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# 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
Last updated 12/2016
English
English
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
• Nest apply(), lapply() and sapply() functions within each other
• Use the which.max() and which.min() functions
Requirements
• Basic knowledge of R
• Knowledge of the GGPlot2 package is recommended
• Knowledge of dataframes
• Knowledge of vectors and vectorized operations
Description

Want to truly become proficient at Data Science and Analytics with R?

This course is for you!

Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.

In this course you will learn:

• How to prepare data for analysis in R
• How to perform the median imputation method in R
• How to work with date-times in R
• What Lists are and how to use them
• What the Apply family of functions is
• How to use apply(), lapply() and sapply() instead of loops
• How to nest your own functions within apply-type functions
• How to nest apply(), lapply() and sapply() functions within each other
• And much, much more!

The more you learn the better you will get. After every module you will already have a strong set of skills to take with you into your Data Science career.

Who is the target audience?
• Anybody who has basic R knowledge and would like to take their skills to the next level
• Anybody who has already completed the R Programming A-Z course
• This course is NOT for complete beginners in R

Size: 1.48G