I am quite new to R and I have found one vector operation frustrating:
I just want to know an index of current element of the list while using sapply, let's say: to print an index, but all my trials do not work, e.g.:
> test <- sapply(my.list.of.matrices,
function(x) print(which(my.list.of.matrices == x)))
Error in which(my.list.of.matrices == x) :
(list) object cannot be coerced to type 'logical'
In addition: Warning message:
In my.list.of.matrices == x :
longer object length is not a multiple of shorter object length
That's not possible unless you pass an index vector. sapply and lapply just pass the elements. And in that case it becomes a disguised for-loop.
Related
Using the default "iris" DataFrame in R, how come when creating a new column "NewCol"
iris[,'NewCol'] = as.POSIXlt(Sys.Date()) # throws Warning
BUT
iris$NewCol = as.POSIXlt(Sys.Date()) # is correct
This issue doesn't exist when assigning Primitive types like chr, int, float, ....
First, notice as #sindri_baldur pointed, as.POSIXlt returns a list.
From R help ($<-.data.frame):
There is no data.frame method for $, so x$name uses the default method which treats x as a list (with partial matching of column names if the match is unique, see Extract). The replacement method (for $) checks value for the correct number of rows, and replicates it if necessary.
So, if You try iris[, "NewCol"] <- as.POSIClt(Sys.Date()) You get warning that You're trying assign a list object to a vector. So only the first element of the list is used.
Again, from R help:
"For [ the replacement value can be a list: each element of the list is used to replace (part of) one column, recycling the list as necessary".
And in Your case, only one column is specified meaning only the first element of the as.POSIXlt's result (list) will be used. And You are warned of that.
Using $ syntax the iris data.frame is treated as a list and then the result of as.POSIXlt - a list again - is appended to it. Finally, the result is data.frame, but take a look at the type of the NewCol - it's a list.
iris[, "NewCol"] <- as.POSIXlt(Sys.Date()) # warning
iris$NewCol2 <- as.POSIXlt(Sys.Date())
typeof(iris$NewCol) # double
typeof(iris$NewCol2) # list
Suggestion: maybe You wanted to use as.POSIXct()?
I'm trying to convert a data.frame in R to mpfr format by multiplying by an mpfr unit constant. This works, as demonstrated in the code below, when applied to a column (result variable 'mpfr_col'), but for both approaches shown for working with a data.frame, it does not. The relevant errors for each attempt are listed in comment.
library(Rmpfr)
prec <- 256
m1 <- mpfr(1,prec)
col_build <- 1:10
test_df <- data.frame(col_build, col_build, col_build)
mpfr_col <- m1*(col_build)
mpfr_df <- m1*test_df # (list) object cannot be coerced to type 'double'
for(colnum in 1:length(colnames(test_df))){
test_df[,colnum] <- m1*test_df[,colnum] # attempt to replicate an object of type 'S4'
}
Answer:
Use [[colnum]] to access the columns instead of [,colnum]:
for(colnum in length(colnames(test_df))){
test_df[[colnum]] <- m1*test_df[[colnum]]
}
(Note: the print method of data.frame will fail, but the 'mpfr-izing' work. You can print it either by printing the columns individually or using as_tibble(test_df).
Explanation
The original fails because the [,colnum] assignment doesn't coerce the argument, I think. Using [[ returns an element (aka a column) of the list (aka the data.frame).
See this bit of Hadley Wickham's Advanced R book:
[ selects sub-lists. It always returns a list; if you use it with a
single positive integer, it returns a list of length one. [[ selects
an element within a list. $ is a convenient shorthand: x$y is
equivalent to x[["y"]].
And the help from Extract.data.frame {base}:
When [ and [[ are used to add or replace a whole column, no coercion
takes place but value will be replicated (by calling the generic
function rep) to the right length if an exact number of repeats can be
used.
Normally I wonder where mysterious errors come from but now my question is where a mysterious lack of error comes from.
Let
numbers <- c(1, 2, 3)
frame <- as.data.frame(numbers)
If I type
subset(numbers, )
(so I want to take some subset but forget to specify the subset-argument of the subset function) then R reminds me (as it should):
Error in subset.default(numbers, ) :
argument "subset" is missing, with no default
However when I type
subset(frame,)
(so the same thing with a data.frame instead of a vector), it doesn't give an error but instead just returns the (full) dataframe.
What is going on here? Why don't I get my well deserved error message?
tl;dr: The subset function calls different functions (has different methods) depending on the type of object it is fed. In the example above, subset(numbers, ) uses subset.default while subset(frame, ) uses subset.data.frame.
R has a couple of object-oriented systems built-in. The simplest and most common is called S3. This OO programming style implements what Wickham calls a "generic-function OO." Under this style of OO, an object called a generic function looks at the class of an object and then applies the proper method to the object. If no direct method exists, then there is always a default method available.
