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R Programming: Advanced Analytics In R For Data Science
Rating: 4.6 out of 5(9,212 ratings)
63,318 students

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
Created byKirill Eremenko, SuperDataScience Team, Ligency ​
Last updated 9/2025
English

What you'll 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

Course content

4 sections • 50 lectures • 5h 53m total length

  • Welcome to the Advanced R Programming Course!5:44
  • Learning Paths0:34
  • Extra: Interview with Hadley Wickham0:39

    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

  • Get the materials0:17

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 have a robust set of skills to take with you into your Data Science career.


We prepared real-life case studies.

In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries.

In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks.

In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that.

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

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