R Programming For Absolute Beginners

R Programming For Absolute Beginners

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

Requirements

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

Description

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.

In the first section of this course you will get started with R: you will install the program (in case you didn’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.

The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So you’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.

After finishing with the data structures we’ll get to the programming structures in R. In this section you’ll learn about loops, conditional statements and functions. You’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.

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.

In the following section you’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.

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.

So if you want to learn R from scratch, you need this course. Enroll right now and begin a fantastic R programming journey!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Getting Started with R

Lecture 2 Installing R and RStudio

Lecture 3 The RStudio Interface

Lecture 4 Installing and Activating R Packages

Lecture 5 Setting the Working Directory

Lecture 6 Basic Operations in R

Lecture 7 Working With Variables

Section 3: Vectors

Lecture 8 Creating Vectors With the c() Function

Lecture 9 Creating Vectors Using the Colon Operator

Lecture 10 Creating Vectors With the rep() Function

Lecture 11 Creating Vectors With the seq() Function

Lecture 12 Creating Vectors of Random Numbers

Lecture 13 Creating Empty Vectors

Lecture 14 Indexing Vectors With Numeric Indices

Lecture 15 Indexing Vectors With Logical Indices

Lecture 16 Naming Vector Components

Lecture 17 Filtering Vectors

Lecture 18 The Functions all() and any()

Lecture 19 Sum and Product of Vector Components

Lecture 20 Vectorized Operations

Lecture 21 Treating Missing Values in Vectors

Lecture 22 Sorting Vectors

Lecture 23 Minimum and Maximum Values

Lecture 24 The ifelse() Function

Lecture 25 Adding and Multiplying Vectors

Lecture 26 Testing Vector Equality

Lecture 27 Vector Correlation

Lecture 28 Bonus Lecture: Learn Statistics with R

Lecture 29 Practical Exercises

Section 4: Matrices and Arrays

Lecture 30 Creating Matrices With the matrix() Function

Lecture 31 Creating Matrices With the rbind() and cbind() Functions

Lecture 32 Naming Matrix Rows and Columns

Lecture 33 Indexing Matrices

Lecture 34 Filtering Matrices

Lecture 35 Editing Values in Matrices

Lecture 36 Adding and Deleting Rows and Columns

Lecture 37 Minima and Maxima in Matrices

Lecture 38 Applying Functions to Matrices (1)

Lecture 39 Applying Functions to Matrices (2)

Lecture 40 Applying Functions to Matrices (3)

Lecture 41 Adding and Multiplying Matrices

Lecture 42 Other Matrix Operations

Lecture 43 Creating Multidimensional Arrays

Lecture 44 Indexing Multidimensional Arrays

Lecture 45 Practical Exercises

Section 5: Lists

Lecture 46 Create Lists With the list() Function

Lecture 47 Create Lists With the vector() Function

Lecture 48 Indexing Lists With Brackets

Lecture 49 Indexing Lists Using Objects Names

Lecture 50 Editing Values in Lists

Lecture 51 Adding and Removing List Objects

Lecture 52 Applying Functions to Lists

Lecture 53 Practical Example of List: the Regression Analysis Output

Lecture 54 Bonus Lecture: Data Analysis in R

Lecture 55 Practical Exercises

Section 6: Factors

Lecture 56 Working With Factors

Lecture 57 Splitting a Vector By a Factor Levels

Lecture 58 The tapply() Function

Lecture 59 The by() Function

Lecture 60 Practical Exercises

Section 7: Data Frames

Lecture 61 Creating Data Frames

Lecture 62 Loading Data Frames From External Files

Lecture 63 Writing Data Frames in External Files

Lecture 64 Indexing Data Frames As Lists

Lecture 65 Indexing Data Frames As Matrices

Lecture 66 Selecting a Random Sample of Entries

Lecture 67 Filtering Data Frames

Lecture 68 Editing Values in Data Frames

Lecture 69 Adding Rows and Columns to Data Frames

Lecture 70 Naming Rows and Columns in Data Frames

Lecture 71 Applying Functions to Data Frames

Lecture 72 Sorting Data Frames

Lecture 73 Shuffling Data Frames

Lecture 74 Merging Data Frames

Lecture 75 Practical Exercises

Section 8: Programming Structures

Lecture 76 For Loops

Lecture 77 While Loops

Lecture 78 Repeat Loops

Lecture 79 Nested For Loops

Lecture 80 Conditional Statements

Lecture 81 Nested Conditional Statements

Lecture 82 Loops and Conditional Statements

Lecture 83 User Defined Functions

Lecture 84 The Return Command

Lecture 85 More Complex Functions Examples

Lecture 86 Checking Whether an Integer Is a Perfect Square

Lecture 87 A Custom Function That Solves Quadratic Equations

Lecture 88 Binary Operations

Lecture 89 Practical Exercises

Section 9: Working With Strings

Lecture 90 Creating Strings

Lecture 91 Printing Strings

Lecture 92 Concatenating Strings

Lecture 93 String Manipulation (1)

Lecture 94 String Manipulation (2)

Lecture 95 String Manipulation (3)

Lecture 96 Functions for Finding Patterns in Strings

Lecture 97 Functions for Replacing Patterns in Strings

Lecture 98 Regular Expressions

Lecture 99 Practical Exercises

Section 10: Plotting in Base R

Lecture 100 Building Scatterplot Charts

Lecture 101 Setting Graphical Parameters (1)

Lecture 102 Setting Graphical Parameters (2)

Lecture 103 Adding a Trend Line to a Scatterplot

Lecture 104 Building a Clustered Scatterplot

Lecture 105 Plotting a Line Chart

Lecture 106 Setting the Line Parameters

Lecture 107 Overplotting Lines and Dots

Lecture 108 Plotting Two Lines in the Same Chart

Lecture 109 Plotting Bar Charts

Lecture 110 Setting the Bar Parameters

Lecture 111 Plotting Histograms

Lecture 112 Plotting Density Lines

Lecture 113 Plotting Pie Charts

Lecture 114 Plotting Boxplot Charts

Lecture 115 Plotting Functions

Lecture 116 Exporting Charts

Lecture 117 Bonus Lecture: More Advanced Plotting

Lecture 118 Practical Exercises

Section 11: Download Links

Lecture 119 R Files and Data Frames

Wannabe data scientists,Academic researchers,Doctoral researchers,Students,Anyone who wants to master R

Course Information:

Udemy | English | 9h 33m | 1.16 GB
Created by: Bogdan Anastasiei

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