R Programming

R Programming For Absolute Beginners

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

Overall Rating: 
4.3/5 stars

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HD

cost

R Programming For Absolute Beginners

What you'll learn

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

Requirements

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

ConClusion

If you have decided to learn R as your data science programming language, you have made an excellent decision!  

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.  

The course is meant for absolute beginners, so you don’t have to know anything about R before starting. (You don’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 – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.   

This course contains about 100 video lectures in nine sections.  

11 sections • 119 lectures • 9h 32m total length

  • Preview04:22

  • Preview05:44
  • The RStudio Interface

    12:58
  • Installing and Activating R Packages

    04:44
  • Setting the Working Directory

    02:14
  • Basic Operations in R

    03:17
  • Working With Variables

    10:23

  • Creating Vectors With the c() Function

    05:57
  • Creating Vectors Using the Colon Operator

    04:03
  • Creating Vectors With the rep() Function

    04:30
  • Creating Vectors With the seq() Function

    07:42
  • Creating Vectors of Random Numbers

    08:05
  • Creating Empty Vectors

    03:13
  • Indexing Vectors With Numeric Indices

    09:44
  • Indexing Vectors With Logical Indices

    01:33
  • Naming Vector Components

    01:51
  • Filtering Vectors

    08:11
  • The Functions all() and any()

    06:24
  • Sum and Product of Vector Components

    02:56
  • Vectorized Operations

    07:13
  • Treating Missing Values in Vectors

    03:24
  • Sorting Vectors

    03:35
  • Minimum and Maximum Values

    02:10
  • The ifelse() Function

    07:00
  • Adding and Multiplying Vectors

    03:09
  • Testing Vector Equality

    09:16
  • Vector Correlation

    04:11
  • Bonus Lecture: Learn Statistics with R

    00:05
  • Practical Exercises

    00:05

  • Creating Matrices With the matrix() Function

    07:42
  • Creating Matrices With the rbind() and cbind() Functions

    03:26
  • Naming Matrix Rows and Columns

    02:32
  • Indexing Matrices

    10:14
  • Filtering Matrices

    04:37
  • Editing Values in Matrices

    03:17
  • Adding and Deleting Rows and Columns

    07:46
  • Minima and Maxima in Matrices

    04:34
  • Applying Functions to Matrices (1)

    03:27
  • Applying Functions to Matrices (2)

    10:25
  • Applying Functions to Matrices (3)

    04:08
  • Adding and Multiplying Matrices

    03:08
  • Other Matrix Operations

    04:52
  • Creating Multidimensional Arrays

    06:31
  • Indexing Multidimensional Arrays

    05:12
  • Practical Exercises

    00:06

  • Create Lists With the list() Function

    07:30
  • Create Lists With the vector() Function

    02:00
  • Indexing Lists With Brackets

    06:32
  • Indexing Lists Using Objects Names

    03:49
  • Editing Values in Lists

    02:48
  • Adding and Removing List Objects

    03:32
  • Applying Functions to Lists

    09:30
  • Practical Example of List: the Regression Analysis Output

    07:08
  • Bonus Lecture: Data Analysis in R

    00:05
  • Practical Exercises

    00:05

  • Working With Factors

    12:50
  • Splitting a Vector By a Factor Levels

    03:41
  • The tapply() Function

    03:03
  • The by() Function

    03:03
  • Practical Exercises

    00:05

  • Creating Data Frames

    06:00
  • Loading Data Frames From External Files

    06:56
  • Writing Data Frames in External Files

    03:40
  • Indexing Data Frames As Lists

    04:34
  • Indexing Data Frames As Matrices

    06:50
  • Selecting a Random Sample of Entries

    04:04
  • Filtering Data Frames

    05:56
  • Editing Values in Data Frames

    03:20
  • Adding Rows and Columns to Data Frames

    06:14
  • Naming Rows and Columns in Data Frames

    02:47
  • Applying Functions to Data Frames

    05:28
  • Sorting Data Frames

    06:49
  • Shuffling Data Frames

    02:02
  • Merging Data Frames

    06:06
  • Practical Exercises

    00:05

  • For Loops

    11:29
  • While Loops

    05:59
  • Repeat Loops

    03:00
  • Nested For Loops

    06:52
  • Conditional Statements

    07:51
  • Nested Conditional Statements

    02:24
  • Loops and Conditional Statements

    04:09
  • User Defined Functions

    07:47
  • The Return Command

    04:40
  • More Complex Functions Examples

    05:04
  • Checking Whether an Integer Is a Perfect Square

    03:29
  • A Custom Function That Solves Quadratic Equations

    04:11
  • Binary Operations

    05:10
  • Practical Exercises

    00:06

  • Creating Strings

    07:14
  • Printing Strings

    11:31
  • Concatenating Strings

    08:21
  • String Manipulation (1)

    03:51
  • String Manipulation (2)

    06:42
  • String Manipulation (3)

    02:13
  • Functions for Finding Patterns in Strings

    11:35
  • Functions for Replacing Patterns in Strings

    02:21
  • Regular Expressions

    16:30
  • Practical Exercises

    00:05

  • Building Scatterplot Charts

    03:21
  • Setting Graphical Parameters (1)

    07:50
  • Setting Graphical Parameters (2)

    06:44
  • Adding a Trend Line to a Scatterplot

    01:32
  • Building a Clustered Scatterplot

    06:30
  • Plotting a Line Chart

    01:54
  • Setting the Line Parameters

    04:12
  • Overplotting Lines and Dots

    02:40
  • Plotting Two Lines in the Same Chart

    03:11
  • Plotting Bar Charts

    02:35
  • Setting the Bar Parameters

    02:04
  • Plotting Histograms

    03:07
  • Plotting Density Lines

    02:38
  • Plotting Pie Charts

    04:58
  • Plotting Boxplot Charts

    03:53
  • Plotting Functions

    02:14
  • Exporting Charts

    04:13
  • Bonus Lecture: More Advanced Plotting

    00:08
  • Practical Exercises

    00:05

  • R Files and Data Frames

    00:01

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