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Looking for help on building a regex that captures a 1-line string after a specific word.
The challenge I'm running into is that the program where I need to build this regex uses a single line format, in other words dot matches new line. So the formula I created isn't working. See more details below. Any advice or tips?
More specific regex task:
I'm trying to grab the line that comes after the word Details from entries like below. The goal is pull out 100% Silk, or 100% Velvet. This is the material of the product that always comes after Details.
Raw data:
<p>Loose fitted blouse green/yellow lily print.
V-neck opening with a closure string.
Small tie string on left side of top.</p>
<h3>Details</h3> <p>100% Silk.</p>
<p>Made in Portugal.</p> <h3>Fit</h3>
<p>Model is 5‰Ûª10,‰Û size 2 wearing size 34.</p> <p>Size 34 measurements</p>
OR
<p>The velvet version of this dress. High waist fit with hook and zipper closure.
Seams run along edges of pants to create a box-like.</p>
<h3>Details</h3> <p>100% Velvet.</p>
<p>Made in the United States.</p>
<h3>Fit</h3> <p>Model is 5‰Ûª10‰Û, size 2 and wearing size M pants.</p> <p>Size M measurements Length: 37.5"åÊ</p>
<p>These pants run small. We recommend sizing up.</p>
Here is the current formula I created that's not working:
Replace (.)(\bDetails\s+(.)) with $3
The output gives the below:
<p>100% Silk.</p>
<p>Made in Portugal.</p>
<h3>Fit</h3>
<p>Model is 5‰Ûª10,‰Û size 2 wearing size 34.</p>
<p>Size 34 measurements</p>
OR
<p>100% Velvet.</p>
<p>Made in the United States.</p>
<h3>Fit</h3> <p>Model is 5‰Ûª10‰Û, size 2 and wearing size M pants.</p> <p>Size M measurements Length: 37.5"åÊ</p>
<p>These pants run small. We recommend sizing up.</p>
`
How do I capture just the desired string? Let me know if you have any tips! Thank you!
Difficult to provide a working solution in your situation as you mention your program has "limited regex features" but don't explain what limitations.
Here is a Regex you can try to work with to capture the target string
^(?:<h3>Details<\/h3>)(.*)$
I would personally use BeautifulSoup for something like this, but here are two solutions you could use:
Match the line after "Details", then pull out the data.
matches = re.findall('(?<=Details<).*$', text)
matches = [i.strip('<>') for i in matches]
matches = [i.split('<')[0] for i in [j.split('>')[-1] for j in matches]]
Replace "Details<...>data" with "Detailsdata", then find the data.
text = re.sub('Details<.*?<.*>', '', text)
matches = re.findall('(?<=Details).*?(?=<)', text)
I have a information in the format -
AAMOD, Robert Kevin; Salt Lake, '91; Sales Associate, Xyz, UT; r: 101 Williams Ave, Salt Lake City, UT 84105, cell: (xxx) xxx- xxxx, abc#yahoo.com.
I am trying to convert the information to CSV.
I have converted the information to a list by splitting with respect to ';' and now for each item of the list, I am using regex to convert it to another list holding only required information and in particular sequence and none if that information is not present.
for item in list_1:
direct = []
if re.search(r'([A-Z]{3,}),([\d\w\s.]+)', item):
match = re.search(r'([A-Z]{3,}),([\d\w\s.]+)', item)
direct.append(match.group(1))
direct.append(match.group(2))
break
else:
match1 = re.search(r'\'(\d+)', item)
if match1:
direct.append(match1.group(1))
break
else:
match2 = re.search(r'(r:[\w\d\s.]*,*\s*([\w]*)\s*([A-Z]{2})\s([\d]{5}),\s*(.\d{3}.\s\d{3}-\d{4}),\s*(\w+[\w\d\s.]+#+\s*[\w\d.]+\.+\w+))', item)
if match2:
direct.append(match2.group(2))
direct.append(match2.group(3))
direct.append(match2.group(4))
direct.append(match2.group(5))
direct.append(match2.group(6))
break
else:
direct.append('')
break
print direct
when I run this code, list only shows first match.
And if I run each re.search operation individually, it is working. But the moment I try to combine them using nested if-else, nothing happens. So can anyone suggest where is the logic wrong?
