which list element is being processed when using snowfall::sfLapply? - list

Assume we have a list (mylist) that is use as input object for a lapply function. Is there a way to know which element in mylist is being evaluated? The method should work on lapply and snowfall::sfApply (and possible others apply family members) as well.
On chat, Gavin Simpson suggested the following method. This works great for lapply but not so much for sfApply. I would like to avoid extra packages or fiddling with the list. Any suggestions?
mylist <- list(a = 1:10, b = 1:10)
foo <- function(x) {
deparse(substitute(x))
}
bar <- lapply(mylist, FUN = foo)
> bar
$a
[1] "X[[1L]]"
$b
[1] "X[[2L]]"
This is the parallel version that isn't cutting it.
library(snowfall)
sfInit(parallel = TRUE, cpus = 2, type = "SOCK") # I use 2 cores
sfExport("foo", "mylist")
bar.para <- sfLapply(x = mylist, fun = foo)
> bar.para
$a
[1] "X[[1L]]"
$b
[1] "X[[1L]]"
sfStop()

I think you are going to have to use Shane's solution/suggestion in that chat session. Store your objects in a list such that each component of the top list contains a component with the name or ID or experiment contained in that list component, plus a component containing the object you want to process:
obj <- list(list(ID = 1, obj = 1:10), list(ID = 2, obj = 1:10),
list(ID = 3, obj = 1:10), list(ID = 4, obj = 1:10),
list(ID = 5, obj = 1:10))
So we have the following structure:
> str(obj)
List of 5
$ :List of 2
..$ ID : num 1
..$ obj: int [1:10] 1 2 3 4 5 6 7 8 9 10
$ :List of 2
..$ ID : num 2
..$ obj: int [1:10] 1 2 3 4 5 6 7 8 9 10
$ :List of 2
..$ ID : num 3
..$ obj: int [1:10] 1 2 3 4 5 6 7 8 9 10
$ :List of 2
..$ ID : num 4
..$ obj: int [1:10] 1 2 3 4 5 6 7 8 9 10
$ :List of 2
..$ ID : num 5
..$ obj: int [1:10] 1 2 3 4 5 6 7 8 9 10
The have something like the first line in the following function, followed by your
foo <- function(x) {
writeLines(paste("Processing Component:", x$ID))
sum(x$obj)
}
Which will do this:
> res <- lapply(obj, foo)
Processing Component: 1
Processing Component: 2
Processing Component: 3
Processing Component: 4
Processing Component: 5
Which might work on snowfall.

I could also alter the attributes like so.
mylist <- list(a = 1:10, b = 1:10)
attr(mylist[[1]], "seq") <- 1
attr(mylist[[2]], "seq") <- 2
foo <- function(x) {
writeLines(paste("Processing Component:", attributes(x)))
}
bar <- lapply(mylist, FUN = foo)
(and the parallel version)
mylist <- list(a = 1:10, b = 1:10)
attr(mylist[[1]], "seq") <- 1
attr(mylist[[2]], "seq") <- 2
foo <- function(x) {
x <- paste("Processing Component:", attributes(x))
}
sfExport("mylist", "foo")
bar <- sfLapply(mylist, fun = foo)

