R Programming: Advanced Analytics In R For Data Science

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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

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What Will I 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