R Programming: Advanced Analytics In R For Data Science

Name: R Programming: Advanced Analytics In R For Data Science
Rating: 4.6 (9223 reviews)

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 2/2025

English

German [Auto],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

Welcome to this section. This is what you will learn!2:43
Project Brief: Financial Review2:49
Import Data into R5:10
What are Factors (Refresher)7:37
Learn how factors convert categorical variables into numeric levels in R, identify levels, handle empty categories, and convert non-factors to factors with the factor() function.
The Factor Variable Trap10:09
FVT Example6:34
gsub() and sub()9:44
Use gsub and sub to clean revenue, expenses, and growth by removing dollar signs, commas, and spaces, turning them into numeric variables; learn how sub handles first versus all matches.
Dealing with Missing Data9:32
Explore practical strategies for handling missing data in data science. Predict missing values, keep or remove records, or impute with mean, median, or model-based methods.
What is an NA?5:15
An Elegant Way To Locate Missing Data10:01
Data Filters: which() for Non-Missing Data8:57
Data Filters: is.na() for Missing Data5:52
Removing records with missing data4:47
Reseting the dataframe index5:03
Reset the dataframe index in R by reassigning row names to a 1 through n sequence or by setting rownames(fin) <- NULL for an automatic reset.
Replacing Missing Data: Factual Analysis Method6:48
Apply factual analysis method to replace missing state values by matching city names, filtering NA rows, and updating the state column in R.
Replacing Missing Data: Median Imputation Method (Part 1)13:09
Replacing Missing Data: Median Imputation Method (Part 2)4:29
Replacing Missing Data: Median Imputation Method (Part 3)6:14
Replacing Missing Data: Deriving Values Method4:33
Impute missing data by deriving values from known relationships, such as revenue minus expenses equals profit, and choose between using industry medians as proxies or calculating from the equation.
Visualizing results10:49
Section Recap5:49
Recap data preparation: convert factors to numeric safely, clean fields with sub to remove dollar signs, spaces, and commas, handle missing data with median imputation and complete.cases filtering, derive values.
Data Preparation

Welcome to this section. This is what you will learn!1:44
Explore lists in R, a flexible data structure that stores any type of data, unlike vectors. Learn to create, access, subset, rename, and operate on lists for versatile data storage.
Project Brief: Machine Utilization17:44
Analyze coal-terminal machine utilization in R. Build an R list containing machine name, min/mean/max utilization, a below 90 percent flag, unknown utilization, plus subsetting and plotting for machines.
Import Data Into R5:58
Import data into R and build a list. Capture name, mean, min, max, 90% check, NA hours, data frame subset, plot, and derive utilization from percent idle.
Handling Date-Times in R10:17
R programming: What is a List?11:19
In this lecture, you will know what the lists in R programming language are and how to create them
Naming components of a list4:35
Extracting components of lists: [] vs [[]] vs $6:46
Adding and deleting components9:35
Learn to add and delete components in an R list, including new information, unknown hours, and a data frame, then remove components with the null method and note index shifting.
Subsetting a list8:05
Master subsetting lists in R for data science by using double and single brackets and dollar notation to access components by name. Extract vectors and statistics efficiently.
Creating a Timeseries Plot9:12
Section Recap3:32
Lists in R

Welcome to this section. This is what you will learn!2:40
Explore the apply family of functions in R, using their shortcuts for applying analytics to matrices, lists, and vectors, enabling fast, sophisticated data science insights.
Project Brief: Weather Patterns8:49
Import Data into R9:46
R programming: What is the Apply family?7:34
Here you will know about 3 main functions from Apply family and will know how to use them
Using apply()8:33
Apply shows how to use the apply function in R to compute row or column statistics on a matrix, using mean, max, and min with row or column margins.
Recreating the apply function with loops (advanced topic)7:39
Using lapply()11:02
Combining lapply() with []7:10
Adding your own functions9:33
Add your own functions into the ply family functions by nesting custom functions within apply family functions and apply them to weather matrices to extract the first row.
Using sapply()10:58
Explore how sapply wraps lapply to simplify outputs into vectors or matrices, producing deliverables from weather data and temperature analyses.
Nesting apply() functions8:19
Learn to nest apply functions in R by applying row-wise maxima and minima across a list of matrices, using lapply and apply with inline functions to produce tidy results.
which.max() and which.min() (advanced topic)11:32
Section Recap5:12
"Apply" Family of Functions
THANK YOU Video2:40
Celebrate completing the course and mastering the journey from complex to simple. Enjoy Tasmania scenery compilations and consider rating the course to help others learn.

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

R Programming: Advanced Analytics In R For Data Science

What you'll learn

Explore related topics

Course content

Welcome To The Course4 lectures • 7min

Data Preparation21 lectures • 2hr 26min

Lists in R11 lectures • 1hr 29min

"Apply" Family of Functions14 lectures • 1hr 51min

Requirements

Description

Who this course is for: