I have a dataset of 80 variables, and I want to loop though a subset of 50 of them and construct returns. I have a list of the names of the variables for which I want to construct returns, and am attempting to use the dplyr command mutate to construct the variables in a loop. Specifically my code is:
for (i in returnvars) {
alldta <- mutate(alldta,paste("r",i,sep="") = (i - lag(i,1))/lag(i,1))}
where returnvars is my list, and alldta is my dataset. When I run this code outside the loop with just one of the `i' values, it works fine. The code for that looks like this:
alldta <- mutate(alldta,rVar = (Var- lag(Var,1))/lag(Var,1))
However, when I run it in the loop (e.g., attempting to do the previous line of code 50 times for 50 different variables), I get the following error:
Error: unexpected '=' in:
"for (i in returnvars) {
alldta <- mutate(alldta,paste("r",i,sep="") ="
I am unsure why this issue is coming up. I have looked into a number of ways to try and do this, and have attempted solutions that use lapply as well, without success.
Any help would be much appreciated! If there is an easy way to do this with one of the apply commands as well, that would be great. I did not provide a dataset because my question is not data specific, I'm simply trying to understand, as a relative R beginner, how to construct many transformed variables at once and add them to my data frame.
EDIT: As per Frank's comment, I updated the code to the following:
for (i in returnvars) {
varname <- paste("r",i,sep="")
alldta <- mutate(alldta,varname = (i - lag(i,1))/lag(i,1))}
This fixes the previous error, but I am still not referencing the variable correctly, so I get the error
Error in "Var" - lag("Var", 1) :
non-numeric argument to binary operator
Which I assume is because R sees my variable name Var as a string, rather than as a variable. How would I correctly reference the variable in my dataset alldta? I tried get(i) and alldta$get(i), both without success.
I'm also still open to (and actively curious about), more R-style ways to do this entire process, as opposed to using a loop.
Using mutate inside a loop might not be a good idea either. I am not sure if mutate makes a copy of the data frame but its generally not a good practice to grow a data frame inside a loop. Instead create a separate data frame with the output and then name the columns based on your logic.
result = do.call(rbind,lapply(returnvars,function(i) {...})
names(result) = paste("r",returnvars,sep="")
After playing around with this more, I discovered (thanks to Frank's suggestion), that the following works:
extended <- alldta # Make a copy of my dataset
for (i in returnvars) {
varname <- paste("r",i,sep="")
extended[[varname]] = (extended[[i]] - lag(extended[[i]],1))/lag(extended[[i]],1)}
This is still not very R-styled in that I am using a loop, but for a task that is only repeating about 50 times, this shouldn't be a large issue.
Related
I wrote a for loop to create empty multiple data frames, using a vector of names, but even though it seemed really easy at start I got an error message : Error in ID_names[i] <- data.frame() : replacement has length zero
To be more specific I' ll provide you with a reproducable example:
ID_names <- c("Athens","Rome","Barcelona","London","Paris","Madrid")
for(i in 1:length(ID_names){
ID_names[i] <- data.frame()
}
Do you have any idea why this is wrong? I would like to ask you not only provide a solution, but specify me why this for loop is wrong in order to avoid such kind of mistakes in the future.
You are trying to store a dataframe in one element of a vector (ID_names[i]) which is not possible. You might want to create a list of empty dataframes and assign names to it which can be done using replicate.
ID_names <- c("Athens","Rome","Barcelona","London","Paris","Madrid")
list_data <- setNames(replicate(length(ID_names), data.frame()), ID_names)
However, very rarely such initialisation of empty dataframes will be useful. It ends up creating more confusion down the road. Depending on your actual use case there might be other better ways to handle this.
im super newbie on R and i have been learning for myself for a few weeks already due my work degree.
Im almost done with the statistical analysis that i need, but it is through an ugly and messy code, that is, repeating lot of codes for several data frames, to apply different statistical tests, save results, etc.
Well now, for personal interest, want to write this better, but im totally trapped in my ignorance and really need a push to get the idea, please.
For example, i want to create a function that measure the correlation on all the data tables im using and save those results as a tables using the input name as part of the output name.
I mean, if we had the iris data but measured on different seasons, e.g. iris_fall, iris_winter, iris_spring and iris_summer, after apply cor(X) method to each one, i want to save those results as tables called like "mCoriris_fall.txt", "mCoriris_winter.txt", "mCoriris_spring.txt" and "mCoriris_summer.txt" respectively.
