Stuck on doing a Recursive action in R - r

I have written a code and it works fine. However, for practical reasons (and I also want to learn more) it is ideal if I have a shorter way of doing things. Here is an example of a text file I am reading:
Analysis Date: Tue Oct 16 09:39:06 EDT 2018
Input file(s): 012-915-8-rep1.fastq
Output file(s): 012-915-8-rep1.vdjca
Version: 2.1.12; built=Wed Aug 22 08:47:36 EDT 2018; rev=99f9cc0;
lib=repseqio.v1.5
Command line arguments: align -c IGH -r report012-915-8-rep1.txt 012-915-8-
rep1.fastq 012-915-8-rep1.vdjca
Analysis time: 45.45s
Total sequencing reads: 198274
Successfully aligned reads: 167824 (84.64%)
Alignment failed, no hits (not TCR/IG?): 12122 (6.11%)
Alignment failed because of absence of J hits: 18235 (9.2%)
Alignment failed because of low total score: 93 (0.05%)
Overlapped: 0 (0%)
Overlapped and aligned: 0 (0%)
Alignment-aided overlaps: 0 (?%)
Overlapped and not aligned: 0 (0%)
IGH chains: 167824 (100%)
======================================
Analysis Date: Tue Oct 16 09:39:52 EDT 2018
Input file(s): 012-915-8-rep1.vdjca
Output file(s): 012-915-8-rep1.clns
Version: 2.1.12; built=Wed Aug 22 08:47:36 EDT 2018; rev=99f9cc0; lib=repseqio.v1.5
Command line arguments: assemble -OaddReadsCountOnClustering=true -r
report012-915-8-rep1.txt 012-915-8-rep1.vdjca 012-915-8-rep1.clns
Analysis time: 7.50s
Final clonotype count: 1227
Average number of reads per clonotype: 124.77
Reads used in clonotypes, percent of total: 153096 (77.21%)
Reads used in clonotypes before clustering, percent of total: 153096 (77.21%)
Number of reads used as a core, percent of used: 113699 (74.27%)
Mapped low quality reads, percent of used: 39397 (25.73%)
Reads clustered in PCR error correction, percent of used: 14522 (9.49%)
Reads pre-clustered due to the similar VJC-lists, percent of used: 0 (0%)
Reads dropped due to the lack of a clone sequence: 8958 (4.52%)
Reads dropped due to low quality: 0 (0%)
Reads dropped due to failed mapping: 5770 (2.91%)
Reads dropped with low quality clones: 0 (0%)
Clonotypes eliminated by PCR error correction: 5550
Clonotypes dropped as low quality: 0
Clonotypes pre-clustered due to the similar VJC-lists: 0
======================================
I basically want just line 7,8 and 26, which are: "Total Sequencing Reads", "Successfully Aligned Reads", and "Reads used in clonotypes, percent of total". Everything else can be eliminated. My code to do this, for several text files is as such:
> # Put in your actual path where the text files are saved
> mypath = "C:/Users/ME/Desktop/REPORTS/text files/"
> setwd(mypath)
> #############################################################
> #Functional Code
> #Establish the dataframe
> data <- data.frame("Total seq Reads"=integer(), "Successful Reads"=integer(), "Clonotypes"=integer())
>
> #this should be a loop, I think, same action repeats, I just dont know how to format
>
> wow <- readLines("C:/Users/ME/Desktop/REPORTS/text files/report012-915-8-rep1.txt")
> woah <- wow[-c(1:6,9:25,27:39)]
> blah <- as.numeric(gsub("\\D", "", gsub("\\(.*\\)", "", woah)))
> data[nrow(data)+1,] <- blah
>
> wow <- readLines("C:/Users/ME/Desktop/REPORTS/text files/report012-915-8-rep2.txt")
> woah <- wow[-c(1:6,9:25,27:39)]
> blah <- as.numeric(gsub("\\D", "", gsub("\\(.*\\)", "", woah)))
> data[nrow(data)+1,] <- blah
>
> row.names(data) <- c("012-915-8-rep1","012-915-8-rep2")
>
># Write CSV in R
> write.csv(data, file = "Report_Summary.csv")
Is there a more efficient way of doing this? I only placed 2 files as examples here, but in reality I am utilizing around 20-80 files, which means that this process I would have to do manually. Any help would be appreciated! Thank you!