To get a better idea of how S3 works and the other OO systems work, you might check out the relevant portion of the Advanced R site. The procedure of finding the proper method for an object is referred to as method dispatch. You can read more about this in the help file ?UseMethod.
As noted in the Details section of ?subset, the subset function "is a generic function." This means that subset examines the class of the object in the first argument and then uses method dispatch to apply the appropriate method to the object.
The methods of a generic function are encoded as
< generic function name >.< class name >
and can be found using methods(<generic function name>). For subset, we get
methods(subset)
[1] subset.data.frame subset.default subset.matrix
see '?methods' for accessing help and source code
which indicates that if the object has a data.frame class, then subset calls the subset.data.frame the method (function). It is defined as below:
subset.data.frame
function (x, subset, select, drop = FALSE, ...)
{
r <- if (missing(subset))
rep_len(TRUE, nrow(x))
else {
e <- substitute(subset)
r <- eval(e, x, parent.frame())
if (!is.logical(r))
stop("'subset' must be logical")
r & !is.na(r)
}
vars <- if (missing(select))
TRUE
else {
nl <- as.list(seq_along(x))
names(nl) <- names(x)
eval(substitute(select), nl, parent.frame())
}
x[r, vars, drop = drop]
}
Note that if the subset argument is missing, the first lines
r <- if (missing(subset))
rep_len(TRUE, nrow(x))
produce a vector of TRUES of the same length as the data.frame, and the last line
x[r, vars, drop = drop]
feeds this vector into the row argument which means that if you did not include a subset argument, then the subset function will return all of the rows of the data.frame.
As we can see from the output of the methods call, subset does not have methods for atomic vectors. This means, as your error
Error in subset.default(numbers, )
that when you apply subset to a vector, R calls the subset.default method which is defined as
subset.default
function (x, subset, ...)
{
if (!is.logical(subset))
stop("'subset' must be logical")
x[subset & !is.na(subset)]
}
The subset.default function throws an error with stop when the subset argument is missing.
I am using lapply to select elements from vectors in a list, but not all vectors in the list include the same number of elements. I typically use:
lapply(some.list,"[[",n)
were n is the index of the element in the vectors I am trying to parse out. However, this time my list looks more like this:
some.vect <- c("aaa_elem1","aa_elem2","elem3","bb_elem4","ccc_elem5","abc_elem6")
some.list <- strsplit(some.vect,"_")
When I use my normal lapply method:
lapply(some.list,"[[",2)
I get the following error: Error in FUN(X[[3L]], ...) : subscript out of bounds as expected, because not all vectors in the list have two elements. What I would like is a way to declare the index in lapply as the length of the vector.
I also tried defining a vector of the list vector lengths, and assigning it to index:
vect.length <- unlist(lapply(some.list,length))
lapply(some.list,"[[",vect.length)
(Error in FUN(X[[1L]], ...) : attempt to select more than one element)
and not assigning an index at all:
lapply(some.list,"[[")
(Error in FUN(X[[1L]], ...) : no index specified)
Is there a way to select all of the last elements of vectors in a list?
Use tail...
lapply(some.list, tail , 1 )
I'd like to know the reason why the following does not work on the matrix structure I have posted here (I've used the dput command).
When I try running:
apply(mymatrix, 2, sum)
I get:
Error in FUN(newX[, i], ...) : invalid 'type' (list) of argument
However, when I check to make sure it's a matrix I get the following:
is.matrix(mymatrix)
[1] TRUE
I realize that I can get around this problem by unlisting the data into a temp variable and then just recreating the matrix, but I'm curious why this is happening.
?is.matrix says:
'is.matrix' returns 'TRUE' if 'x' is a vector and has a '"dim"'
attribute of length 2) and 'FALSE' otherwise.
Your object is a list with a dim attribute. A list is a type of vector (even though it is not an atomic type, which is what most people think of as vectors), so is.matrix returns TRUE. For example:
> l <- as.list(1:10)
> dim(l) <- c(10,1)
> is.matrix(l)
[1] TRUE
To convert mymatrix to an atomic matrix, you need to do something like this:
mymatrix2 <- unlist(mymatrix, use.names=FALSE)
dim(mymatrix2) <- dim(mymatrix)
# now your apply call will work
apply(mymatrix2, 2, sum)
# but you should really use (if you're really just summing columns)
colSums(mymatrix2)
The elements of your matrix are not numeric, instead they are list, to see this you can do:
apply(m,2, class) # here m is your matrix
So if you want the column sum you have to 'coerce' them to be numeric and then apply colSums which is a shortcut for apply(x, 2, sum)
colSums(apply(m, 2, as.numeric)) # this will give you the sum you want.