I have a script I have to run periodically where I use a varying number of assignment statements in R, like this:
r5$NWord_1<-ifelse(r5$Match==1,NA,r5$NWord_1)
r5$NWord_2<-ifelse(r5$Match==2,NA,r5$NWord_2)
r5$NWord_3<-ifelse(r5$Match==3,NA,r5$NWord_3)
r5$NWord_4<-ifelse(r5$Match==4,NA,r5$NWord_4)
r5$NWord_5<-ifelse(r5$Match==5,NA,r5$NWord_5)
r5$NWord_6<-ifelse(r5$Match==6,NA,r5$NWord_6)
r5$NWord_7<-ifelse(r5$Match==7,NA,r5$NWord_7)
The problem is that the number of "NWord" variables changes from run to run usually between 5 and 7). I have the number of "NWord" variables stored separately as Size.
Size<-5
I have tried the following, but get() only works on objects, not columns of dataframes.
for(i in 1:Size){
get(paste("r5$NWord_",i,sep=""))<-ifelse(r5$Match==i,NA,get(paste("r5$NWord_",i,sep="")))
}
I am curious: What is the best way to automate this process so I do not have to manually run a subset of these statements every time?
For those interested: 1) The import data are in wide format (that is what the system allows me to download). 2) The export data have to be in wide format in order to upload to the system. Since these are just a few of some ~200 variables in the data set, going back and forth between wide and long and then long back to wide (possibly multiple times) seems cumbersome and prone to error. Therefore, I came up with this:
idx1<-which(colnames(r5)=="NWord_1")
idx2<-which(colnames(r5)==paste("NWord_",Size,sep=""))
for(i in idx1:idx2){
r5[,i]<-ifelse(r5$Match==1,NA,r5[,i])
}
Seems to work just fine, however, I'm not sure that it is the most efficient way to code this.
Related
I've got a dataframe, which is stored in a csv, of 63 columns and 1.3 million rows. Each row is a chess game, each column is details about the game (e.g. who played in the game, what their ranking was, the time it was played, etc). I have a column called "Analyzed", which is whether someone later analyzed the game, so it's a yes/no variable.
I need to use the API offered by chess.com to check whether a game is analyzed. That's easy. However, how do I systematically update the csv file, without wasting huge amounts of time reading in and writing out the csv file, while accounting for the fact that this is going to take a huge amount of time and I need to do it in stages? I believe a best practice for chess.com's API is to use Sys.sleep after every API call so that you lower the likelihood that you are accidentally making concurrent requests, which the API doesn't handle very well. So I have Sys.sleep for a quarter of a second. If we assume the API call itself takes no time, then this means this program will need to run for 90 hours because of the sleep time alone. My goal is to make it so that I can easily run this program in chunks, so that I don't need to run it for 90 hours in a row.
The code below works great to get whether a game has been analyzed, but I don't know how to intelligently update the original csv file. I think my best bet would be to rewrite the new dataframe and replace the old Games.csv every 1000 or say API calls. See the commented code below.
My overall question is, when I need to update a column in csv that is large, what is the smart way to update that column incrementally?
library(bigchess)
library(rjson)
library(jsonlite)
df <- read.csv <- "Games.csv"
for(i in 1:nrow(df)){
data <- read_json(df$urls[i])
if(data$analysisLogExists == TRUE){
df$Analyzed[i] <- 1
}
if(data$analysisLogExists==FALSE){
df$Analyzed[i] = 0
}
Sys.sleep(.25)
##This won't work because the second time I run it then I'll just reread the original lines
##if i try to account for this by subsetting only the the columns that haven't been updated,
##then this still doesn't work because then the write command below will not be writing the whole dataset to the csv
if(i%%1000){
write.csv(df,"Games.csv",row.names = F)
}
}
first question, I'll try to go straight to the point.
I'm currently working with tables and I've chosen R because it has no limit with dataframe sizes and can perform several operations over the data within the tables. I am happy with that, as I can manipulate it at my will, merges, concats and row and column manipulation works fine; but I recently had to run a loop with 0.00001 sec/instruction over a 6 Mill table row and it took over an hour.
Maybe the approach of R was wrong to begin with, and I've tried to look for the most efficient ways to run some operations (using list assignments instead of c(list,new_element)) but, since as far as I can tell, this is not something that you can optimize with some sort of algorithm like graphs or heaps (is just tables, you have to iterate through it all) I was wondering if there might be some other instructions or other basic ways to work with tables that I don't know (assign, extract...) that take less time, or configuration over RStudio to improve performance.
This is the loop, just so if it helps to understand the question:
my_list <- vector("list",nrow(table[,"Date_of_count"]))
for(i in 1:nrow(table[,"Date_of_count"])){
my_list[[i]] <- format(as.POSIXct(strptime(table[i,"Date_of_count"]%>%pull(1),"%Y-%m-%d")),format = "%Y-%m-%d")
}
The table, as aforementioned, has over 6 Mill rows and 25 variables. I want the list to be filled to append it to the table as a column once finished.
Please let me know if it lacks specificity or concretion, or if it just does not belong here.
In order to improve performance (and properly work with R and tables), the answer was a mixture of the first comments:
use vectors
avoid repeated conversions
if possible, avoid loops and apply functions directly over list/vector
I just converted the table (which, realized, had some tibbles inside) into a dataframe and followed the aforementioned keys.
df <- as.data.frame(table)
In this case, by doing this the dates were converted directly to character so I did not have to apply any more conversions.
New execution time over 6 Mill rows: 25.25 sec.
