my problem is, that some code gets executed outside a function, but not in it. In my example, the content of certain cells should be transferred from the input table to the output table. In case of removal or adding of rows/cols I don't access the cells by their index (e.g input[3,4]), but by application of a condition (e.g. input[(which(input$code=="A1")),(which(colnames(input)=="kg"))].
so here's a minimized version of my data:
input<-data.frame(animal=c("cat","dog","mouse","deer","lion"),
m=c(0.5,1,0.1,1.5,3),
kg=c(5,20,0.2,50,100),
code=c("A4","A5","A3","A1","A2"))
output<-data.frame(code=c("A1","A2","A3","A4","A5"),
kg=numeric(5))
execution outside the function, that works (the content of a cell of the input table should be copied to a suitable one in the output table):
row_out<-which(output$code=="A1")
col_out<-which(colnames(output)=="kg")
row_in<-which(input$code=="A1")
col_in<-which(colnames(input)=="kg")
output[row_out,col_out]<-input[row_in,col_in]
and the function, that contains the same code, which worked outside, except for the substitution of the quoted code expression for a function argument (codeexpression):
fun_transfer<-function(codeexpression){
row_out<-which(output$code==codeexpression)
col_out<-which(colnames(output)=="kg")
row_in<-which(input$code==codeexpression)
col_in<-which(colnames(input)=="kg")
output[row_out,col_out]<-input[row_in,col_in]
}
Problem: now the execution of
fun_transfer("A4")
does not lead to an error, nor to a result in the output table.
Why doesn't this function work or rather what does it do? Is there a problem with quotation marks?
any help would be appreciated
thanks,
Michel
In the best case, data enters a function as argument and leaves it as a return value.
Outside of a function
output[row_out,col_out] <- input[row_in,col_in]
changes the existing data.frame. You can (or better: should) not change some variable outside the function from within the function.
Just end your function with a return statement to return the changed dataframe to the caller
Edit
It appears as if what you try to write is a lesser version of merge. If the following answers your question it will probably be more concise, faster and more idiomatic:
input<-data.frame(animal=c("cat","dog","mouse","deer","lion"),
m=c(0.5,1,0.1,1.5,3),
kg=c(5,20,0.2,50,100),
code=c("A4","A5","A3","A1","A2"))
output<-data.frame(code=c("A1","A2","A3","A4","A5"))
output <- merge(output, input[, c("code", "kg")], by = "code",
all.x = TRUE, all.y = FALSE)
print(output)
Related
I have a list of identifiers as follows:
url_num <- c('85054655', '85023543', '85001177', '84988480', '84978776', '84952756', '84940316', '84916976', '84901819', '84884081', '84862066', '84848942', '84820189', '84814935', '84808144')
And from each of these I'm creating a unique variable:
for (id in url_num){
assign(paste('test_', id, sep = ""), FUNCTION GOES HERE)
}
This leaves me with my variables which are:
test_8505465, test_85023543, etc, etc
Each of them hold the correct output from the function (I've checked), however my next step is to combine them into one big vector which holds all of these created variables as a seperate element in the vector. This is easy enough via:
c(test_85054655,test_85023543,test_85001177,test_84988480,test_84978776,test_84952756,test_84940316,test_84916976,test_84901819,test_84884081,test_84862066,test_84848942,test_84820189,test_84814935,test_84808144)
However, as I update the original 'url_num' vector with new identifiers, I'd also have to come down to the above chunk and update this too!
Surely there's a more automated way I can setup the above chunk?
Maybe some sort of concat() function in the original for-loop which just adds each created variable straight into an empty vector right then and there?
