I am trying to create a simple csv table output in R that contains a listing of only files through a directory (recursively). The output should contain, at minimum 3 columns:
The Full Path (e.g. \path\to\file\somefile.txt)
File size
MD5 Hash of file
(additional file.info properties (data created, modified etc.) would be helpful, but not strictly necessary
I have the following script that I hacked together from various places on the internet, which works, but I think is not the 'best' way to do it and/or might be brittle. I am seeking any comments/suggests on how to clean this up and help improve my R-skills. Thanks!
*I am particularly concerned about how cbind works, and how does it "know" if row arrangement/order is preserved?
library(digest)
library(tidyverse)
library(magrittr)
test_dir <- "C:\\Path\\To\\Folder"
outfile <- "out.csv"
file.names <- list.files(test_dir, recursive = TRUE, full.names = TRUE)
md5s <- sapply(file.names, digest, file = TRUE, algo = "md5")
q <- map(file.names, file.info)
file.sizes <- map_df(q, extract, c("size"))
output <- cbind(file.names, file.sizes, md5s)
write_csv(output, str_c("./R/", outfile))
The chosen answer did not give me the md5 of the actual file but of the file's names! I got the real md5 (which matched the md5 generated from other sources) using the following command. This seems to work with only one file at a time.
library(openssl)
md5 <- as.character(md5(file(file.name, open="rb")))
For multiple files the following command worked for me
library(tools)
md5 = as.vector(md5sum(file.names))
One tip might be to use the openssl md5 function instead of digest.
library(openssl)
md5s <- md5(file.names)
It's already vectorised so you won't need to use sapply which may improve your processing speed (depending on how big a file you want to hash).
In terms of cbind, it will keep the order of the first column you are binding to using your key (md5) so the output will have the order that file.names has.
Related
I get .xlsx files from various sources, to read and analyse the data in R, working great. Files are big, 10+ MB. So far, readxl::read_xlsx was the only solution that worked. xlsx::read.xls produced only error messages: Error in .jcall("RJavaTools", "Ljava/lang/Object;", "invokeMethod", cl, : java.lang.OutOfMemoryError: GC overhead limit exceeded)
Problem: some files have non-standard letters in the filename, e.g. displayed in Windows 10/explorer as '...ü...xlsx' (the character 'ü' somewhere in the filename). When I read all filenames in the folder in R, I get '...u"...xlsx'). I check for doublettes of the filenames from different folders before I actualle read the files. However, when it comes to read the above file, I get an error message '... file does not exist', no matter if I use
the path/filename character variable directly obtained from list.files (showing '...u"...xlsx')
the string constant '...u"...xlsx'
the string constant '...ü...xlsx'
As far as I understand, the problem arises from aequivalent, yet not identical, unicode compositions. I have no influence on how these characters are originally encoded. Therefore I see no way to read the file, other than (so far manually) rename the file in Windows explorer, changing an 'ü' coded as 'u+"' to 'ü'.
Questions:
is there a workaround within R? (keep in mind the requirement to use read_xlsx, unless a yet unknown package works with huge files.
if not possible within R, what would be the best option to change filenames automatically ('u+"' to 'ü') - I need to keep the 'ü' (or ä, ö, and others) in order to connect the analysis results back to the input), preferrably without additional (non-standard) software (e.g. command shell).
EDIT:
To read the list of files, dir_ls works (as suggested), but it returns an even stranger filename: 'ö' instead of 'ö', which in turn cannot be read (found) by read_xlsx either.
try using the fs library. My workflow looks something like this:
library(tidyverse)
library(lubridate)
library(fs)
library(readxl)
directory_to_read <- getwd()
file_names_to_read <- dir_ls(path = directory_to_read,
recurse = FALSE, # set this to TRUE to read all subdirectories
glob = "*.xls*",
ignore.case = TRUE) %>% # This is to ignore upper/lower case extensions
# Use this to weed out temp files - I constantly have this probles
str_subset(string = .,
regex(pattern = "\\/~\\$", ignore_case = TRUE), #use \\ before $ else it will not work
negate = TRUE) # TRUE Returns non-matching patterns
map(file_names_to_red[4], read_excel)
I'm writing a loop script which involves reading a file from a workbook (using the package XLConnect). The challenge is that the file names contain characters (representing time) that I want to ignore.
