R Programming: Advanced Analytics In R For Data Science Free Tutorials

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R programming & Data Science take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2

R Programming: Advanced Analytics In R For Data Science Free Tutorials

R Programming: Advanced Analytics In R For Data Science Free Tutorials

What 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

Requirements For This Course:

  • Basic knowledge of R
  • Knowledge of the GGPlot2 package is recommended
  • Knowledge of dataframes
  • Knowledge of vectors and vectorized operations

Description:

Prepared to take your R Programming aptitudes to the following level?

Need to genuinely get capable at Data Science and Analytics with R?

This course is for you!

Proficient R Video preparing, novel datasets structured in light of long periods of industry experience, connecting with practices that are both fun and furthermore give you a preference for Analytics of the REAL WORLD.

In this course you will learn:

  • Instructions to get ready information for examination in R
  • Step by step instructions to play out the middle attribution strategy in R
  • Instructions to work with date-times in R
  • What Lists are and how to utilize them
  • What the Apply group of capacities is
  • The most effective method to utilize apply(), lapply() and sapply() rather than circles
  • The most effective method to settle your own capacities inside apply-type capacities
  • Instructions to settle apply(), lapply() and sapply() capacities inside one another

What’s more, a whole lot more!

The more you get familiar with the better you will get. After each module you will as of now have a solid arrangement of abilities to take with you into your Data Science vocation.

Who this course is for:

  • 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

Course Content:

1. Introduction of R programming :

  • Learning Paths
  • Interview with Hadley Wickham
  • Get the materials
  • Your Shortcut To Becoming A Better Data Scientist
  • Welcome to this section. This is what you will learn!
  • Updates on Udemy Reviews
  • Import Data into R
  • What are Factors (Refresher)
  • 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
  • Data Preparation
  • Project Brief: Machine Utilization
  • Import Data Into R
  • Handling Date-Times in R
  • Naming components of a list
  • Extracting components lists: [] vs [[]] vs $
  • Adding and deleting components
  • Subsetting a list
  • Creating A Timeseries Plot
  • Section Recap
  • Lists in R
  • Welcome to this section. This is what you will learn!
  • Project Brief: Weather Patterns
  • Import Data into R
  • 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
  • “Apply” Family of Functions

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