Related

Reproduce list of data frame

today I wanted to run TropFishR package, the problem is (to me), every data must be arranged in list. So I tried to reconstruct the alba dataset in order to replicate with my own data in the future. Here is what I have done:
library(TropFishR)
data("alba")
str(alba) #the list contain 4 variables
List of 4
$ sample.no : int [1:14] 1 2 3 4 5 6 7 8 9 10 ...
$ midLengths: num [1:14] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 ...
$ dates : Date[1:7], format: "1976-04-17" "1976-07-02" "1976-09-19" ...
$ catch : num [1:14, 1:7] 0 0 0 1 1 1 3 9 5 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:7] "1976.29315068493" "1976.50136986301" "1976.71780821918" "1976.95616438356" ...
- attr(*, "class")= chr "lfq"
And this is what I did:
#1 We create sample.no
sample.no <- c(1:14)
sample.no
#2 We create "midlengths"
midlengths <- seq(from = 1.5, to = 14.5, by = 1)
midlengths
#3 We create "dates"
dates <- as.Date(c("1976-04-17","1976-07-02", "1976-09-19", "1976-12-15", "1977-02-18",
"1977-04-30", "1977-06-24"))
dates
#4 We create "catch"
catch <- as.matrix(read.csv(file.choose(), header=T))
#I copied the alba length freq data, move it to excel and imported as csv file
colnames(catch)<-NULL
print(catch)
#5 create list files
synLFQb <- list(sample.no,midlengths,dates,catch)
synLFQb #just checked if it turned out to be as desired format
#6 create a name for the data list
names(synLFQb) <- c("sample.no","midlengths","dates","catch")
#Finally, we need to assign the class lfq to our new object in order to allow it to be recognized by other TropFishR functions, e.g. plot.lfq:
class(synLFQb) <- "lfq"
it will produce "similar" data list
str(synLFQb)
List of 4
$ sample.no : int [1:14] 1 2 3 4 5 6 7 8 9 10 ...
$ midlengths: num [1:14] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 ...
$ dates : Date[1:7], format: "1976-04-17" "1976-07-02" "1976-09-19" ...
$ catch : int [1:14, 1:7] 0 0 0 1 1 1 3 9 5 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : NULL
- attr(*, "class")= chr "lfq"
However, when everytime I tried to do this simple command:
plot(synLFQb, Fname="catch", hist.sc = 1)
It resulted in error:
> plot(synLFQb, Fname="catch", hist.sc = 1)
Error in plot.window(...) : need finite 'ylim' values
In addition: Warning messages:
1: In min(x, na.rm = na.rm) :
no non-missing arguments to min; returning Inf
2: In max(x, na.rm = na.rm) :
no non-missing arguments to max; returning -Inf
Any help will be much appreciated.
Please make sure that you call the mid lengths vector in your list "midLengths" with a capital "L". I hope that will does the trick in your example.

