.mp4

03:46

47 - Practical Exercises Time to test your skills on Lists.html

10 - Course Break

48 - Course Break.mp4

02:19

11 - Libraries

49 - Installing Libraries.mp4

09:17

50 - Loading Libraries.mp4

01:50

12 - Working with Data Frames

10 - Working with Data Frames Questions.html

51 - Introduction.mp4

02:00

52 - Inspecting Data Frames.mp4

12:12

53 - Filtering Data Frames.mp4

14:12

54 - Creating New Columns in a Data Frame.mp4

07:25

55 - Apply Family.mp4

12:49

56 - New Column with Sapply.mp4

11:29

57 - Aggregating and Sorting.mp4

09:37

58 - Merging Data Frames.mp4

12:20

59 - Extra Merging Data Frames using a SQLLike Library.mp4

03:58

60 - Plotting Overview Base R.mp4

12:12

60 - R Colors Cheatsheet.txt

61 - GGPlot 2 Overview.mp4

07:09

62 - Practical Exercises Time to test your skills on manipulating Data Frames.html

13 - Loading External Data

63 - Exploring Working Directories.mp4

11:01

64 - Loading CSV Files.mp4

07:15

65 - Loading Excel xlsxlsx Files.mp4

09:06

66 - Loading data from a MySQL Database.mp4

11:40

14 - Real World Data Frame Analysis Walmart Data

67 - Introduction.mp4

02:46

68 - Loading the Data.mp4

03:46

69 - Extracting the Number of Rows.mp4

00:58

70 - Checking the First Rows.mp4

00:49

71 - Counting Rows by Store.mp4

01:28

72 - Converting to a Data Frame.mp4

01:23

73 - Extracting the Store with most Rows.mp4

02:23

74 - Summing up the Total Sales by Store.mp4

02:07

75 - Modifying Column Names.mp4

01:09

76 - Plotting Total Sales per Store.mp4

02:33

77 - Extracting the Mean of every Column.mp4

02:15

78 - Creating a New Column.mp4

03:22

79 - Plotting the Sales by Date for a Specific Store.mp4

07:04

80 - Using Ggplot to Enhance the Sales Plot.mp4

07:00

81 - Plotting the Sales By Date for Multiple Departments.mp4

12:49

15 - Working with Excel Files

82 - Reading the Crime Data Excel File.mp4

02:48

83 - Setting Column Names.mp4

01:38

84 - Building a Pipeline and Dropping Null Values.mp4

06:41

85 - Preparing Row Names.mp4

02:39

86 - Drop Columns.mp4

03:48

87 - Convert Data to Numeric.mp4

02:39

88 - Assigning Row Names.mp4

02:05

89 - Automating Column Selection.mp4

05:10

90 - Hardcoded Selection vs Automated Rule Based Selection.mp4

02:27

91 - Aggregating Data and Bar Plotting.mp4

10:44

92 - Creating a Data Pipeline Exercise.html

16 - Functions

11 - Functions Questions.html

93 - Functions Intro.mp4

12:04

94 - Functions Arguments.mp4

05:22

95 - Functions Control Flow Loops.mp4

11:01

96 - Functions Control Flow If Statements.mp4

06:16

97 - Functions Control Flow While Loops.mp4

06:54

98 - Functions Working with Data 1.mp4

09:43

99 - Functions Working with Data 2.mp4

04:01

17 - Statistics Overview

100 - Visualizing Data Distributions.mp4

10:12

101 - Mean and Median.mp4

04:15

102 - Mode.mp4

03:19

103 - Comparing Distributions.mp4

08:28

104 - Spread of a Data Distribution.mp4

07:49

105 - Boxplots.mp4

07:21

105 - Box Plot Explanation.txt

106 - Detailed Feedback.html

18 - Course Preview R For Data Science

107 - R for Data Science Course Preview.mp4

01:34

108 - Training a Linear Regression Using R.mp4

04:53

109 - Training a Decision Tree using R.mp4

05:01

19 - Course Ending

110 - Where to go from here Learning Paths after Learning R.mp4

03:13

111 - Bonus Section Other Courses and Resources.html

112 - Final Notes.mp4

00:27

Description

R Programming course suitable for everyone, no coding experience or a statistics background needed

What You'll Learn?

Installing R and R Studio
Manipulating R Vectors, Arrays and Matrixes
Manipulating R Data Frames
Plotting Data Using R
Analyzing Real Datasets using R
Organizing your Code in R
Develop your own Functions in R
Summarizing Data with R
Load Excel Files with R
Compute Basic Statistics about a Dataset
Install External Libraries to Power up R
Aggregate and Sort Data
Subset Data

Who is this for?

Data Analysts

Business Analysts

Financial Managers

Statisticians

Researchers

Software Engineering Undergraduates

Data Scientists

Data Engineers

R learners

Entry-level Data Scientists

What You Need to Know?

Computer with at least 4GB Ram

More details

Description
So, you've decided that you want to learn RÂ or you want to get familiar with it, but don't know where to start?Â 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!

This course was designed to be your first step into the RÂ programming world!Â We will delve deeper into the concepts of RÂ objects, understand the RÂ user interface and play around with several datasets. This course contains lectures around the following groups:Â
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 RÂ - 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 RÂ objects.Â
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.
Who this course is for:
Data Analysts
Business Analysts
Financial Managers
Statisticians
Researchers
Software Engineering Undergraduates
Data Scientists
Data Engineers
R learners
Entry-level Data Scientists

User Reviews

Rating

average 0

Total votes0

Focused display

Ivo Bernardo is a professional with a passion for Data Science and Analytics and is currently a Partner at DareData Engineering, a startup that implements machine learning systems all around world for companies of different sizes (from startups to large enterprises). He holds a master Degree in Statistics and Business Intelligence from New University of Lisbon and has been an instructor in several data science academies throughout the years. His main teaching passion is helping beginners or professionals from other industries taking their first leap into the Data Science and Analytics space. Technically he has worked with Python, R, SQL and main cloud providers infrastructure. Feel free to contact him on LinkedIn or via Udemy for potential business ventures or collaborations._________________________________________________________________________Ivo Bernardo é um profissional com grande interesse por Data Science e Analytics. De momento, é Partner da DareData Engineering, uma startup que implementa sistemas de machine learning em todo o mundo para empresas de diferentes tamanhos (desde startups a grandes organizações). É mestre em Estatística e Business Intelligence pela Universidade Nova de Lisboa e participou como instructor em algumas academias data science em Portugal. A sua principal paixão enquanto instrutor é ajudar iniciantes ou profissionais de outras áreas a dar o seu primeiro passo no mundo de Data Science e Analytics. Tecnicamente, trabalha maioritariamente com Python, R, SQL e com a infraestrutura das plataformas cloud mais relevantes do mundo. Pode contactá-lo directamente no LinkedIn ou via Udemy para potenciais colaborações ou outros assuntos relacionados com Data Science.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.