How could someone use a for loop within a reactive expression ?
I mean if i want to build repeated procedure for a dataset that depend on users input values.
data <- reactive({
for (i in 1:5) {initial_data$input$valuei}
})
r you need to express this in another way?
Are you list looking to change valuei each time in the loop? If so then the dollar sign operator isn't a good choice. You'll want to use proper list indexing. How about
data <- reactive({
for (i in 1:5) {
initial_data$input[[paste0("value",i]]
}
})
of course that doesn't actually do anything with those values, but i wasn't sure what your intent was from your sample.
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.
So I'm trying to develop with R Shiny, I ask the user to do 5 file inputs. And then I have a reactive function mergeFun() which will cast and merge the data in one data set once the inputs are done.
Then I have multiple buttons, boxplots, renderUI, outputs, PCA, regression trees,... in my different tabPanels and each one of these calls the function mergeFun() in order to do the statistic analysis.
So as it is making quite some time after the inputs to load everyone of these panels at once, I was wondering if there was a way to just call the function globally and stock the merged data in one global variable.
I was thinking maybe because I call this function x times once in each analysis in the tabPanels, it was causing the time lapse.
So I would just be able to call the data instead of calling the function mergeFun()
I'm posting the code modified, to give you an idea about what I am doing
mergeFun <- reactive({
#So there I had my test about if my files existed
#I delete for visibility
cast <- dcast(input,var1 + var2,value.var = "var3")
mergeAll <- merge(input2,cast,by=c("var1","var2","var3"))
data.frame(mergeAll)
})
So this is my mergeFun() and then I have many reactive and renderUI which will all start like this :
output$xcol <- renderUI({
df <-mergeFun()
if (is.null(df)) return(NULL)
...
...
...
})
So maybe calling mergeFun() x times isn't programatically efficient?
I'm running into problem of storing results of multiple queries into a list.
Currently i have the results from previously executed query stored in a list.
Currently i can't find a way to store the results into a list or anything of that like.
queryResults2 return a 2D list.
#Sample code for 2nd select
for(i in length(queryResults[[1]]){
query_pd <-paste("Select price_date,price from price_master where stock_id ='",queryResults[[1]][i],"' order by price_date")
queryResults2 <- dbGetQuery(conn, query_pd)
#storing value here
}
First of all, don't worry too much about the whole "don't use loops" thing. Here are three basic options:
for loop. The basic template would be:
result <- vector("list",length(queryResults[[1]])
for (i in ...){
#Do stuff
result[[i]] <- something
}
lapply Here the basic format would be:
lapply(seq_len(queryResults[[1]]),function(i) dbGetQuery(conn,paste(...)))
You don't necessarily need the function to take the query and connection object as arguments, R's scoping will be able to find them if they exist in the calling environment.
Run it all as one query with an IN clause and then split is afterwards.
Personally, I try to use (3) as much as possible.
I am trying to combine several for loops into a single loop or function. Each loop is evaluating if an individual is present at a site that is protected, and based on that is assigning a number (numbers represent sites) at each time step. After that, the results for each time step are stored in a matrix and later used in other analysis. The problem that I am having is that I am repeating the same loop several times to evaluate the different scenarios (10%, 50%, 100% of sites protected). Since I need to store my results for each scenario I can't think of a better way to simplify this into a single loop or function. Any ideas or suggestions will be appreciated. This is a very small and simplify idea of the problem. I would like to keep the structure of the loop since my original loop is using several if statements. The only thing that is changing is the proportion of sites that are protected.
