Change the last line to
q + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
By default, the axes are aligned at the center of the text, even when rotated. When you rotate +/- 90 degrees, you usually want it to be aligned at the edge instead:
The image above is from this blog post.
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.
Best Answer
I too have been looking at Julia ever since Doug Bates sent me a heads-up in January. But like @gsk3, I measure this on an "Rcpp scale" as I would like to pass rich R objects to Julia. And that does not seem to be supported at all right now.
Julia has a nice and simple C interface. So that gets us something like
.C(). But as recently discussed on r-devel, you really do not want.C(), in most cases you rather want.Call()in order to pass actual SEXP variables representing real R objects. So right now I see little scope for Julia from R because of this limitation.Maybe an indirect interface using tcp/ip to Rserve could be a first start before Julia matures a little and we get a proper C++ interface. Or we use something based on Rcpp to get from from R to C++ before we enter an intermediate layer [which someone would have to write] from which we data feed to Julia, just like the actual R API only offers a C layer. I don't know.
And the end of the day, some patience may be needed. I started to look at R around 1996 or 1997 when Fritz Leisch made the first announcements on the comp.os.linux.announce newsgroup. And R had rather limited facilities then (but the full promise of the S language, of course, si we knew we had a winner). And a few years later I was ready to make it my primary modeling language. At that time CRAN had still way less than 100 packages...
Julia may well get there. But for now I suspect many of us will get work done in R, and have just a few curious glimpses at Julia.