R Programming For Absolute Beginners

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Bogdan Anastasiei

9:32:03

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1. Introduction.mp4

04:22

2. Installing R and RStudio.mp4

05:45

3. The RStudio Interface.mp4

12:58

4. Installing and Activating R Packages.mp4

04:44

5. Setting the Working Directory.mp4

02:15

6. Basic Operations in R.mp4

03:17

7. Working With Variables.mp4

10:23

8. Creating Vectors With the c() Function.mp4

05:57

9. Creating Vectors Using the Colon Operator.mp4

04:03

10. Creating Vectors With the rep() Function.mp4

04:31

11. Creating Vectors With the seq() Function.mp4

07:42

12. Creating Vectors of Random Numbers.mp4

08:05

13. Creating Empty Vectors.mp4

03:13

14. Indexing Vectors With Numeric Indices.mp4

09:44

15. Indexing Vectors With Logical Indices.mp4

01:33

16. Naming Vector Components.mp4

01:51

17. Filtering Vectors.mp4

08:11

18. The Functions all() and any().mp4

06:24

19. Sum and Product of Vector Components.mp4

02:56

20. Vectorized Operations.mp4

07:14

21. Treating Missing Values in Vectors.mp4

03:24

22. Sorting Vectors.mp4

03:35

23. Minimum and Maximum Values.mp4

02:11

24. The ifelse() Function.mp4

07:00

25. Adding and Multiplying Vectors.mp4

03:09

26. Testing Vector Equality.mp4

09:16

27. Vector Correlation.mp4

04:12

30. Creating Matrices With the matrix() Function.mp4

07:43

31. Creating Matrices With the rbind() and cbind() Functions.mp4

03:26

32. Naming Matrix Rows and Columns.mp4

02:32

33. Indexing Matrices.mp4

10:14

34. Filtering Matrices.mp4

04:37

35. Editing Values in Matrices.mp4

03:17

36. Adding and Deleting Rows and Columns.mp4

07:46

37. Minima and Maxima in Matrices.mp4

04:34

38. Applying Functions to Matrices (1).mp4

03:27

39. Applying Functions to Matrices (2).mp4

10:25

40. Applying Functions to Matrices (3).mp4

04:08

41. Adding and Multiplying Matrices.mp4

03:08

42. Other Matrix Operations.mp4

04:52

43. Creating Multidimensional Arrays.mp4

06:31

44. Indexing Multidimensional Arrays.mp4

05:12

46. Create Lists With the list() Function.mp4

07:30

47. Create Lists With the vector() Function.mp4

02:00

48. Indexing Lists With Brackets.mp4

06:32

49. Indexing Lists Using Objects Names.mp4

03:49

50. Editing Values in Lists.mp4

02:48

51. Adding and Removing List Objects.mp4

03:32

52. Applying Functions to Lists.mp4

09:30

53. Practical Example of List the Regression Analysis Output.mp4

07:08

56. Working With Factors.mp4

12:50

57. Splitting a Vector By a Factor Levels.mp4

03:41

58. The tapply() Function.mp4

03:03

59. The by() Function.mp4

03:03

61. Creating Data Frames.mp4

06:01

62. Loading Data Frames From External Files.mp4

06:56

63. Writing Data Frames in External Files.mp4

03:40

64. Indexing Data Frames As Lists.mp4

04:34

65. Indexing Data Frames As Matrices.mp4

06:50

66. Selecting a Random Sample of Entries.mp4

04:04

67. Filtering Data Frames.mp4

05:56

68. Editing Values in Data Frames.mp4

03:20

69. Adding Rows and Columns to Data Frames.mp4

06:14

70. Naming Rows and Columns in Data Frames.mp4

02:47

71. Applying Functions to Data Frames.mp4

05:28

72. Sorting Data Frames.mp4

06:49

73. Shuffling Data Frames.mp4

02:02

74. Merging Data Frames.mp4

06:06

76. For Loops.mp4

11:29

77. While Loops.mp4

05:59

78. Repeat Loops.mp4

03:00

79. Nested For Loops.mp4

06:52

80. Conditional Statements.mp4

07:51

81. Nested Conditional Statements.mp4

02:24

82. Loops and Conditional Statements.mp4

04:09

83. User Defined Functions.mp4

07:47

84. The Return Command.mp4

04:40

85. More Complex Functions Examples.mp4

05:04

86. Checking Whether an Integer Is a Perfect Square.mp4

03:29

87. A Custom Function That Solves Quadratic Equations.mp4

04:12

88. Binary Operations.mp4

05:10

90. Creating Strings.mp4

07:14

91. Printing Strings.mp4

11:31

92. Concatenating Strings.mp4

08:22

93. String Manipulation (1).mp4

03:51

94. String Manipulation (2).mp4

06:42

95. String Manipulation (3).mp4

02:13

96. Functions for Finding Patterns in Strings.mp4

11:35

97. Functions for Replacing Patterns in Strings.mp4

02:21

98. Regular Expressions.mp4

16:31

100. Building Scatterplot Charts.mp4

03:21

101. Setting Graphical Parameters (1).mp4

