I have a small number of csv files, each containing two columns with numeric values. I want to write a for loop that reads the files, sums the columns, and stores the sum totals for each csv in a numeric vector. This is the closest I've come:
allfiles <- list.files()
for (i in seq(allfiles)) {
total <- numeric()
total[i] <- sum(subset(read.csv(allfiles[i]), select=Gift.1), subset(read.csv(allfiles[i]), select=Gift.2))
total
}
My result is all NA's save a value for the last file. I understand that I'm overwriting each iteration each time the for loop executes and I think* I need to do something with indexing.
The first problem is that you are not pre-allocating the right length of (or properly appending to) total. Regardless, I recommend against that method.
There are several ways to do this, but the R-onic (my term, based on pythonic ... I know, it doesn't flow well) is based on vectors/lists.
alldata <- sapply(allfiles, read.csv, simplify = FALSE)
totals <- sapply(alldata, function(a) sum(subset(a, select=Gift.1), subset(a, select=Gift.2)))
I often like to that, keeping the "raw/unaltered" data in one list and then repeatedly extract from it. For instance, if the files are huge and reading them is a non-trivial amount of time, then if you realize you also need Gift.3 and did it your way, then you'd need to re-read the entire dataset. Using my method, however, you just update the second sapply to include the change and rerun on the already-loaded data. (Most of the my rationale is based on untrusted data, portions that are typically unused, or other factors that may not be there for you.)
If you really wanted to reduce the code to a single line, something like:
totals <- sapply(allfiles, function(fn) {
x <- read.csv(fn)
sum(subset(x, select=Gift.1), subset(x, select=Gift.2))
})
allfiles <- list.files()
total <- numeric()
for (i in seq(allfiles)) {
total[i] <- sum(subset(read.csv(allfiles[i]), select=Gift.1), subset(read.csv(allfiles[i]), select=Gift.2))
}
total
if possible try and give the total a known length before hand ie total<-numeric(length(allfiles))
Related
I'm in need of some R for-loop and grep optimisation assistance.
I have a data.frame made up of columns of different data types. 42 of these columns have the name "treatmentmedication_code_#", where # is a number 1 to 42.
There is a lot of code so a reproducible example is quite tricky. As a compromise, the following code is the precise operation I need to optimise.
for(i in 1:nTreatments) {
...lots of code...
controlsDrugStatusDF <- cbind(controlsTreatmentDF, Drug=0)
for(n in 1:nControls) {
if(treatment %in% controlsDrugStatusDF[n,grep(pattern="^treatmentmedication_code*",x=colnames(controlsDrugStatusDF))]) {
controlsDrugStatusDF$Drug[n] <- 1
} else {
controlsDrugStatusDF$Drug[n] <- 0
}
}
}
treatment is some coded medication e.g., 145374524. The condition inside the if statement is very slow. It checks to see whether the treatment value is present in any one of those columns defined by the grep for the row n. To make matters worse, this is done for every treatment, thus the i for-loop.
Short of launching multiple processes or massacring my data.frames into lots of separate matrices then pasting them together and converting them back into a data.frame, are there any notable improvements one could make on the if statement?
As part of optimization, the grep for selecting the columns can be done outside the loop. Regarding the treatments part it is not clear. Consider that it is a vector of values. We can use
nm1 <- grep("^treatmentmedication_code*",
colnames(controlsDrugStatusDF), values = TRUE)
nm2 <- paste0("Drug", seq_along(nm1))
controlsDrugStatusDF[nm2] <- lapply(controlsDrugStatusDF[nm1],
function(x)
+(x %in% treatments))
I am trying to obtain the number of cases for each variable in a df. There are 275 cases in the df but most columns have some missing data. I am trying to run a for loop to obtain the information as follows:
idef_id<-readxl::read_xlsx("IDEF.xlsx")
casenums <- for (i in names(idef_id)) {
nas<- sum(is.na(i))
275-nas
}
however the output for casenums is
> summary(casenums)
Length Class Mode
0 NULL NULL
Any help would be much appreciated!