So far I've just been trying to list all the variable names and somehow get the output to be in an acceptable format to get thrown straight into the c() function.
for (id in url_num){
cat(as.name(paste('test_', id, ",", sep = "")))
}
...which results in:
test_85054655,test_85023543,test_85001177,test_84988480,test_84978776,test_84952756,test_84940316,test_84916976,test_84901819,test_84884081,test_84862066,test_84848942,test_84820189,test_84814935,test_84808144,
This is close to the output I'm looking for but because it's using the cat() function it's essentially a print statement and its output can't really get put anywhere. Not to mention I feel like this method I've attempted is wrong to begin with and there must be something simpler I'm missing.
Thanks in advance for any help you guys can give me!
Troy
I want to, essentially, pass a value untouched through a function. So in the following example (in Rstudio):
example_function <- function(datain){
as.environment("package:utils")$View(datain)
}
I want the inner function to act as if I'm passing it the original object, in particular so the name which appears in the View window will have the name of the original object (X, say) rather than datain which is what currently occurs.
With deparse(substitute(datain)) you can get the original name of the argument passed.
Then, to accomplish what you asked for, you can simply do
example_function <- function(datain){
as.environment("package:utils")$View(datain, deparse(substitute(datain)))
}
Now the View window will be titled appropriately as you wanted.
However note that "I want the inner function to act as if I'm passing it the original object" request of yours is not possible in R. R does not support pass-by-reference. There are some workarounds, but if you only needed if for naming the View, the above fix should be fine.
You can also use get for this.
example_function <- function(datain){
as.environment("package:utils")$View(get(datain),datain)
}
in this case you don't pass the variable but rather the name of the variable as a string.
example_function("X")
Suppose I have in my shiny ui.R an input:
selectInput("choice","Select Values",choices=c('a','b','c',FALSE),
selected="FALSE")
And I want to use this input as a parameter to a function in server.R. The default value for this parameter is FALSE, but can take in character values. I want to set it so that, for example, if the user select 'a', the value 'a' would be passed on to the parameter in the function, and if the user select 'FALSE', the default value of FALSE is passed on to the parameter.
The way I tried to do this is by:
choice <- ifelse(input$choice=='FALSE',FALSE,input$choice)
and then use it in the function:
sample_func(param=choice)
However, this gives me an error "param has the wrong format". The error is the result of choice not being a character value or FALSE.
What might be the reason for this?
The first problem is:
choice <- ifelse(input$choice=='FALSE',FALSE,input$choice)
This must be written in a reactive context as it depends on user input. So you have to wrap your ifelse(...) part in reactive(). Of course I don't know if you had done so already since you didn't post the entire code and I assume you just wrote that code without the wrapping of reactive().
Another thing concerning this same line is not really a bug, but a habit. It's a bad idea to use choice again. The point is, it already existed as in input$choice and now you assign it to a reactive value of the same name. That could be confusing if you have a complex and long code.
So, I would suggest something like this below.
fun_choice <- reactive(ifelse(input$choice=='FALSE',FALSE,input$choice))
The next problem is in:
sample_func(param=fun_choice) ## I changed the argument name to fun_choice
If you had treated fun_choice as reactive, That should be replaced by fun_choice(). Also, that would mean that this function result should also be in reactive context. So the line should become:
variable_name <- reactive( sample_func(param=fun_choice()) )
Of course, it could be wrapped in observe if what you want is the side effect and some other things but not the function return.
Hope this help.
One last thing, if fun_choice is of no other use elsewhere but just passing into sample_func, I would suggest you put them in one reactive(). i.e.
variable_name <- reactive({
fun_choice <- ifelse(input$choice == 'FALSE', FALSE, input$choice)
sample_func(param = fun_choice)
})
Note that in this method, fun_choice, within the same reactive(), need not be expressed as fun_choice(), but simply fun_choice.
Am R newb. I coded a function that uses 3 parameters. In my code i use one of the parameters to help me read files from a directory. There are 100 files in the directory. The code works fine when I pass it all the function parameters and specify the files i want to read.
functionX(var1, var2, id) and functionX(var1, var2, id = 1:100)
## Below is the first line of code for me that uses "id".
sub.file.names <- file.names[id] ### Get file names
The odd thing is that when a value for "id" is not passed to the function initially (or set with a 1:100 default), the code seems to read all the file names anyway. And it does so even though a value for "id" has never been established.
