Harness the Power of Tidyverse for Data Preprocessing and Visualisation in R [Video] – Minerva Singh

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Read in data into the R environment from different sources

Carry out basic data pre-processing & wrangling in R Studio

Use some of the most important R data wrangling & visualization packages such as dplyr and ggplot2

Gain proficiency in data preprocessing, data wrangling, and data visualization in R and put what you've learned into action straightaway

About    

This is your roadmap to becoming highly proficient in data preprocessing, data wrangling, and data visualization using two of the most in-demand R data science packages

What this course will do for you:

It will take you from a basic level to a level where you'll perform some of the most common data wrangling tasks in R—with two of the most well-known R data science packages: Tidyverse and dplyr.

It will equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2.

It will Introduce you, in a practical way, to some of the most important data visualization concepts so that you can apply them to practical data analysis and interpretation.

You will also be able to decide which wrangling and visualization techniques are best suited to answering your research questions and most applicable to your data, so that you can interpret the results.

The course will mostly focus on helping you implement different techniques on real-life data

After each video, you will have learned a new concept or technique and will be able to apply it to your own projects immediately!

All the code and supporting files for this course are available at – https://github.com/PacktPublishing/Harness-the-Power-of-Tidyverse-for-Data-Preprocessing-and-Visualisation-in-R

Features    

Minimal mathematical jargon. The course focuses on teaching you basic time series concepts and hands-on applications for the most important concepts in R

People with no prior exposure to time series can use this course to get started

A thorough grounding in how to use both statistical and machine learning techniques on time series data

Table of contents

Welcome to The Course

• Introduction to the Course
• Install R and RStudio
• Common data types
• Quick Pointers

Read in Data from Different Sources

• Read in CSV and Excel Data
• Read in Data from Online HTML Tables-Part 1
• Read in Data from Online HTML Tables-Part 2
• Read in Data from Databases
• Read in Data from JSON

Data Processing With dplyr

• Introduction to Pipe Operators
• Get acquainted with our data using "dplyr"
• More selections with dplyr
• Row filtering
• More row filtering
• Select desired rows and columns
• Add new variables/columns
• Making sense of data by grouping different categories
• Grouping Data-Part 2
• Introduction to dplyr for Data Summarizing-Part 1
• Introduction to dplyr for Data Summarizing-Part 2

Data Processing the Tidy Way: The "tidyr" Package

• Start with Tidyverse
• Column Renaming
• Tidy Data: Long and Wide
• Joining Tables
• Nesting
• Brief Reminder: Hypothesis Testing
• Implement t-test On Different Categories

Dealing with Missing Values

• Removing NAs- the ordinary way
• Remove NAs- using "dplyr"
• Data imputation with dplyr
• More data imputation

Data Visualisation and Explorations

• What is Data Visualisation?
• Some Principles of Data Visualisation
• Data Visualisation with dplyr and ggplot2
• Mining and Visualising Information About the Olympic Games
• Of Winter and Summer Olympic Games
• Of Men and Women
• Theory of Ordinary Least Square (OLS) Regression
• Implement OLS on Different Categories

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