**Harness the Power of Tidyverse for Data Preprocessing and Visualisation in R**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 4h 21m | 996 MB

Data wrangling and data visualization with the Tidyverse R data science package

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!

Learn

- 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

**Welcome to The Course**

1 Introduction to the Course

2 Install R and RStudio

3 Common data types

4 Quick Pointers

**Read in Data from Different Sources**

5 Read in CSV and Excel Data

6 Read in Data from Online HTML Tables-Part 1

7 Read in Data from Online HTML Tables-Part 2

8 Read in Data from Databases

9 Read in Data from JSON

**Data Processing With dplyr**

10 Introduction to Pipe Operators

11 Get acquainted with our data using ‘dplyr’

12 More selections with dplyr

13 Row filtering

14 More row filtering

15 Select desired rows and columns

16 Add new variables columns

17 Making sense of data by grouping different categories

18 Grouping Data-Part 2

19 Introduction to dplyr for Data Summarizing-Part 1

20 Introduction to dplyr for Data Summarizing-Part 2

**Data Processing the Tidy Way – The ‘tidyr’ Package**

21 Start with Tidyverse

22 Column Renaming

23 Tidy Data – Long and Wide

24 Joining Tables

25 Nesting

26 Brief Reminder – Hypothesis Testing

27 Implement t-test On Different Categories

**Dealing with Missing Values**

28 Removing NAs- the ordinary way

29 Remove NAs- using ‘dplyr’

30 Data imputation with dplyr

31 More data imputation

**Data Visualisation and Explorations**

32 What is Data Visualisation

33 Some Principles of Data Visualisation

34 Data Visualisation with dplyr and ggplot2

35 Mining and Visualising Information About the Olympic Games

36 Of Winter and Summer Olympic Games

37 Of Men and Women

38 Theory of Ordinary Least Square (OLS) Regression

39 Implement OLS on Different Categories

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