For example, here are 3 paths to those files:
G://User//Documents//daily_data//Op_Schedule_20160520_132025.xlsx
G://User//Documents//daily_data//Op_Schedule_20160521_142805.xlsx
G://User//Documents//daily_data//Op_Schedule_20160522_103052.xlsx
I need to import hundreds of those files. I can easily account for the character string representing the date (e.g. 20160522), but not the time.
Is there a way to tell R to ignore some characters located in the file path? Here is how I was thinking of writing my script (the "???" is where i need help). I know a loop is probably not the most efficient way, but i'm open to suggestions, should you have any:
require(XLConnect)
path= "G://User//Documents//daily_data//Op_Schedule_"
wd.seq = format(seq(as.Date("2014-01-01"),as.Date("2016-12-31"),"days"),format="%Y%m%d")
scheduleList = rep(list(matrix(1,1,1)),length(wd.seq))
for(i in 1:length(wd.seq)) {
wb = loadWorkbook(file= paste0(path,wd.seq[i],"???",".xlxs"))
scheduleList[[i]] = readWorksheet(wb,sheet='=SCHEDULE', header = TRUE)
}
`
Thanks for reading and suggestions, if any.
Mathieu
I don't know if this is helpful, but if you want to read all the files in a certain directory (which it seems to me is what you're after), you can read all the filenames into a list using the list.files() function, for example
fileList <- list.files(""G://User//Documents//daily_data//")
And then load the xlsx files looping through the list with a for loop
for(i in fileList) {
loadWorkbook(file = i)
}
I haven't used the XLConnect function before so that exact code probably doesn't work, but the loop will iterate through all the files in that directory and so you can construct your loading call using the i variable for the filename (it won't be an absolute path though, so you might need to use paste to add the first part of the filepath)
I realize there might be other files in the directory that are not excel files, you could use grepl to select only files containg "OP_Schedule_"
fileListClean <- fileList[grepl("Op_Schedule_",fileList)]
or perhaps only selecting .xlsx files in the directory:
fileListClean <- fileList[grepl(".xlsx",fileList)]
Edit to fit your reply:
Since you need to fit it to a sequence, you can do it as you did earlier:
wd.seq = format(seq(as.Date("2014-01-01"),as.Date("2016-12-31"),"days"),format="%Y%m%d")
wd.seq2 <- paste("Op_Schedule_", wd.seq, sep = "")
And then use grepl to only pick files starting with that extensions:
fileListClean <- fileList[grepl(paste(wd.seq2, collapse = "|"), fileList)]
Full disclosure: The last part i got from this SO answer: grep using a character vector with multiple patterns
Is it possible to parse text data from PDF files in R? There does not appear to be a relevant package for such extraction, but has anyone attempted or seen this done in R?
In Python there is PDFMiner, but I would like to keep this analysis all in R if possible.
Any suggestions?
Linux systems have pdftotext which I had reasonable success with. By default, it creates foo.txt from a give foo.pdf.
That said, the text mining packages may have converters. A quick rseek.org search seems to concur with your crantastic search.
This is a very old thread, but for future reference: the pdftools R package extracts text from PDFs.
A colleague turned me on to this handy open-source tool: http://tabula.nerdpower.org/. Install, upload the PDF, and select the table in the PDF that requires data-ization. Not a direct solution in R, but certainly better than manual labor.
