When I have to bind two data.tables (or data.frames), I use:
data.full<- rbindlist(list(data.v1, data.v2), fill = T)
rm(data.v1)
rm(data.v2)
gc()
This works, but the data is duplicated in memory for a moment, and this can be a huge problem if the data is too big. Is there a way to do this without copying the data?
I would strictly prefer a solution that does not involve binding the data in an external software, such as an SQL database. A perfect solution would be something like data.table::setorder(data, id) that replaces data <- data[order(id)].
I have 900000 csv files which i want to combine into one big data.table. For this case I created a for loop which reads every file one by one and adds them to the data.table. The problem is that it is performing to slow and the amount of time used is expanding exponentially. It would be great if someone could help me make the code run faster. Each one of the csv files has 300 rows and 15 columns.
The code I am using so far:
library(data.table)
setwd("~/My/Folder")
WD="~/My/Folder"
data<-data.table(read.csv(text="X,Field1,PostId,ThreadId,UserId,Timestamp,Upvotes,Downvotes,Flagged,Approved,Deleted,Replies,ReplyTo,Content,Sentiment"))
csv.list<- list.files(WD)
k=1
for (i in csv.list){
temp.data<-read.csv(i)
data<-data.table(rbind(data,temp.data))
if (k %% 100 == 0)
print(k/length(csv.list))
k<-k+1
}
Presuming your files are conventional csv, I'd use data.table::fread since it's faster. If you're on a Linux-like OS, I would use the fact it allows shell commands. Presuming your input files are the only csv files in the folder I'd do:
dt <- fread("tail -n-1 -q ~/My/Folder/*.csv")
You'll need to set the column names manually afterwards.
If you wanted to keep things in R, I'd use lapply and rbindlist:
lst <- lapply(csv.list, fread)
dt <- rbindlist(lst)
You could also use plyr::ldply:
dt <- setDT(ldply(csv.list, fread))
This has the advantage that you can use .progress = "text" to get a readout of progress in reading.
All of the above assume that the files all have the same format and have a header row.
Building on Nick Kennedy's answer using plyr::ldply there is roughly a 50% speed increase by enabling the .parallel option while reading 400 csv files roughly 30-40 MB each.
Original answer with progress bar
dt <- setDT(ldply(csv.list, fread, .progress="text")
Enabling .parallel also with a text progress bar
library(plyr)
library(data.table)
library(doSNOW)
cl <- makeCluster(4)
registerDoSNOW(cl)
pb <- txtProgressBar(max=length(csv.list), style=3)
pbu <- function(i) setTxtProgressBar(pb, i)
dt <- setDT(ldply(csv.list, fread, .parallel=TRUE, .paropts=list(.options.snow=list(progress=pbu))))
stopCluster(cl)
As suggested by #Repmat, use rbind.fill. As suggested by #Christian Borck, use fread for faster reads.
require(data.table)
require(plyr)
files <- list.files("dir/name")
df <- rbind.fill(lapply(files, fread, header=TRUE))
Alternatively you could use do.call, but rbind.fill is faster (http://www.r-bloggers.com/the-rbinding-race-for-vs-do-call-vs-rbind-fill/)
df <- do.call(rbind, lapply(files, fread, header=TRUE))
Or you could use the data.table package, see this
You are growing your data table in a for loop - this is why it takes forever. If you want to keep the for loop as is, first create a empty data frame (before the loop), which has the dimensions you need (rows x columns), and place it in the RAM.
Then write to this empty frame in each iteration.
Otherwise use rbind.fill from package plyr - and avoid the loop altogehter.
To use rbind.fill:
require(plyr)
data <- rbind.fill(df1, df2, df3, ... , dfN)
To pass the names of the df's, you could/should use an apply function.
I go with #Repmat as your current solution using rbind() is copying the whole data.table in memory every time it is called (this is why time is growing exponentially). Though another way would be to create an empty csv file with only the headers first and then simply append the data of all your files to this csv-file.
write.table(fread(i), file = "your_final_csv_file", sep = ";",
col.names = FALSE, row.names=FALSE, append=TRUE, quote=FALSE)
This way you don't have to worry about putting the data to the right indexes in your data.table. Also as a hint: fread() is the data.table file reader which is much faster than read.csv.
In generell R wouldn't be my first choice for this data munging tasks.
One suggestion would be to merge them first in groups of 10 or so, and then merge those groups, and so on. That has the advantage that if individual merges fail, you don't lose all the work. The way you are doing it now not only leads to exponentially slowing execution, but exposes you to having to start over from the very beginning every time you fail.
This way will also decrease the average size of the data frames involved in the rbind calls, since the majority of them will be being appended to small data frames, and only a few large ones at the end. This should eliminate the majority of the execution time that is growing exponentially.
I think no matter what you do it is going to be a lot of work.
Some things to consider under the assumption you can trust all the input data and that each record is sure to be unique:
Consider creating the table being imported into without indexes. As indexes get huge the time involved to manage them during imports grows -- so it sounds like this may be happening. If this is your issue it would still take a long time to create indexes later.
