Welcome To The Course
- Welcome to the Advanced R Programming Course!
- BONUS: Learning Paths
- BONUS: Interview with Hadley Wickham
- Get the materials
- Your Shortcut To Becoming A Better Data Scientist!
Data Preparation
- Welcome to this section. This is what you will learn!
- Project Brief: Financial Review
- Updates on Udemy Reviews
- Import Data into R
- What are Factors (Refresher)
- The Factor Variable Trap
- FVT Example
- gsub() and sub()
- Dealing with Missing Data
- What is an NA?
- An Elegant Way To Locate Missing Data
- Data Filters: which() for Non-Missing Data
- Data Filters: is.na() for Missing Data
- Removing records with missing data
- Reseting the dataframe index
- Replacing Missing Data: Factual Analysis Method
- Replacing Missing Data: Median Imputation Method (Part 1)
- Replacing Missing Data: Median Imputation Method (Part 2)
- Replacing Missing Data: Median Imputation Method (Part 3)
- Replacing Missing Data: Deriving Values Method
- Visualizing results
- Section Recap
- Data Preparation
Lists in R
- Welcome to this section. This is what you will learn!
- Project Brief: Machine Utilization
- Import Data Into R
- Handling Date-Times in R
- R programming: What is a List?
- Naming components of a list
- Extracting components lists: [] vs [[]] vs $
- Adding and deleting components
- Subsetting a list
- Creating A Timeseries Plot
- Section Recap
- Lists in R
"Apply" Family of Functions
- Welcome to this section. This is what you will learn!
- Project Brief: Weather Patterns
- Import Data into R
- R programming: What is the Apply family?
- Using apply()
- Recreating the apply function with loops (advanced topic)
- Using lapply()
- Combining lapply() with []
- Adding your own functions
- Using sapply()
- Nesting apply() functions
- which.max() and which.min() (advanced topic)
- Section Recap
- "Apply" Family of Functions
- THANK YOU bonus video
Bonus Lectures
- ***YOUR SPECIAL BONUS***