Expected output:
[AAMOD, Robert Kevin, 91, Sales Associate, Salt Lake City, UT, 84105, (xxx) xxx- xxxx, abc#yahoo.com]
I currently searching for a method in R which let's me match/merge two data frames. Helas both of these data frames contain non optimal data. They can have certain abbreviations of even typo's in them. Therefore I would like to define a list for each abbreviation and if a string contains one of those elements. If the original entries don't match, R should check if any of the other options of the abbreviation has a match. To illustrate: the name of a company could end with "Limited" but also with "Ltd." of "Ltd" etc.
EXAMPLE
Data
The Original "Address" file contains:
Company name Address
Deloitte Ltd. New York
Coca-Cola New York
Tesla ltd California
Microsoft Limited Washington
Would have to be merged with "EnterpriseNrList"
Company name EnterpriseNumber
Deloitte Ltd. 221
Coca-Cola 334
Tesla ltd 725
Microsoft Limited 127
So the abbreviations should work in "both directions". That's why I said, if R recognises any of the abbreviations, R should try to match all of them.
All of the matches should be reported as the return.
Therefore I would make up a list "Abbreviations" for each possible abbreviation
Limited.
limited
Ltd.
ltd.
Ltd
ltd
Questions
1) Would this be a good method, or would there be a more efficient way?
2) How can I check a list against a list of possible abbreviations (step 1, see below), sort of a containsx from excel?
3) How could I make up a list that replaces for the entries that do not match the abbreviation with all other abbreviatinos (step 2, see below)?
Thoughts for solution
Step 1
As I am still very new to this kind of work, I was thinking the following: use a regex expression to filter out wether a string contains any of the abbreviation options and create a list which will then contain either -1 if no match could be found and >0 if match is found. The no pattern matching can already be matched against the "Address" list. With the other entries I continue to step 2.
In this step I don't really know how to check against a list of options ("Abbreviations" list).
Step 2
Next I would create a list with the matches from step 1 and rbind together all options. In this step I don't really know to I could create a list that combines f.e. Coca-Cola with all it's possible abbreviations.
Coca-Cola Limited
Coca-Cola Ltd.
Coca-Cola Ltd
etc.
Step 3
Lastly I would match/merge this more complete list of companies again with the original "Data" list. With the introduction of step 2 I thought It might be a bit easier on the required computing power, as the original list is about 8000 rows.
I would go in a different approach, fixing the tables first before the merge.
To fix with abreviations, I would use a regex, case insensitive, the final dot being optionnal, I start with a list of 'Normal word' = vector of abbreviations.
abbrevs <- list('Limited'=c('Limited','Ltd'),'Incorporated'=c('Incorporated','Inc'))
The I build the corresponding regex (alternations with an optional dot at end, the case will be ignored by parameter in gsub and agrep later):
regexes <- lapply(abbrevs,function(x) { paste0("(",paste0(x,collapse='|'),")[.]?") })
Which gives:
$Limited
[1] "(Limited|Ltd)[.]?"
$Incorporated
[1] "(Incorporated|Inc)[.]?"
Now we have to apply each regex to the company.name column of each df:
for (i in seq_along(regexes)) {
Address$Company.name <- gsub(regexes[[i]], names(regexes[i]), Address$Company.name, ignore.case=TRUE)
Enterprise$Company.name <- gsub(regexes[[i]], names(regexes[i]), Enterprise$Company.name, ignore.case=TRUE)
}
This does not take into account typos. Here you'll need to work on with agrepor adist to manage it.
Result for Address example data set:
> Address
Company.name Address
1 Deloitte Limited New York
2 Coca-Cola New York
3 Tesla Limited California
4 Microsoft Limited Washington
Input data used:
Address <- structure(list(Company.name = c("Deloitte Ltd.", "Coca-Cola",
"Tesla ltd", "Microsoft Limited"), Address = c("New York", "New York",
"California", "Washington")), .Names = c("Company.name", "Address"
), class = "data.frame", row.names = c(NA, -4L))
Enterprise <- structure(list(Company.name = c("Deloitte Ltd.", "Coca-Cola",
"Tesla ltd", "Microsoft Limited"), EnterpriseNumber = c(221L,
334L, 725L, 127L)), .Names = c("Company.name", "EnterpriseNumber"
), class = "data.frame", row.names = c(NA, -4L))
I would say that the answer depends on whether you have a list of abbreviations or not.