A for loop isn't a function - it doesn't return anything, so x <- for(... doesn't ever make sense. You can do that with, e.g., sapply, like this
casenums <- sapply(idef_id, function(x) sum(!is.na(x)))
Or you can do it in a for loop, but you need to assign to a particular value inside the loop:
casenums = rep(NA, ncol(idef_id))
names(casenums) = names(idef_id)
for(i in names(idef_id)) {
casenums[i] = sum(!is.na(idef_id[[i]]))`
}
You also had a problem that i is taking on column names, so sum(is.na(i)) is asking if the value of the column name is missing. You need to use idef_id[[i]] to access the actual column, not just the column name, as I show above.
You seem to want the answer to be the number of non-NA values, so I switched to sum(!is.na(...)) to count that directly, rather than hard-coding the number of rows of the data frame and doing subtraction.
The immediate fix for your for loop is that your i is a column name, not the data within. On your first pass through the for loop, your i is class character, always length 1, so sum(is.na(i)) is going to be 0. Due to how frames are structured, there is very little likelihood that a name is NA (though it is possible ... with manual subterfuge).
I suggest a literal fix for your code could be:
casenums <- for (i in names(idef_id)) {
nas<- sum(is.na(idef_id[[i]]))
275-nas
}
But this has the added problem that for loops don't return anything (as Gregor's answer also discusses). For the sake of walking through things, I'll keep that (for the first bullet), and then fix it (in the second):
Two things:
hard-coding 275 (assuming that's the number of rows in the frame) will be problematic if/when your data ever changes. Even if you're "confident" it never will ... I still recommend not hard-coding it. If it's based on the number of rows, then perhaps
OUT_OF <- 275 # should this be nrow(idef_id)?
casenums <- for (i in names(idef_id)) {
nas<- sum(is.na(idef_id[[i]]))
OUT_OF - nas
}
at least in a declarative sense, where the variable name (please choose something better) is clear as to how you determined 275 and how (if necessary) it should be fixed in the future.
(Or better, use Gregor's logic of sum(!is.na(...)) if you just need to count not-NA.)
doing something for each column of a frame is easily done using sapply or lapply, perhaps
OUT_OF <- 275 # should this be nrow(idef_id)?
OUT_OF - sapply(idef_id, function(one_column) sum(is.na(one_column)))
## or
sapply(idef_id, function(one_column) OUT_OF - sum(is.na(one_column)))
I currently have a for loop as below and it does not run as fast as I would like it to.
library(dplyr)
DF<-data.frame(Name=c('Bob','Joe','Sally')) #etc
PrimaryResult <- Function1(DF)
ResultsDF<-Function2(PrimaryResult)
for(i in 1:9)
{
Filtered<-filter(DF,Name!=PrimaryResult[i,2])
NextResult <- Function1(Filtered)
ResultsDF<-rbind(ResultsDF,Function2(NextResult))
}
The code takes an initial result of Function1 (which is a list of names) and tries it again with each name in the initial result being excluded individually to provide alternative results. These are returned as a one row data frame via Function2 and appended to the Results data frame.
How can I make this faster?
It seems like your main problem is the appending results from function 2 each iteration with rbind. This is classically slow because you are telling R to rewrite a bunch of information at each time step and R does not really know how large of a vector you are going to end up with.
Try making your results into a list vector. I don't really know what your functions do so I can't really assist with that part.
results_list <- vector("list", 10)
results_list[[1]] <- Function2(PrimaryResult)
for(i in 1:9){
Filtered<-filter(DF,Name!=PrimaryResult[i,2])
NextResult <- Function1(Filtered)
results_list[[i+1]]<-rbind(results_list[[i]],Function2(NextResult))
}
This is not perfect, but it should speed things up a bit.
I've been learning R for my project and have been unable to google a solution to my current problem.
