I am using R and trying to debug a function that I call with do.call() for convenience.
Combining do.call() and browser() is problematic. Basically, all the elements of the list of arguments passed to do.call() are printed, which, if the list contains for example a very large data table, is not sustainable.
Here is a reprex. I create a simple getsum() function that sums elements of a vector. I create a func() function that calls getsum() for a list of vectors.
#getsum returns the sum of a vector's elements
#func returns the vector of the sum of a list of vectors
func <- function(vec_list){
browser()
sums = lapply(FUN=getsum, X=vec_list)
sum = unlist(sums)
return(sum)
}
getsum <- function(vec){
sum = sum(vec)
return(sum)
}
args = list(vec_list=list(rnorm(5), rnorm(5)))
do.call(func, args)
That's the output I get :
Called from: (function(vec_list){
browser()
sums = lapply(FUN=getsum, X=vec_list)
sum = unlist(sums)
return(sum)
})(vec_list = list(c(-0.0801864335418185, 0.448324209935905,
-2.86518616779484, -0.359284963520417, -0.620062639582574), c(1.74835180362954,
-0.904288222154223, 0.746007117029027, 0.625889703799832, -0.908748727897187
)))
Browse[1]>
One might tell me "why are you using do.call()?". Indeed, if I simply call the function myself, the problem does not arise (see below). In this example I don't need to use do.call(), but sometimes it's extremely convenient.
#instead of do.call() use :
func(vec_list=args$vec_list)
The output is then :
Called from: func(vec_list = args$vec_list)
Browse[1]>
EDIT :
I have tried the argument browser(skipCalls=TRUE) that solves the problem but defies the purpose of browser(). It makes R executing all the function's command at once. Other suggestions welcome.
I currently have a basic script written in R, which has two functions embedded within another:
FunctionA <- Function() {
results_from_B <- FunctionB()
results_from_C <- FunctionC()
}
Function B generates some data which is then analysed in Function C.
If I stop the code within function A, I can see the structure of results_from_C - this appears under 'values' and I can refer to different elements using the syntax results_from_C$column_name1.
I achieved this within Function C by specifying the returned values using:
return(list(column_name_1 = value1, column_name_2 = value2)
However, I cannot work out how I can return these same values (in the same structure) from Function A - everything I try returns a list which is formatted as 'Data' rather than 'Values' and cannot be indexed using the syntax results_from_A$column_name1.
Can anyone help me to understand what I need to do in order to extract results from Function C outside of Function A?
Thanks in advance
I don't understand what you mean by formatted as 'Data' rather than 'Values'.
There's nothing wrong with the setup you describe, I every now and then use functions inside functions, it's perfectly OK.
(Note that R is case sensitive, it's function not Function.)
FunctionA <- function() {
FunctionB <- function() 1:2*pi
FunctionC <- function(x)
list(column_name_1 = x[1], column_name_2 = x[2])
results_from_B <- FunctionB()
results_from_C <- FunctionC(results_from_B)
results_from_C
}
result <- FunctionA()
result
$column_name_1
[1] 3.141593
$column_name_2
[1] 6.283185
result$column_name_1
[1] 3.141593
Is this it? If not, please clarify your question.
I'm trying to use as.name(x) to refer to a list to input into a function. Here's an example of my simplified version of my stats function followed by the for loop I'm using to output all the data at once.
get<-function(data,x) {
for (i in x) {
lm(as.formula(paste(i,'~',variable)),data)
}
}
lists<-c("a","b","c")
# where each of a, b, and c are lists that refer to column names of my data
for (j in lists) {
get(data,as.name(j))
}
I keep getting the following error:
Error in for (i in x) { : invalid for() loop sequence
If I just do get(data,a) each time it works but not when I try and do a loop.
Are each of a, b and c a list that contains only one value? I ask because your lm() formula has i on the left hand side, and can only be a vector.
If that's the case, then replacing as.name(j) with j should make your code work.
I am trying to write a function and apply it to a list. Inside my function is a function written by some one else. If I make my list very easy, everything will work fine. But if I use all the real data I have, there are some bad objects and the outside function doesn't work and my whole function won't go through.
What do I type to say "If the outside function doesn't work, skip that object and move to the next one in the list."? With or without NA, doesn't matter.
I cannot figure out how to write a reproducible example that would result in a list of dataframes, which is what happens inside this function. I'm willing to take any help to improve this question.