N<-10 # number of sites
sites<-factor(seq(from=1,to=N))
sites10<-as.factor(sample(sites,N*1))
sites5<-as.factor(sample(sites,N*0.5))
sites1<-as.factor(sample(sites,N*0.1))
steps<-10
P.stay<-0.9
# storing results
result<-matrix(0,nrow=steps)
time.step<-seq(1,steps)
time.step<-data.frame(time.step)
time.step$event<-0
j<-numeric(steps)
j[1]<-sample(1:N,1)
time.step$event[1]<-j[1]
for(i in 1:(steps-1)){
if(j[i] %in% sites1){
if(rbinom(1,1,P.stay)==1){time.step$event[i+1]<-j[i+1]<-j[i]} else
time.step$event[i+1]<-0
}
time.step$event[i+1]<-j[i+1]<-sample(1:N,1)
}
results.sites1<-as.factor(result)
###
result<-matrix(0,nrow=steps)
time.step<-seq(1,steps)
time.step<-data.frame(time.step)
time.step$event<-0
j<-numeric(steps)
j[1]<-sample(1:N,1)
time.step$event[1]<-j[1]
for(i in 1:(steps-1)){
if(j[i] %in% sites5){
if(rbinom(1,1,P.stay)==1){time.step$event[i+1]<-j[i+1]<-j[i]} else
time.step$event[i+1]<-0
}
time.step$event[i+1]<-j[i+1]<-sample(1:N,1)
}
results.sites5<-as.factor(result)
###
result<-matrix(0,nrow=steps)
time.step<-seq(1,steps)
time.step<-data.frame(time.step)
time.step$event<-0
j<-numeric(steps)
j[1]<-sample(1:N,1)
time.step$event[1]<-j[1]
for(i in 1:(steps-1)){
if(j[i] %in% sites10){
if(rbinom(1,1,P.stay)==1){time.step$event[i+1]<-j[i+1]<-j[i]} else
time.step$event[i+1]<-0
}
time.step$event[i+1]<-j[i+1]<-sample(1:N,1)
}
results.sites10<-as.factor(result)
#
results.sites1
results.sites5
results.sites10
Instead of doing this:
sites10<-as.factor(sample(sites,N*1))
sites5<-as.factor(sample(sites,N*0.5))
sites1<-as.factor(sample(sites,N*0.1))
and running distinct loops for each of the three variables, you can make a general loop and put it in a function, then use one of the -apply functions to call it with specific parameters. For example:
N<-10 # number of sites
sites<-factor(seq(from=1,to=N))
steps<-10
P.stay<-0.9
simulate.n.sites <- function(n) {
n.sites <- sample(sites, n)
result<-matrix(0,nrow=steps)
time.step<-seq(1,steps)
time.step<-data.frame(time.step)
time.step$event<-0
j<-numeric(steps)
j[1]<-sample(1:N,1)
time.step$event[1]<-j[1]
for(i in 1:(steps-1)){
if(j[i] %in% n.sites){
...etc...
return(result)
}
results <- lapply(c(1, 5, 10), simulate.n.sites)
Now results will be a list, with three matrix elements.
The key is to identify places where you repeat yourself, and then refactor those areas into functions. Not only is this more concise, but it's easy to extend in the future. Want to sample for 2 site? Put a 2 in the vector you pass to lapply.
If you're unfamiliar with the -apply family of functions, definitely look into those.
I also suspect that much of the rest of your code could be simplified, but I think you've gutted it too much for me to make sense of it. For example, you define an element of time.step$event based on a condition, but then you overwrite that element. Surely this isn't what the actual code does?
I am wondering if it is possible in R to use a value that is declared in a function call as a "variable" part of the function itself, similar to the functionality that is available in SAS IML.
Given something like this:
put.together <- function(suffix, numbers) {
new.suffix <<- as.data.frame(numbers)
return(new.suffix)
}
x <- c(seq(1000,1012, 1))
put.together(part.a, x)
new.part.a ##### does not exist!!
new.suffix ##### does exist
As it is written, the function returns a dataframe called new.suffix, as it should because that is what I'm asking it to do.
I would like to get a dataframe returned that is called new.part.a.
EDIT: Additional information was requested regarding the purpose of the analysis
The purpose of the question is to produce dataframes that will be sent to another function for analysis.
There exists a data bank where elements are organized into groups by number, and other people organize the groups
into a meaningful set.
Each group has an id number. I use the information supplied by others to put the groups together as they are specified.
For example, I would be given a set of id numbers like: part-1 = 102263, 102338, 202236, 302342, 902273, 102337, 402233.
So, part-1 has seven groups, each group having several elements.
I use the id numbers in a merge so that only the groups of interest are extracted from the large data bank.
The following is what I have for one set:
### all.possible.elements.bank <- .csv file from large database ###
id.part.1 <- as.data.frame(c(102263, 102338, 202236, 302342, 902273, 102337, 402233))
bank.names <- c("bank.id")
colnames(id.part.1) <- bank.names
part.sort <- matrix(seq(1,nrow(id.part.1),1))
sort.part.1 <- cbind(id.part.1, part.sort)
final.part.1 <- as.data.frame(merge(sort.part.1, all.possible.elements.bank,
by="bank.id", all.x=TRUE))
The process above is repeated many, many times.
I know that I could do this for all of the collections that I would pull together, but I thought I would be able to wrap the selection process into a function. The only things that would change would be the part numbers (part-1, part-2, etc..) and the groups that are selected out.
It is possible using the assign function (and possibly deparse and substitute), but it is strongly discouraged to do things like this. Why can't you just return the data frame and call the function like:
new.part.a <- put.together(x)
Which is the generally better approach.
If you really want to change things in the global environment then you may want a macro, see the defmacro function in the gtools package and most importantly read the document in the refrences section on the help page.
This is rarely something you should want to do... assigning to things out of the function environment can get you into all sorts of trouble.
However, you can do it using assign:
put.together <- function(suffix, numbers) {
assign(paste('new',
deparse(substitute(suffix)),
sep='.'),
as.data.frame(numbers),
envir=parent.env(environment()))
}
put.together(part.a, 1:20)
But like Greg said, its usually not necessary, and always dangerous if used incorrectly.