I have ~ 100 csv files and need to perform an exact set of operations across them. I've read them in as separate objects (which I assume is probably improper r style) but I've been unable to write a function that can loop through. Each csv is a dataframe that contain information, including a column with dates in decimal year form. I need to create 2 new columns containing year and day of year. I've figured out how to do it manually I would like to find a way to automate the process. Here's what I've been doing:
#setup
library(lubridate) #Used to check for leap years
df.00 <- data.frame( site = seq(1:10), date = runif(10,1980,2000 ))
#what I need done
df.00$doy <- NA # make an empty column which I'm going to place the day of the year
df.00$year <- floor(df.00$date) # grabs the year from the date column
df.00$dday <- df.00$date - df.00$year # get the year fraction. intermediate step.
# multiply the fraction year by 365 or 366 if it's a leap year to give me the day of the year
df.00$doy[which(leap_year(df.00$year))] <- round(df.00$dday[which(leap_year(df.00$year))] * 366)
df.00$doy[which(!leap_year(df.00$year))] <- round(df.00$dday[which(!leap_year(df.00$year))] * 365)
The above, while inelegant, does what I would like it to. However, I need to do this to the other data frames, df.01 - df.99. So far I've been unable to place it in a function or for loop. If I place it into a function:
funtest <- function(x) {
x$doy <- NA
}
funtest(df.00) does nothing. Which is what I would expect from my understanding of how functions work in r but if I wrap it up in a for loop:
for(i in c(df.00)) {
i$doy <- NA }
I get "In i$doy <- NA : Coercing LHS to a list" several times which tells me that the loop isn't treat the dataframe as a single unit but perhaps looking at each column in the frame.
I would really appreciate some insight on what I should be doing. I feel that I could have solved this easily using bash and awk but I would like to be less incompetent using r
the most efficient and direct way is to use a list.
Put all of your CSV's into one folder
grab a list of the files in that folder
eg: files <- dir('path/to/folder', full.names=TRUE)
iterativly read in all those files into a list of data.frames
eg: df.list <- lapply(files, read.csv, <additional args>)
apply your function iteratively over each data.frame
eg: lapply(df.list, myFunc, <additional args>)
Since your df's are already loaded, and they have nice convenient names, you can grab them easily using the following:
nms <- c(paste0("df.0", 0:9), paste0("df.", 10:99))
df.list <- lapply(nms, get)
Then take everything you have in the #what I need done portion and put inside a function, eg:
myFunc <- function(DF) {
# what you want done to a single DF
return(DF)
}
And then lapply accordingly
df.list <- lapply(df.list, myFunc)
On a separate notes, regarding functions:
The reason your funTest "does nothing" is that it you are not having it return anything. That is to say, it is doing something, but when it finishes doing that, then it does "nothing".
You need to include a return(.) statement in the function. Alternatively, the output of last line of the function, if not assigned to an object, will be used as the return value -- but this last sentence is only loosely true and hence one needs to be cautious. The cleanest option (in my opinion) is to use return(.)
regarding the for loop over the data.frame
As you observed, using for (i in someDataFrame) {...} iterates over the columns of the data.frame.
You can iterate over the rows using apply:
apply(myDF, MARGIN=1, function(x) { x$doy <- ...; return(x) } ) # dont forget to return
I am confused by the behavior of is.na() in a for loop in R.
I am trying to make a function that will create a sequence of numbers, do something to a matrix, summarize the resulting matrix based on the sequence of numbers, then modify the sequence of numbers based on the summary and repeat. I made a simple version of my function because I think it still gets at my problem.
library(plyr)
test <- function(desired.iterations, max.iterations)
{
rich.seq <- 4:34 ##make a sequence of numbers
details.table <- matrix(nrow=length(rich.seq), ncol=1, dimnames=list(rich.seq))
##generate a table where the row names are those numbers
print(details.table) ##that's what it looks like
temp.results <- matrix(nrow=10, ncol=2, dimnames=list(1:10))
##generate some sample data to summarize and fill into details.table
temp.results[,1] <- rep(5:6, 5)
temp.results[,2] <- rnorm(10)
print(temp.results) ##that's what it looks like
details.table[,1][row.names(details.table) %in% count(temp.results[,1])$x] <-
count(temp.results[,1])$freq
##summarize, subset to the appropriate rows in details.table, and fill in the summary
print(details.table)
for (i in 1:max.iterations)
{
rich.seq <- rich.seq[details.table < desired.iterations | is.na(details.table)]
## the idea would be to keep cutting this sequence of numbers down with
## successive iterations until the desired number of iterations per row in
## details.table was reached. in other words, in the real code i'd do
## something to details.table in the next line
print(rich.seq)
}
}
##call the function
test(desired.iterations=4, max.iterations=2)
On the first run through the for loop the rich.seq looks like I'd expect it to, where 5 & 6 are no longer in the sequence because both ended up with more than 4 iterations. However, on the second run, it spits out something unexpected.
