I successfully wrote a for loop in R. That is okay and I am very happy that it works. But I also want to understand what I've done exactly because I will have to work with loops later on in my analysis as well.
I work with Raster Data (DEMs). I load them into the environment as rasters and then I use the getValues function in the loop as I want to do some calculations. Looks as follows:
list <- dir(pattern=".tif", full.names=T)
tif.files <- list()
tif.files.values <- tif.files
for (i in 1: length(list)){
tif.files[[i]] <- raster (list[[i]])
tif.files.values[[i]] <- getValues(tif.files[[i]])
}
Okay, so far so good. I don't get why I have to specify tif.files and tif.files.values before I use them in the loop and I don't know why to specify them exactly how I did that. For the first part, the raster operation, I had a pattern. Maybe someone can explain the context. I really want to understand R.
When you do:
tif.files[[i]] <- raster (list[[i]])
then tif.files[[i]] is the result of running raster(list[[i]]), so that is storing the raster object. This object contains the metadata (extent, number of rows, cols etc) and the data, although if the tiff is huge it doesn't actually read it in at the time.
tif.files.values[[i]] <- getValues(tif.files[[i]])
that line calls getValues on the raster object, which reads the values from the raster and returns a vector. The values of the grid cells are now in tif.files.values[[i]].
Experiment by printing tif.files[[1]] and tif.files.values[[1]] at the R prompt.
Note
This is R, not RStudio, which is the interface you are using that has all the buttons and menus. The R language exists quite happily without it, and your question is just a language question. I've edited and tagged it now for you.
Related
I am running several calculations and ML algorithms in R and store their results in four distinctive tables.
For each calculation, I obtain four tables, which I store in a single list.
According to R, all of my lists are labelled as "Large List (4 elements, 971.2 kB)" in the upper right quadrant in RStudio where all my objects, functions, etc. are displayed.
I have five of these lists and save them for later use with the save() function.
I use the function:
save(list1, list2, list3, list4, list5, file="mypath/mylists.RData")
For some reason, which I do not understand, R takes more than 24 hours to save these four lists with only 971.2 kB each.
Maybe, I should add that apparently more than 10GB of my RAM are used by R at the time. However, the lists are as small as I indicated above.
Does anyone have an idea why it takes so long to save the lists to my harddrive and what I could do about it?
Thank you
This is just a guess, because we don't have your data.
Some objects in r contain references to environments. The most common examples are functions and formulas. If you save one of those, r may need to save the whole environment. This can drastically increase the size of what is being saved. If you are short of memory that could take a very long time due to swapping.
Example:
F <- function () {
X <- rnorm(1000000)
Y ~ z
}
This function returns a small formula which references the environment holding X, so saving it will take a lot of space.
Thanks for your answers.
I solved my problem by writing a function which extracts the tables from the objects and saves them as .csv files in a folder. I cleaned the environment and shut down the computer. Afterwards, I restarted the computer, started R and loaded all the .csv files again. I then saved the thus created objects with the familiar save() command.
It is probably not the most elegant way, but it worked and was quite quick.
I am desesperate, because my problem seems very simple, but I cannot find out how to manage it.
Aim:
I would like to read 1 to 4 raster files from a folder. The names of the one that I need are listed in a list as character.
After having opened the files, I would like to create a new raster corresponding to the mean of the files.
I can manage it on QGIS, but I need to automatize hte process, as I have a lot of individuals!
1) It should work with list.files(pattern = ) but as the names are in a list, I do not know how to do.
Ex: for the first individual, I have to read 2 files named 2018-12-27_sic.tif and 2018-12-27_sic_con.tif
I tried to read with readGDAL , open.GDAL it didn't work
thanks a lot for your valuable help
I would use the stack and calc functions from the raster package. The function stack creates a stack of rasters, all with the same resolution and extent, and makes it easy to do operations like take the mean of every cell. So:
library(raster)
fs <- list.files(pattern='tif$')
rasterstack <- stack(fs)
rastermean <- calc(rasterstack, fun=mean)
Note, if your rasters are not the same resolution, you will have to use the resample function, and if they are not the same extent, you will have to use crop. Typing in ?resample and ?crop in RStudio will show you instructions for using those functions.
I'm brand new to programming and an picking up Rstudio as a stats tool.
I have a dataset which includes multiple questionnaires divided by weeks, and I'm trying to organize the data into meaningful chunks.
Right now this is what my code looks like:
w1a=table(qwest1,talm1)
w2a=table(qwest2,talm2)
w3a=table(quest3,talm3)
Where quest and talm are the names of the variable and the number denotes the week.
Is there a way to compress all those lines into one line of code so that I could make w1a,w2a,w3a... each their own object with the corresponding questionnaire added in?