How to replicate column-names, split them at delimiter '/', into multiple column-names, in R?

I have this matrix (it's big in size) "mymat". I need to replicate the columns that have "/" in their column name matching at "/" and make a "resmatrix". How can I get this done in R?
mymat
a b IID:WE:G12D/V GH:SQ:p.R172W/G c
1 3 4 2 4
22 4 2 2 4
2 3 2 2 4
resmatrix
a b IID:WE:G12D IID:WE:G12V GH:SQ:p.R172W GH:SQ:p.R172G c
1 3 4 4 2 2 4
22 4 2 2 2 2 4
2 3 2 2 2 2 4
Find out which columns have the "/" and replicate them, then rename. To calculate the new names, just split on / and replace the last letter for the second name.
# which columns have '/' in them?
which.slash <- grep('/', names(mymat), value=T)
new.names <- unlist(lapply(strsplit(which.slash, '/'),
function (bits) {
# bits[1] is e.g. IID:WE:G12D and bits[2] is the V
# take bits[1] and replace the last letter for the second colname
c(bits[1], sub('.$', bits[2], bits[1]))
}))
# make resmat by copying the appropriate columns
resmat <- cbind(mymat, mymat[, which.slash])
# order the columns to make sure the names replace properly
resmat <- resmat[, order(names(resmat))]
# put the new names in
names(resmat)[grep('/', names(resmat))] <- sort(new.names)
resmat looks like this
# a b c GH:SQ:p.R172G GH:SQ:p.R172W IID:WE:G12D IID:WE:G12V
# 1 1 3 4 2 2 4 4
# 2 22 4 4 2 2 2 2
# 3 2 3 4 2 2 2 2
You could use grep to get the index of column names with / ('nm1'), replicate the column names in 'nm1' by using sub/scan to create 'nm2'. Then, cbind the columns that are not 'nm1', with the replicated columns ('nm1'), change the column names with 'nm2', and if needed order the columns.
#get the column index with grep
nm1 <- grepl('/', names(df1))
#used regex to rearrange the substrings in the nm1 column names
#removed the `/` and use `scan` to split at the space delimiter
nm2 <- scan(text=gsub('([^/]+)(.)/(.*)', '\\1\\2 \\1\\3',
names(df1)[nm1]), what='', quiet=TRUE)
#cbind the columns that are not in nm1, with the replicate nm1 columns
df2 <- cbind(df1[!nm1], setNames(df1[rep(which(nm1), each= 2)], nm2))
#create another index to find the starting position of nm1 columns
nm3 <- names(df1)[1:(which(nm1)[1L]-1)]
#we concatenate the nm3, nm2, and the rest of the columns to match
#the expected output order
df2N <- df2[c(nm3, nm2, setdiff(names(df1)[!nm1], nm3))]
df2N
# a b IID:WE:G12D IID:WE:G12V GH:SQ:p.R172W GH:SQ:p.R172G c
#1 1 3 4 4 2 2 4
#2 22 4 2 2 2 2 4
#3 2 3 2 2 2 2 4
data
df1 <- structure(list(a = c(1L, 22L, 2L), b = c(3L, 4L, 3L),
`IID:WE:G12D/V` = c(4L,
2L, 2L), `GH:SQ:p.R172W/G` = c(2L, 2L, 2L), c = c(4L, 4L, 4L)),
.Names = c("a", "b", "IID:WE:G12D/V", "GH:SQ:p.R172W/G", "c"),
class = "data.frame", row.names = c(NA, -3L))

