The difference in assignment operators is clearer when you use them to set an argument value in a function call. For example:
median(x = 1:10)
x
## Error: object 'x' not found
In this case, x is declared within the scope of the function, so it does not exist in the user workspace.
median(x <- 1:10)
x
## [1] 1 2 3 4 5 6 7 8 9 10
In this case, x is declared in the user workspace, so you can use it after the function call has been completed.
There is a general preference among the R community for using <- for assignment (other than in function signatures) for compatibility with (very) old versions of S-Plus. Note that the spaces help to clarify situations like
x<-3
# Does this mean assignment?
x <- 3
# Or less than?
x < -3
Most R IDEs have keyboard shortcuts to make <- easier to type. Ctrl + = in Architect, Alt + - in RStudio (Option + - under macOS), Shift + - (underscore) in emacs+ESS.
If you prefer writing = to <- but want to use the more common assignment symbol for publicly released code (on CRAN, for example), then you can use one of the tidy_* functions in the formatR package to automatically replace = with <-.
library(formatR)
tidy_source(text = "x=1:5", arrow = TRUE)
## x <- 1:5
The answer to the question "Why does x <- y = 5 throw an error but not x <- y <- 5?" is "It's down to the magic contained in the parser". R's syntax contains many ambiguous cases that have to be resolved one way or another. The parser chooses to resolve the bits of the expression in different orders depending on whether = or <- was used.
To understand what is happening, you need to know that assignment silently returns the value that was assigned. You can see that more clearly by explicitly printing, for example print(x <- 2 + 3).
Secondly, it's clearer if we use prefix notation for assignment. So
x <- 5
`<-`(x, 5) #same thing
y = 5
`=`(y, 5) #also the same thing
The parser interprets x <- y <- 5 as
`<-`(x, `<-`(y, 5))
We might expect that x <- y = 5 would then be
`<-`(x, `=`(y, 5))
but actually it gets interpreted as
`=`(`<-`(x, y), 5)
This is because = is lower precedence than <-, as shown on the ?Syntax help page.
There's not much of one in everyday work.
However, according to the documentation for both functions (accessed by putting a ? before the function name and hitting enter), require is used inside functions, as it outputs a warning and continues if the package is not found, whereas library will throw an error.
Best Answer
As far as I understand and from my setup there is no coding difference. The difference is in the rendering. The file extension is the same.
When you make a new R Notebook it adds
html_notebookin the output option in the header. That's the difference. You can then preview the rendering quickly without having to knit it. It also refreshes the preview every time you save. However in that preview you don't have the code output (no figures, no tables..) (at least in my setup). Withouthtml_notebookin the output there is no button previewas you can see the Preview options shows up but you can also knit it in any format you want. It will add it to the header code when you do so.
However if you don't have that
html_notebookin your header, you can only knit your code to see what it looks like (the entire book) (please ignore the additional default option I put in with the picture)and the option to preview doesn't show in the drop down menu
Otherwise it works the same. For some default configuration the output is also hidden by default in the code section.
Note that you can mix several output options in your header so that you can keep the preview and keep your knit options for export.