Advanced R
Advanced R, available at $79.99, has an average rating of 4.7, with 19 lectures, 9 quizzes, based on 79 reviews, and has 751 subscribers.
You will learn about Build R packages Write C++ code in R via Rcpp Do complex date parsing Profile and benchmark their programs Build parallel code Parse complex text via Regex And much more! This course is ideal for individuals who are Intermediate and advanced R users or Basic R users (with a few weeks of experience) can also take this course. They might find some parts difficult, specially if they lack programming experience It is particularly useful for Intermediate and advanced R users or Basic R users (with a few weeks of experience) can also take this course. They might find some parts difficult, specially if they lack programming experience.
Enroll now: Advanced R
Summary
Title: Advanced R
Price: $79.99
Average Rating: 4.7
Number of Lectures: 19
Number of Quizzes: 9
Number of Published Lectures: 19
Number of Published Quizzes: 8
Number of Curriculum Items: 28
Number of Published Curriculum Objects: 27
Original Price: £44.99
Quality Status: approved
Status: Live
What You Will Learn
- Build R packages
- Write C++ code in R via Rcpp
- Do complex date parsing
- Profile and benchmark their programs
- Build parallel code
- Parse complex text via Regex
- And much more!
Who Should Attend
- Intermediate and advanced R users
- Basic R users (with a few weeks of experience) can also take this course. They might find some parts difficult, specially if they lack programming experience
Target Audiences
- Intermediate and advanced R users
- Basic R users (with a few weeks of experience) can also take this course. They might find some parts difficult, specially if they lack programming experience
This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don’t recommend this course on beginners.
We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R’s libraries. After that we introduce the necessary skills for profiling your R code. We then move into C++ and Rcpp, and we show how to write super fast C++ parallel code that uses OpenMP. Understanding and mastering Rcpp will allow you to push your R skills to another dimension. When your colleagues are writing R functions, you will be able to get Rcpp+OpenMP equivalent code running 4-8X times faster. We then move into Python and Java, and show how these can be called from R and vice-versa. This will be really helpful for writing code that leverages the excellent object oriented features from this pair of languages. You will be able to build your own classes in Java or Python that store the data that you get from R. Since the Python community is growing so fast, and producing so wonderful packages, it’s great to know that you will be able to call any function from any Python package directly from R. We finally explain how to use sqldf, which is a wonderful package for doing serious, production grade data processing in R. Even though it has its limitations, we will be able to write SQL queries directly in R. We will certainly show how to bypass those limitations, such as its inability to write full joins using specific tricks.
All the code (R,JAVA,C++,.csv) used in this course is available for download, and all the lectures can be downloaded as well. Our teaching strategy is to present you with examples carrying the minimal complexity, so we hope you can easily follow each lecture. In case you have doubts or comments, feel free to send us a message
Course Curriculum
Chapter 1: General R topics
Lecture 1: Introduction
Lecture 2: Creating Packages
Lecture 3: Functionals and closures
Lecture 4: Environments
Chapter 2: Dates
Lecture 1: Parsing Dates
Chapter 3: Regex
Lecture 1: Regex – Part 1
Lecture 2: Regex – Part 2
Chapter 4: Intenet
Lecture 1: Parsing Websites
Chapter 5: Profiling and memory
Lecture 1: Profiling
Chapter 6: Rcpp and high performance R-C++ computing
Lecture 1: Rcpp – Part 1
Lecture 2: Rcpp 2 – Part 2
Lecture 3: Rcpp sugar
Lecture 4: Parallel computing
Chapter 7: Interacting with other programming languages
Lecture 1: Calling Python from R
Lecture 2: Calling R from Python
Lecture 3: Executing Java code in R
Lecture 4: Calling R from Java using Rserve
Chapter 8: Data processing
Lecture 1: The Sqldf package – Part 1
Lecture 2: The Sqldf package – Part 2
Instructors
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 7 votes
- 3 stars: 12 votes
- 4 stars: 19 votes
- 5 stars: 40 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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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