Which pattern occurs the most in a matrix - R (UPDATE)

UPDATE 2
*I've added some code (and explanation) I wrote myself at the end of this question, this is however a suboptimal solution (both in coding efficiency as resulting output) but kind of manages to make a selection of items that adhere to the constraints. If you have any ideas on how to improve it (again both in efficiency as resulting output) please let me know.
1. Updated Post
Please look below for the initial question and sample code. Thx to alexis_laz his answer the problem was solved for a small number of items. However when the number of items becomes to large the combn function in R cannot calculate it anymore because of the invalid 'ncol' value (too large or NA) error. Since my dataset has indeed a lot of items, I was wondering whether replacing some of his code (shown after this) with C++ provides a solution to this, and if this is the case what code I should use for this? Tnx!
This is the code as provided by alexis_laz;
ff = function(x, No_items, No_persons)
{
do.call(rbind,
lapply(No_items:ncol(x),
function(n) {
col_combs = combn(seq_len(ncol(x)), n, simplify = F)
persons = lapply(col_combs, function(j) rownames(x)[rowSums(x[, j, drop = F]) == n])
keep = unlist(lapply(persons, function(z) length(z) >= No_persons))
data.frame(persons = unlist(lapply(persons[keep], paste, collapse = ", ")),
items = unlist(lapply(col_combs[keep], function(z) paste(colnames(x)[z], collapse = ", "))))
}))
}
2. Initial Post
Currently I'm working on a set of data coming from adaptive measurement, which means that not all persons have made all of the same items. For my analysis however I need a dataset that contains only items that have been made by all persons (or a subset of these persons).
I have a matrix object in R with rows = persons (100000), and columns = items(220), and a 1 in a cell if the person has made the item and a 0 if the person has not made the item.
How can I use R to determine which combination of at least 15 items, is made by the highest amount of persons?
Hopefully the question is clear (if not please ask me for more details and I will gladly provide those).
Tnx in advance.
Joost
Edit:
Below is a sample matrix with the items (A:E) as columns and persons (1:5) as rows.
mat <- matrix(c(1,1,1,0,0,1,1,0,1,1,1,1,1,0,1,0,1,1,0,0,1,1,1,1,0),5,5,byrow=T)
colnames(mat) <- c("A","B","C","D","E")
rownames(mat) <- 1:5
> mat
A B C D E
"1" 1 1 1 0 0
"2" 1 1 0 1 1
"3" 1 1 1 0 1
"4" 0 1 1 0 0
"5" 1 1 1 1 0
mat[1,1] = 1 means that person 1 has given a response to item 1.
Now (in this example) I'm interested in finding out which set of at least 3 items is made by at least 3 people. So here I can just go through all possible combinations of 3, 4 and 5 items to check how many people have a 1 in the matrix for each item in a combination.
This will result in me choosing the item combination A, B and C, since it is the only combination of items that has been made by 3 people (namely persons 1, 3 and 5).
Now for my real dataset I want to do this but then for a combination of at least 10 items that a group of at least 75 people all responded to. And since I have a lot of data preferably not by hand as in the example data.
I'm thus looking for a function/code in R, that will let me select the minimal amount of items, and questions, and than gives me all combinations of items and persons that adhere to these constraints or have a greater number of items/persons than the constrained.
Thus for the example matrix it would be something like;
f <- function(data,no.items,no.persons){
#code
}
> f(mat,3,3)
no.item no.pers items persons
1 3 3 A, B, C 1, 3, 5
Or in case of at least 2 items that are made by at least 3 persons;
> f(mat,2,3)
no.item no.pers items persons
1 2 4 A, B 1, 2, 3, 5
2 2 3 A, C 1, 3, 5
3 2 4 B, C 1, 3, 4, 5
4 3 3 A, B, C 1, 3, 5
Hopefully this clears up what my question actually is about. Tnx for the quick replies that I already received!
3. Written Code
