Hands-On R and its Libraries in 4 Hours
R is a popular choice of tool for data science professionals, offering a large variety of libraries pertaining to each and every task in data science. With practical examples based on various real-world domains and use-cases where R and its libraries can be used, this course will be your companion for implementing those tasks using the free, open source libraries provided by R.
This comprehensive 2-in-1 course equips you with all the essential skills required to perform data science tasks with ease using practical, real-world scenarios. It follows a step-by-step approach to teach you the fundamental concepts of R programming and usage of its libraries such as, setup, working with complex data, performing data analysis, data mining, and data visualization with ggplot2, Plotly, Leaflet, GoogleVis, Motion Chart, and Joy of Stats.
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Speaking ‘R’ – The Language of Data Science, begins with an introduction to R and setting up the development platform. You will then be introduced commands in R, which are very useful and not as common in traditional languages since manipulating data is more important in R. You will also look at an example of Titanic dataset, which is the kind of thing you’ll come across in R, a multidimensional collection of variables of different types. Using the tools that we cover we can form a picture, a story behind the data. Next, you will learn how to clean up the data. Finally, you will learn some basic statistics with R using special features for data visualization.
In the second course, Heavy-Lifting Using R Libraries, you will begin by looking at high-performance computing in the classic, computationally intensive scenario: finding prime numbers. You will then learn how to use R, before moving on to using C++, which is far faster. Next, you will use the power of parallel, though that varies from problem to problem since some are more suitable for parallelization. Finally, you will look at some powerful options available in R where you don’t just produce a static result but instead respond to user selections.
By the end of this course, you will be able to confidently use the R programming language for performing data mining, data analysis, and data visualization.
Meet Your Expert(s):
We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:
Dr. Samik Sen is a Theoretical Physicist and loves hard problems to think about. After his Phd, which was about developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle the maths problem while lecturing. He developed algorithms to generate problem sets and solutions, and learned how to create video lessons. He has developed a large Facebook community teaching school maths around Ireland, with associated e-learning products and YouTube channel. Samik is currently fascinated by machine and deep Learning.He has developed a machine learning system which is performing better than he can himself which was the hope.
Speaking 'R' - The Language of Data Science
This video gives an overview of the entire course.
The aim of the video is to introduce the section and overview of the language R.
Understand how R is state of the art in whatever you do
Learn how R is the language of data science
Understand the origins of R
We need to have the core programs before we can begin and in this video,we show where to get them.
Go to the www.r-project.org website
Follow installation process for your OS
Repeat for RStudio
In this video, we look at where to begin, so that we can get started.
Understand how a data scientist's work is different from coding
Introduce the four panels
Write our first program
In this video, you will learn how RStudio has packages which avoid the problems and how we'll work on them.
Look at how we can work with RStudio
Show problems with this
Show how packages avoid these and how we'll work
In this video, you will learn how similar R is to other languages.
Learn that R has numbers, strings, and arrays
Learn how R uses “=” to assign
Learn how R uses arrays
In this video, we see more familiar things in R.
Learn how R uses the “if” statement
Learn how R uses “for” loops
Understand that R has functions
In this video, we are now ready to write programs.
Write our program
Check manually for a few numbers to see that seems to be working
Test it for larger size against known answers to confirm that we're right
In this video, we will look at R data types which are new.
Look at Vectors, Matrices, data frames, and lists
Learn how to initialize and index them to select elements
Look at mtcars, one of the included data sets with R
In this video, we will introduce some key commands to study data.
Understand the use of thestructure and summary commands
Understand the use of the head and tail commands with few examples
Learn how the View command lets us see the data in a spreadsheet style
In this video, we willintroduce various commands to help us pick out elements in which we are interested in.
Understand how the sequence and repeat commands generate a list of numbers, so we can pick out those entries
Learn how the subset and which commands let us pick out elements with certain properties
In this video, we will investigate the Titanic dataset to see what it says.
See that this is not simple one dimensional information
Find out how to index through the data and get a handle on it
Produce plots which tell us a story
In this video, we willadd a value by processing our data.
Download the tennis data and study it
Create your own league table to gain insight, which is not readily available
In this video,we will download football results from a web page.
See that this data is not nice and structured
Try to navigate it and extract what we want
See how random and tricky it is compared with the other data we used so far
In this video, we will use R to do some statistics.
Perform basic operations
Fit a model to global warming data
See the vast range of functions available
In this video, we will work with distributions using R.
Generate random numbers from distributions
Perform chi squared and F test
Simulate football matches using Poisson distributions
In this video, we will see some of R's graphical power.
Produce standard plots
Use pairs to look at complex data
Pick a palette
In this video, we will use the plotting package, ggplot2.
Plot complex data using traditional plotting
Install and use ggplot2
Map the data not only using coordinates but also colour
In this video, we will see another plotting technique known as Facets.
Plot multidimensional data using Facets
Use both grid and wrap
Provide links to a handy reference with a list of useful commands
Heavy-Lifting Using R Libraries
This video gives an overview of an entire course.
In this video, we will compare speed of functions.
Write a program looking at prime numbers
Write another version doing the same thing in a different way
Compare both using system.time to compare
This video will speed up our code using C++.
Install rcpp package
Write function in C++ and call from r
Quantify the massive improvement using system.time
In this video, we will speed up code by going Parallel.
Learn how to time program normally and in Parallel, using simple doParallel
Measure using micro-benchmark which is better showing small gains
Use RcppParallel for more sophisticated parallel with better results
In this video, we will create an interactive web app.
Run a Shiny app
Look at the UI.r and server.r scripts that power it
Sample the rich range of widgets available
In this video, we will include code, data, and plots in our document for greater transparency.
Create an RMarkdown document
Write a program analysing data
Embed the code in our RMarkdown document
In this video, we will create an interactive document.
Create a Shiny RMarkdown document
Look at the structure like Shiny which looked at before
Publish with free account and use from smartphone
In this video, we will use leading data visualisation package ggplot2.
Visualise complex data set using colours
Visualise complex data set using Facets
In this video, we will produce interactive charts.
Plot using Plotly
See that not static but can zoom in and out as well as move around
Convert ggplot command to interactive plots using ggplotly
In this video, we will produce interactive maps.
Plot maps using ggmap
Produce dynamic maps using leaflet with overlaid crime data
Produce interactive maps with data relevant to where mouse is
In this video, we will produce interactive charts.
See how can visualise data in multiple ways
In this video, we will produce animation.
View data to see structure
Visualise with Motion chart
Try setting different fields to different dimensions like colour and size
In this video, we will apply what we learned to complex data set.
Load world development indicators data
Investigate various data fields