R Programming: Advanced Analytics In R For Data Science

R Programming: Advanced Analytics In R For Data Science, Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2

Created by Kirill Eremenko, SuperDataScience Team, English, English, French [Auto]

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What you'll 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