You can make it a function and loop it on your files. One thing you should be aware of is growing vectors/data.frames, like with this data[nrow(data)+1,] <- blah. It's generally inefficient, so either start with a vector, etc. of the desired size and write output to it, or bind/reshape. For a small number of rows you may not notice it, but you will the more rows you have. If interested, read up on vectorization.
textfunction <- function(x) {
wow <- readLines(x)
woah <- wow[c(9:10,29)] # I think these are the lines you are referencing
blah <- as.numeric(gsub("\\D", "", gsub("\\(.*\\)", "", woah)))
}
Then get your directory, get the filenames, apply your function, and transpose/rename.
library(data.table)
dir = "C:/Users/ME/Documents/"
filenames <- list.files(path = dir, pattern = "*.txt", full.names = FALSE)
textreads <- lapply(filenames, function(x) textfunction(x))
data <- as.data.frame(data.table::transpose(textreads), col.names = c("Total seq Reads", "Successful Reads", "Clonotypes"), row.names = filenames)
data
Total.seq.Reads Successful.Reads Clonotypes
text1.txt 198274 167824 153096
text2.txt 198274 167824 153096

Related

Slow wordcloud in R

Trying to create a word cloud from a 300MB .csv file with text, but its taking hours on a decent laptop with 16GB of RAM. Not sure how long this should typically take...but here's my code:
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")
dfTemplate <- read.csv("CleanedDescMay.csv", header=TRUE, stringsAsFactors = FALSE)
template <- dfTemplate
template <- Corpus(VectorSource(template))
template <- tm_map(template, removeWords, stopwords("english"))
template <- tm_map(template, stripWhitespace)
template <- tm_map(template, removePunctuation)
dtm <- TermDocumentMatrix(template)
m <- as.matrix(dtm)
v <- sort(rowSums(m), decreasing=TRUE)
d <- data.frame(word = names(v), freq=v)
head(d, 10)
par(bg="grey30")
png(file="WordCloudDesc1.png", width=1000, height=700, bg="grey30")
wordcloud(d$word, d$freq, col=terrain.colors(length(d$word), alpha=0.9), random.order=FALSE, rot.per = 0.3, max.words=500)
title(main = "Top Template Words", font.main=1, col.main="cornsilk3", cex.main=1.5)
dev.off()
Any advice is appreciated!
Step 1: Profile
Have you tried profiling your full workflow yet with a small subset to figure out which steps are taking the most time? Profiling with RStudio here
If not, that should be your first step.
If the tm_map() functions are taking a long time:
If I recall correctly, I found working with stringi to be faster than the dedicated corpus tools.
My workflow wound up looking like the following for the pre-cleaning steps. This could definitely be optimized further -- magrittr pipes %>% do contribute to some additional processing time, but I feel like that's an acceptable trade-off for the sanity of not having dozens of nested parenthesis.
library(data.table)
library(stringi)
library(parallel)
## This function handles the processing pipeline
textCleaner <- function(InputText, StopWords, Words, NewWords){
InputText %>%
stri_enc_toascii(.) %>%
toupper(.) %>%
stri_replace_all_regex(.,"[[:cntrl:]]"," ") %>%
stri_replace_all_regex(.,"[[:punct:]]"," ") %>%
stri_replace_all_regex(.,"[[:space:]]+"," ") %>% ## Replaces multiple spaces with
stri_replace_all_regex(.,"^[[:space:]]+|[[:space:]]+$","") %>% ## Remove leading and trailing spaces
stri_replace_all_regex(.,"\\b"%s+%StopWords%s+%"\\b","",vectorize_all = FALSE) %>% ## Stopwords
stri_replace_all_regex(.,"\\b"%s+%Words%s+%"\\b",NewWords,vectorize_all = FALSE) ## Replacements
}
## Replacement Words, I would normally read in a .CSV file
Replace <- data.table(Old = c("LOREM","IPSUM","DOLOR","SIT"),
New = c("I","DONT","KNOW","LATIN"))
## These need to be defined globally
GlobalStopWords <- c("AT","UT","IN","ET","A")
GlobalOldWords <- Replace[["Old"]]
GlobalNewWords <- Replace[["New"]]
## Generate some sample text
DT <- data.table(Text = stringi::stri_rand_lipsum(500000))
## Running Single Threaded
system.time({
DT[,CleanedText := textCleaner(Text, GlobalStopWords,GlobalOldWords, GlobalNewWords)]
})
# user system elapsed
# 66.969 0.747 67.802
The process of cleaning text is embarrassingly parallel, so in theory you should be able some big time savings possible with multiple cores.
