$200 | Duration: 6 hours | Video: h264, 1920x1080 | Audio: AAC, 44100 Hz, 2 Ch | 1.4 GB

Genre: eLearning | Language: English | Project Files

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

Requirements

Basic knowledge of R

Knowledge of the GGPlot2 package is recommended

Knowledge of dataframes

Knowledge of vectors and vectorized operations

Description

Ready to take your R Programming skills to the next level?

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