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
Table of Contents
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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