I used to run this pipeline in parallel, but looking back at it today, it turns out that the communication overhead makes this take twice as long with 8 cores as it does single threaded. I'm not sure if this was the same for my original use case, but I guess this may simply serve as a good example of why trying to parallelize instead of optimize can lead to more trouble than value.
## This function handles the cluster creation
## and exporting libraries, functions, and objects
parallelCleaner <- function(Text, NCores){
cl <- parallel::makeCluster(NCores)
clusterEvalQ(cl, library(magrittr))
clusterEvalQ(cl, library(stringi))
clusterExport(cl, list("textCleaner",
"GlobalStopWords",
"GlobalOldWords",
"GlobalNewWords"))
Text <- as.character(unlist(parallel::parLapply(cl, Text,
fun = function(x) textCleaner(x,
GlobalStopWords,
GlobalOldWords,
GlobalNewWords))))
parallel::stopCluster(cl)
return(Text)
}
## Run it Parallel
system.time({
DT[,CleanedText := parallelCleaner(Text = Text,
NCores = 8)]
})
# user system elapsed
# 6.700 5.099 131.429
If the TermDocumentMatrix(template) is the chief offender:
Update: I mentioned Drew Schmidt and Christian Heckendorf also submitted an R package named ngram to CRAN recently that might be worth checking out: ngram Github Repository. Turns out I should have just tried it before explaining the really cumbersome process of building a command line tool from source-- this would have saved me a lot of time had been around 18 months ago!
It is a good deal more memory intensive and not quite as fast -- my memory usage peaked around 31 GB so that may or may not be a deal-breaker for you. All things considered, this seems like a really good option.
For the 500,000 paragraph case, ngrams clocks in at around 7 minutes of runtime:
#install.packages("ngram")
library(ngram)
library(data.table)
system.time({
ng1 <- ngram::ngram(DT[["CleanedText"]],n = 1)
ng2 <- ngram::ngram(DT[["CleanedText"]],n = 2)
ng3 <- ngram::ngram(DT[["CleanedText"]],n = 3)
pt1 <- setDT(ngram::get.phrasetable(ng1))
pt1[,Ngrams := 1L]
pt2 <- setDT(ngram::get.phrasetable(ng2))
pt2[,Ngrams := 2L]
pt3 <- setDT(ngram::get.phrasetable(ng3))
pt3[,Ngrams := 3L]
pt <- rbindlist(list(pt1,pt2,pt3))
})
# user system elapsed
# 411.671 12.177 424.616
pt[Ngrams == 2][order(-freq)][1:5]
# ngrams freq prop Ngrams
# 1: SED SED 75096 0.0018013693 2
# 2: AC SED 33390 0.0008009444 2
# 3: SED AC 33134 0.0007948036 2
# 4: SED EU 30379 0.0007287179 2
# 5: EU SED 30149 0.0007232007 2
You can try using a more efficient ngram generator. I use a command line tool called ngrams (available on github here) by Zheyuan Yu- partial implementation of Dr. Vlado Keselj 's Text-Ngrams 1.6 to take pre-processed text files off disk and generate a .csv output with ngram frequencies.
You'll need to build from source yourself using make and then interface with it using system() calls from R, but I found it to run orders of magnitude faster while using a tiny fraction of the memory. Using it, I was was able generate 5-grams from ~700MB of text input in well under an hour, the CSV result with all the output was 2.9 GB file with 93 million rows.
