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)
}
Related
I am using the R programming language. I am interested in seeing if it is somehow possible to take an html file (generated using the "plotly" library) and then insert that file into a MS Powerpoint slideshow.
I was looking at other stackoverflow questions where similar things were attempted: Exporting PNG files from Plotly in R
Suppose I generate this simple, interactive plot using plotly in R:
library(plotly)
x <- rnorm(100,10,10)
color <- rnorm(100, 2,1)
frame = data.frame(x,color)
p = plot_ly(type = "scatter", mode = "markers", data = frame, x = ~x, y = " ", color = ~color )
Using the "htmlwidget" library, I can save the object "p" as an html file. But is there a way to insert this object "p" into a MS Powerpoint presentation? Preferably, an offline way that does not use the internet or requires any additional software to be installed?
I tried using the "insert html" functionality in MS Powerpoint, but this just produces a "grey square" that doesn't load when you play the presentation.
Can someone please tell me if this is possible?
Thanks
I have a large .json file and I only want to read in a part of it.
I tried the the following solutions but they didn´t work:
yelp <- stream_in(file("yelp_academic_dataset_review.json"), paigesize = 500)
yelp <- stream_in(file("yelp_academic_dataset_review.json"), nrows = 500)
Anyone know how it works?
First off- always helpful to provide the packages you are using, in your case jsonlite.
One solution is parsing the data file (as a .txt file) prior to streaming it in.
yelp <- readLines("yelp_academic_dataset_review.json")[1:500]
yelp <- stream_in(textConnection(gsub("\\n", "", yelp)))
I'm assuming your file is local?
I have had success with actual piping/streaming json in the past. Ie, from the command line,
cat x.json | parse_json.py
Then you write your python script:
import json,sys
for line in sys.stdin:
js_line = json.loads(line.rstrip())
try:
# do something with js_line['x']['y']
except ValueError:
pass
I'm not sure why you want to use stream_in, but this somewhat manual approach can be effective
I use this code for extracting 1400001 to 1450000 lines of yelp:
setwd("d:/yelp_dataset")
rm(list=ls())
library(jsonlite)
rev<- 'd:/yelp_dataset/review.JSON'
revu<-jsonlite::stream_in(textConnection(readLines(rev)[1400001:1450000],verbose=F)
The official Premier league website provides data with various statistics for league's teams over seasons (e.g. this one). I used the function readHTMLTable from XML R package to retrieve those tables. However, I noticed that the function can not read tables for May months while for others it works well. Here is an example:
april2007.url <- "http://www.premierleague.com/en-gb/matchday/league-table.html?season=2006-2007&month=APRIL&timelineView=date&toDate=1177887600000&tableView=CURRENT_STANDINGS"
april.df <- readHTMLTable(april2007.url, which = 1)
april.df[complete.cases(april.df),] ## correct table
march2014.url <- "http://www.premierleague.com/en-gb/matchday/league-table.html?season=2013-2014&month=APRIL&timelineView=date&toDate=1398639600000&tableView=CURRENT_STANDINGS"
march.df <- readHTMLTable(march2014.url, which = 1)
march.df[complete.cases(march.df), ] ## correct table
may2007.url <- "http://www.premierleague.com/en-gb/matchday/league-table.html?season=2006-2007&month=MAY&timelineView=date&toDate=1179010800000&tableView=CURRENT_STANDINGS"
may.df1 <- readHTMLTable(may2007.url, which = 1)
may.df1 ## Just data for the first team
may2014.url <- "http://www.premierleague.com/en-gb/matchday/league-table.html?season=2013-2014&month=MAY&timelineView=date&toDate=1399762800000&tableView=CURRENT_STANDINGS"
may.df2 <- readHTMLTable(may2014.url, which =1)
may.df2 ## Just data for the first team
As you can see, the function can not retrieve data for May month.
Please, can someone explain why this happens and how it can be fixed?
EDIT AFTER #zyurnaidi answer:
Below is the code that can do the job without manual editing.
url <- "http://www.premierleague.com/en-gb/matchday/league-table.html?season=2009-2010&month=MAY&timelineView=date&toDate=1273359600000&tableView=CURRENT_STANDINGS" ## data for the 09-05-2010.
con <- file (url)
raw <- readLines (con)
close (con)
pattern <- '<span class=" cupchampions-league= competitiontooltip= qualifiedforuefachampionsleague=' ## it seems that this part of the webpage source code mess the things up
raw <- gsub (pattern = pattern, replacement = '""', x = raw)
df <- readHTMLTable (doc = raw, which = 1)
df[complete.cases(df), ] ## correct table
OK. There are few hints for me to find the problem here:
1. The issues happen consistently on May. This is the last month of each season. It means that there should be something unique in this particular case.
2. Direct parsing (htmlParse, from both link and downloaded file) produces a truncated file. The table and html file are just suddenly closed after the first team in the table is reported.
The parsed data always differs from the original right after this point:
<span class=" cupchampions-league=
After downloading and carefully checking the html file itself, I found that there are (uncoded?) character issues there. My guess, this is caused by the cute little trophy icons seen after the team names.
Anyway, to solve this issue, you need to take out these error characters. Instead of editing the downloaded html files, my suggestion is:
1. View page source the EPL url for May's league table
2. Copy all and paste to the text editor, save as an html file
3. You can now use either htmlParse or readHTMLTable
There might be better way to automate this, but hope it can help.