My function is something like this:
do_this<- function(x){
outside_function(x))%>% #this returns a dataframe for each object
filter()%>%
select()%>%
summarise_each(funs(mean(., na.rm = TRUE))) #by the end the df is down to one row
}
This is how I apply the function to the list to come up with my final dataframe.
df<-bind_rows(lapply(my_list, do_this))
An example:
myfun <- function(x) {if (x == 1) {stop("bad")} else x}
throws error on input of 1:
lapply(1:4, myfun) # stops from error
Just wrap it in try (as long as you don't need more complex error handling):
L <- lapply(1:4, function(x) try(myfun(x)))
And then you can use Filter to get rid of the "bad" cases:
Filter(function(x) !inherits(x, "try-error"), L)
Although you may want to just make your wrapper function more robust, or return NULL (or some other appropriate value) under the condition that makes the inner function fail.
I have two lists of lists. humanSplit and ratSplit. humanSplit has element of the form::
> humanSplit[1]
$Fetal_Brain_408_AGTCAA_L001_R1_report.txt
humanGene humanReplicate alignment RNAtype
66 DGKI Fetal_Brain_408_AGTCAA_L001_R1_report.txt 6 reg
68 ARFGEF2 Fetal_Brain_408_AGTCAA_L001_R1_report.txt 5 reg
If you type humanSplit[[1]], it gives the data without name $Fetal_Brain_408_AGTCAA_L001_R1_report.txt
RatSplit is also essentially similar to humanSplit with difference in column order. I want to apply fisher's test to every possible pairing of replicates from humanSplit and ratSplit. Now I defined the following empty vector which I will use to store the informations of my fisher's test
humanReplicate <- vector(mode = 'character', length = 0)
ratReplicate <- vector(mode = 'character', length = 0)
pvalue <- vector(mode = 'numeric', length = 0)
For fisher's test between two replicates of humanSplit and ratSplit, I define the following function. In the function I use `geneList' which is a data.frame made by reading a file and has form:
> head(geneList)
human rat
1 5S_rRNA 5S_rRNA
2 5S_rRNA 5S_rRNA
Now here is the main function, where I use a function getGenetype which I already defined in other part of the code. Also x and y are integers :
fishertest <-function(x,y) {
ratReplicateName <- names(ratSplit[x])
humanReplicateName <- names(humanSplit[y])
## merging above two based on the one-to-one gene mapping as in geneList
## defined above.
mergedHumanData <-merge(geneList,humanSplit[[y]], by.x = "human", by.y = "humanGene")
mergedRatData <- merge(geneList, ratSplit[[x]], by.x = "rat", by.y = "ratGene")
## [here i do other manipulation with using already defined function
## getGenetype that is defined outside of this function and make things
## necessary to define following contingency table]
contingencyTable <- matrix(c(HnRn,HnRy,HyRn,HyRy), nrow = 2)
fisherTest <- fisher.test(contingencyTable)
humanReplicate <- c(humanReplicate,humanReplicateName )
ratReplicate <- c(ratReplicate,ratReplicateName )
pvalue <- c(pvalue , fisherTest$p)
}
After doing all this I do the make matrix eg to use in apply. Here I am basically trying to do something similar to double for loop and then using fisher
eg <- expand.grid(i = 1:length(ratSplit),j = 1:length(humanSplit))
junk = apply(eg, 1, fishertest(eg$i,eg$j))
Now the problem is, when I try to run, it gives the following error when it tries to use function fishertest in apply
Error in humanSplit[[y]] : recursive indexing failed at level 3
Rstudio points out problem in following line:
mergedHumanData <-merge(geneList,humanSplit[[y]], by.x = "human", by.y = "humanGene")
Ultimately, I want to do the following:
result <- data.frame(humanReplicate,ratReplicate, pvalue ,alternative, Conf.int1, Conf.int2, oddratio)
I am struggling with these questions:
In defining fishertest function, how should I pass ratSplit and humanSplit and already defined function getGenetype?
And how I should use apply here?
Any help would be much appreciated.
Up front: read ?apply. Additionally, the first three hits on google when searching for "R apply tutorial" are helpful snippets: one, two, and three.
Errors in fishertest()
The error message itself has nothing to do with apply. The reason it got as far as it did is because the arguments you provided actually resolved. Try to do eg$i by itself, and you'll see that it is returning a vector: the corresponding column in the eg data.frame. You are passing this vector as an index in the i argument. The primary reason your function erred out is because double-bracket indexing ([[) only works with singles, not vectors of length greater than 1. This is a great example of where production/deployed functions would need type-checking to ensure that each argument is a numeric of length 1; often not required for quick code but would have caught this mistake. Had it not been for the [[ limit, your function may have returned incorrect results. (I've been bitten by that many times!)