Continuing the example above, In my working directory, I have a folder, ngrams-master, in my working directory that contains the ngrams executable created with make.
writeLines(DT[["CleanedText"]],con = "ExampleText.txt")
system2(command = "ngrams-master/ngrams",args = "--type=word --n = 3 --in ExampleText.txt", stdout = "ExampleGrams.csv")
# ngrams have been generated, start outputing.
# Subtotal: 165 seconds for generating ngrams.
# Subtotal: 12 seconds for outputing ngrams.
# Total 177 seconds.
Grams <- fread("ExampleGrams.csv")
# Read 5917978 rows and 3 (of 3) columns from 0.160 GB file in 00:00:06
Grams[Ngrams == 3 & Frequency > 10][sample(.N,5)]
# Ngrams Frequency Token
# 1: 3 11 INTERDUM_NEC_RIDICULUS
# 2: 3 18 MAURIS_PORTTITOR_ERAT
# 3: 3 14 SOCIIS_AMET_JUSTO
# 4: 3 23 EGET_TURPIS_FERMENTUM
# 5: 3 14 VENENATIS_LIGULA_NISL
I think I may have made a couple tweaks to get the output format how I wanted it, if you're interested I can try to find the changes I made to generate a .csvoutputs that differ from the default and upload to Github. (I did that project before I was familiar with the platform so I don't have a good record of the changes I made, live and learn.)
Update 2: I created a fork on Github, msummersgill/ngrams that reflects the slight tweaks I made to output results in a .CSV format. If someone was so inclined, I have a hunch that this could be wrapped up in a Rcpp based package that would be acceptable for CRAN submission -- any takers? I honestly have no clue how Ternary Search Trees work, but they seem to be significantly more memory efficient and faster than any other N-gram implementation currently available in R.
Drew Schmidt and Christian Heckendorf also submitted an R package named ngram to CRAN, I haven't used it personally but it might be worth checking out as well: ngram Github Repository.
The Whole Shebang:
Using the same pipeline described above but with a size closer to what you're dealing with (ExampleText.txt comes out to ~274MB):
DT <- data.table(Text = stringi::stri_rand_lipsum(500000))
system.time({
DT[,CleanedText := textCleaner(Text, GlobalStopWords,GlobalOldWords, GlobalNewWords)]
})
# user system elapsed
# 66.969 0.747 67.802
writeLines(DT[["CleanedText"]],con = "ExampleText.txt")
system2(command = "ngrams-master/ngrams",args = "--type=word --n = 3 --in ExampleText.txt", stdout = "ExampleGrams.csv")
# ngrams have been generated, start outputing.
# Subtotal: 165 seconds for generating ngrams.
# Subtotal: 12 seconds for outputing ngrams.
# Total 177 seconds.
Grams <- fread("ExampleGrams.csv")
# Read 5917978 rows and 3 (of 3) columns from 0.160 GB file in 00:00:06
Grams[Ngrams == 3 & Frequency > 10][sample(.N,5)]
# Ngrams Frequency Token
# 1: 3 11 INTERDUM_NEC_RIDICULUS
# 2: 3 18 MAURIS_PORTTITOR_ERAT
# 3: 3 14 SOCIIS_AMET_JUSTO
# 4: 3 23 EGET_TURPIS_FERMENTUM
# 5: 3 14 VENENATIS_LIGULA_NISL
While the example may not be a perfect representation due to the limited vocabulary generated by stringi::stri_rand_lipsum(), the total run time of ~4.2 minutes using less than 8 GB of RAM on 500,000 paragraphs has been fast enough for the corpuses (corpi?) I've had to tackle in the past.
If wordcloud() is the source of the slowdown:
I'm not familiar with this function, but #Gregor's comment on your original post seems like it would take care of this issue.
library(wordcloud)
GramSubset <- Grams[Ngrams == 2][1:500]
par(bg="gray50")
wordcloud(GramSubset[["Token"]],GramSubset[["Frequency"]],color = GramSubset[["Frequency"]],
rot.per = 0.3,font.main=1, col.main="cornsilk3", cex.main=1.5)

How to read unquoted extra \r with data.table::fread

Data I have to process has unquoted text with some additional \r character. Files are big (500MB), copious (>600), and changing the export is not an option. Data might look like
A,B,C
blah,a,1
bloo,a\r,b
blee,c,d
How can this be handled with data.table's fread?
Is there a better R read CSV function for this, that's similarly performant?
