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R Programming For Absolute Beginners

Learn the basics of writing code in R - your first step to become a data scientist
Instructor:
Bogdan Anastasiei
98,938 students enrolled
English [Auto]
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

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!

Introduction

1
Introduction

What we are going to cover in this course.

Getting Started with R

1
Installing R and RStudio

How to download and install R and RStudio.

2
The RStudio Interface

The RStudio work interface, explained in detail.

3
Installing and Activating R Packages

How to work with packages in R - you will need that.

4
Setting the Working Directory

How to setup the working directory in R, so you can access the files in that directory.

5
Basic Operations in R

How to perform the basic mathematical operations in R.

6
Working With Variables

The basic stuff about variables in R.

Vectors

1
Creating Vectors With the c() Function

How to use the c() function - one of the most common ways to create vectors.

2
Creating Vectors Using the Colon Operator

Build sequences of integers with the colon operator.

3
Creating Vectors With the rep() Function

Create vectors of replicated values with the rep() function.

4
Creating Vectors With the seq() Function

Create sequences of real numbers with the seq() function.

5
Creating Vectors of Random Numbers

Build vectors of discrete and continuous random numbers.

6
Creating Empty Vectors

Create vectors with no elements.

7
Indexing Vectors With Numeric Indices

How to access vector components using numeric indices.

8
Indexing Vectors With Logical Indices

How to access vector components using logical indices.

9
Naming Vector Components

How to name vector components - and remove names when you don't need them.

10
Filtering Vectors

How to access the vector components using various criteria.

11
The Functions all() and any()

Use these two function to check whether the vector components meet your conditions.

12
Sum and Product of Vector Components

Besides sum and product, you will learn how to compute basic statistical indicators for a numeric vector.

13
Vectorized Operations

One of the most important topics in R: how to apply mathematical operations to all the components in a vector.

14
Treating Missing Values in Vectors

How to deal with unknown values in a vector.

15
Sorting Vectors

How to order vector components.

16
Minimum and Maximum Values

How to get the minimum and maximum values in vectors and pairs of vectors.

17
The ifelse() Function

A great way to use the if-then-else statement on a vector.

18
Adding and Multiplying Vectors

Useful operations with vectors  - and something about recycling vectors.

19
Testing Vector Equality

How to check whether two vectors have equal components or not.

20
Vector Correlation

Compute the Pearson correlation for two numeric vectors.

21
Bonus Lecture: Learn Statistics with R

How to perform statistical analyses in R like an expert.

22
Practical Exercises

Practical exercises for the section "Vectors".

Matrices and Arrays

1
Creating Matrices With the matrix() Function

The most used way to create matrices - the matrix() function.

2
Creating Matrices With the rbind() and cbind() Functions

Other two useful functions for creating matrices.

3
Naming Matrix Rows and Columns

How to name rows and columns in a matrix.

4
Indexing Matrices

How to access matrix elements.

5
Filtering Matrices

How to find the elements that meet one or several conditions.

6
Editing Values in Matrices

How to change any data value in a matrix.

7
Adding and Deleting Rows and Columns

How to add new rows or columns, and how to remove rows and columns.

8
Minima and Maxima in Matrices

Find the minimum and maximum values in a matrix.

9
Applying Functions to Matrices (1)

Using the apply() function to perform mathematical operations on the matrix rows and columns.

10
Applying Functions to Matrices (2)

Some more important stuff about the apply() function.

11
Applying Functions to Matrices (3)

Apply the swipe() function to matrices.

12
Adding and Multiplying Matrices

How to add and multiply two matrices (when these operations are possible).

13
Other Matrix Operations

How to compute the determinant and the inverse of a quadratic matrix - and a couple more operations.

14
Creating Multidimensional Arrays

How to build an array with two (or more) matrices.

15
Indexing Multidimensional Arrays

How to access any element (or group of elements) in an array.

16
Practical Exercises

Practical exercises for the section "Matrices and Arrays".

Lists

1
Create Lists With the list() Function

What is a list and how to use the list() function to create one.

2
Create Lists With the vector() Function

Other way to create a list - the vector() function.

3
Indexing Lists With Brackets

How to access list elements.

4
Indexing Lists Using Objects Names

Other possible way to access list elements.

5
Editing Values in Lists

How to modify values (or entire objects) in a list.

6
Adding and Removing List Objects

How to add objects to a list, or remove existing objects.

7
Applying Functions to Lists

When and how you can use the lapply() function on a list.

8
Practical Example of List: the Regression Analysis Output

Use what you know about lists to "read" the results of a linear regression analysis.

9
Bonus Lecture: Data Analysis in R

Learn to perform simple and advanced data analyses in R.

10
Practical Exercises

Practical exercises for the section "Lists".

Factors

1
Working With Factors

How to create unordered and ordered factors.

2
Splitting a Vector By a Factor Levels

How to split a vector in several objects using the levels of a factor.

3
The tapply() Function

How to compute summary values for a vector components by a factor level with the tapply() function.

4
The by() Function

How to compute summary values for a vector components by a factor level, this time using the by() function.

5
Practical Exercises

Practical exercises for the section "Factors".



Data Frames

1
Creating Data Frames

How to create data frames using the data.frame() function.

2
Loading Data Frames From External Files

How to read data frames from the files on your hard disk (CSV or text format).

3
Writing Data Frames in External Files

How to save a data frame on your hard disk as a CSV file.

.
4
Indexing Data Frames As Lists

The first way to index a data frame.

5
Indexing Data Frames As Matrices

The second way to index a data frame.

6
Selecting a Random Sample of Entries

How to draw a random sample of observation form any data frame.

7
Filtering Data Frames

Find the rows in a data frame that meet certain criteria.

8
Editing Values in Data Frames

Modify values in data frames.

9
Adding Rows and Columns to Data Frames

Adding new observations and variables to an existing data frame.

10
Naming Rows and Columns in Data Frames

Naming (and renaming) observations and variables in a data frame.

11
Applying Functions to Data Frames

Using the functions apply(), lapply() and sapply() with data frames.

12
Sorting Data Frames

Arrange the data frame entries in any order you want.

13
Shuffling Data Frames

Arrange the data frame entries in a random order.

14
Merging Data Frames

Join two data frames based on a common variable.

15
Practical Exercises

Practical exercises for the section "Data Frames".


Programming Structures

1
For Loops

Use the for loops to go through a sequence and perform various operations.

2
While Loops

Learn how to work with a while loop.

3
Repeat Loops

Learn how to use a repeat loop.

4
Nested For Loops

Get more serious - build a few nested for loops.

5
Conditional Statements

  Using if-else statements in R.

6
Nested Conditional Statements

More complex if-else statements.

7
Loops and Conditional Statements

Combining for loops and conditional statements to perform really useful tasks.

8
User Defined Functions

Create custom functions that you can reuse later.

9
The Return Command

Why is the return command useful often times.

10
More Complex Functions Examples

Using nested loops and conditional statements in a function.

11
Checking Whether an Integer Is a Perfect Square

A function that checks whether a positive whole number is a perfect square or not.

12
A Custom Function That Solves Quadratic Equations

A function that solves any quadratic equation.

13
Binary Operations

How to create custom binary operations using functions.

14
Practical Exercises

Practical exercises for the section "Programming Structures".


Working With Strings

1
Creating Strings

Various ways to create string variables.

2
Printing Strings

Useful functions to print and format string variables.

You can view and review the lecture materials indefinitely, like an on-demand channel.
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