R Programming For Absolute Beginners
4.3 (1,922 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
59,160 students enrolled

R Programming For Absolute Beginners

Learn the basics of writing code in R - your first step to become a data scientist
4.3 (1,922 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
59,160 students enrolled
Created by Bogdan Anastasiei
Last updated 6/2017
English [Auto]
Current price: $27.99 Original price: $39.99 Discount: 30% off
23 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 9.5 hours on-demand video
  • 12 articles
  • 11 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 119 lectures 09:32:57
+ Introduction
1 lecture 04:22

What we are going to cover in this course.

Preview 04:22
+ Getting Started with R
6 lectures 39:20

How to download and install R and RStudio.

Preview 05:44

The RStudio work interface, explained in detail.

The RStudio Interface

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

Installing and Activating R Packages

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

Setting the Working Directory

How to perform the basic mathematical operations in R.

Basic Operations in R

The basic stuff about variables in R.

Working With Variables
+ Vectors
22 lectures 01:44:18

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

Creating Vectors With the c() Function

Build sequences of integers with the colon operator.

Creating Vectors Using the Colon Operator

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

Creating Vectors With the rep() Function

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

Creating Vectors With the seq() Function

Build vectors of discrete and continuous random numbers.

Creating Vectors of Random Numbers

Create vectors with no elements.

Creating Empty Vectors

How to access vector components using numeric indices.

Indexing Vectors With Numeric Indices

How to access vector components using logical indices.

Indexing Vectors With Logical Indices

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

Naming Vector Components

How to access the vector components using various criteria.

Filtering Vectors

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

The Functions all() and any()

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

Sum and Product of Vector Components

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

Vectorized Operations

How to deal with unknown values in a vector.

Treating Missing Values in Vectors

How to order vector components.

Sorting Vectors

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

Minimum and Maximum Values

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

The ifelse() Function

Useful operations with vectors  - and something about recycling vectors.

Adding and Multiplying Vectors

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

Testing Vector Equality

Compute the Pearson correlation for two numeric vectors.

Vector Correlation

How to perform statistical analyses in R like an expert.

Bonus Lecture: Learn Statistics with R

Practical exercises for the section "Vectors".

Practical Exercises
+ Matrices and Arrays
16 lectures 01:21:57

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

Creating Matrices With the matrix() Function

Other two useful functions for creating matrices.

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

How to name rows and columns in a matrix.

Naming Matrix Rows and Columns

How to access matrix elements.

Indexing Matrices

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

Filtering Matrices

How to change any data value in a matrix.

Editing Values in Matrices

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

Adding and Deleting Rows and Columns

Find the minimum and maximum values in a matrix.

Minima and Maxima in Matrices

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

Applying Functions to Matrices (1)

Some more important stuff about the apply() function.

Applying Functions to Matrices (2)

Apply the swipe() function to matrices.

Applying Functions to Matrices (3)

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

Adding and Multiplying Matrices

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

Other Matrix Operations

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

Creating Multidimensional Arrays

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

Indexing Multidimensional Arrays

Practical exercises for the section "Matrices and Arrays".

Practical Exercises
+ Lists
10 lectures 43:00

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

Create Lists With the list() Function

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

Create Lists With the vector() Function

How to access list elements.

Indexing Lists With Brackets

Other possible way to access list elements.

Indexing Lists Using Objects Names

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

Editing Values in Lists

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

Adding and Removing List Objects

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

Applying Functions to Lists

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

Practical Example of List: the Regression Analysis Output

Learn to perform simple and advanced data analyses in R.

Bonus Lecture: Data Analysis in R

Practical exercises for the section "Lists".

Practical Exercises
+ Factors
5 lectures 22:42

How to create unordered and ordered factors.

Working With Factors

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

Splitting a Vector By a Factor Levels

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

The tapply() Function

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

The by() Function

Practical exercises for the section "Factors".

Practical Exercises
+ Data Frames
15 lectures 01:10:51

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

Creating Data Frames

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

Loading Data Frames From External Files

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

Writing Data Frames in External Files

The first way to index a data frame.

Indexing Data Frames As Lists

The second way to index a data frame.

Indexing Data Frames As Matrices

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

Selecting a Random Sample of Entries

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

Filtering Data Frames

Modify values in data frames.

Editing Values in Data Frames

Adding new observations and variables to an existing data frame.

Adding Rows and Columns to Data Frames

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

Naming Rows and Columns in Data Frames

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

Applying Functions to Data Frames

Arrange the data frame entries in any order you want.

Sorting Data Frames

Arrange the data frame entries in a random order.

Shuffling Data Frames

Join two data frames based on a common variable.

Merging Data Frames

Practical exercises for the section "Data Frames".

Practical Exercises
+ Programming Structures
14 lectures 01:12:11

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

For Loops

Learn how to work with a while loop.

While Loops

Learn how to use a repeat loop.

Repeat Loops

Get more serious - build a few nested for loops.

Nested For Loops

  Using if-else statements in R.

Conditional Statements

More complex if-else statements.

Nested Conditional Statements

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

Loops and Conditional Statements

Create custom functions that you can reuse later.

User Defined Functions

Why is the return command useful often times.

The Return Command

Using nested loops and conditional statements in a function.

More Complex Functions Examples

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

Checking Whether an Integer Is a Perfect Square

A function that solves any quadratic equation.

A Custom Function That Solves Quadratic Equations

How to create custom binary operations using functions.

Binary Operations

Practical exercises for the section "Programming Structures".

Practical Exercises
+ Working With Strings
10 lectures 01:10:23

Various ways to create string variables.

Creating Strings

Useful functions to print and format string variables.

Printing Strings

A few functions used to concatenate string variables (and vectors).

Concatenating Strings

How to change characters in a string.

String Manipulation (1)

How to extract a substring from a string (and replace it, if necessary).

String Manipulation (2)

How to split strings based on a substring.

String Manipulation (3)

How to find any sequence of characters in a given string.

Functions for Finding Patterns in Strings

How to replace any sequence of characters in a given string.

Functions for Replacing Patterns in Strings

Use regular expression to define patterns.

Regular Expressions

Practical exercises for the section "Working With Strings".

Practical Exercises
+ Plotting in Base R
19 lectures 01:03:49

How to create a simple dot chart.

Building Scatterplot Charts

How to set some parameters to make your dot chart more good looking.

Setting Graphical Parameters (1)

Set a few more parameters in your dot chart.

Setting Graphical Parameters (2)

Find the trend in your dot chart and build a trend line.

Adding a Trend Line to a Scatterplot

Create a grouped dot chart.

Building a Clustered Scatterplot

Create a line chart with some made-up data.

Plotting a Line Chart

Make your line chart a bit more interesting.

Setting the Line Parameters

Make chart with both lines and dots.

Overplotting Lines and Dots

Represent two lines in the same graph.

Plotting Two Lines in the Same Chart

How to create bar charts in R.

Plotting Bar Charts

A few parameters you can manipulate in your bar chart.

Setting the Bar Parameters

Creating and editing histogram charts.

Plotting Histograms

Creating and editing density line charts.

Plotting Density Lines

Creating and editing pie charts.

Plotting Pie Charts

How to draw boxplot charts.

Plotting Boxplot Charts

How to plot any function of one variable.

Plotting Functions

How to save charts on your hard disk.

Exporting Charts

How to draw complex charts in R.

Bonus Lecture: More Advanced Plotting
Practical Exercises
  • No special prerequisite - you should only know how to use a computer

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!

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