A purely R solution could be:
library('tm')
file <- 'namefile.pdf'
Rpdf <- readPDF(control = list(text = "-layout"))
corpus <- VCorpus(URISource(file),
readerControl = list(reader = Rpdf))
corpus.array <- content(content(corpus)[[1]])
then you'll have pdf lines in an array.
install.packages("pdftools")
library(pdftools)
download.file("http://www.nfl.com/liveupdate/gamecenter/56901/DEN_Gamebook.pdf",
"56901.DEN.Gamebook", mode = "wb")
txt <- pdf_text("56901.DEN.Gamebook")
cat(txt[1])
The tabula PDF table extractor app is based around a command line application based on a Java JAR package, tabula-extractor.
The R tabulizer package provides an R wrapper that makes it easy to pass in the path to a PDF file and get data extracted from data tables out.
Tabula will have a good go at guessing where the tables are, but you can also tell it which part of a page to look at by specifying a target area of the page.
Data can be extracted from multiple pages, and a different area can be specified for each page, if required.
For an example use case, see: When Documents Become Databases – Tabulizer R Wrapper for Tabula PDF Table Extractor.
I used an external utility to do the conversion and called it from R. All files had a leading table with the desired information
Set path to pdftotxt.exe and convert pdf to text
exeFile <- "C:/Projects/xpdfbin-win-3.04/bin64/pdftotext.exe"
for(i in 1:length(pdfFracList)){
fileNumber <- str_sub(pdfFracList[i], start = 1, end = -5)
pdfSource <- paste0(reportDir,"/", fileNumber, ".pdf")
txtDestination <- paste0(reportDir,"/", fileNumber, ".txt")
print(paste0("File number ", i, ", Processing file ", pdfSource))
system(paste(exeFile, "-table" , pdfSource, txtDestination, sep = " "), wait = TRUE)
}
I'm using the mclapply function in the multicore package to do parallel processing. It seems that all child processes started produce the same names for temporary files given by the tempfile function. i.e. if I have four processors,
library(multicore)
mclapply(1:4, function(x) tempfile())
will give four exactly same filenames. Obviously I need the temporary files to be different so that the child processes don't overwrite each others' files. When using tempfile indirectly, i.e. calling some function that calls tempfile I have no control over the filename.
Is there a way around this? Do other parallel processing packages for R (e.g. foreach) have the same problem?
Update: This is no longer an issue since R 2.14.1.
CHANGES IN R VERSION 2.14.0 patched:
[...]
o tempfile() on a Unix-alike now takes the process ID into account.
This is needed with multicore (and as part of parallel) because
the parent and all the children share a session temporary
directory, and they can share the C random number stream used to
produce the uniaue part. Further, two children can call
tempfile() simultaneously.
I believe multicore spins off a separate process for each subtask. If that assumption is correct, then you should be able to use Sys.getpid() to "seed" tempfile:
tempfile(pattern=paste("foo", Sys.getpid(), sep=""))
Use the x in your function:
mclapply(1:4, function(x) tempfile(pattern=paste("file",x,"-",sep=""))
Because the parallel jobs all run at the same time, and because the random seed comes from the system time, running four instances of tempfile in parallel will typically produce the same results (if you have 4 cores, that is. If you only have two cores, you'll get two pairs of identical temp file names).
Better to generate the tempfile names first and give them to your function as an argument:
filenames <- tempfile( rep("file",4) )
mclapply( filenames, function(x){})
If you're using someone else's function that has a tempfile call in it, then working the PID into the tempfile name by modifying the tempfile function, as previously suggested, is probably the simplest plan:
tempfile <- function( pattern = "file", tmpdir = tempdir(), fileext = ""){
.Internal(tempfile(paste("pid", Sys.getpid(), pattern, sep=""), tmpdir, fileext))}
mclapply( 1:4, function(x) tempfile() )
At least for now, I chose to monkey-patch my way around this by using the following code in my .Rprofile following Daniel's advice to use PID values.
assignInNamespace("tempfile.orig", tempfile, ns="base")
.tempfile = function(pattern="file", tmpdir=tempdir())
tempfile.orig(paste(pattern, Sys.getpid(), sep=""), tmpdir)
assignInNamespace("tempfile", .tempfile, ns="base")
Obviously it's not a good option for any package you'd distribute, but for a single user's need it's the best option thus far since it works in all cases.
