By accident i've encountered strange behaviour of "[<-"
operator. It behaves differently depending on order of calls and whether i'm using RStudio or just ordinary RGui. I will make myself clear with an example.
x <- 1:10
"[<-"(x, 1, 111)
x[5] <- 123
As far as i know, first assigment shouldn't change x
(or maybe i'm wrong?), while the second should do. And in fact the result of above operations is
x
[1] 1 2 3 4 123 6 7 8 9 10
However, when we perform these operations in different order, results are different and x
has changed! Meaningly:
x <- 1:10
x[5] <- 123
"[<-"(x, 1, 111)
x
[1] 111 2 3 4 123 6 7 8 9 10
But it only happens when i'm using plain R! In RStudio the behaviour is the same in both options. I've checked it on two machines (one with Fedora one with Win7) and the situation looks exactly the same.
I know the 'functional' version ("[<-"(x..)
) is probably never used but i'm very curious why it is happening. Could anyone explain that?
==========================
EDIT:
Ok, so from comments i get that the reason was that x <- 1:10
has type 'integer' and after replacing x[5] <- 123
it's 'double'.
But still remains question why behaviour is different in RStudio? I restart R session and it doesn't change anything.
The result of the %% operator is the REMAINDER of a division, Eg. 75%%4 = 3. I noticed if the dividend is lower than the divisor, then R returns the same dividend value.
%>% is called the forward pipe operator in R. It provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression.
OR Operator “|” The OR operator (|) works similarly, but the difference is that only at least one of the logical values should be equal to TRUE for the entire OR operation to evaluate to TRUE . This means that TRUE | TRUE equals TRUE , but also, TRUE | FALSE and FALSE | TRUE evaluates to TRUE .
Generally speaking, the $ operator is used to extract or subset a specific part of a data object in R. For instance, this can be a data frame object or a list. In this example, I'll explain how to extract the values in a data frame columns using the $ operator.
Moreover, logical operators allow us to change or compare the results. There are various types of operators available in R, and logical is one of them; we will talk about the OR operator. The OR is a built-in R logical operator that returns TRUE if one of the conditions is TRUE. If both conditions are FALSE, then it will return FALSE.
Following table shows the logical operators supported by R language. It is applicable only to vectors of type logical, numeric or complex. All numbers greater than 1 are considered as logical value TRUE.
The R packages dplyr and sf import the operator %>% from the R package magrittr. The documentation of the magrittr forward-pipe operator gives a good example: When functions require only one argument, x %>% f is equivalent to f (x)
Operation on Vectors. The above mentioned operators work on vectors. The variables used above were in fact single element vectors. We can use the function c() (as in concatenate) to make vectors in R. All operations are carried out in element-wise fashion.
Rstudio's object browser modifies objects it examines in a way that forces copying upon modification. Specifically, the object browser employs at least one R function whose call internally forces evaluation of the object, in the process resetting the value of the object's named field from 1 to 2. From the R-Internals manual:
When an object is about to be altered, the named field is consulted. A value of 2 means that the object must be duplicated before being changed. [...] A value of 1 is used for situations [...] where in principle two copies of a exist for the duration of the computation [...] but for no longer, and so some primitive functions can be optimized to avoid a copy in this case.
To see that the object browser modifies the named field ([NAM()]
in the next code block), compare the results of running the following lines. In the first, both 'lines' are run together, so that Rstudio has no time to 'touch' X
before its structure is queried. In the second, each line is pasted in separately, so X
is modified before it is examined.
## Pasted in together
x <- 1:10; .Internal(inspect(x))
# @46b47b8 13 INTSXP g0c4 [NAM(1)] (len=10, tl=0) 1,2,3,4,5,...
## Pasted in with some delay between lines
x <- 1:10
.Internal(inspect(x))
# @42111b8 13 INTSXP g0c4 [NAM(2)] (len=10, tl=0) 1,2,3,4,5,...
Once the named field is set to 2, [<-(X, ...)
will not modify the original object. Pasting the following into Rstudio all at once modifies X
, while pasting it in line-by-line does not:
x <- 1:10
"[<-"(x, 1, 111)
One more consequence of all this is that Rstudio's object browser actually makes some operations slower than they would otherwise be. Again, compare the same two commands first pasted in together, and then one at a time:
## Pasted in together
x <- 1:5e7
system.time(x[1] <- 9L)
# user system elapsed
# 0 0 0
## Pasted in one at a time
x <- 1:5e7
system.time(x[1] <- 9L)
# user system elapsed
# 0.11 0.04 0.16
The behavior of [<-
w.r.t. modifying a vector X
depends on the storage types of X
and of the element being assigned into it. This explains R
's behavior but not Rstudio's.
In R, when [<-
either appends to a vector X
, or performs a subassignment that requires that X
's type be modified, X
is copied and the value that is returned does not overwrite the pre-existing variable X
. (To do that you need to do something like X <- "[<-(X, 2, 100)
.
So, neither of the following modify X
X <- 1:2 ## Note: typeof(X) --> "integer"
## Subassignment that requires that X be coerced to "numeric" type
"[<-"(X, 2, 100) ## Note: typeof(100) --> "numeric"
X
# [1] 1 2
## Appending to X
"[<-"(X, 3, 100L)
X
# [1] 1 2
Whenever possible, though, R does allow the [<-
function to modify X
directly by reference (i.e. without making a copy). "Possible" here includes cases in which a sub-assignment doesn't require that X
's type be modified.
So all of the following modify X
X <- c(0i, 0i, 0i, 0i)
"[<-"(X, 1, TRUE)
"[<-"(X, 2, 20L)
"[<-"(X, 3, 3.14)
"[<-"(X, 4, 5+5i)
X
# [1] 1.00+0i 20.00+0i 3.14+0i 5.00+5i
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