Advanced R

  • Course provided by Udemy
  • Study type: Online
  • Starts: Anytime
  • Price: See latest price on Udemy
Udemy

Course Description

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


Who this course is for:

  • 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

Instructor

  • 3.8 Instructor Rating
  • 424 Reviews
  • 21,908 Students
  • 9 Courses

I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software.

Expected Outcomes

  1. 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! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6778fb264c77ce3b',m:'6c36f4b1077b3786b530017f92545747c9ac4c0a-1627757098-1800-ATYGT608DYM2FM00ucsDRMj9VSYbq//eTcDe6fXcKaabht4kWzkjclBCPY1pGDRnzgkO8R+EzaQbG2T0lktNZecJGKFH74TlwIeWWEMGoMv4xj2/duFzXKo3gQn9Ib9ECsyj5p+XcBaOcec9wPPSIk0=',s:[0x654470ff40,0xe097e4ee5a],}})();
  2. 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! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6778fb264c77ce3b',m:'6c36f4b1077b3786b530017f92545747c9ac4c0a-1627757098-1800-ATYGT608DYM2FM00ucsDRMj9VSYbq//eTcDe6fXcKaabht4kWzkjclBCPY1pGDRnzgkO8R+EzaQbG2T0lktNZecJGKFH74TlwIeWWEMGoMv4xj2/duFzXKo3gQn9Ib9ECsyj5p+XcBaOcec9wPPSIk0=',s:[0x654470ff40,0xe097e4ee5a],}})();
  3. Do complex date parsing Profile and benchmark their programs Build parallel code Parse complex text via Regex And much more! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6778fb264c77ce3b',m:'6c36f4b1077b3786b530017f92545747c9ac4c0a-1627757098-1800-ATYGT608DYM2FM00ucsDRMj9VSYbq//eTcDe6fXcKaabht4kWzkjclBCPY1pGDRnzgkO8R+EzaQbG2T0lktNZecJGKFH74TlwIeWWEMGoMv4xj2/duFzXKo3gQn9Ib9ECsyj5p+XcBaOcec9wPPSIk0=',s:[0x654470ff40,0xe097e4ee5a],}})();
  4. Profile and benchmark their programs Build parallel code Parse complex text via Regex And much more! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6778fb264c77ce3b',m:'6c36f4b1077b3786b530017f92545747c9ac4c0a-1627757098-1800-ATYGT608DYM2FM00ucsDRMj9VSYbq//eTcDe6fXcKaabht4kWzkjclBCPY1pGDRnzgkO8R+EzaQbG2T0lktNZecJGKFH74TlwIeWWEMGoMv4xj2/duFzXKo3gQn9Ib9ECsyj5p+XcBaOcec9wPPSIk0=',s:[0x654470ff40,0xe097e4ee5a],}})();
  5. Build parallel code Parse complex text via Regex And much more! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6778fb264c77ce3b',m:'6c36f4b1077b3786b530017f92545747c9ac4c0a-1627757098-1800-ATYGT608DYM2FM00ucsDRMj9VSYbq//eTcDe6fXcKaabht4kWzkjclBCPY1pGDRnzgkO8R+EzaQbG2T0lktNZecJGKFH74TlwIeWWEMGoMv4xj2/duFzXKo3gQn9Ib9ECsyj5p+XcBaOcec9wPPSIk0=',s:[0x654470ff40,0xe097e4ee5a],}})();
  6. Parse complex text via Regex And much more! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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  7. And much more! Course content 8 sections • 19 lectures • 4h 41m total length Expand all sections General R topics 4 lectures • 57min Introduction Preview 04:29 Creating Packages 19:55 Packages 4 questions Functionals and closures 12:27 Functionals 2 questions Environments 19:58 Environments 3 questions Dates 1 lecture • 15min Parsing Dates 14:42 Dates 3 questions Regex 2 lectures • 18min Regex - Part 1 11:35 Regex - Part 2 06:03 Regex 5 questions Intenet 1 lecture • 20min Parsing Websites 19:59 Profiling and memory 1 lecture • 9min Profiling 09:17 Rcpp and high performance R-C++ computing 4 lectures • 1hr 9min Rcpp - Part 1 Preview 15:01 Rcpp 2 - Part 2 19:53 Rcpp sugar 14:04 Parallel computing 19:59 Rcpp 9 questions Interacting with other programming languages 4 lectures • 59min Calling Python from R 08:25 Calling R from Python 19:50 Executing Java code in R 19:49 Calling R from Java using Rserve 11:08 Python - R - Java 8 questions Data processing 2 lectures • 35min The Sqldf package - Part 1 16:50 The Sqldf package - Part 2 17:55 Sqldf 5 questions Requirements A few weeks experience with R is absolutely necessary, and ideally some months of experience would be better Being able to code functions, manipulate data, and be comfortable writing complex R code Some experience with other programming languages (such as Python - Java) would be beneficial, but it is not necessary Description 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 Who this course is for: 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 Show more Show less Instructor Francisco Juretig Mr 3.8 Instructor Rating 424 Reviews 21,908 Students 9 Courses I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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