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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

Created byBogdan Anastasiei

Last updated 6/2017

English

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

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

Introduction4:22
What we are going to cover in this course.

Installing R and RStudio5:44
How to download and install R and RStudio.
The RStudio Interface12:58
The RStudio work interface, explained in detail.
Installing and Activating R Packages4:44
How to work with packages in R - you will need that.
Setting the Working Directory2:14
How to setup the working directory in R, so you can access the files in that directory.
Basic Operations in R3:17
How to perform the basic mathematical operations in R.
Working With Variables10:23
The basic stuff about variables in R.

Creating Vectors With the c() Function5:57
How to use the c() function - one of the most common ways to create vectors.
Creating Vectors Using the Colon Operator4:03
Build sequences of integers with the colon operator.
Creating Vectors With the rep() Function4:30
Create vectors of replicated values with the rep() function.
Creating Vectors With the seq() Function7:42
Create sequences of real numbers with the seq() function.
Creating Vectors of Random Numbers8:05
Build vectors of discrete and continuous random numbers.
Creating Empty Vectors3:13
Create vectors with no elements.
Indexing Vectors With Numeric Indices9:44
How to access vector components using numeric indices.
Indexing Vectors With Logical Indices1:33
How to access vector components using logical indices.
Naming Vector Components1:51
How to name vector components - and remove names when you don't need them.
Filtering Vectors8:11
How to access the vector components using various criteria.
The Functions all() and any()6:24
Use these two function to check whether the vector components meet your conditions.
Sum and Product of Vector Components2:56
Besides sum and product, you will learn how to compute basic statistical indicators for a numeric vector.
Vectorized Operations7:13
One of the most important topics in R: how to apply mathematical operations to all the components in a vector.
Treating Missing Values in Vectors3:24
How to deal with unknown values in a vector.
Sorting Vectors3:35
How to order vector components.
Minimum and Maximum Values2:10
How to get the minimum and maximum values in vectors and pairs of vectors.
The ifelse() Function7:00
A great way to use the if-then-else statement on a vector.
Adding and Multiplying Vectors3:09
Useful operations with vectors - and something about recycling vectors.
Testing Vector Equality9:16
How to check whether two vectors have equal components or not.
Vector Correlation4:11
Compute the Pearson correlation for two numeric vectors.
Bonus Lecture: Learn Statistics with R0:05
How to perform statistical analyses in R like an expert.
Practical Exercises0:05
Practical exercises for the section "Vectors".

Creating Matrices With the matrix() Function7:42
The most used way to create matrices - the matrix() function.
Creating Matrices With the rbind() and cbind() Functions3:26
Other two useful functions for creating matrices.
Naming Matrix Rows and Columns2:32
How to name rows and columns in a matrix.
Indexing Matrices10:14
How to access matrix elements.
Filtering Matrices4:37
How to find the elements that meet one or several conditions.
Editing Values in Matrices3:17
How to change any data value in a matrix.
Adding and Deleting Rows and Columns7:46
How to add new rows or columns, and how to remove rows and columns.
Minima and Maxima in Matrices4:34
Find the minimum and maximum values in a matrix.
Applying Functions to Matrices (1)3:27
Using the apply() function to perform mathematical operations on the matrix rows and columns.
Applying Functions to Matrices (2)10:25
Some more important stuff about the apply() function.
Applying Functions to Matrices (3)4:08
Apply the swipe() function to matrices.
Adding and Multiplying Matrices3:08
How to add and multiply two matrices (when these operations are possible).
Other Matrix Operations4:52
How to compute the determinant and the inverse of a quadratic matrix - and a couple more operations.
Creating Multidimensional Arrays6:31
How to build an array with two (or more) matrices.
Indexing Multidimensional Arrays5:12
How to access any element (or group of elements) in an array.
Practical Exercises0:06
Practical exercises for the section "Matrices and Arrays".

Create Lists With the list() Function7:30
What is a list and how to use the list() function to create one.
Create Lists With the vector() Function2:00
Other way to create a list - the vector() function.
Indexing Lists With Brackets6:32
How to access list elements.
Indexing Lists Using Objects Names3:49
Other possible way to access list elements.
Editing Values in Lists2:48
How to modify values (or entire objects) in a list.
Adding and Removing List Objects3:32
How to add objects to a list, or remove existing objects.
Applying Functions to Lists9:30
When and how you can use the lapply() function on a list.
Practical Example of List: the Regression Analysis Output7:08
Use what you know about lists to "read" the results of a linear regression analysis.
Bonus Lecture: Data Analysis in R0:05
Learn to perform simple and advanced data analyses in R.
Practical Exercises0:05
Practical exercises for the section "Lists".

Working With Factors12:50
How to create unordered and ordered factors.
Splitting a Vector By a Factor Levels3:41
How to split a vector in several objects using the levels of a factor.
The tapply() Function3:03
How to compute summary values for a vector components by a factor level with the tapply() function.
The by() Function3:03
How to compute summary values for a vector components by a factor level, this time using the by() function.
Practical Exercises0:05
Practical exercises for the section "Factors".

