this might be quiet a strange question but...
I have 3 vectors:
myseq=seq(8,22,1)
myseqema3=seq(3,4,1)
myseqema15=seq(10,20,1)
And I want to assign the results to my list:
SLResultsloop=vector(mode="list")
With this loop:
for (i in myseq){
for(j in myseqema3){
for( k in myseqema15){
SLResultsloop[[i-7]]= StopLoss(data=mydata,n=i,EMA3=j,EMA15=k)
names(SLResultsloop[[i-7]])=rep(paste("RSI=",i,"EMA3=",j,"EMA15=",k,sep="|"),
length=length(SLResultsloop[[i-7]]))
}
}
}
The problem is as follows: the loop above overrides the list elements. So does any one have a clever solution about how to assign the loopresults to unique list elements (without overriding previous results)?
One solution could be to assign the output to different lists but it is a bit of an ugly solution...
Best Regards
You can skip the loops entirely by using expand.grid and apply (or something similar):
g <-
expand.grid(myseq = myseq,
myseqema3 = myseqema3,
myseqema15 = myseqema15)
apply(g, 1, function(a) {
StopLoss(data=mydata, n=a[1], EMA3=a[2], EMA15=a[3])
})
You can then build your names for each element of the return value from apply using something like:
paste("RSI=",g[,1], "EMA3=", g[,2],"EMA15=", g[,3], sep="|")
Related
I used to work in C++ and I think I am misunderstanding how for-loops (or iterations) work in R. I want to change list items in a for loop, but the for loop seems to make a temporary copy and only change that? How can I prevent this? This seems like a trivial beginners question, but I was unable to find a tutorial / question on stackoverflow about why this happens.
Code:
myList <- list(a=1, b=1, c=1, d=1)
for(item in myList){item <- 3}
myList
# Expected output: 3,3,3,3 - Real output: 1,1,1,1
# Additionally, I now have a variable "item" with value 3.
for(item in myList) creates a new object called item
If you want to refer to the items from the list, it would be better to do it by calling either their position with myList[1], or their name with myList[["a"]].
You can for-loop through the list by using the index (as one of the comments suggested).
myList <- list(a=1, b=2, c=4, d=5)
for(i in 1:length(myList)){
myList[i] <- 3
}
myList
But I would recomment a vector approach. Check this out:
myList <- list(a=1, b=2, c=1, d=5)
myList=='1'
myList[myList=='1']=3
myList
myList[names(myList)=='a']=9
myList
Now you do not have any redundant variables.
This is actually the recommended approach in R. For-loops are too computationally expensive.
As stated by #nicola, lapply should be a good option. Here is an example based on your question.
myList <- list(a = 1, b = 1, c = 1, d = 1) # output: 1,1,1,1
lapply(myList, function(x) 3) # output: 3,3,3,3
# lapply iterates over every list item
Say I created a list and gave some names to the objects in the list.
list = rep(0, times = 20)
list = lapply(list, initialize_list)
names(list) = names
Now I want to iterate through all the objects in the list. I can perform any operation on the object, but I just can't find a way to get the name of the object at the same time. Is this possible to achieve in either a for loop or a lapply call without resorting to explicitly writing
for (name in names(myList)) {
v = myList[[name]]
...
}
?
The existing answers don't seem to be exactly what I want, and I seemed to only find a relevant blog post which provided the above seemingly clumsy solution.
You can just use the index of the name.
lst = rep(0, times = 20)
lst = lapply(lst, sum)
names(lst) = letters[1:20]
for (id in seq_along(names(lst))) {
v = names(lst)[id]
}
When I have data.frame objects, I can simply do View(df), and then I get to see the data.frame in a nice table (even if I can't see all of the rows, I still have an idea of what variables my data contains).
But when I have a list object, the same command does not work. And when the list is large, I have no idea what the list looks like.
I've tried head(mylist) but my console simply cannot display all of the information at once. What's an efficient way to look at a large list in R?
Here's a few ways to look at a list:
Look at one element of a list:
myList[[1]]
Look at the head of one element of a list:
head(myList[[1]])
See the elements that are in a list neatly:
summary(myList)
See the structure of a list (more in depth):
str(myList)
Alternatively, as suggested above you could make a custom print method as such:
printList <- function(list) {
for (item in 1:length(list)) {
print(head(list[[item]]))
}
}
The above will print out the head of each item in the list.
