R Programming: Advanced Analytics In R For Data Science

Description

In this course, you will learn

• Perform Data Preparation in R

• Identify missing records in dataframes

• Locate missing data in your dataframes

• Apply the Median Imputation method to replace missing records

• Apply the Factual Analysis method to replace missing records

• Understand how to use the which() function

• Know how to reset the dataframe index

• Work with the gsub() and sub() functions for replacing strings

• Explain why NA is a third type of logical constant

• Deal with date-times in R

• Convert date-times into POSIXct time format

• Create, use, append, modify, rename, access and subset Lists in R

• Understand when to use [] and when to use [[]] or the $ sign when working with Lists

• Create a timeseries plot in R

• Understand how the Apply family of functions works

• Recreate an apply statement with a for() loop

• Use apply() when working with matrices

• Use lapply() and sapply() when working with lists and vectors

• Add your own functions into apply statements

• Nest apply(), lapply() and sapply() functions within each other

• Use the which.max() and which.min() functions