Conditional Statements in R: if_else() and case_when() Functions
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- Опубликовано: 11 сен 2024
- Learn the if_else() and case_when() functions in R! Whether you're a beginner in R programming or an experienced data scientist looking to brush up on your skills, this video is perfect for you.
In this video, we delve into the essential conditional functions in R that every data analyst and data scientist should know. The if_else() and case_when() functions are powerful tools for data manipulation and statistical analysis. We'll cover:
Understanding the syntax and usage of if_else() and case_when().
Practical examples demonstrating how to apply these functions in real-world data analysis scenarios.
Tips and tricks for efficient coding in the R language.
Enhancing your data wrangling capabilities with conditional statements.
Comparison between if_else() and case_when(): when to use which function.
By the end of this video, you'll have a solid understanding of how to use these functions to streamline your data workflows and make your R programming more efficient. This tutorial is part of our R programming for beginners series, designed to help you build a strong foundation in data science using R.
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Thank you! The only confusing this the final argument: TRUE ~. This is a bit counterintuitive.....
I've had issues with if_else() being more restrictive with outputs and have switched to ifelse().
Let's say you want to output either a character or a number depending on your condition. For example, I want to convert all my 0 values (considering them as below LOQ) in variable_1 to "NA" and leave the rest as numeric values so I ca then use "na.exclude()":
mutate(variable_2 = if_else(variable_1 == 0, "NA", variable_2) will give me an error.
This same code works if I use "ifelse()" instead.
I was initially using embedded if else statements and eventually realized case_when is a lot more compact.
Fantastic video as always. However, why use if_else() from dplyr when there is ifelse() from base R?
Sure, if_else() has an extra argument option for processing missing values, that could still be handled with the base R version without much effort too. Sometimes I think the Tidyverse over-complicates code by offering more code choices when the base R version is equivalent or nearly as good.
Your content submission is rocket!
Thanks.. :)
Fantastic. I'm curious then, if you like the table you've made, and want to use it in a paper or presentation, how do you export that? Is it simply copy and paste, or is there a command we can use to export the table?
oh there is a way of getting the tables out - I'll do a video all about it soon (hard to get into the details here in the comments)
@@RProgramming101 fantastic. Appreciate it!
In the case_when example, both FALSE actions (when the 2 conditions were not met) were the same (putting original hair colour)
what if the FALSE action of the 2 conditions is different?
🎉 always love every post and always looking forward to more. Thank Greg# R_ Super hero 🦸🏽♂️
Very helpful.