It's as if R somehow treats the two functions below the same when the user omits passing a value to "id" when executing the function ... eg, functionx("var1", "var2") ## and does not pass any id variable
functionx(var1, var2, id)
functionx(var1, var2, id = 1:100)
Any pointers on why this is happening would be great to know. I feel the answer is obvious, but have not been able to figure it out.
Let me try to explain what is happening with a simple example. Consider the following function
foo = function(i){
LETTERS[i]
}
When you try foo(), you will notice that the function returns all 26 uppercase letters. Why does that happen? Well, everything in R is a function. So when you say LETTERS[i], you are essentially calling the function [. So, the function call is
`[`(LETTERS, i)
Since i is missing, this call is executed as [(LETTERS) (essentially LETTERS[]) which returns all elements of the vector. Note that this occurs because the [ function allows for the i argument to be missing while calling it. Check ?[
If you want the function to act differently when id is missing, either check for missing(id), or explicitly set it to NULL as default. So, if you do
foo2 = function(i = NULL){
LETTERS[i]
}
foo2() will return a zero length character vector.
While testing a simulation in R using randomly generated input data, I have found and fixed a few bugs and would now like to re-run the simulation with the same data, but with all intermediate variables removed to ensure it's a clean test.
Is there a way to remove several dozen manually selected variables from the workspace without having to:
a) clobber the entire workspace, e.g. rm(list=ls()), or b) type each variable name, e.g. remove(name1, name2, ...)?
Ideal solution would be to use ls() to inspect the definitions and then pick out the indices of the ones I want to remove, e.g.
ls() # inspect definitions
delme <- c(3,5,7:9,11,13) # names selected for removal
remove(ls()[delme]) # DESIRED SOLUTION -- doesn't quite work this way
(In hindsight, I should have used a fixed seed to generate the random input data, which allow clearing everything and then re-running the test...)
There is a much simpler and more direct solution:
vars.to.remove <- ls()
vars.to.remove <- temp[c(1,2,14:15)]
rm(list = vars.to.remove)
Or, better yet, if you are good about variable naming schemes, you can use the following pattern matching strategy:
E.g. I name all temporary variables with the starting string "Temp."
... so, you can have Temp.Names, Temp.Values, Temp.Whatever
The following produces the list of variables that match this pattern
ls(pattern = "^Temp\\.")
So, you can remove all unneeded variables using ONE line of code, as follows:
rm(list = ls(pattern = "^Temp\\."))
Hope this helps.
Assad, while I think the actual answer to the question is in the comments, let me suggest this pattern as a broader solution:
rm(list=
Filter(
Negate(is.na), # filter entries corresponding to objects that don't meet function criteria
sapply(
ls(pattern="^a"), # only objects that start with "a"
function(x) if(is.matrix(get(x))) x else NA # return names of matrix objects
) ) )
In this case, I'm removing all matrix object that start with "a". By modifying the pattern argument and the function used by sapply here, you can get pretty fine control over what you delete, without having to specify many names.
If you are concerned that this could delete something you don't want to delete, you can store the result of the Filter(... operation in a variable, review the contents, and then execute the rm(list=...) command.
Try
eval(parse(text=paste("rm(",paste(ls()[delme],sep=","),")")))
I had a similar requirement. I pulled all the elements I needed to a list:
varsToPurge = as.list(ls())
I then reassign the few values I wish to keep with new variable names which will not be in the variable varsToPurge. After that I looped through the elements
for (j in 1:length(varsToPurge)){
rm(list = as.character(varsToPurge[j]))
}
Do a little garbage collecting, and you maintain a clean environment as you go through your code.
gc()
You can also use a vector of row numbers you wish to keep instead and run through the vector in the loop but it won't be as dynamic if you add rough work you wish to remove.