07:50

102. Setting Graphical Parameters (2).mp4

06:44

103. Adding a Trend Line to a Scatterplot.mp4

01:32

104. Building a Clustered Scatterplot.mp4

06:30

105. Plotting a Line Chart.mp4

01:54

106. Setting the Line Parameters.mp4

04:12

107. Overplotting Lines and Dots.mp4

02:40

108. Plotting Two Lines in the Same Chart.mp4

03:11

109. Plotting Bar Charts.mp4

02:35

110. Setting the Bar Parameters.mp4

02:04

111. Plotting Histograms.mp4

03:07

112. Plotting Density Lines.mp4

02:38

113. Plotting Pie Charts.mp4

04:58

114. Plotting Boxplot Charts.mp4

03:53

115. Plotting Functions.mp4

02:14

116. Exporting Charts.mp4

04:14

Description

Learn the basics of writing code in R - your first step to become a data scientist

What You'll Learn?

Work with vectors, matrices and lists
Work with factors
Manage data frames
Write complex programming structures (loops and conditional statements)
Build their own functions and binary operations
Work with strings
Create charts in base R

Who is this for?

Wannabe data scientists

Academic researchers

Doctoral researchers

Students

Anyone who wants to master R

What You Need to Know?

No special prerequisite - you should only know how to use a computer

More details

Description
If you have decided to learn R as your data science programming language, you have made an excellent decision! Â
R is the most widely used tool for statistical programming. It is powerful, versatile and easy to use. It is the first choice for thousands of data analysts working in both companies and academia. This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist. Â
The course is meant for absolute beginners, so you donâ€™t have to know anything about R before starting. (You donâ€™t even have to have the R program on your computer; I will show you how to install it.) But after graduating this course you will have the most important R programming skills â€“ and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.Â Â

This course contains about 100 video lectures in nine sections. Â
In the first section of this course you will get started with R: you will install the program (in case you didnâ€™t do it already), you will familiarize with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory. Moreover, you will learn how to perform simple operations in R and how to work with variables. Â

The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So youâ€™ll learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), how to apply functions to data and much more. These are very important topics, because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling. Â

After finishing with the data structures weâ€™ll get to the programming structures in R. In this section youâ€™ll learn about loops, conditional statements and functions. Youâ€™ll learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later. We will also study some practical examples of functions. Â
The next section is about working with strings. Here we will cover the most useful functions that allow us to manipulate strings. So you will learn how to format strings for printing, how to concatenate strings, how to extract substrings from a given string and especially how to create regular expressions that identify patterns in strings. Â

In the following section youâ€™ll learn how to build charts in R. We are going to cover seven types of charts: dot chart (scatterplot), line chart, bar chart, pie chart, histogram, density line and boxplot. Moreover, you will learn how to plot a function of one variable and how to export the charts you create.Â Â
Every command and function is visually explained: you can see the output live. At the end of each section you will find a PDF file with practical exercises that allow you to apply and strengthen your knowledge. Â
Â So if you want to learn R from scratch, you need this course. Enroll right now and begin a fantastic R programming journey!

Who this course is for:
Wannabe data scientists
Academic researchers
Doctoral researchers
Students
Anyone who wants to master R

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R Programming

Bogdan Anastasiei

Instructor's Courses

My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting.

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