I use str to see the structure of any object, especially complex list's
Rstudio shows you the structure by clicking at the blue arrow in the data-window:
You can also use a package called listviewer
library(listviewer)
jsonedit( myList )
If you have a really large list, you can look at part of it using
str(myList, max.level=1)
(If you don't feel like typing out the second argument, it can be written as max=1 since there are no other arguments that start with max.)
I do this often enough that I have an alias in my .Rprofile for it:
str1 <- function(x, ...) str(x, max.level=1, ...)
And a couple others that limit the printed output (see example(str) for an example of using list.len):
strl <- function(x, len=10L, ...) str(x, list.len=len, ...) # lowercase L in the func name
str1l <- function(x, len=10L, ...) str(x, max.level=1, list.len=len, ...)
you can check the "head" of your dataframes using lapply family:
lapply(yourList, head)
which will return the "heads" of you list.
For example:
df1 <- data.frame(x = runif(3), y = runif(3))
df2 <- data.frame(x = runif(3), y = runif(3))
dfs <- list(df1, df2)
lapply(dfs, head)
Returns:
> lapply(dfs, head)
[[1]]
x y
1 0.3149013 0.8418625
2 0.8807581 0.5048528
3 0.2490966 0.2373453
[[2]]
x y
1 0.4132597 0.5762428
2 0.0303704 0.3399696
3 0.9425158 0.5465939
Instead of "head" you can use any function related to the data.frames, i.e. names, nrow...
Seeing as you explicitly specify that you want to use View() with a list, this is probably what you are looking for:
View(myList[[x]])
Where x is the number of the list element that you wish to view.
For example:
View(myList[[1]])
will show you the first element of the list in the standard View() format that you will be used to in RStudio.
If you know the name of the list item you wish to view, you can do this:
View(myList[["itemOne"]])
There are several other ways, but these will probably serve you best.
This is a simple edit of giraffehere's excellent answer.
For some lists it is convenient to only print the head of a subset of the nested objects, to print the name of the given slot above the output of head().
Arguments:
#'#param list a list object name
#'#param n an integer - the the objects within the list that you wish to print
#'#param hn an integer - the number of rows you wish head to print
USAGE: printList(mylist, n = 5, hn = 3)
printList <- function(list, n = length(list), hn = 6) {
for (item in 1:n) {
cat("\n", names(list[item]), ":\n")
print(head(list[[item]], hn))
}
}
For numeric lists, output may be more readable if the number of digits is limited to 3, eg:
printList <- function(list, n = length(list), hn = 6) {
for (item in 1:n) {
cat("\n", names(list[item]), ":\n")
print(head(list[[item]], hn), digits = 3)
}
}
I had a similar problem and managed to solve it using as_tibble() on my list (dplyr or tibble packages), then just use View() as usual.
In recent versions of RStudio, you can just use View() (or alternatively click on the little blue arrow beside the object in the Global Environment pane).
For example, if we create a list with:
test_list <- list(
iris,
mtcars
)
Then either of the above methods will show you:
I like using as.matrix() on the list and then can use the standard View() command.
I have a problem with my loop. It just delete some rows that have 0 or NA values in my desire column and I don't know why:
for (i in 1:105) {
for (j in 1:l[i+1]){
if(m[[i]][j,12]==0 | is.na(m[[i]][j,12])) {
m[[i]]=m[[i]][-j,]
}
}
}
Searching on the web I saw that maybe I could use apply function... something like:
for( i in 1:105){m[[i]]<-m[[i]][!apply(is.na(m[[i]]), 1, any),]}
for( i in 1:105){
as.null(0)
m[[i]]<-m[[i]][!apply(is.null(m[[i]]), 1, any),]
}
This throws me a dim(x) error... I want to set Zero number as NULL
I was thinking something as follows but clearly it isn't good... it just the idea.... I really don't know how to use apply function well
for( i in 1:105){as.null(0) m[[i]]<-!apply(m[[i]],1,is.null(m[[i]])) }
Thanks a lot for your useful help !