I am trying to learn R and want to bring in an SPSS file, which I can open in SPSS.
I have tried using read.spss from foreign and spss.get from Hmisc. Both error messages are the same.
Here is my code:
## install.packages("Hmisc")
library(foreign)
## change the working directory
getwd()
setwd('C:/Documents and Settings/BTIBERT/Desktop/')
## load in the file
## ?read.spss
asq <- read.spss('ASQ2010.sav', to.data.frame=T)
And the resulting error:
Error in read.spss("ASQ2010.sav", to.data.frame = T) : error
reading system-file header In addition: Warning message: In
read.spss("ASQ2010.sav", to.data.frame = T) : ASQ2010.sav: position
0: character `\000' (
Also, I tried saving out the SPSS file as a SPSS 7 .sav file (was previously using SPSS 18).
Warning messages: 1: In read.spss("ASQ2010_test.sav", to.data.frame =
T) : ASQ2010_test.sav: Unrecognized record type 7, subtype 14
encountered in system file 2: In read.spss("ASQ2010_test.sav",
to.data.frame = T) : ASQ2010_test.sav: Unrecognized record type 7,
subtype 18 encountered in system file
I had a similar issue and solved it following a hint in read.spss help.
Using package memisc instead, you can import a portable SPSS file like this:
data <- as.data.set(spss.portable.file("filename.por"))
Similarly, for .sav files:
data <- as.data.set(spss.system.file('filename.sav'))
although in this case I seem to miss some string values, while the portable import works seamlessly. The help page for spss.portable.file claims:
The importer mechanism is more flexible and extensible than read.spss and read.dta of package "foreign", as most of the parsing of the file headers is done in R. They are also adapted to load efficiently large data sets. Most importantly, importer objects support the labels, missing.values, and descriptions, provided by this package.
The read.spss seems to be outdated a little bit, so I used package called memisc.
To get this to work do this:
install.packages("memisc")
data <- as.data.set(spss.system.file('yourfile.sav'))
You may also try this:
setwd("C:/Users/rest of your path")
library(haven)
data <- read_sav("data.sav")
and if you want to read all files from one folder:
temp <- list.files(pattern = "*.sav")
read.all <- sapply(temp, read_sav)
I know this post is old, but I also had problems loading a Qualtrics SPSS file into R. R's read.spss code came from PSPP a long time ago, and hasn't been updated in a while. (And Hmisc's code uses read.spss(), too, so no luck there.)
The good news is that PSPP 0.6.1 should read the files fine, as long as you specify a "String Width" of "Short - 255 (SPSS 12.0 and earlier)" on the "Download Data" page in Qualtrics. Read it into PSPP, save a new copy, and you should be in business. Awkward, but free.
,
You can read SPSS file from R using above solutions or the one you are currently using. Just make sure that the command is fed with the file, that it can read properly. I had same error and the problem was, SPSS could not access that file. You should make sure the file path is correct, file is accessible and it is in correct format.
library(foreign)
asq <- read.spss('ASQ2010.sav', to.data.frame=TRUE)
As far as warning message is concerned, It does not affect the data. The record type 7 is used to store features in newer SPSS software to make older SPSS software able to read new data. But does not affect data. I have used this numerous times and data is not lost.
You can also read about this at http://r.789695.n4.nabble.com/read-spss-warning-message-Unrecognized-record-type-7-subtype-18-encountered-in-system-file-td3000775.html#a3007945
It looks like the R read.spss implementation is incomplete or broken. R2.10.1 does better than R2.8.1, however. It appears that R gets upset about custom attributes in a sav file even with 2.10.1 (The latest I have). R also may not understand the character encoding field in the file, and in particular it probably does not work with SPSS Unicode files.
