R Programming: Advanced Analytics In R For Data Science

About Course

Learn advanced R programming techniques and unlock the power of data analytics for a successful career in data science. This free course from Udemy will equip you with the skills needed to handle real-world data analysis challenges. Discover how to prepare data for analysis, master data imputation methods, work with dates and times in R, and leverage powerful functions like apply(), lapply(), and sapply() to streamline your code.

This comprehensive course features:

Professional video training
Unique datasets based on industry experience
Engaging exercises for practical application
Real-world case studies from various industries

By the end of this course, you will be able to:

Prepare data for analysis using R
Implement the median imputation method in R
Work confidently with dates and times in R
Master lists and their applications in R
Utilize the apply family of functions to enhance efficiency
Nest functions within apply-type functions for advanced operations
And much more!

This free course provides the foundation you need to excel in data science. Enroll today and start your journey to becoming an R programming expert!

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

Course Content

Welcome To The Course

A Message from the Professor
Welcome to the Advanced R Programming Course

05:44

Data Preparation

Welcome to this section. This is what you will learn

02:43
Project Brief Financial Review

02:49
Updates on Udemy Reviews

02:51
Import Data into R

05:11
What are Factors (Refresher)

07:37
The Factor Variable Trap

10:09
FVT Example

06:34
gsub() and sub()

09:45
Dealing with Missing Data

09:32
What is an NA

05:15
An Elegant Way To Locate Missing Data

10:01
Data Filters which() for Non-Missing Data

08:58
Data Filters is.na() for Missing Data

05:52
Removing records with missing data

04:47
Reseting the dataframe index

05:03
Replacing Missing Data Factual Analysis Method

06:48
Replacing Missing Data Median Imputation Method (Part 1)

13:09
Replacing Missing Data Median Imputation Method (Part 2)

04:30
Replacing Missing Data Median Imputation Method (Part 3)

06:14
Replacing Missing Data Deriving Values Method

04:33
Visualizing results

10:50
Section Recap

05:49

Lists in R

Welcome to this section. This is what you will learn

01:44
Project Brief Machine Utilization

17:44
Import Data Into R

05:58
Handling Date-Times in R

10:17
R programming What is a List

11:19
Naming components of a list

04:35
Extracting components lists () vs (()) vs

06:46
Adding and deleting components

09:36
Subsetting a list

08:05
Creating A Timeseries Plot

09:12
Section Recap

03:32

Apply Family of Functions

Welcome to this section. This is what you will learn

02:40
Project Brief Weather Patterns

08:50
Import Data into R

09:46
R programming What is the Apply family

07:34
Using apply()

08:33
Recreating the apply function with loops (advanced topic)

07:39
Using lapply()

11:02
Combining lapply() with ()

07:11
Adding your own functions

09:33
Using sapply()

10:58
Nesting apply() functions

08:19
which.max() and which.min() (advanced topic)

11:33
Section Recap

05:12
THANK YOU bonus video

02:40

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About Course

What Will You Learn?

Course Content

Welcome To The Course

A Message from the Professor

Welcome to the Advanced R Programming Course

Data Preparation

Welcome to this section. This is what you will learn

Project Brief Financial Review

Updates on Udemy Reviews

Import Data into R

What are Factors (Refresher)

The Factor Variable Trap

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

Lists in R

Welcome to this section. This is what you will learn

Project Brief Machine Utilization

Import Data Into R

Handling Date-Times in R

R programming What is a List

Naming components of a list

Extracting components lists () vs (()) vs

Adding and deleting components

Subsetting a list

Creating A Timeseries Plot

Section Recap

Apply Family of Functions

Welcome to this section. This is what you will learn

Project Brief Weather Patterns

Import Data into R

R programming What is the Apply family

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

THANK YOU bonus video

Earn a certificate

Student Ratings & Reviews

Be Smart, Be Theeta

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