Essential Fundamentals of R

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

Course Description

Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary.

Who this course is for:

  • Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R
  • People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets
  • College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software
  • Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software.

Instructor

Associate Professor of Information Systems
  • 4.2 Instructor Rating
  • 3,752 Reviews
  • 29,037 Students
  • 27 Courses

Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.

Expected Outcomes

  1. Install R and RStudio and engage in a basic R session Understand the characteristics of different data types and structures in R Be able to read in data and write out data files from various sources Sort, select, filter, subset, and manipulate tables of data in R Create and execute their own user-defined functions in an R session Understand how to use the apply() family of functions to execute various actions against different R data structures Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();
  2. Understand the characteristics of different data types and structures in R Be able to read in data and write out data files from various sources Sort, select, filter, subset, and manipulate tables of data in R Create and execute their own user-defined functions in an R session Understand how to use the apply() family of functions to execute various actions against different R data structures Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();
  3. Be able to read in data and write out data files from various sources Sort, select, filter, subset, and manipulate tables of data in R Create and execute their own user-defined functions in an R session Understand how to use the apply() family of functions to execute various actions against different R data structures Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();
  4. Sort, select, filter, subset, and manipulate tables of data in R Create and execute their own user-defined functions in an R session Understand how to use the apply() family of functions to execute various actions against different R data structures Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();
  5. Create and execute their own user-defined functions in an R session Understand how to use the apply() family of functions to execute various actions against different R data structures Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();
  6. Understand how to use the apply() family of functions to execute various actions against different R data structures Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();
  7. Know how to use reshaping and recoding "short cuts" for changing data types and for rearranging data structures. Course content 7 sections • 46 lectures • 10h 32m total length Expand all sections Introduction and Orientation 8 lectures • 2hr 1min Introduction to R Software Preview 14:56 What is R? Preview 14:24 Workspace Management Controls 15:37 Workspace Management R Manuals 12:58 Hands-On Tutorial of R Basics (part 1) 14:35 Hands-On Tutorial of R Basics (part 2) 14:53 Tutorial with R Functions 13:54 Distributional Functions and Plotting 19:25 Input and Output, Data and Data Structures 9 lectures • 1hr 46min Data Input and Output 14:44 Accessing Data Sets in R 14:40 Basic Data Structures (part 1) 14:47 Basic Data Structures (part 2) Preview 14:42 Basic Data Structures (part 3) 14:35 Manipulating Dataframes (part 1) 16:04 Manipulating Dataframes (part 2) 10:48 Input Output Exercises 01:04 Dataframe Manipulation Exercises 04:11 Manipulating Dataframes in Depth 6 lectures • 1hr 22min Input Output Exercises Solution 14:21 Data Manipulation Exercise Solution 14:25 Manipulating Dataframes (part 3) Preview 07:48 Manipulating Dataframes (part 4) 14:24 Manipulating Dataframes (part 5) 18:37 Manipulating Dataframes (part 6) 12:12 User-Defined Functions in R 6 lectures • 1hr 26min Remaining Data Manipulation Exercises Solutions 14:58 User-Defined Function Exercise and Finish Manipulating Dataframes 15:43 Begin User-Defined Functions Demonstrations 14:06 The 'Scope' of a Function 14:16 Formal, Local and Free Parameters Preview 14:42 Flexible Arguments to Functions 12:06 Writing Functions in R 6 lectures • 1hr 27min User-Defined Functions Exercise Solution 13:22 More on User-Defined Functions 14:31 Loops and Repeats 15:13 Control Statements 16:05 Returning Values from a Function Preview 15:29 Anonymous Functions 12:33 The Apply Family of Functions 6 lectures • 1hr 25min Some Short Programs in R (part 1) 15:13 Some Short Programs in R (part 2) 16:02 The Apply family of Functions (part 1) 16:11 The Apply Family of Functions (part 2) Preview 17:35 The Apply Family of Functions (part 3) 11:47 Apply Functions Exercises 08:22 Reshaping and Recoding Data 5 lectures • 1hr 6min Apply Functions Exercises Solutions 14:24 The Reshape Package in R 12:04 Recoding Data in R (part 1) 16:57 Recoding Data in R (part 2) Preview 15:50 More Vector-Maker Exercises 06:58 Requirements Students will need to install both R software and RStudio (instructions are provided) Description Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. Who this course is for: Anyone who is interested in learning to use R software who is relatively new (or 'brand new') to using R People who wish to learn the essential fundamentals of using R including data types and structures, inputting and outputting data and files, writing user-defined functions, and manipulating data sets College undergrads and/or graduate students who are looking for an alternative to using SAS or SPSS software Professionals engaged in quantitative analyses and/or data analyses tasks who seek an alternative to using SAS and/or SPSS software. Show more Show less Instructor Geoffrey Hubona, Ph.D. Associate Professor of Information Systems 4.2 Instructor Rating 3,752 Reviews 29,037 Students 27 Courses Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. 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:'677816cd090240c6',m:'1da06e931bab0390a91d04bc10bc6658cc07210b-1627747746-1800-ATyORO2SExyknKm6T1F4bkh9V/aWgSVh8PA334ykcTsOgrenvv/YwciOtnBXIyp+53A6mNQdmxEa1FcCEUGo9in9SPeDxWDA8tRfF7U6ZZKfcwkPTvBKaK6Z0E/bRmUFlIsgQ9IbrsAGf2EbrA5TXHvhDs/ohHpEvEuhzyZITcRn',s:[0x6675349ce1,0x21ba06422d],}})();