COURSE INFORMATION
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COURSE
Data Analytics in R
DESCRIPTION
This course is suitable for anybody who has basic R knowledge and would like to take their skills to the next level, or anyone who has already completed the R Programming A-Z course.
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PREREQUISITES
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Basic knowledge of R
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Knowledge of the GGPlot2 package is recommended
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Knowledge of dataframes
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Knowledge of vectors and vectorized operations
CURRICULUM
DATA PREPARATION
- Project Brief: Financial Review
- 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
LISTS IN R
- Project Brief: Machine Utilization
- Import Data Into R
- Handling Date-Times in R
- 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
"APPLY" FAMILY OF FUNCTIONS
- Project Brief: Weather Patterns
- Import Data into R
- 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
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COURSE FEES
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INSTRUCTIONS FOR ENROLMENT