Creating Data Frames6:00
How to create data frames using the data.frame() function.
Loading Data Frames From External Files6:56
How to read data frames from the files on your hard disk (CSV or text format).
Writing Data Frames in External Files3:40
How to save a data frame on your hard disk as a CSV file.
.
Indexing Data Frames As Lists4:34
The first way to index a data frame.
Indexing Data Frames As Matrices6:50
The second way to index a data frame.
Selecting a Random Sample of Entries4:04
How to draw a random sample of observation form any data frame.
Filtering Data Frames5:56
Find the rows in a data frame that meet certain criteria.
Editing Values in Data Frames3:20
Modify values in data frames.
Adding Rows and Columns to Data Frames6:14
Adding new observations and variables to an existing data frame.
Naming Rows and Columns in Data Frames2:47
Naming (and renaming) observations and variables in a data frame.
Applying Functions to Data Frames5:28
Using the functions apply(), lapply() and sapply() with data frames.
Sorting Data Frames6:49
Arrange the data frame entries in any order you want.
Shuffling Data Frames2:02
Arrange the data frame entries in a random order.
Merging Data Frames6:06
Join two data frames based on a common variable.
Practical Exercises0:05
Practical exercises for the section "Data Frames".

For Loops11:29
Use the for loops to go through a sequence and perform various operations.
While Loops5:59
Learn how to work with a while loop.
Repeat Loops3:00
Learn how to use a repeat loop.
Nested For Loops6:52
Get more serious - build a few nested for loops.
Conditional Statements7:51
Using if-else statements in R.
Nested Conditional Statements2:24
More complex if-else statements.
Loops and Conditional Statements4:09
Combining for loops and conditional statements to perform really useful tasks.
User Defined Functions7:47
Create custom functions that you can reuse later.
The Return Command4:40
Why is the return command useful often times.
More Complex Functions Examples5:04
Using nested loops and conditional statements in a function.
Checking Whether an Integer Is a Perfect Square3:29
A function that checks whether a positive whole number is a perfect square or not.
A Custom Function That Solves Quadratic Equations4:11
A function that solves any quadratic equation.
Binary Operations5:10
How to create custom binary operations using functions.
Practical Exercises0:06
Practical exercises for the section "Programming Structures".

Creating Strings7:14
Various ways to create string variables.
Printing Strings11:31
Useful functions to print and format string variables.
Concatenating Strings8:21
A few functions used to concatenate string variables (and vectors).
String Manipulation (1)3:51
How to change characters in a string.
String Manipulation (2)6:42
How to extract a substring from a string (and replace it, if necessary).
String Manipulation (3)2:13
How to split strings based on a substring.
Functions for Finding Patterns in Strings11:35
How to find any sequence of characters in a given string.
Functions for Replacing Patterns in Strings2:21
How to replace any sequence of characters in a given string.
Regular Expressions16:30
Use regular expression to define patterns.
Practical Exercises0:05
Practical exercises for the section "Working With Strings".

Building Scatterplot Charts3:21
How to create a simple dot chart.
Setting Graphical Parameters (1)7:50
How to set some parameters to make your dot chart more good looking.
Setting Graphical Parameters (2)6:44
Set a few more parameters in your dot chart.
Adding a Trend Line to a Scatterplot1:32
Find the trend in your dot chart and build a trend line.
Building a Clustered Scatterplot6:30
Create a grouped dot chart.
Plotting a Line Chart1:54
Create a line chart with some made-up data.
Setting the Line Parameters4:12
Make your line chart a bit more interesting.
Overplotting Lines and Dots2:40
Make chart with both lines and dots.
Plotting Two Lines in the Same Chart3:11
Represent two lines in the same graph.
Plotting Bar Charts2:35
How to create bar charts in R.
Setting the Bar Parameters2:04
A few parameters you can manipulate in your bar chart.
Plotting Histograms3:07
Creating and editing histogram charts.
Plotting Density Lines2:38
Creating and editing density line charts.
Plotting Pie Charts4:58
Creating and editing pie charts.
Plotting Boxplot Charts3:53
How to draw boxplot charts.
Plotting Functions2:14
How to plot any function of one variable.
Exporting Charts4:13
How to save charts on your hard disk.
Bonus Lecture: More Advanced Plotting0:08
How to draw complex charts in R.
Practical Exercises0:05

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!

Who this course is for:

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

R Programming For Absolute Beginners

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 4min

Getting Started with R6 lectures • 39min

Vectors22 lectures • 1hr 44min

Matrices and Arrays16 lectures • 1hr 22min

Lists10 lectures • 43min

Factors5 lectures • 23min

Data Frames15 lectures • 1hr 11min

Programming Structures14 lectures • 1hr 12min

Working With Strings10 lectures • 1hr 10min

Plotting in Base R19 lectures • 1hr 4min

Requirements

Description

Who this course is for: