Introduction to Writing Functions in R
Take your R skills up a notch by learning to write efficient, reusable functions.
- 4 hours
- 14 Videos
- 52 Exercises
- 9,952 Participants
- 4,350 XP
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Course Description
Being able to write your own functions makes your analyses more readable, with fewer errors, and more reusable from project to project. Function writing will increase your productivity more than any other skill! In this course you'll learn the basics of function writing, focusing on the arguments going into the function and the return values. You'll be writing useful data science functions, and using real-world data on Wyoming tourism, stock price/earnings ratios, and grain yields.
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1
How to write a function
FreeLearn why writing your own functions is useful, how to convert a script into a function, and what order you should include the arguments.
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Return values and scope
Learn how to return early from a function, how to return multiple values, and understand how R decides which variables exist.
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All about arguments
Learn how to set defaults for arguments, how to pass arguments between functions, and how to check that users specified arguments correctly.
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Case study on grain yields
Apply your function writing skills to a case study involving data preparation, visualization, and modeling.
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1
How to write a function
FreeLearn why writing your own functions is useful, how to convert a script into a function, and what order you should include the arguments.
-
All about arguments
Learn how to set defaults for arguments, how to pass arguments between functions, and how to check that users specified arguments correctly.
-
Return values and scope
Learn how to return early from a function, how to return multiple values, and understand how R decides which variables exist.
-
Case study on grain yields
Apply your function writing skills to a case study involving data preparation, visualization, and modeling.
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Richie Cotton
Curriculum Architect at DataCamp
Richie is a Curriculum Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
See MoreCollaborators
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Marianna Lamnina