I would like to use R to download the HTML code of any Yahoo Finance Headlines webpage, select the "headlines" and collect them in Excel. Unfortunately I cannot find and select the HTML nodes corresponding to the headlines once I download the source file to R.
Let me show the problem with an example.
I started with
source <- "http://finance.yahoo.com/q/h?s=AAPL+Headlines"
file <- "destination/finance_file.cvs"
download.file(url = source, destfile = file)
x = scan(file, what = "", sep = "\n")
producing the Excel file finance_file.cvs and, most importantly, the character x.
Using x I would like to collect the headlines and write them into a column in a second Excel file, called headlines.cvs.
My problem now is the following: if I select any headline I can find it in the HTML code of the webpage itself, but I lose its track in x. Therefore, I do not know how to extract it.
For the extraction I was thinking of
x = x[grep("some string of characters to do the job", x)]
but I am no expert in web scraping.
Any ideas/suggestions?
I thank you very much!
You can use the XML package and write the XPath query needed to extract the headlines.
Since the web page looks like:
...
<ul class="newsheadlines"/>
<ul>
<li>First headline</li>
...
you get the following query.
library(XML)
source <- "http://finance.yahoo.com/q/h?s=AAPL+Headlines"
d <- htmlParse(source)
xpathSApply(d, "//ul[contains(#class,'newsheadlines')]/following::ul/li/a", xmlValue)
free(d)
I would like to create a Corpus for the collection of downloaded HTML files, and then read them in R for future text mining.
Essentially, this is what I want to do:
Create a Corpus from multiple html files.
I tried to use DirSource:
library(tm)
a<- DirSource("C:/test")
b<-Corpus(DirSource(a), readerControl=list(language="eng", reader=readPlain))
but it returns "invalid directory parameters"
Read in html files from the Corpus all at once.
Not sure how to do it.
Parse them, convert them to plain text, remove tags.
Many people suggested using XML, however, I didn't find a way to process multiple files. They are all for one single file.
Thanks very much.
This should do it. Here I've got a folder on my computer of HTML files (a random sample from SO) and I've made a corpus out of them, then a document term matrix and then done a few trivial text mining tasks.
# get data
setwd("C:/Downloads/html") # this folder has your HTML files
html <- list.files(pattern="\\.(htm|html)$") # get just .htm and .html files
# load packages
library(tm)
library(RCurl)
library(XML)
# get some code from github to convert HTML to text
writeChar(con="htmlToText.R", (getURL(ssl.verifypeer = FALSE, "https://raw.github.com/tonybreyal/Blog-Reference-Functions/master/R/htmlToText/htmlToText.R")))
source("htmlToText.R")
# convert HTML to text
html2txt <- lapply(html, htmlToText)
# clean out non-ASCII characters
html2txtclean <- sapply(html2txt, function(x) iconv(x, "latin1", "ASCII", sub=""))
# make corpus for text mining
corpus <- Corpus(VectorSource(html2txtclean))
# process text...
skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords)
a <- tm_map(a, PlainTextDocument)
a <- tm_map(corpus, FUN = tm_reduce, tmFuns = funcs)
a.dtm1 <- TermDocumentMatrix(a, control = list(wordLengths = c(3,10)))
newstopwords <- findFreqTerms(a.dtm1, lowfreq=10) # get most frequent words
# remove most frequent words for this corpus
a.dtm2 <- a.dtm1[!(a.dtm1$dimnames$Terms) %in% newstopwords,]
inspect(a.dtm2)
# carry on with typical things that can now be done, ie. cluster analysis
a.dtm3 <- removeSparseTerms(a.dtm2, sparse=0.7)
a.dtm.df <- as.data.frame(inspect(a.dtm3))
a.dtm.df.scale <- scale(a.dtm.df)
d <- dist(a.dtm.df.scale, method = "euclidean")
fit <- hclust(d, method="ward")
plot(fit)
# just for fun...
library(wordcloud)
library(RColorBrewer)
m = as.matrix(t(a.dtm1))
# get word counts in decreasing order
word_freqs = sort(colSums(m), decreasing=TRUE)
# create a data frame with words and their frequencies
dm = data.frame(word=names(word_freqs), freq=word_freqs)
# plot wordcloud
wordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, "Dark2"))
This will correct the error.
b<-Corpus(a, ## I change DireSource(a) by a
readerControl=list(language="eng", reader=readPlain))
But I think to read your Html you need to use xml reader. Something like :
r <- Corpus(DirSource('c:\test'),
readerControl = list(reader = readXML),spec)
But you need to supply the spec argument, which depends with your file structure.
see for example readReut21578XML. It is a good example of xml/html parser.
To read all the html files into an R object you can use
# Set variables
folder <- 'C:/test'
extension <- '.htm'
# Get the names of *.html files in the folder
files <- list.files(path=folder, pattern=extension)
# Read all the files into a list
htmls <- lapply(X=files,
FUN=function(file){
.con <- file(description=paste(folder, file, sep='/'))
.html <- readLines(.con)
close(.con)
names(.html) <- file
.html
})
That will give you a list, and each element is the HTML content of each file.
I'll post later on parsing it, I'm in a hurry.
I found the package boilerpipeR particularly useful to extract only the "core" text of an html page.