Advanced Tools and Techniques Beyond Base R
3.5 (2 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.
12 students enrolled

Advanced Tools and Techniques Beyond Base R

Explore functional and meta-programming and see it will simplify and fasten your data analysis code.
3.5 (2 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.
12 students enrolled
Created by Packt Publishing
Last updated 11/2017
English
English [Auto-generated]
Current price: $80.99 Original price: $124.99 Discount: 35% off
3 hours left at this price!
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This course includes
  • 2 hours on-demand video
  • 2 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Working on recent R packages
  • Working with Tidyverse package collection
  • Produce high-quality statistical graphics
  • Use functional programming and meta-programming to fasten data analysis code
Course content
Expand all 24 lectures 02:19:20
+ Tidying Data and Analyzing Tidy Data
11 lectures 01:02:17

This video provides an overview of the entire course.

Preview 02:04
This video aims to explain the properties of tidy data.
Preview 04:28

In this video, we will learn how to install R packages, which provide new functions and capabilities.

Installing R Packages
04:01

In this video, we will learn how to import data from common file formats into an R session.

Reading Data from Files with readr and readxl
05:20

In this video, we will learn how to use the tidyr package to turn messy datasets into tidy ones.

Tidying Data with tidyr
05:45
In this video, we will learn how to use the magrittr package to write readable and effective R pipelines.
Writing Code Pipelines with magrittr
05:35
In this video, we will learn how to use the stringr package to manipulate strings.
Manipulating Strings with stringr
07:51

This video aims to explain how to use the forcats package to manipulate factors.

Manipulating Factors with forcats
06:32
In this video, we will understand how to use the lubridate package to manipulate dates and times.
Manipulating Dates and Times with Lubridate
04:47

In this video, we will understand the powerful split-apply-combine strategy for data analysis and how to implement it with the dplyr package.

Analyzing Data with dplyr and the split-apply-combine Strategy
12:14
Tidying data and analysing tidy data – Exercises
03:40
+ Using ggplot2 to Draw Statistical Graphics
8 lectures 53:54

This video will help us understand the grammar of graphics and how it is implemented in ggplot2.

Preview 07:53
In this video, we will learn how to implement the grammar of graphics concepts of geoms, stats, and scales in ggplot2.
Drawing Basic Plots and Scaling Plot Elements
08:16

This video will help you learn about five useful ggplot2 geoms that implement useful statistical transformations, or ‘stats’.

Transforming Plot Data with Stats
07:14

This video will help you learn how to control large scale plot elements including facets and non-data elements such as labels and color schemes.

Faceting Plots and Controlling Plot Themes and Labels
06:41

This video will help you learn how to save your ggplot2 plots to file.

Writing Plots to File
02:23
This video will help you learn how to extend ggplot2 with packaged from CRAN and GitHub to draw more advanced and complex plots.
Drawing Advanced Plots with ggplot2 Extensions
06:26
This video will help you learn how to use the gganimate package to create animated plots.
Creating Animated Plots with gganimate and tweenr - I
06:48
This video will help you learn how to use the tweenr package to create smooth transitions between states in animations created with gganimate.
Creating Animated Plots with gganimate and tweenr - II
08:13

Now that you are done with the videos of section 2, let’s assess your learning. Here, are a few questions with options, out of which 1 is the correct option. Select the right option and validate your learning! The answers are provided in a separate

Section 2: Using ggplot2 to draw statistical graphics – Questions
15 questions
+ Writing Functional R
5 lectures 23:08

This video will help you write our own R functions, including arguments, and learn how to manage missing arguments.

Writing an R Function
06:36
This video will help you apply the lapply function to run a function on a list of inputs.
Understanding the lapply() Function
04:12
This video will help you learn return the magrittr pipe, learn how to compose your own functions into practical data analysis pipelines.
Using Function Composition in Data Analysis
04:37

This video will help you understand how to capture R code in an expression and manipulate it to compose functions and expressions.

Using Expressions and Metaprogramming for Advanced Function Composition
05:58
Writing functional R – Exercises
01:45
Requirements
  • Should have basic knowledge of programming and statistics.
Description

Advanced Tools and Techniques Beyond Base R introduces a number of recently developed R  packages and paradigms, in particular the concept of tidy data and the  Tidyverse collection of packages, which are rapidly becoming  indispensable to R data analysts. You will learn how to efficiently  process and analyze data in ways not possible with base R and produce  high-quality statistical graphics. The course will finish with a taste  of how functional programming and meta-programming with R can simplify  and speed up your data analysis code.

About the Author :

Dr. David Wilkins is a microbial ecologist currently based in Sydney, Australia. The author has worked with the R technology for around five years.

Who this course is for:
  • Data scientists/professionals with a basic knowledge of programming and statistics and looking to take their R programming skills to the next level.