Repro
library(data.table)
csv<-"A,B,C\r\n
blah,a,1\r\n
bloo,a\r,b\r\n
blee,c,d\r\n"
fread(csv)
Error in fread(csv) :
Expected sep (',') but new line, EOF (or other non printing character) ends field 1 when detecting types from point 0:
bloo,a
Advanced repro
The simple repro might be too trivial to give a sense of scale...
samplerecs<-c("blah,a,1","bloo,a\r,b","blee,c,d")
randomcsv<-paste0(c("A,B,C",rep(samplerecs,2000000)))
write(randomcsv,file = "sample.csv")
# Naive approach
fread("sample.csv")
# Akrun's approach with needing text read first
fread(gsub("\r\n|\r", "", paste0(randomcsv,collapse="\r\n")))
#>Error in file.info(input) : file name conversion problem -- name too long?
# Julia's approach with needing text read first
readr::read_csv(gsub("\r\n|\r", "", paste0(randomcsv,collapse="\r\n")))
#> Error: C stack usage 48029706 is too close to the limit
Further to #dirk-eddelbuettel & #nrussell's suggestions, a way of solving this is to is to pre-process the file. The processor could also be called within fread() but here it is performed in seperate steps:
samplerecs<-c("blah,a,1","bloo,a\r,b","blee,c,d")
randomcsv<-paste0(c("A,B,C",rep(samplerecs,2000000)))
write(randomcsv,file = "sample.csv")
# Remove errant `\r`'s with tr - shown here is the Windows R solution
shell("C:/Rtools/bin/tr.exe -d '\\r' < sample.csv > sampleNEW.csv")
fread("sampleNEW.csv")
We can try with gsub
fread(gsub("\r\n|\r", "", csv))
# A B C
#1: blah a 1
#2: bloo a b
#3: blee c d
You can also do this with tidyverse packages, if you'd like.
> library(readr)
> library(stringr)
> read_csv(str_replace_all(csv, "\r", ""))
# A tibble: 3 × 3
A B C
<chr> <chr> <chr>
1 blah a 1
2 bloo a b
3 blee c d
If you do want to do it purely in R, you could try working with connections. As long as a connection is kept open, it will start reading/writing from its previous position. Of course, this means the burden of opening and closing connections falls on you.
In the following code, the file is processed by chunks:
library(data.table)
input_csv <- "sample.csv"
in_conn <- file(input_csv)
output_csv <- "out.csv"
out_conn <- file(output_csv, "w+")
open(in_conn)
chunk_size <- 1E6
return_pattern <- "(?<=^|,|\n)([^,]*(?<!\n)\r(?!\n)[^,]*)(?=,|\n|$)"
buffer <- ""
repeat {
new_chars <- readChar(in_conn, chunk_size)
buffer <- paste0(buffer, new_chars)
while (grepl("[\r\n]$", buffer, perl = TRUE)) {
next_char <- readChar(in_conn, 1)
buffer <- paste0(buffer, next_char)
if (!length(next_char))
break
}
chunk <- gsub("(.*)[,\n][^,\n]*$", "\\1", buffer, perl = TRUE)
buffer <- substr(buffer, nchar(chunk) + 1, nchar(buffer))
cleaned <- gsub(return_pattern, '"\\1"', chunk, perl = TRUE)
writeChar(cleaned, out_conn, eos = NULL)
if (!length(new_chars))
break
}
writeChar('\n', out_conn, eos = NULL)
close(in_conn)
close(out_conn)
result <- fread(output_csv)
Process:
If a chunk ends with a \r or \n, another character is added until it doesn't.
Quotes are put around values containing a \r which isn't adjacent to a
\n.
The cleaned chunk is added to the end of another file.
Rinse and repeat.
This code simplifies the problem by assuming no quoting is done for any field in sample.csv. It's not especially fast, but not terribly slow. Larger values for chunk_size should reduce the amount of time spent in I/O operations. If used for anything beyond this toy example, I'd strongly suggesting wrapping it in a tryCatch(...) call to make sure the files are closed afterwards.

Fast reading (by chunk?) and processing of a file with dummy lines at regular interval in R

I have a file with regular numeric output (same format) of many arrays, each separated by a single line (containing some info).