UPDATE
Thanks for your help and also my apologies. After re-reading my original post it is not only less than clear, but I hadn't realized count was part of the plyr package, which I call in my full function but wasn't calling here. I'll try and explain better.
What I have working at the moment is a function that takes a matrix, randomizes it (in any of a number of different ways), then calculates some statistics on it. These stats are temporarily stored in a table--temp.results--where temp.results[,1] is the sum of the non zero elements in each column, and temp.results[,2] is a different summary statistic for that column. I save these results to a csv file (and append them to the same file at subsequent iterations), because looping through it and rbinding hogs a lot of memory.
The problem is that certain column sums (temp.results[,1]) are sampled very infrequently. In order to sample those sufficiently requires many many iterations, and the resulting .csv files would stretch into the hundreds of gigabytes.
What I want to do is create and then update a table (details.table) at each iteration that keeps track of how many times each column sum actually got sampled. When a given element in the table reaches the desired.iterations, I want it to be excluded from the vector rich.seq, so that only columns that haven't received the desired.iterations are actually saved to the csv file. The max.iterations argument will be used in a break() statement in case things are taking too long.
So, what I was expecting in the example case is the exact same line for rich.seq for both iterations, since I didn't actually do anything to change it. I believe that flodel is definitely right that my problem lies in comparing a matrix (details.table) of length longer than rich.seq, leading to unexpected results. However, I don't want the dimensions of details.table to change. Perhaps I can solve the problem implementing %in% somehow when I redefine rich.seq in the for loop?
I agree you should improve your question. However, I think I can spot what is going wrong.
You compute details.table before the for loop. It is a matrix with same length as rich.seq when it was first initialized (length(4:34), i.e. 31).
Inside the for loop, details.table < desired.iterations | is.na(details.table) is then a logical vector of length 31. On the first loop iteration,
rich.seq <- rich.seq[details.table < desired.iterations | is.na(details.table)]
will result in reducing the length of rich.seq. But on the second loop iteration, unless details.table is redefined (not the case), you are trying to subset rich.seq by a logical vector of longer length than rich.seq. This will certainly lead to unexpected results.
You probably meant to redefine details.table as part of your for loop.
(Also I am surprised to see you never used temp.results[,2].)
Thanks to flodel for setting me off on the right track. It had nothing to do with is.na but rather the lengths of vectors I was comparing.
That said, I set the initial values of the details.table to zero to avoid the added complexity of the is.na statement.
This code works, and can be modified to do what I described above.
library(plyr)
test <- function(desired.iterations, max.iterations)
{
rich.seq <- 4:34 ##make a sequence of numbers
details.table <- matrix(nrow=length(rich.seq), ncol=1, dimnames=list(rich.seq)) ##generate a table where the row names are those numbers
details.table[,1] <- 0
print(details.table) ##that's what it looks like
temp.results <- matrix(nrow=10, ncol=2, dimnames=list(1:10)) ##generate some sample data to summarize and fill into details.table
temp.results[,1] <- rep(5:6, 5)
temp.results[,2] <- rnorm(10)
print(temp.results) ##that's what it looks like
details.table[,1][row.names(details.table) %in% count(temp.results[,1])$x] <- count(temp.results[,1])$freq ##summarize, subset to the appropriate rows in details.table, and fill in the summary
print(details.table)
for (i in 1:max.iterations)
{
rich.seq <- row.names(details.table)[details.table[,1] < desired.iterations]
print(rich.seq)
}
}
Rather than trying to cut down the rich.seq I just redefine it every iteration based on whatever happens with details.table during the previous iteration.