My useless code for now say:
cor_PQ<-function(X) {
cor_PQ<-cor(X, use="pairwise.complete.obs")
return(cor_PQ)
}
savecor<-function(t) {
outputname<-(paste0("mCor",t)) #HOW DO I CALL THE NAME OF THE INPUT? t is cor_PQ result matrix.
savecor<-write.table(t, file=paste0(outputname,".txt"))
return(savecor)
}
cor_PQ(Iris_fall)
I expect to get cor result and save it as a table in my workspace, using the input name as part of the output name.
Im aware this are 2 separates functions and the one to write table should be inside the function for cor(x), but i cant understand how.
I have been reading a lot but i just cant fit all in my head.
Thanks to anyone who can help me.
Regards.
UNTIL HERE IT HAS BEEN SOLVED...
But after making a list with my 14 data frames to apply cor and other methods, the write.table function overwrite the 14 cor results on 1 single doc. This is my code.
PQ_files<-list.files(path="C:/Users/Sol/Documents/ProyectoTÃtulo/CalidadAgua/Matrices/Regs",pattern="\\_PQ.txt")
PQ_data<-lapply(PQ_files, read.table)
names(PQ_data)<-gsub("\\_PQ.txt","", PQ_files)
PQ_data
cor_PQ<-function(X) {
cor_PQ<-cor(X, use="pairwise.complete.obs")
outputname.txt<-paste0("mCor",deparse(substitute(X)),".txt")
write.table(cor_PQ, file=outputname.txt)
outputname.pdf<-paste0("Cor",deparse(substitute(X)),".pdf")
pdf(outputname.pdf)
plot(X)
dev.off()
return(cor_PQ)
}
for (i in seq_along(PQ_data)){
Correlaciones<-lapply(PQ_data,cor_PQ)
}
Correlaciones
On SUM: seems to work almost good, until the write.table and plot(x) overwrite the outputs from the 14 dataframes on my PQ_data withe the name mCor[[i]] and CorX[[i]], respectively.
Should i define [i] somehow to have each results with the right name?
Also, when i run Correlaciones at the end, i can see the cor result for the 14 dataframes in one single dataframe, but i dont know how to split them correctly.
I guess almost there.
THANKS AGAIN!
You can combine the two functions and use deparse substitute to get input names as string
cor_PQ <- function(X) {
cor_PQ<-cor(X, use="pairwise.complete.obs")
outputname<- paste0("mCor",deparse(substitute(X)), ".txt")
write.table(t, file=outputname)
return(cor_PQ)
}
and then call
cor_PQ(Iris_fall)
I'm learning R and trying to use it for a statistical analysis at the same time.
Here, I am in the first part of the work: I am writing matrices and doing some simple things with them, in order to work later with these.
punti<-c(0,1,2,4)
t1<-matrix(c(-8,36,-8,-20,51,-17,-17,-17,57,-19,-19,-19,35,-8,-19,-8,0,0,0,0,-20,-20,-20,60,
-8,-8,-28,44,-8,-8,39,-23,-8,-19,35,-8,57,-8,-41,-8,-8,55,-8,-39,-8,-8,41,-25,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),ncol=4,byrow=T)
colnames(t1) <- c("20","1","28","19")
r1<-matrix(c(12,1,19,9,20,20,11,20,20,11,20,28,0,0,0,12,19,19,20,19,28,15,28,19,11,28,1,
33,20,28,31,1,19,17,28,19,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),ncol=3,byrow=T)
pt1<-rbind(sort(colSums(t1)),sort(punti))
colnames(r1)<-c("Valore","Vincitore","Perdente")
r1<-as.data.frame(r1)
But I have more matrices t_ and r_ so I would like to run a for-loop like:
for (i in 1:150)
{
pt[i]<-rbind(sort(colSums(t[i])),sort(punti))
colnames(r[i])<-c("Valore","Vincitore","Perdente")
r[i]<-as.data.frame(r[i])
}
This one just won't work because r_, t_ and pt_ are strings, but you get both the idea and that I would not like to copy-paste these three lines and manually edit the [i] 150 times. Is there a way to do it?
personally i don't advise dynamically and automatically creating lots of variables in the global environment, and would advise you to think about how you can accomplish your goals without such an approach. with that said, if you feel you really need to dynamically create all these variables, you may benefit from the assign function.
it could work like so:
for (i in 1:150)
{
assign(paste0('p',i),rbind(sort(colSums(t[i])),sort(punti)))
}
the first argument in the assign function is the formula for the variable name and how it is created; the second argument is what you wish to assign to the variable being created.