If you have one, you could just look which element of your list contains an abbreviation with grep or greplfunctions. (grep return all indexes that have a matching pattern whereas grepl returns a logical vector).
Also, use the ignore.case= TRUE parameter of these function, so you don't have to try all capitalized/lowercase possibilities.
If you don't have such a list, my first guest would be to extract the first "word" of each company (I would guess that there is a single "Deloitte" company, and that it is "Deloitte Ltd"). You can do so with:
unlist(strsplit(CompanyNames,split = " "))
If you wanted to also correct for typos, this is more a question of string distance.
Hope that it helped!
As a relative novice in R and programming, my first ever question in this forum is about regex pattern matching, specifically line breaks. First some background. I am trying to perform some preprocessing on a corpus of texts using R before processing them further on the NLP platform GATE. I convert the original pdf files to text as follows (the text files, unfortunately, go into the same folder):
dest <- "./MyFolderWithPDFfiles"
myfiles <- list.files(path = dest, pattern = "pdf", full.names = TRUE)
lapply(myfiles, function(i) system(paste('"C:/Program Files (x86)/xpdfbin-win-3.04/bin64/pdftotext.exe"', paste0('"', i, '"')), wait = FALSE))
Then, having loaded the tm package and physically(!) moved the text files to another folder, I create a corpus:
TextFiles <- "./MyFolderWithTXTfiles"
EU <- Corpus(DirSource(TextFiles))
I then want to perform a series of custom transformations to clean the texts. I succeeded to replace a simple string as follows:
ReplaceText <- content_transformer(function(x, from, to) gsub(from, to, x, perl=T))
EU2 <- tm_map(EU, ReplaceText, "Table of contents", "TOC")
However, a pattern that is a 1-3 digit page number followed by two line breaks and a page break is causing me problems. I want to replace it with a blank space:
EU2 <- tm_map(EU, ReplaceText, "[0-9]{1,3}\n\n\f", " ")
The ([0-9]{1,3}) and \f alone match. The line breaks don't. If I copy text from one of the original .txt files into the RegExr online tool and test the expression "[0-9]{1,3}\n\n\f", it matches. So the line breaks do exist in the original .txt file.
But when I view one of the .txt files as read into the EU corpus in R, there appear to be no line breaks even though the lines are obviously breaking before the margin, e.g.
[3] "PROGRESS TOWARDS ACCESSION"
[4] "1"
[5] ""
[6] "\fTable of contents"
Seeing this, I tried other patterns, e.g. to detect one or more blank space ("[0-9]{1,3}\s*\f"), but no patterns worked.
So my questions are:
Am I converting and reading the files into R correctly? If so, what has happened to the line breaks?
If no line breaks is normal, how can I pattern match the character on line 5? Is that not a blank
space?
(A tangential concern:) When converting the pdf files, is there code that will put them directly in a new folder?
Apologies for extending this, but how can one print or inspect only a few lines of the text object? The tm commands and head(EU) print the entire object, each a very long text.
I know my problem(s) must appear simple and perhaps stupid, but one has to start somewhere and extensive searching has not revealed a source that explains comprehensively how to use RegExes to modify text objects in R. I am so frustrated and hope someone here will take pity and can help me.
Thanks for any advice you can offer.