Thank you for your help, I'm very new to coding and I don't know the etiquette or all the vocabulary.
This might do what you wanted (but not what you asked for):
tbl_list <- mapply(table, list(qwest1, qwest2, quest3),
list(talm1, talm2, talm3) )
names(tbl_list) <- c('w1a', 'w2a','w3a')
You are committing a fairly typical new-R-user error in creating multiple similarly named and structured objects but not putting them in a list. This is my effort at pushing you in that direction. Could also have been done via:
qwest_lst <- list(qwest1, qwest2, quest3)
talm_lst <- list(talm1, talm2, talm3)
tbl_lst <- mapply(table, qwest_lst, talm_lst)
names(tbl_list) <- paste0('w', 1:3, 'a')
There are other ways to programmatically access objects with character vectors using get or wget.
I'm trying to create a "summary" data frame that holds some high-level stats about a few objects in my R project. I'm having trouble even accomplishing this simple task and I've tried using For loops and Apply functions with no luck.
After searching (a lot) on SO I'm seeing that For loops might not be the best performing option, so I'm open to any solution that gets the job done.
I have three objects: text1 text2 and text3 of class "Large Character (vectors)" (imagine I might be exploring these objects and will create a NLP predictive model from them). Each are > 250 MB in size (upwards of 1 million "rows" each) once loaded into R.
My goal: Store the results of object.size() length() and max(nchar()) in a table for my 3 objects.
Method 1: Use an Apply() Function
Issue: I haven't successfully applied multiple functions to a single object. I understand how to do simple applies like lapply(x, mean) but I'm falling short here.
Method 2: Bind Rows Using a For loop
I'm liking this solution because I almost know how to implement it. A lot of SO users say this is a bad approach, but I'm lacking other ideas.
sources <- c("text1", "text2", "text3")
text.summary <- data.frame()
for (i in sources){
text.summary[i ,] <- rbind(i, object.size(get(i)), length(get(i)),
max(nchar(get(i))))
}
Issue: This returns the error data length exceeds size of matrix - I know I could define the structure of my data frame (on line 2), but I've seen too much feedback on other questions that advise against doing this.
Thanks for helping me understand the proper way to accomplish this. I know I'm going to have trouble doing NLP if I can't even figure out this simple problem, but R is my first foray into programming. Oof!
Just try for example:
do.call(rbind, lapply(list(text1,text2,text3),
function(x) c(objectSize=c(object.size(x)),length=length(x),max=max(nchar(x)))))
You'll obtain a matrix. You can coerce to data.frame later if you need.
I am not sure there are any R users out there, but just in case:
I am a novice at R and was kindly "handed down" the following R code snippet:
Beta <- exp(as.matrix(read.table('beta.transpose')))
WordFreq <- read.table('freq-matrix')
WordProbs <- WordFreq$V1 / sum(WordFreq)
infile <- file('freq-matrix')
outfile <- file('doc_topic_prob_matrix', 'w')
open(infile)
open(outfile)
for (i in 1:93049) {
vec <- t(scan(infile, nlines=1))
topics <- (vec/WordProbs) %*% Beta
write.table(topics, outfile, append=T, row.names=F, col.names=F)
}
When I tried running this on my dataset, the system thrashed and swapped like crazy. Now I realize that has a simple reason: the file freq-matrix holds a large (22GB) matrix and I was trying to read it into memory.
I have been told to use the Matrix package, because freq-matrix has many, many zeros all over the place and it handles such cases well. Will that help? If so, any hints on how to change this code would be most welcome. I have no R experience and just started reading through the introduction PDF available on the site.
Many thanks
~l
My suggestion might be completely off, because you don't give enough details about the contents of your files, and I had to guess from the code. Anyway, here it goes.
You don't state it, but I would assume that your code crashes on the second line, when you read in the big matrix. The loop reads the lines one-at-a-time, and should not crash. The only reason you need that big matrix is to calculate the WordProbs vector. So why don't you rewrite that part using the same looping using scan? In fact, you could probably don't even need to store the WordProbs vector, just sum(WordFreq) - you can get that using an initial run through hte file. Then rewrite the formula within the loop to calculate the current WordProb.
Belated answer, but I'd recommend reading the data into a memory mapped file, using the bigmemory package. After that, I'd look for the non-zero entries, which can then be represented as a 3 column matrix: (ix_row, ix_col, value). This is called a coordinate object list (COO), though the name is unimportant. From there, Matrix supports the creation of sparse matrices (via sparseMatrix). After you get the COO, you're pretty much set - conversion to and from the sparse matrix format is reasonably fast. Multiplying the matrix by Beta should be reasonably fast. If you need even greater speed, you could use an optimized BLAS library, but that opens up more questions. :)