Below is the code I've written today. It takes each item once as a starting point and then looks to the item that has been answered most by people who also responded to the start item. It the takes these two items and looks to a third item, and repeats this until the number of people that responded to all selected questions drops below the given limit. One drawback of the code is that it takes some time to run, (it goes up somewhat exponentially when the number of items grows). The second drawback is that this still does not evaluate all possible combinations of items, in the sense that the start item, and the subsequently chosen item may have a lot of persons that answered to these items in common, however if the chosen item has almost no similarities with the other (not yet chosen) items, the sample might shrink very fast. While if an item was chosen with somewhat less persons in common with the start item, and this item has a lot of connections to other items, the final collection of selected items might be much bigger than the one based on the code used below. So again suggestions and improvements in both directions are welcome!
set.seed(512)
mat <- matrix(rbinom(1000000, 1, .6), 10000, 100)
colnames(mat) <- 1:100
fff <- function(data,persons,items){
xx <- list()
for(j in 1:ncol(data)){
d <- matrix(c(j,length(which(data[,j]==1))),1,2)
colnames(d) <- c("item","n")
t = persons+1
a <- j
while(t >= persons){
b <- numeric(0)
for(i in 1:ncol(data)){
z <- c(a,i)
if(i %in% a){
b[i] = 0
} else {
b[i] <- length(which(rowSums(data[,z])==length(z)))
}
}
c <- c(which.max(b),max(b))
d <- rbind(d,c)
a <- c(a,c[1])
t <- max(b)
}
print(j)
xx[[j]] = d
}
x <- y <- z <- numeric(0)
zz <- matrix(c(0,0,rep(NA,ncol(data))),length(xx),ncol(data)+2,byrow=T)
colnames(zz) <- c("n.pers", "n.item", rep("I",ncol(data)))
for(i in 1:length(xx)){
zz[i,1] <- xx[[i]][nrow(xx[[i]])-1,2]
zz[i,2] <- length(unname(xx[[i]][1:nrow(xx[[i]])-1,1]))
zz[i,3:(zz[i,2]+2)] <- unname(xx[[i]][1:nrow(xx[[i]])-1,1])
}
zz <- zz[,colSums(is.na(zz))<nrow(zz)]
zz <- zz[which((rowSums(zz,na.rm=T)/rowMeans(zz,na.rm=T))-2>=items),]
zz <- as.data.frame(zz)
return(zz)
}
fff(mat,110,8)
> head(zz)
n.pers n.item I I I I I I I I I I
1 156 9 1 41 13 80 58 15 91 12 39 NA
2 160 9 2 27 59 13 81 16 15 6 92 NA
3 158 9 3 59 83 32 25 80 14 41 16 NA
4 160 9 4 24 27 71 32 10 63 42 51 NA
5 114 10 5 59 66 27 47 13 44 63 30 52
6 158 9 6 13 56 61 12 59 8 45 81 NA
#col 1 = number of persons in sample
#col 2 = number of items in sample
#col 3:12 = which items create this sample (NA if n.item is less than 10)
to follow up on my comment, something like:
set.seed(1618)
mat <- matrix(rbinom(1000, 1, .6), 100, 10)
colnames(mat) <- sample(LETTERS, 10)
rownames(mat) <- sprintf('person%s', 1:100)
mat1 <- mat[rowSums(mat) > 5, ]
head(mat1)
# A S X D R E Z K P C
# person1 1 1 1 0 1 1 1 1 1 1
# person3 1 0 1 1 0 1 0 0 1 1
# person4 1 0 1 1 1 1 1 0 1 1
# person5 1 1 1 1 1 0 1 1 0 0
# person6 1 1 1 1 0 1 0 1 1 0
# person7 0 1 1 1 1 1 1 1 0 0
table(rowSums(mat1))
# 6 7 8 9
# 24 23 21 5
tab <- table(sapply(1:nrow(mat1), function(x)
paste(names(mat1[x, ][mat1[x, ] == 1]), collapse = ',')))
data.frame(tab[tab > 1])
# tab.tab...1.
# A,S,X,D,R,E,P,C 2
# A,S,X,D,R,E,Z,P,C 2
# A,S,X,R,E,Z,K,C 3
# A,S,X,R,E,Z,P,C 2
# A,S,X,Z,K,P,C 2
Here is another idea that matches your output:
ff = function(x, No_items, No_persons)
{
do.call(rbind,
lapply(No_items:ncol(x),
function(n) {
col_combs = combn(seq_len(ncol(x)), n, simplify = F)
persons = lapply(col_combs, function(j) rownames(x)[rowSums(x[, j, drop = F]) == n])
keep = unlist(lapply(persons, function(z) length(z) >= No_persons))
data.frame(persons = unlist(lapply(persons[keep], paste, collapse = ", ")),
items = unlist(lapply(col_combs[keep], function(z) paste(colnames(x)[z], collapse = ", "))))
}))
}
ff(mat, 3, 3)
# persons items
#1 1, 3, 5 A, B, C
ff(mat, 2, 3)
# persons items
#1 1, 2, 3, 5 A, B
#2 1, 3, 5 A, C
#3 1, 3, 4, 5 B, C
#4 1, 3, 5 A, B, C