Brigitte
p.s. I think it's not possible to upload attachments in this forum, therefore, here is a link to one of the original PDF documents: http://ec.europa.eu/enlargement/archives/pdf/key_documents/1998/czech_en.pdf
Because the doc is long, I created a snippet of the first 3 pages of the TXT doc, read it into the R corpus ('EU') and printed it to the console and this is it:
dput(EU[[2]])
structure(list(content = c("REGULAR REPORT", "FROM THE COMMISSION ON",
"CZECH REPUBLIC'S", "PROGRESS TOWARDS ACCESSION ***********************",
"1", "", "\fTable of contents", "A. Introduction", "a) Preface The Context of the Progress Report",
"b) Relations between the European Union and the Czech Republic The enhanced Pre-Accession Strategy Recent developments in bilateral relations",
"B. Criteria for membership", "1. Political criteria", "1.1. Democracy and the Rule of Law Parliament The Executive The judicial system Anti-Corruption measures",
"1.2. Human Rights and the Protection of Minorities Civil and Political Rights Economic, Social and Cultural Rights Minority Rights and the Protection of Minorities",
"1.3. General evaluation", "2. Economic criteria", "2.1. Introduction 2.2. Economic developments since the Commission published its Opinion",
"Macroeconomic developments Structural reforms 2.3. Assessment in terms of the Copenhagen criteria The existence of a functioning market economy The capacity to cope with competitive pressure and market forces 2.4. General evaluation",
"3. Ability to assume the obligations of Membership", "3.1. Internal Market without frontiers General framework The Four Freedoms Competition",
"3.2. Innovation Information Society Education, Training and Youth Research and Technological Development Telecommunications Audio-visual",
"3.3. Economic and Fiscal Affairs Economic and Monetary Union",
"2", "", "\fTaxation Statistics "), meta = structure(list(author = character(0),
datetimestamp = structure(list(sec = 50.1142621040344, min = 33L,
hour = 15L, mday = 3L, mon = 10L, year = 114L, wday = 1L,
yday = 306L, isdst = 0L), .Names = c("sec", "min", "hour",
"mday", "mon", "year", "wday", "yday", "isdst"), class = c("POSIXlt",
"POSIXt"), tzone = "GMT"), description = character(0), heading = character(0),
id = "CZ1998ProgressSnippet.txt", language = "en", origin = character(0)), .Names = c("author",
"datetimestamp", "description", "heading", "id", "language",
"origin"), class = "TextDocumentMeta")), .Names = c("content",
"meta"), class = c("PlainTextDocument", "TextDocument"))
Yes, working with text in R is not always a smooth experience! But you can get a lot done quickly with some effort (maybe too much effort!)
If you could share one of your PDF files or the output of dput(EU), that might help to identify exactly how to capture your page numbers with regex. That would also add a reproducible example to your question, which is an important thing to have in questions here so that people can test their answers and make sure they work for your specific problem.
No need to put PDF and text files in separate folders, instead you can use a pattern like so:
EU <- Corpus(DirSource(pattern = ".txt"))
This will only read the text files and ignore the PDF files
There is no 'snippet view' method in tm, which is annoying. I often use just names(EU) and EU[[1]] for quick looks
UPDATE
With the data you've just added, I'd suggest a slightly tangential approach. Do the regex work before passing the data to the tm package formats, like so:
# get the PDF
download.file("http://ec.europa.eu/enlargement/archives/pdf/key_documents/1998/czech_en.pdf", "my_pdf.pdf", method = "wget")
# get the file name of the PDF
myfiles <- list.files(path = getwd(), pattern = "pdf", full.names = TRUE)
# convert to text (not my pdftotext is in a different location to you)
lapply(myfiles, function(i) system(paste('"C:/Program Files/xpdf/bin64/pdftotext.exe"', paste0('"', i, '"')), wait = FALSE))
# read plain text int R
x1 <- readLines("my_pdf.txt")
# make into a single string
x2 <- paste(x1, collapse = " ")
# do some regex...
x3 <- gsub("Table of contents", "TOC", x2)
x4 <- gsub("[0-9]{1,3} \f", "", x3)
# convert to corpus for text mining operations
x5 <- Corpus(VectorSource(x4))
With the snippet of data your provided using dput, the output from this method is
inspect(x5)
<<VCorpus (documents: 1, metadata (corpus/indexed): 0/0)>>
[[1]]
<<PlainTextDocument (metadata: 7)>>
REGULAR REPORT FROM THE COMMISSION ON CZECH REPUBLIC'S PROGRESS TOWARDS ACCESSION *********************** TOC A. Introduction a) Preface The Context of the Progress Report b) Relations between the European Union and the Czech Republic The enhanced Pre-Accession Strategy Recent developments in bilateral relations B. Criteria for membership 1. Political criteria 1.1. Democracy and the Rule of Law Parliament The Executive The judicial system Anti-Corruption measures 1.2. Human Rights and the Protection of Minorities Civil and Political Rights Economic, Social and Cultural Rights Minority Rights and the Protection of Minorities 1.3. General evaluation 2. Economic criteria 2.1. Introduction 2.2. Economic developments since the Commission published its Opinion Macroeconomic developments Structural reforms 2.3. Assessment in terms of the Copenhagen criteria The existence of a functioning market economy The capacity to cope with competitive pressure and market forces 2.4. General evaluation 3. Ability to assume the obligations of Membership 3.1. Internal Market without frontiers General framework The Four Freedoms Competition 3.2. Innovation Information Society Education, Training and Youth Research and Technological Development Telecommunications Audio-visual 3.3. Economic and Fiscal Affairs Economic and Monetary Union Taxation Statistics
I am using perl to scrape the following through .txt which I'd ultimately bring into Stata. What format option works? I have many such observations, so would like to use an approach over which I can generalize.