For example:
library(gdata)
nx = 150 # ncol of my arrays
ny = 130 # nrow of my arrays
myfile = 'bigFileWithRowsToSkip.txt'
niter = 10
for (i in 1:niter) {
write(paste(i, 'is the current iteration'), myfile, append=T)
z = matrix(runif(nx*ny), nrow = ny) # random numbers with dim(nx, ny)
write.fwf(z, myfile, append=T, rownames=F, colnames=F) #write in fixed width format
}
With nx=5 and ny=2, I would have a file like this:
# 1 is the current iteration
# 0.08051668 0.19546772 0.908230985 0.9920930408 0.386990316
# 0.57449532 0.21774728 0.273851698 0.8199024885 0.441359571
# 2 is the current iteration
# 0.655215475 0.41899060 0.84615044 0.03001664 0.47584591
# 0.131544592 0.93211342 0.68300161 0.70991368 0.18837031
# 3 is the current iteration
# ...
I want to read the successive arrays as fast as possible to put them in a single data.frame (in reality, I have thousands of them). What is the most efficient way to proceed?
Given the output is regular, I thought readr would be a good idea (?).
The only way I can think of, is to do it manually by chunks in order to eliminate the useless info lines:
library(readr)
ztot = numeric(niter*nx*ny) # allocate a vector with final size
# (the arrays will be vectorized and successively appended to each other)
for (i in 1:niter) {
nskip = (i-1)*(ny+1) + 1 # number of lines to skip, including the info lines
z = read_table(myfile, skip = nskip, n_max = ny, col_names=F)
z = as.vector(t(z))
ifirst = (i-1)*ny*nx + 1 # appropriate index
ztot[ifirst:(ifirst+nx*ny-1)] = z
}
# The arrays are actually spatial rasters. Compute the coordinates
# and put everything in DF for future analysis:
x = rep(rep(seq(1:nx), ny), niter)
y = rep(rep(seq(1:ny), each=nx), niter)
myDF = data.frame(x=x, y=y, z=z)
But this is not fast enough. How can I achieve this faster?
Is there a way to read everything at once and delete the useless rows afterwards?
Alternatively, is there no reading function accepting a vector with precise locations as skip argument, rather than a single number of initial rows?
PS: note the reading operation is to be repeated on many files (same structure) located in different directories, in case it influences the solution...
EDIT
The following solution (reading all lines with readLines and removing the undesirable ones and then processing the rest) is a faster alternative with niter very high:
bylines <- readLines(myfile)
dummylines = seq(1, by=(ny+1), length.out=niter)
bylines = bylines[-dummylines] # remove dummy, undesirable lines
asOneChar <- paste(bylines, collapse='\n') # Then process output from readLines
library(data.table)
ztot <- fread(asOneVector)
ztot <- c(t(ztot))
Discussion on how to proceed results from the readLines can be found here
Pre-processing the file with a command line tool (i.e., not in R) is actually way faster. For example with awk:
tmpfile <- 'cleanFile.txt'
mycommand <- paste("awk '!/is the current iteration/'", myfile, '>', tmpfile)
# "awk '!/is the current iteration/' bigFileWithRowsToSkip.txt > cleanFile.txt"
system(mycommand) # call the command from R
ztot <- fread(tmpfile)
ztot <- c(t(ztot))
Lines can be removed on the basis of a pattern or of indices for example.
This was suggested by #Roland from here.
Not sure if I still understood your problem correctly. Running your script created a file with 1310 lines. With This is iteration 1or2or3 printed at lines
Line 1: This is iteration 1
Line 132: This is iteration 2
Line 263: This is iteration 3
Line 394: This is iteration 4
Line 525: This is iteration 5
Line 656: This is iteration 6
Line 787: This is iteration 7
Line 918: This is iteration 8
Line 1049: This is iteration 9
Line 1180: This is iteration 10
Now there is data between these lines that you want to read and skip this 10 strings.
You can do this by tricking read.table saying your comment.char is "T" which will make read.table thinks all lines starting with letter "T" are comments and will skip those.
data<-read.table("bigFile.txt",comment.char = "T")
this will give you a data.frame of 1300 observations with 150 variables.