You might try opening the file in SPSS, deleting any custom attributes, and resaving the file.
You can see whether there are custom attributes with the SPSS command
display attributes.
If so, delete them (see VARIABLE ATTRIBUTE and DATAFILE ATTRIBUTE commands), and try again.
HTH,
Jon Peck
If you have access to SPSS, save file as .csv, hence import it with read.csv or read.table. I can't recall any problem with .sav file importing. So far it was working like a charm both with read.spss and spss.get. I reckon that spss.get will not give different results, since it depends on foreign::read.spss
Can you provide some info on SPSS/R/Hmisc/foreign version?
Another solution not mentioned here is to read SPSS data in R via ODBC. You need:
IBM SPSS Statistics Data File Driver. Standalone driver is enough.
Import SPSS data using RODBC package in R.
See the example here. However I have to admit that, there could be problems with very big data files.
For me it works well using memisc!
install.packages("memisc")
load('memisc')
Daten.Februar <-as.data.set(spss.system.file("NPS_Februar_15_Daten.sav"))
names(Daten.Februar)
I agree with #SDahm that the haven package would be the way to go. I myself have struggled a bit with string values when starting to use it, so I thought I'd share my approach on that here, too.
The "semantics" vignette has some useful information on this topic.
library(tidyverse)
library(haven)
# Some interesting information in here
vignette('semantics')
# Get data from spss file
df <- read_sav(path_to_file)
# get value labels
df <- map_df(.x = df, .f = function(x) {
if (class(x) == 'labelled') as_factor(x)
else x})
# get column names
colnames(df) <- map(.x = spss_file, .f = function(x) {attr(x, 'label')})
There is no such problem with packages you are using. The only requirement for read a spss file is to put the file into a PORTABLE format file. I mean, spss file have *.sav extension. You need to transform your spss file in a portable document that uses *.por extension.
There is more info in http://www.statmethods.net/input/importingdata.html
In my case this warning was combined with a appearance of a new variable before first column of my data with values -100, 2, 2, 2, ..., a shift in the correspondence between labels and values and the deletion of the last variable. A solution that worked was (using SPSS) to create a new dump variable in the last column of the file, fill it with random values and execute the following code:
(filename is the path to the sav file and in my case the original SPSS file had 62 columns, thus 63 with the additional dumb variable)
library(memisc)
data <- as.data.set(spss.system.file(filename))
copyofdata = data
for(i in 2:63){
names(data)[i] <- names(copyofdata)[i-1]
}
data[[1]] <- NULL
newcopyofdata = data
for(i in 2:62){
labels(data[[i]]) <- labels(newcopyofdata[[i-1]])
}
labels(data[[1]]) <- NULL
Hope the above code will help someone else.
Turn your UNICODE in SPSS off
Open SPSS without any data open and run the code below in your syntax editor
SET UNICODE OFF.
Open the data set and resave it to remove the Unicode
read.spss('yourdata.sav', to.data.frame=T) works correctly then
I just came came across an SPSS file that I couldn't get open using haven, foreign, or memisc, but readspss::read.por did the trick for me:
download.file("http://www.tcd.ie/Political_Science/elections/IMSgeneral92.zip",
"IMSgeneral92.zip")
unzip("IMSgeneral92.zip", exdir = "IMSgeneral92")
# rio, haven, foreign, memisc pkgs don't work on this file! But readspss does:
if(!require(readspss)) remotes::install_git("https://github.com/JanMarvin/readspss.git")
ims92 <- readspss::read.por("IMSgeneral92/IMS_Nov7 92.por", convert.factors = FALSE)
Nice! Thanks, #JanMarvin!
1)
I've found the program, stat-transfer, useful for importing spss and stata files into R.
It resolves the issue you mention by converting spss to R dataset. Also very useful for subsetting super large datasets into smaller portions consumable by R. Not free, but a very useful tool for working with datasets from different programs -- especially if you don't have access to them.
2)
Memisc package also has an spss function worth trying.