Is it possible to return 4 different data frames from one function?
Scenario:
I am trying to read a file, parse it, and return some parts of the file.
My function looks something like this:
parseFile <- function(file){
carFile <- read.table(file, header=TRUE, sep="\t")
carNames <- carFile[1,]
carYear <- colnames(carFile)
return(list(carFile,carNames,carYear))
}
I don't want to have to use list(carFile,carNames,carYear). Is there a way return the 3 data frames without returning them in a list first?
R does not support multiple return values. You want to do something like:
foo = function(x,y){return(x+y,x-y)}
plus,minus = foo(10,4)
yeah? Well, you can't. You get an error that R cannot return multiple values.
You've already found the solution - put them in a list and then get the data frames from the list. This is efficient - there is no conversion or copying of the data frames from one block of memory to another.
This is also logical, the return from a function should conceptually be a single entity with some meaning that is transferred to whatever function is calling it. This meaning is also better conveyed if you name the returned values of the list.
You could use a technique to create multiple objects in the calling environment, but when you do that, kittens die.
Note in your example carYear isn't a data frame - its a character vector of column names.
There are other ways you could do that, if you really really want, in R.
assign('carFile',carFile,envir=parent.frame())
If you use that, then carFile will be created in the calling environment. As Spacedman indicated you can only return one thing from your function and the clean solution is to go for the list.
In addition, my personal opinion is that if you find yourself in such a situation, where you feel like you need to return multiple dataframes with one function, or do something that no one has ever done before, you should really revisit your approach. In most cases you could find a cleaner solution with an additional function perhaps, or with the recommended (i.e. list).
In other words the
envir=parent.frame()
will do the job, but as SpacedMan mentioned
when you do that, kittens die
The zeallot package does what you need in a similar that Python can unpack variables from a function. Reproducible example below.
parseFile <- function(){
carMPG <- mtcars$mpg
carName <- rownames(mtcars)
carCYL <- mtcars$cyl
return(list(carMPG,carName,carCYL))
}
library(zeallot)
c(myFile, myName, myYear) %<-% parseFile()
I'm trying to subset a dataframe within a function using a mixture of fixed variables and some variables which are created within the function (I only know the variable names, but cannot vectorise them beforehand). Here is a simplified example:
a<-c(1,2,3,4)
b<-c(2,2,3,5)
c<-c(1,1,2,2)
D<-data.frame(a,b,c)
subbing<-function(Data,GroupVar,condition){
g=Data$c+3
h=Data$c+1
NewD<-data.frame(a,b,g,h)
subset(NewD,select=c(a,b,GroupVar),GroupVar%in%condition)
}
Keep in mind that in my application I cannot compute g and h outside of the function. Sometimes I'll want to make a selection according to the values of h (as above) and other times I'll want to use g. There's also the possibility I may want to use both, but even just being able to subset using 1 would be great.
subbing(D,GroupVar=h,condition=5)
This returns an error saying that the object h cannot be found. I've tried to amend subset using as.formula and all sorts of things but I've failed every single time.
Besides the ease of the function there is a further reason why I'd like to use subset.
In the function I'm actually working on I use subset twice. The first time it's the simple subset function. It's just been pointed out below that another blog explored how it's probably best to use the good old data[colnames()=="g",]. Thanks for the suggestion, I'll have a go.
There is however another issue. I also use subset (or rather a variation) in my function because I'm dealing with several complex design surveys (see package survey), so subset.survey.design allows you to get the right variance estimation for subgroups. If I selected my group using [] I would get the wrong s.e. for my parameters, so I guess this is quite an important issue.
Thank you
It's happening right as the function is trying to define GroupVar in the beginning. R is looking for the object h by itself (not within the dataframe).
The best thing to do is refer to the column names in quotes in the subset function. But of course, then you'd have to sidestep the condition part:
subbing <- function(Data, GroupVar, condition) {
....
DF <- subset(Data, select=c("a","b", GroupVar))
DF <- DF[DF[,3] %in% condition,]
}
That will do the trick, although it can be annoying to have one data frame indexing inside another.