You use apply to apply a function over a margin of an array, but I think is not the best idea here, since you only need to subset the matrix properly. Let's focus in just one matrix m.
ind = m[,12] == 0 | is.na(m[,12])
ind will have TRUE where appropiate and the you can do
m = m[!ind, ] # m is a matrix, not the list
to remove the rows. You can put this inside the loop, or use lapply (to apply a function over a list), but first you need a function to be applied to every element in the list (all your 105 matrix), so
removeRows = function(m) {
ind = m[,12] == 0 | is.na(m[,12])
m = m[!ind, ]
return(m)
}
m = lapply(m, FUN=removeRows)
That should work.
I am trying to come up with a variant of mapply (call it xapply for now) that combines the functionality (sort of) of expand.grid and mapply. That is, for a function FUN and a list of arguments L1, L2, L3, ... of unknown length, it should produce a list of length n1*n2*n3 (where ni is the length of list i) which is the result of applying FUN to all combinations of the elements of the list.
If expand.grid worked to generate lists of lists rather than data frames, one might be able to use it, but I have in mind that the lists may be lists of things that won't necessarily fit into a data frame nicely.
This function works OK if there are exactly three lists to expand, but I am curious about a more generic solution. (FLATTEN is unused, but I can imagine that FLATTEN=FALSE would generate nested lists rather than a single list ...)
xapply3 <- function(FUN,L1,L2,L3,FLATTEN=TRUE,MoreArgs=NULL) {
retlist <- list()
count <- 1
for (i in seq_along(L1)) {
for (j in seq_along(L2)) {
for (k in seq_along(L3)) {
retlist[[count]] <- do.call(FUN,c(list(L1[[i]],L2[[j]],L3[[k]]),MoreArgs))
count <- count+1
}
}
}
retlist
}
edit: forgot to return the result. One might be able to solve this by making a list of the indices with combn and going from there ...
I think I have a solution to my own question, but perhaps someone can do better (and I haven't implemented FLATTEN=FALSE ...)
xapply <- function(FUN,...,FLATTEN=TRUE,MoreArgs=NULL) {
L <- list(...)
inds <- do.call(expand.grid,lapply(L,seq_along)) ## Marek's suggestion
retlist <- list()
for (i in 1:nrow(inds)) {
arglist <- mapply(function(x,j) x[[j]],L,as.list(inds[i,]),SIMPLIFY=FALSE)
if (FLATTEN) {
retlist[[i]] <- do.call(FUN,c(arglist,MoreArgs))
}
}
retlist
}
edit: I tried #baptiste's suggestion, but it's not easy (or wasn't for me). The closest I got was
xapply2 <- function(FUN,...,FLATTEN=TRUE,MoreArgs=NULL) {
L <- list(...)
xx <- do.call(expand.grid,L)
f <- function(...) {
do.call(FUN,lapply(list(...),"[[",1))
}
mlply(xx,f)
}
which still doesn't work. expand.grid is indeed more flexible than I thought (although it creates a weird data frame that can't be printed), but enough magic is happening inside mlply that I can't quite make it work.
Here is a test case:
L1 <- list(data.frame(x=1:10,y=1:10),
data.frame(x=runif(10),y=runif(10)),
data.frame(x=rnorm(10),y=rnorm(10)))
L2 <- list(y~1,y~x,y~poly(x,2))
z <- xapply(lm,L2,L1)
xapply(lm,L2,L1)
#ben-bolker, I had a similar desire and think I have a preliminary solution worked out, that I've also tested to work in parallel. The function, which I somewhat confusingly called gmcmapply (g for grid) takes an arbitrarily large named list mvars (that gets expand.grid-ed within the function) and a FUN that utilizes the list names as if they were arguments to the function itself (gmcmapply will update the formals of FUN so that by the time FUN is passed to mcmapply it's arguments reflect the variables that the user would like to iterate over (which would be layers in a nested for loop)). mcmapply then dynamically updates the values of these formals as it cycles over the expanded set of variables in mvars.