The original data are of the form:
First Name: Allen
Last Name: Von Schmidt
Birth Year: 1965
Location: District 1, Ocean City, Cape May, New Jersey, USA
First Name: Lee Roy
Last Name: McBride
Birth Year: 1967
Location: Precinct 5, District 2, Chicago, Cook, Illinois, USA
The goal is to create the variables in Stata:
First Name: Allen
Last Name: Von Schmidt
Birth Year: 1965
County: Cape May
State: New Jersey
First Name: Allen
Last Name: McBride
Birth Year: 1967
County: Cook
State: Illinois
What possible .txt might lead to such, and how would I load it into Stata?
Also, the amount of terms vary in Location as in these 2 examples, but I always want the 2 before USA.
At the moment, I am putting "", around each variable from the table for the .txt.
"Allen","Von Schmidt","1965","District 1, Ocean City, Cape May, New Jersey, USA"
"Lee Roy","McBride","1967","Precinct 5, District 2, Chicago, Cook, Illinois, USA"
Is there a better way to format the .txt? How would I create the corresponding variables in Stata?
Thank you for your help!
P.S. I know that stata uses infile or insheet and can handle , or tabs to separate variables. I did not know how to scrape a variable like Location in perl with all of the those so I added the ""
There are two ways to do this. The first is to paste the data into your do-file and use input. Assuming the format is fairly regular, you can clean it up easily using commas to parse. Note that I removed the commas:
#delimit;
input
str100(first_name last_name yob geo);
"Allen" "Von Schmidt" "1965" "District 1, Ocean City, Cape May, New Jersey, USA";
end;
compress;
destring, replace;
split geo, parse(,);
rename geo1 district;
rename geo2 city;
rename geo3 county;
rename geo4 state;
rename geo5 country;
drop geo;
The second way is to insheet the data from the txt file directly, which is probably easier. This assumes that the commas were not removed:
#delimit;
insheet first_name last_name yob geo using "raw_data.txt", clear comma nonames;
Then clean it up as in the first example.
This isn't a complete answer, but I need more space and flexibility than comments (easily) allow.
One trick is based on peeling off elements from the end. The easiest way to do that could be to start looking for the last comma, which is in turn the first comma in the reversed string. Use strpos(reverse(stringvar), ",").
For example the first commma is found by strpos() like this
. di strpos("abcd,efg,h", ",")
5
and the last comma like this
. di strpos(reverse("abcd,efg,h"), ",")
2
Once you know where the last comma is you can peel off the last element. If the last comma is at position # in the reversed string, it is at position -# in the string.
. di substr("abcd,efg,h", -2, 2)
,h
These examples clearly are calculator-style examples for single strings. But the last element can be stripped off similarly for entire string variables.
. gen poslastcomma = strpos(reverse(var), ",")
. gen var_end = substr(var, -poslastcomma, poslastcomma)
. gen var_begin = substr(var, 1, length(var) - poslastcomma)
Once you get used to stuff like this you can write more complicated statements with fewer variables, but slowly, slowly step by step is better when you are learning.
By the way, a common Stata learner error (in my view) is to assume that a solution to a string problem must entail the use of regular expressions. If you are very fluent at regular expressions, you can naturally do wonderful things with them, but the other string functions in conjunction can be very powerful too.
In your specific example, it sounds as if you want to ignore a last element such as "USA" and then work in turn on the next elements working backwards.
split in Stata is fine too (I am a fan and indeed am its putative author) but can be awkward if a split yields different numbers of elements, which is where I came in.