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Practical Data Pre-Processing & Visualization Training With R
Welcome To The Course
Introduction To The Course and Instructor (1:59)
Data and Code Used in the Course
Install R and RStudio (6:36)
Read in Data From Different Sources
Read in CSV and Excel Data (9:56)
Read Unzipped Folder (3:00)
Read Online CSV (4:04)
Read in Googlesheets (3:53)
Read in Data from Online HTML Tables-Part 1 (4:13)
Read in Data from Online HTML Tables-Part 2 (6:24)
Read Data from a Database (8:23)
Common Data Pre-Processing Techniques
Basic Data Cleaning in R: Remove NA (17:12)
Additional Data Cleaning (8:05)
Indexing and Subsetting Data (11:59)
Summarising Based on Qualitative Attributes (3:40)
Of Long and Wide (5:36)
Pre-processing Tasks and the Pipe Operator (9:14)
Introduction to dplyr for Data Summarizing-Part 1 (6:11)
Introduction to dplyr for Data Summarizing-Part 2 (4:44)
Start with Tidyverse (3:17)
Column Renaming (6:59)
Tidy Data: Long and Wide (5:03)
Joining Tables (5:58)
Summarising Based on Qualitative Attributes (3:40)
Of Long and Wide (5:36)
Basic Data Visualization
What is Data Visualisation? (9:33)
Some Principles of Data Visualisation (6:46)
Exploratory Data Analysis (EDA) in R (9:02)
More Exploratory Data Analysis with xda (4:16)
Grammar of Graphics: ggplot2
Start with qplot (4:45)
More qplot Visualizations (7:24)
Start with ggplot (4:59)
Scatterplots with ggplot2 (5:38)
Faceting With ggplot2 (4:42)
More Faceting (11:51)
Insert a Smoothing Line (7:08)
Boxplots (3:50)
More Boxplots (11:21)
Histograms (11:58)
Error Bars (7:08)
Barplots For Discrete Numbers (14:12)
Line Charts (5:57)
Additional ggplot2 Themes (4:32)
Real Life Data
Use dplyr and ggplot2 (6:07)
nobel1 (16:26)
nobel2 (7:35)
Mining and Visualising Information About the Olympic Games-Part 1 (12:49)
Of Winter and Summer Olympic Games (4:16)
Of Men and Women (8:26)
Geographic Visualisations
Brief Introduction (4:17)
Work With R's Inbuilt Geospatial Data-Part 2 (7:32)
Use ggplot2 For Geographic Data Visualisations (14:11)
Exploratory Data Analysis (EDA) in R
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