> dim(data)
[1] 1300 150
For a non-consisted strings. Read your data with read.table with fill=TRUE flag. This will not break your input process.
data<-read.table("bigFile.txt",fill=TRUE)
Your data looks like this
> head(data)
V1 V2 V3 V4 V5 V6 V7
1: 1.0000000 is the current iteration NA NA
2: 0.4231829 0.142353335 0.3813622692 0.07224282 0.037681101 0.7761575 0.1132471
3: 0.1113989 0.587115721 0.2960257430 0.49175715 0.642754463 0.4036675 0.4940814
4: 0.9750350 0.691093967 0.8610487920 0.08208387 0.826175117 0.8789275 0.3687355
5: 0.1831840 0.001007096 0.2385952028 0.85939856 0.646992019 0.5783946 0.9095849
6: 0.7648907 0.204005372 0.8512769730 0.10731854 0.299391995 0.9200760 0.7814541
Now if you see how the strings are distributed in columns. Now you can simply subset your data set with pattern matching. Matching columns that match these strings. For example
library(data.table)
data<-as.data.table(data)
cleaned_data<-data[!(V3 %like% "the"),]
> head(cleaned_data)
V1 V2 V3 V4 V5 V6 V7
1: 0.4231829 0.142353335 0.3813622692 0.07224282 0.037681101 0.7761575 0.1132471
2: 0.1113989 0.587115721 0.2960257430 0.49175715 0.642754463 0.4036675 0.4940814
3: 0.9750350 0.691093967 0.8610487920 0.08208387 0.826175117 0.8789275 0.3687355
4: 0.1831840 0.001007096 0.2385952028 0.85939856 0.646992019 0.5783946 0.9095849
5: 0.7648907 0.204005372 0.8512769730 0.10731854 0.299391995 0.9200760 0.7814541
6: 0.3943193 0.508373900 0.2131134905 0.92474343 0.432134031 0.4585807 0.9811607

How to convert code to more readable form in R

I copy code from the terminal to post here. It is in following form:
> ddf2 = ddf[ddf$stone_ny>'stone',] # this is first command
> ddf2[!duplicated(ddf2$deltnr),] # second command
deltnr us stone_ny stone_mobility
4 1536 63 stone mobile
10 1336 62 stone mobile
First 2 lines are commands while next 3 lines are output. However, this cannot be copied from here back to R terminal since the commands start with '> '. How can I convert this to:
ddf2 = ddf[ddf$stone_ny>'stone',] # this is first command
ddf2[!duplicated(ddf2$deltnr),] # second command
# deltnr us stone_ny stone_mobility
#4 1536 63 stone mobile
#10 1336 62 stone mobile
So that it become suitable for copying from here.
I tried:
text
[1] "> ddf2 = ddf[ddf$stone_ny>'stone',] # this is first command\n> ddf2[!duplicated(ddf2$deltnr),] # second command\n deltnr us stone_ny stone_mobility \n4 1536 63 stone mobile \n10 1336 62 stone mobile "
text2 = gsub('\n','#',text)
text2 = gsub('#>','\n',text2)
text2 = gsub('#','\n#',text2)
text2
[1] "> ddf2 = ddf[ddf$stone_ny>'stone',] \n# this is first command\n
ddf2[!duplicated(ddf2$deltnr),] \n# second command\n# deltnr us stone_ny stone_mobility \n#4 1536 63 stone mobile \n#10 1336 62 stone mobile "
But it cannot get pasted to the terminal.
I've been waiting for an opportunity to share this function I keep in my .Rprofile file. While it may not answer exactly your question, I feel it is accomplishing something very close to what you are after. So you might get some ideas by looking at its code. And others might find it useful just as it is. The function:
SO <- function(script.file = '~/.active-rstudio-document') {
# run the code and store the output in a character vector
tmp <- tempfile()
capture.output(
source(script.file, echo = TRUE,
prompt.echo = "> ",
continue.echo = "+ "), file = tmp)
out <- readLines(tmp)
# identify lines that are comments, code, results
idx.comments <- grep("^> [#]{2}", out)
idx.code <- grep("^[>+] ", out)
idx.blank <- grep("^[[:space:]]*$", out)
idx.results <- setdiff(seq_along(out),
c(idx.comments, idx.code, idx.blank))
# reformat
out[idx.comments] <- sub("^> [#]{2} ", "", out[idx.comments])
out[idx.code] <- sub("^[>+] ", " ", out[idx.code])
out[idx.results] <- sub("^", " # ", out[idx.results])
# output
cat(out, sep = "\n", file = stdout())
}
This SO function is what allows me to quickly format my answers to questions on this very website, StackOverflow. My workflow is as follows:
1) In RStudio, write my answer in an untitled script (that's the top-left quadrant). For example:
## This is super easy, you can do
set.seed(123)
# initialize x
x <- 0
while(x < 0.5) {
print(x)
# update x
x <- runif(1)
}
## And voila.
2) Near the top, click the "Source" button. It will execute the code in the console which is not really what we are after: rather, it will have the side effect of saving the code to the default file '~/.active-rstudio-document'.