Alternately, with the amount of data you are discussing you may want to consider a method of partitioning the data (often done via date ranges). Depending on your database you may then have individually indexed partitions -- easing index efforts.
If your demonstration code doesn't resolve down to a database file import utility then use such a utility.
It may be worth processing files into larger data sets prior to importing them. You could experiment with this by combining 100 files into one larger file before loading, for example, and comparing times.
In the event you can't use partitions (depending on the environment and the experience of the database personnel) you can use a home brewed method of seperating data into various tables. For example data201401 to data201412. However, you'd have to roll your own utilities to query across boundaries.
While decidedly not a better option it is something you could do in a pinch -- and it would allow you to retire/expire aged records easily and without having to adjust the related indexes. it would also let you load pre-processed incoming data by "partition" if desired.
I have two very big files that I have to merge and than eliminate duplicates according to one column. So far I am doing like this
myfiles <- list.files(pattern="*.dat")
myfilesContent <- lapply(myfiles, read.delim, header=F, quote="\"",sep=" ",colClasses="character")
data = as.data.frame(data.table::rbindlist(myfilesContent))
data <- data[!duplicated(data$V1,fromLast=TRUE),]
but reading the two files consumes a lot of memory. Is there a better way of doing it?
Many thanks
but reading the two files consumes a lot of memory. Is there a better way of doing it?
Try fread instead of read.delim.
Yes keep using rbindlist.
Use unique(...,by=V1) on the data.table rather than converting it to data.frame.
Should be a lot faster and more memory efficient.
I have 20 different .csv files and I need to some how stack the data in R so that I can get an overall picture of the data.
Presently I am copying and pasting the columns in excel to make one big data set.
However, I am sure there is a quicker and more efficient way of doing this in R as this would ultimately take a while.
Also, to make things worse some of the variable names are not the same in each data set.
eg VARIABLE1 is written as variable1 in some datasets. How would i rectify this in R as I understand that R is case sensitive?
Any help would be greatly appreciated. Thanks!
The easiest and the fastest way to do this, if you're (or wish you to be) familiar with data.table package is this way (not tested):
require(data.table)
in_pth <- "path_to_csv_files" # directory where CSV files are located, not the files.
files <- list.files(in_pth, full.names=TRUE, recursive=FALSE, pattern="\\.csv$")
out <- rbindlist(lapply(files, fread))
list.files parameters:
full.names = TRUE will return the full path to your file. Suppose your in_pth <- "c:\\my_csv_folder" and inside this you've two files: 01.csv and 02.csv. Then, full.names=TRUE will return c:\\my_csv_folder\\01.csv and c:\\my_csv_folder\\02.csv (full path).
recursive = FALSE will not search inside directories within your in_pth folder. Assume you've two more csv files in c:\\my_csv_folder\\another_folder. Now, if you want to load these files inside this one, then you can set recursive=TRUE, which'll scan for files until you reach all directories searching down.
pattern=\\.csv$: This is a regular expression to tell which sort of files to load. If your folder, in addition to csv files also has text files (.txt), then by specifying this pattern, you'll load only the csv files. If your folder has only CSV files, then this is not necessary.
data.table functions:
rbindlist avoids conflict in column names by retaining the name of the previous data.table. That is, if you've two data.tables dt1, dt2 with column names x,y and a,b respectively, then doing rbindlist(dt1,dt2) will take care of changing a,b to x,y and rbindlist(dt2, dt1) will take care of changing x,y to a,b.
fread takes care of columns, headers separators etc most often automatically.. and is extremely fast (although still experimental, so you may want to check your output to be sure it's all fine (even if stable)).
# Denis:It is also worth looking into the plyr package for the same. rbind.fill(...) allows you to combine data.frames by row.
install.packages("plyr")
library(plyr)
help (rbind.fill) for details gives you following:
rbinds a list of data frames filling missing columns with NA.
Usage
rbind.fill(...)
Arguments
...
input data frames to row bind together. The first argument can be a list of data frames, in which case all other arguments are ignored.
Details
This is an enhancement to rbind that adds in columns that are not present in all inputs, accepts a list of data frames, and operates substantially faster.
Column names and types in the output will appear in the order in which they were encountered. No checking is performed to ensure that each column is of consistent type in the inputs.
To my knowledge,there is no cbind.fill; however, there is the user function cbind.fill that allows you to combine data.frames by column. Details here.
There are two solutions: one depending on rbind.fill in the plyr package and another is independent of rbind.fill.
Another way, without using external packages, is to use the cbind() command: it makes the binding per column.. So if you have to different tables you can just pass them as arguments to cbind() and they will be appended
I am producing a script where I have done many manipulations to a bunch of data and, I do these same manipulations to another dataset. Both data sets have the same rows, columns, and headers. I would like to be able to join the two data sets together where I place dataset A above dataset B. I wouldn't need to headers for dataset B and would instead just clump all of the data together as if they were never really separated in the first place. Is there a simply way to do this?
Yes. Use rbind() command.
combineddataset = rbind(dataset1, dataset2)
Hope that helps.
And for completeness, you could also use the rbind.fill function found in the plyr package.