Hadley Wickham "Data Science with R"
Why Stack Overflow recommends it
Stack Overflow users link it when explaining the %>% pipe and the tidyverse approach to people who are new to R.
Recommended by 1 people across 2 different months, 2018 to 2022.
What it is
A 2016 talk at Reed College in which Hadley Wickham of RStudio walks through the data science workflow of importing, tidying, transforming, visualising and modelling data in R. It centres on the magrittr pipe and how tidyr, dplyr, ggplot2 and purrr fit together in one readable pipeline, and it is pitched so newcomers to R can follow.
What people said
Recommends DJV · 2018-04 · Stack Overflow · on “correlogram with reliabilities in the main diagonal”
Hi, the %>% (pipe) is part of the magrittr/tidyvesre packages. Just install this paackges and you'll be ok :-) Regardless to your question, I recommend you to read about it: [LINK] or [this video] And glad to help :-) if this answer solved your problem please mark it as accepted by clicking the check mark next to the answer ( [LINK] )
Recommends DJV · 2018-04 · Stack Overflow · on “How to add a palette color into a scatterplot graph in R”
The tidyverse package (and all the packages within it) are amazing!. If you want to know more about the tidyverse/tidy-data approach I would have recommend reading this ( [LINK] ), you can also look at this video ( [this video] ) and/or look for things (mainly) by Hadley Wickham. In addition, I'm glad to help, if this answer solved your problem please mark it as accepted by clicking the check mark next to the answer.
Recommends DJV · 2018-05 · Stack Overflow · on “Filter variable based on NA > 20% in a range—R”
About where to go for on tidyverse. I would've youtube "hadley wickham". It's old but the idea is the same: [LINK] or [this video] and you can got to the tidyverse website, they explain the all philosophy with examples: [LINK]
Cites Marian Minar · 2018-11 · Stack Overflow · on “R - How to generate parameterized reports via nested tibbles + pwalk(rmarkdown::render)”
I'm trying to generate assessment reports based on student data. My idea was to create a RDS (nested tibble) to pass along to rmarkdown::render() using purrr::pwalk() . I'm relatively new to purrr and its map family of functions. Here's the part of the code that works (try it out): library(tidyverse) # assignment tibble assignments <- tribble( ~ FullName, ~Assignment, ~Type, ~Score, ~Due, "Friendly, Officer", "First Assignment", "Assignment", 4, "2018-11-01", "Friendly, Officer", "Second Assignment", "Assignment", 2, "2018-11-03", "OLantern, Jack", "First Assignment", "Assignment", 3, "2018-11-01", "OLantern, Jack", "Second Assignment", "Assignment", 3, "2018-11-03" ) # personal data RDS per
Cites TarJae · 2022-02 · Stack Overflow · on “Should I use %$% instead of %>%?”
In addition to the provided comments: %$% also called the Exposition pipe vs. %>% : This is a short summary of this article [LINK] "The key difference in using %$% or %>% lies in the type of arguments of used functions." One advantage, and as far as I can understand it, for me the only one to use %$% over %>% is the fact that we can avoid repetitive input of the dataframe name in functions that have no data as an argument. For example the lm() has a data argument. In this case we can use both %>% and %$% interchangeable . But in functions like the cor() which has no data argument : mtcars %>% cor(disp, mpg) # Will give an Error cor(mtcars$disp, mtcars$mpg) is equivalent to mtcars %$% cor(dis