I have the output from a data submission which is in the form of multiple vector list objects in rda files.
Each list object is in a separate rda file and i have nearly 2000 files.
I want to merge all the objects into a single object in a single rda file in the fastest way (partly because i may need to repeat this several times).
All the rda files are fairly small (~10mb though this will be a compressed size), but it all adds up with the number of files.
Memory isn't a huge problem as am running it on a server with >700GB RAM,
My first approach to incrementally load them one by one concatenate with the merged list object and remove the object that was appended went badly due to the time it was going to take (something like 40 days at a best guess).
My revised approach is below, but wondering if there is a quicker way to do this given that i may need to repeat the process:
load("data_1.rda")
load("data_2.rda")
load("data_3.rda") ...
load("data_2000.rda")
my.list <- list()
my.list <- c(my.list, data.1, data.2, data.3, ... , data.2000)
save(my.list, file="my_list.rda")
And just to add to things i'm getting an error when doing this:
Error: attempt to set index 18446744071562067968/2877912830 in SET_STRING_ELT
It's not a very helpful error message
All the rdas load as objects into the environment fine, but when i try and concatenate them that is when I get the error message, and it seems like it is when it gets to a particular point as it doesn't fail immediately. Wasn't sure if it is some sort of limit in the number of concatenations you can do or rogue data, but troubleshooting it it appears to be syntax rather than data related.
Have chunked it up into 5 batches and then doing a final concatenation before saving the rda. Have seen other answers for this sort of thing suggesting using rbind, mget, and do.Call or list function - would using any of these functions make it faster and achieve the same thing?
Something like this:
my.list <- do.call(rbind, mget(ls(pattern="^data_")))
Thanks
I am trying to manage multiple files in R but am having a difficult time of it. I want to take the data in each of these files and manipulate them through a series of steps (all files receiving the same treatment). I think that I am going about it in a very silly manner though. Is there a way to manage many files (each the same a before) without using 900 apply statements? For example, when is it recommended you merge all the data frames rather that treat each separately? Is there a way to merge more than two, or an uncertain number, as with the way the files are input here? Or is there a better way to handle so many files?
I take files in a standard way:
chosen<-(tk_choose.files(default="", caption="Files:", multi=TRUE, filters=NULL, index=1))
But after that I would like to do several things with the data. As of now I am just apply different things but it is getting confusing. See:
ytrim<-lapply(chosen, function(x) strtrim(y, width=11))
chRead<-lapply(chosen,read.table,header=TRUE)
tmp<-lapply(inputFiles, function(x) stack(fnctn))
etc, etc. This surely can't be the recommended way to go about it. Is there a better way to handle a multitude of files?
You can write one function with all operations, and apply it to all your files like this:
doSomethingWithFile <- function(filename) {
ytrim <- strtrim(filename, width=11))
chRead<- read.table(filename,header=TRUE)
# Return some result
chRead
}
result<-lapply(chosen, doSomethingWithFile)
You will only need to think about how to return the results, as lapply needs to return a list with the same length as the input (chosen, in this case). You could also look at one of the apply functions of the plyr packages for more flexibility.
(BTW: this code is not without errors, but neither is your example... I'll update mine if you give a proper example)
I can't find something to the effect of an undo command in R (neither on An Introduction to R nor in R in a Nutshell). I am particularly interested in undoing/deleting when dealing with interactive graphs.
What approaches do you suggest?
You should consider a different approach which leads to reproducible work:
Pick an editor you like and which has R support
Write your code in 'snippets', ie short files for functions, and then use the facilities of the editor / R integration to send the code to the R interpreter
If you make a mistake, re-edit your snippet and run it again
You will always have a log of what you did
All this works tremendously well in ESS which is why many experienced R users like this environment. But editors are a subjective and personal choice; other people like Eclipse with StatET better. There are other solutions for Mac OS X and Windows too, and all this has been discussed countless times before here on SO and on other places like the R lists.
In general I do adopt Dirk's strategy. You should aim for your code to be a completely reproducible record of how you have transformed your raw data into output.
However, if you have complex code it can take a long time to re-run it all. I've had code that takes over 30 minutes to process the data (i.e., import, transform, merge, etc.).
In these cases, a single data-destroying line of code would require me to wait 30 minutes to restore my workspace.
By data destroying code I mean things like:
x <- merge(x, y)
df$x <- df$x^2
e.g., merges, replacing an existing variable with a transformation, removing rows or columns, and so on. In these cases, it's easy, especially when first learning R to make a mistake.
To avoid having to wait this 30 minutes, I adopt several strategies:
If I'm about to do something where there's a risk of destroying my active objects, I'll first copy the result into a temporary object. I'll then check that it worked with the temporary object and then rerun replacing it with the proper object.
E.g., first run temp <- merge(x, y); check that it worked str(temp); head(temp); tail(temp) and if everything looks good x <- merge(x, y)
As is common in psychological research, I often have large data frames with hundreds of variables and different subsets of cases. For a given analysis (e.g., a table, a figure, some results text), I'll often extract just the subset of cases and variables that I need into a separate object for the analysis and work with that object when preparing and finalising my analysis code. That way, I'm less likely to accidentally damage my main data frame. This assumes that the results of the analysis does not need to be fed back into the main data frame.
If I have finished performing a large number of complex data transformations, I may save a copy of the core workspace objects. E.g., save(x, y, z , file = 'backup.Rdata') That way, If I make a mistake, I only have to reload these objects.
df$x <- NULL is a handy way of removing a variable in a data frame that you did not want to create
However, in the end I still run all the code from scratch to check that the result is reproducible.