BTW: your code is also incorrect in its scoped access to pvalue, et al. If you make your function return just the numbers you need and the aggregate it outside of the function, your life will simplify. (pvalue <- c(pvalue, ...) will find pvalue assigned outside the function but will not update it as you want. You are defeating one purpose of writing this into a function. When thinking about writing this function, try to answer only this question: "how do I compare a single rat record with a single human record?" Only after that works correctly and simply without having to overwrite variables in the parent environment should you try to answer the question "how do I apply this function to all pairs and aggregate it?" Try very hard to have your function not change anything outside of its own environment.
Errors in apply()
Had your function worked properly despite these errors, you would have received the following error from apply:
apply(eg, 1, fishertest(eg$i, eg$j))
## Error in match.fun(FUN) :
## 'fishertest(eg$i, eg$j)' is not a function, character or symbol
When you call apply in this sense, it it parsing the third argument and, in this example, evaluates it. Since it is simply a call to fishertest(eg$i, eg$j) which is intended to return a data.frame row (inferred from your previous question), it resolves to such, and apply then sees something akin to:
apply(eg, 1, data.frame(...))
Now that you see that apply is being handed a data.frame and not a function.
The third argument (FUN) needs to be a function itself that takes as its first argument a vector containing the elements of the row (1) or column (2) of the matrix/data.frame. As an example, consider the following contrived example:
eg <- data.frame(aa = 1:5, bb = 11:15)
apply(eg, 1, mean)
## [1] 6 7 8 9 10
# similar to your use, will not work; this error comes from mean not getting
# any arguments, your error above is because
apply(eg, 1, mean())
## Error in mean.default() : argument "x" is missing, with no default
Realize that mean is a function itself, not the return value from a function (there is more to it, but this definition works). Because we're iterating over the rows of eg (because of the 1), the first iteration takes the first row and calls mean(c(1, 11)), which returns 6. The equivalent of your code here is mean()(c(1, 11)) will fail for a couple of reasons: (1) because mean requires an argument and is not getting, and (2) regardless, it does not return a function itself (in a "functional programming" paradigm, easy in R but uncommon for most programmers).
In the example here, mean will accept a single argument which is typically a vector of numerics. In your case, your function fishertest requires two arguments (templated by my previous answer to your question), which does not work. You have two options here:
Change your fishertest function to accept a single vector as an argument and parse the index numbers from it. Bothing of the following options do this:
fishertest <- function(v) {
x <- v[1]
y <- v[2]
ratReplicateName <- names(ratSplit[x])
## ...
}
or
fishertest <- function(x, y) {
if (missing(y)) {
y <- x[2]
x <- x[1]
}
ratReplicateName <- names(ratSplit[x])
## ...
}
The second version allows you to continue using the manual form of fishertest(1, 57) while also allowing you to do apply(eg, 1, fishertest) verbatim. Very readable, IMHO. (Better error checking and reporting can be used here, I'm just providing a MWE.)
Write an anonymous function to take the vector and split it up appropriately. This anonymous function could look something like function(ii) fishertest(ii[1], ii[2]). This is typically how it is done for functions that either do not transform as easily as in #1 above, or for functions you cannot or do not want to modify. You can either assign this intermediary function to a variable (which makes it no longer anonymous, figure that) and pass that intermediary to apply, or just pass it directly to apply, ala:
.func <- function(ii) fishertest(ii[1], ii[2])
apply(eg, 1, .func)
## equivalently
apply(eg, 1, function(ii) fishertest(ii[1], ii[2]))
There are two reasons why many people opt to name the function: (1) if the function is used multiple times, better to define once and reuse; (2) it makes the apply line easier to read than if it contained a complex multi-line function definition.
As a side note, there are some gotchas with using apply and family that, if you don't understand, will be confusing. Not the least of which is that when your function returns vectors, the matrix returned from apply will need to be transposed (with t()), after which you'll still need to rbind or otherwise aggregrate.
This is one area where using ddply may provide a more readable solution. There are several tutorials showing it off. For a quick intro, read this; for a more in depth discussion on the bigger picture in which ddply plays a part, read Hadley's Split, Apply, Combine Strategy for Data Analysis paper from JSS.