3) Run SO() from the console (bottom-left quadrant) which will source the code (again...) from the saved file, capture the output and print it in a SO-friendly format:
This is super easy, you can do
set.seed(123)
# initialize x
x <- 0
while(x < 0.5) {
print(x)
# update x
x <- runif(1)
}
# [1] 0
# [1] 0.2875775
And voila.
4) Copy-paste into stackoverflow and done.
Note: For code that takes a while to run, you can avoid running it twice by saving your script to a file (e.g. 'xyz.R') instead of clicking the "Source" button. Then run SO("xyz.R").
You could try cat with an ifelse condition.
cat(ifelse(substr(s <- strsplit(text, "\n")[[1]], 1, 1) %in% c("_", 0:9, " "),
paste0("# ", s),
gsub("[>] ", "", s)),
sep = "\n")
which results in
ddf2 = ddf[ddf$stone_ny>'stone',] # this is first command
ddf2[!duplicated(ddf2$deltnr),] # second command
# deltnr us stone_ny stone_mobility
# 4 1536 63 stone mobile
# 10 1336 62 stone mobile
The "_" and 0:9 are in there because one of the rules in R is that a function cannot begin with a _ or a digit. You can adjust it to fit your needs.

ff package dim error (selection)

i search a solution to work with big data. So i tried "ff package". In my normal script i used following code for a selection in a 66896 x 362 data.frame:
setwd(wd)
bf <- read.table("G_BANKFULL_km3month.csv",header=T, sep=",",dec=".")
## read river discharge global, monthly vlaues 1971-2000##
memory.limit(size=16000) # increase RAM
dis <- read.table('RIVER_AVAIL_7100_WG22.txt', header=T, sep="\t", dec=".")
##
## return only grid cells where bankfull is exceeded at least once during the time
## period
test <- cbind(dis,bf$VALUE)
test2 <- test[(test[,-c(1:3)] > test[,length(test)]), ]
It works, if i use enough RAM.
But i dont have always enough RAM for such a operation so i tried the "ff package".
library(ff)
## read Bankfull flow##
setwd(wd)
bf <- read.csv.ffdf(file="G_BANKFULL_km3month.csv",header=TRUE)
## read river discharge global, monthly vlaues 1971-2000##
memory.limit(size=16000) # increase working memory
dis <- read.table.ffdf(file='RIVER_AVAIL_7100_WG22.txt', header=T, sep="\t", dec=".")
##read bankfull values as ff object##
bfvalues <- ff(bf[,2])
##combination of bf and dis ( see test <- cbind(dis,bf$VALUE))
dis_bf <- do.call('ffdf', c(physical(dis), list(bfvalues=bfvalues)))
dis_bf_test <- dis_bf[(dis_bf[,-c(1:3)] > dis_bf[,length(dis_bf)]),]
The ffdf and the normal data.frame have the same structure etc. but if i try to this last selection it doesn't work and i get following error:
Error in as.hi.matrix(i, maxindex = nvw$n, vw = nvw$vw, pack = FALSE, :
argument "dim" is missing, with no default
Perhaps someone of you have worked with ff package and has an idea why it doesn't work. I am also happy about some ideas or information to other packages and solutions for working with big data.
Cheers
Why don't you replace your code
dis_bf_test <- dis_bf[(dis_bf[,-c(1:3)] > dis_bf[,length(dis_bf)]),]
with
require(ffbase)
open(dis_bf_test)
dis_bf_test <- subset(dis_bf_test, yourcolumnname > youothercolumnname)
where yourcolumnname represents the column you indicated by dis_bf[,-c(1:3)] and yourothercolumnname with the column you indicated with dis_bf[,length(dis_bf)]

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