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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
Instructor:
Kirill Eremenko
45,834 students enrolled
English More
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

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.

Welcome To The Course

1
Welcome to the Advanced R Programming Course!
2
BONUS: Learning Paths
3
BONUS: Interview with Hadley Wickham

Hey there!

Since you're taking this Advanced Analytics in R course, I'm guessing you're quite comfortable with R and most likely familiar with the name Hadley Wickham (creator of GGPlot2 and countless other R packages).

Hadley is awesome! Is he not?

Well, then...

I've got a special surprise for you.

Earlier in 2020 Hadley joined me on the SuperDataScience podcast for a deep chat about R Programming, and today I'd like to share this interview with you:

https://www.superdatascience.com/podcast/hadley-wickham-talks-integration-and-future-of-python-and-r

Listen to it during your commute to work, a hike, or simply in the comfort of your home. You can find it via the link or by searching for the SuperDataScience Podcast on any podcast app or even Spotify and navigating to episode number #337.

In this episode you will learn:

  • Hadley’s R packages [8:26]

  • Better integrations between R and Python [20:11]

  • LinkedIn Q&A [33:34]

  • useR Conference vs. RStudio Conference [50:46]

  • LinkedIn Q&A: Career-related questions [1:01:06]

  • LinkedIn Q&A: Future-related questions [1:08:01]

I'm sure you're going to enjoy this inspiring interview!

- Kirill

4
Get the materials
5
Your Shortcut To Becoming A Better Data Scientist!

Data Preparation

1
Welcome to this section. This is what you will learn!
2
Project Brief: Financial Review
3
Updates on Udemy Reviews
4
Import Data into R
5
What are Factors (Refresher)
6
The Factor Variable Trap
7
FVT Example
8
gsub() and sub()
9
Dealing with Missing Data
10
What is an NA?
11
An Elegant Way To Locate Missing Data
12
Data Filters: which() for Non-Missing Data
13
Data Filters: is.na() for Missing Data
14
Removing records with missing data
15
Reseting the dataframe index
16
Replacing Missing Data: Factual Analysis Method
17
Replacing Missing Data: Median Imputation Method (Part 1)
18
Replacing Missing Data: Median Imputation Method (Part 2)
19
Replacing Missing Data: Median Imputation Method (Part 3)
20
Replacing Missing Data: Deriving Values Method
21
Visualizing results
22
Section Recap
23
Data Preparation

Lists in R

1
Welcome to this section. This is what you will learn!
2
Project Brief: Machine Utilization
3
Import Data Into R
4
Handling Date-Times in R
5
R programming: What is a List?

In this lecture, you will know what the lists in R programming language are and how to create them

6
Naming components of a list
7
Extracting components lists: [] vs [[]] vs $
8
Adding and deleting components
9
Subsetting a list
10
Creating A Timeseries Plot
11
Section Recap
12
Lists in R

"Apply" Family of Functions

1
Welcome to this section. This is what you will learn!
2
Project Brief: Weather Patterns
3
Import Data into R
4
R programming: What is the Apply family?

Here you will know about 3 main functions from Apply family and will know how to use them

5
Using apply()
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Recreating the apply function with loops (advanced topic)
7
Using lapply()
8
Combining lapply() with []
9
Adding your own functions
10
Using sapply()
11
Nesting apply() functions
12
which.max() and which.min() (advanced topic)
13
Section Recap
14
"Apply" Family of Functions
15
THANK YOU bonus video

Bonus Lectures

1
***YOUR SPECIAL BONUS***
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Includes

6 hours on-demand video
5 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion