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Pre-Process and Visualize Data With Tidy Techniques in R
Welcome To The Course
Introduction to the Course (2:16)
Data & Scripts
Install R and RStudio (6:36)
Common Data Types We Encounter in Data Analysis (3:37)
Read in Data From Different Sources
Read in CSV and Excel Data (9:56)
Read in Data from Online HTML Tables-Part 1 (4:13)
Read in Data from Online HTML Tables-Part 2 (6:24)
Read in Data from Databases (8:23)
Read in Data from JSON (5:28)
Data Processing With dplyr
Introduction to Pipe Operators (9:14)
Get acquainted with our data using "dplyr" (8:29)
More selections with dplyr (12:28)
Row filtering (7:05)
More row filtering (4:59)
Select desired rows and columns (4:03)
Add new variables/columns (10:02)
Making sense of data by grouping different categories (5:28)
Grouping Data-Part 2 (8:55)
Introduction to dplyr-1 (6:11)
Introduction to dplyr-2 (4:44)
Process your Data the Tidy Way: Start With tidyverse
Getting Started With the tidyverse Package (3:17)
Rename Columns (6:59)
Long and Wide Format (5:03)
Joining Tables (5:58)
Nesting (3:59)
Theory Behind Hypothesis Testing (5:42)
Implement t-test With tidyverse (3:44)
Dealing With Missing Values
Removing NAs- the ordinary way (17:12)
Remove NAs- using "dplyr" (5:15)
Data imputation with dplyr (4:44)
More data imputation (3:53)
Data Visualisation and Explorations
What is Data Visualisation? (9:33)
Some Principles of Data Visualisation (6:46)
Data Visualisation With dplyr and ggplot2 (6:07)
Mining and Visualising Information About the Olympic Games (12:49)
Of Winter and Summer Olympic Games (4:16)
Of Men and Women (8:26)
Theory of Ordinary Least Square (OLS) Regression (10:44)
Implement OLS on Different Categories (7:57)
Remove NAs- using "dplyr"
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