I've posted the preliminary code as a gist (reprinted with an example below) and would be curious to get your feedback on it. I'm a grad student, that is self-described as an intermediately-skilled R enthusiast, so this is pushing my R skills for sure. You or other folks in the community may have suggestions that would improve on what I have. I do think even as it stands, I'll be coming to this function quite a bit in the future.
gmcmapply <- function(mvars, FUN, SIMPLIFY = FALSE, mc.cores = 1, ...){
require(parallel)
FUN <- match.fun(FUN)
funArgs <- formals(FUN)[which(names(formals(FUN)) != "...")] # allow for default args to carry over from FUN.
expand.dots <- list(...) # allows for expanded dot args to be passed as formal args to the user specified function
# Implement non-default arg substitutions passed through dots.
if(any(names(funArgs) %in% names(expand.dots))){
dot_overwrite <- names(funArgs[which(names(funArgs) %in% names(expand.dots))])
funArgs[dot_overwrite] <- expand.dots[dot_overwrite]
#for arg naming and matching below.
expand.dots[dot_overwrite] <- NULL
}
## build grid of mvars to loop over, this ensures that each combination of various inputs is evaluated (equivalent to creating a structure of nested for loops)
grid <- expand.grid(mvars,KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE)
# specify formals of the function to be evaluated by merging the grid to mapply over with expanded dot args
argdefs <- rep(list(bquote()), ncol(grid) + length(expand.dots) + length(funArgs) + 1)
names(argdefs) <- c(colnames(grid), names(funArgs), names(expand.dots), "...")
argdefs[which(names(argdefs) %in% names(funArgs))] <- funArgs # replace with proper dot arg inputs.
argdefs[which(names(argdefs) %in% names(expand.dots))] <- expand.dots # replace with proper dot arg inputs.
formals(FUN) <- argdefs
if(SIMPLIFY) {
#standard mapply
do.call(mcmapply, c(FUN, c(unname(grid), mc.cores = mc.cores))) # mc.cores = 1 == mapply
} else{
#standard Map
do.call(mcmapply, c(FUN, c(unname(grid), SIMPLIFY = FALSE, mc.cores = mc.cores)))
}
}
example code below:
# Example 1:
# just make sure variables used in your function appear as the names of mvars
myfunc <- function(...){
return_me <- paste(l3, l1^2 + l2, sep = "_")
return(return_me)
}
mvars <- list(l1 = 1:10,
l2 = 1:5,
l3 = letters[1:3])
### list output (mapply)
lreturns <- gmcmapply(mvars, myfunc)
### concatenated output (Map)
lreturns <- gmcmapply(mvars, myfunc, SIMPLIFY = TRUE)
## N.B. This is equivalent to running:
lreturns <- c()
for(l1 in 1:10){
for(l2 in 1:5){
for(l3 in letters[1:3]){
lreturns <- c(lreturns,myfunc(l1,l2,l3))
}
}
}
### concatenated outout run on 2 cores.
lreturns <- gmcmapply(mvars, myfunc, SIMPLIFY = TRUE, mc.cores = 2)
Example 2. Pass non-default args to FUN.
## Since the apply functions dont accept full calls as inputs (calls are internal), user can pass arguments to FUN through dots, which can overwrite a default option for FUN.
# e.g. apply(x,1,FUN) works and apply(x,1,FUN(arg_to_change= not_default)) does not, the correct way to specify non-default/additional args to FUN is:
# gmcmapply(mvars, FUN, arg_to_change = not_default)
## update myfunc to have a default argument
myfunc <- function(rep_letters = 3, ...){
return_me <- paste(rep(l3, rep_letters), l1^2 + l2, sep = "_")
return(return_me)
}
lreturns <- gmcmapply(mvars, myfunc, rep_letters = 1)
A bit of additional functionality I would like to add but am still trying to work out is
cleaning up the output to be a pretty nested list with the names of mvars (normally, I'd create multiple lists within a nested for loop and tag lower-level lists onto higher level lists all the way up until all layers of the gigantic nested loop were done). I think using some abstracted variant of the solution provided here will work, but I haven't figured out how to make the solution flexible to the number of columns in the expand.grid-ed data.frame.
I would like an option to log the outputs of the child processesthat get called in mcmapply in a user-specified directory. So you could look at .txt outputs from every combination of variables generated by expand.grid (i.e. if the user prints model summaries or status messages as a part of FUN as I often do). I think a feasible solution is to use the substitute() and body() functions, described here to edit FUN to open a sink() at the beginning of FUN and close it at the end if the user specifies a directory to write to. Right now, I just program it right into FUN itself, but later it would be nice to just pass gmcmapply an argument called something like log_children = "path_to_log_dir. and then editing the body of the function to (pseudocode) sink(file = file.path(log_children, paste0(paste(names(mvars), sep = "_"), ".txt")
Let me know what you think!
-Nate