How to properly manipulate a string column in a data frame in R?

I have a data.frame with a string column that contains periods e.g "a.b.c.X". I want to split out the string by periods and retain the third segment e.g. "c" in the example given. Here is what I'm doing.
> df = data.frame(v=c("a.b.a.X", "a.b.b.X", "a.b.c.X"), b=seq(1,3))
> df
v b
1 a.b.a.X 1
2 a.b.b.X 2
3 a.b.c.X 3
And what I want is
> df = data.frame(v=c("a.b.a.X", "a.b.b.X", "a.b.c.X"), b=seq(1,3))
> df
v b
1 a 1
2 b 2
3 c 3
I'm attempting to use within, but I'm getting strange results. The value in the first row in the first column is being repeated.
> get = function(x) { unlist(strsplit(x, "\\."))[3] }
> within(df, v <- get(as.character(v)))
v b
1 a 1
2 a 2
3 a 3
What is the best practice for doing this? What am I doing wrong?
Update:
Here is the solution I used from #agstudy's answer:
> df = data.frame(v=c("a.b.a.X", "a.b.b.X", "a.b.c.X"), b=seq(1,3))
> get = function(x) gsub(".*?[.].*?[.](.*?)[.].*", '\\1', x)
> within(df, v <- get(v))
v b
1 a 1
2 b 2
3 c 3
Using some regular expression you can do :
gsub(".*?[.].*?[.](.*?)[.].*", '\\1', df$v)
[1] "a" "b" "c"
Or more concise:
gsub("(.*?[.]){2}(.*?)[.].*", '\\2', v)
The problem is not with within but with your get function. It returns a single character ("a") which gets recycled when added to your data.frame. Your code should look like this:
get.third <- function(x) sapply(strsplit(x, "\\."), `[[`, 3)
within(df, v <- get.third(as.character(v)))
Here is one possible solution:
df[, "v"] <- do.call(rbind, strsplit(as.character(df[, "v"]), "\\."))[, 3]
## > df
## v b
## 1 a 1
## 2 b 2
## 3 c 3
The answer to "what am I doing wrong" is that the bit of code that you thought was extracting the third element of each split string was actually putting all the elements of all your strings in a single vector, and then returning the third element of that:
get = function(x) {
splits = strsplit(x, "\\.")
print("All the elements: ")
print(unlist(splits))
print("The third element:")
print(unlist(splits)[3])
# What you actually wanted:
third_chars = sapply(splits, function (x) x[3])
}
within(df, v2 <- get(as.character(v)))

Subset elements in a list based on a logical condition

How can I subset a list based on a condition (TRUE, FALSE) in another list? Please, see my example below:
l <- list(a=c(1,2,3), b=c(4,5,6,5), c=c(3,4,5,6))
l
$a
[1] 1 2 3
$b
[1] 4 5 6 5
$c
[1] 3 4 5 6
cond <- lapply(l, function(x) length(x) > 3)
cond
$a
[1] FALSE
$b
[1] TRUE
$c
[1] TRUE
> l[cond]
Error in l[cond] : invalid subscript type 'list'
This is what the Filter function was made for:
Filter(function(x) length(x) > 3, l)
$b
[1] 4 5 6 5
$c
[1] 3 4 5 6
Another way is to use sapply instead of lapply.
cond <- sapply(l, function(x) length(x) > 3)
l[cond]
[ is expecting a vector, so use unlist on cond:
l[unlist(cond)]
$b
[1] 4 5 6 5
$c
[1] 3 4 5 6
> l[as.logical(cond)]
$b
[1] 4 5 6 5
$c
[1] 3 4 5 6
I recently learned lengths(), which gets the length of each element of a list. This allows us to avoid making another list including logical values as the OP tried.
lengths(l)
#a b c
#3 4 4
Using this in a logical condition, we can subset list elements in l.
l[lengths(l) > 3]
$b
[1] 4 5 6 5
$c
[1] 3 4 5 6
Well im am very new to R but as it is a functional language by far the best solution according to the previous answers is something like:
filter <- function (inputList, selector) sapply(inputList, function (element) selector(element))
Assume you have a complex list like yours:
myList <- list(
a=c(1,2,3),
b=c(4,5,6,5),
c=c(3,4,5,6))
Then you can filter the elements like:
selection <- myList[filter(myList, function (element) length(element) > 3]
Well of course this also works for list that just contain a value at the first level:
anotherList <- list(1, 2, 3, 4)
selection <- myList[filter(anotherList, function (element) element == 2)]
Or you can put it all together like:
filter <- function (inputList, selector) inputList[sapply(inputList, function (element) selector(element))]
cond <- lapply(l, length) > 3
l[cond]
l <- list(a=c(1,2,3), b=c(4,5,6,5), c=c(3,4,5,6))
l[lengths(l) > 3]
$b
[1] 4 5 6 5
$c
[1] 3 4 5 6
If a condition on value is needed:
cond <- lapply(l, function(i) i > 3)
res <- Map(`[`, l, cond)
res
$a
numeric(0)
$b
[1] 4 5 6 5
$c
[1] 4 5 6