participated in the review for their constructive contributions, and to the international organizations. He urged them to continue to show solidarity with C™te dÕIvoire. In closing, he said that, more than ever, C™te dÕIvoire needed their support in order to attain its objectives ahead of 2020.
*II. Conclusions and/or recommendations*
127. The recommendations listed below enjoy the support of C™te dÕIvoire:
127.1 Consider the accession to core human rights instruments (Lesotho); and to other main international human rights treaties that it is not yet a party to (Philippines);
127.2 Make efforts towards the ratification of the OP-CAT (Chile);
127.3 Ratify the OP-CAT (Ghana, Tunisia), as recommended previously in 2009 (Czech Republic) and take policy measures to prevent torture and ill-treatment (Estonia);
This is my text, and I want to extract everything after, II. Conclusions and/or recommendations
I have tried the following;
x <- substring(text, first = "II. Conclusions and/or recommendations\n")
But Im getting the NAs introduced by coercion. what do I do to make it better?
text <- "participated in the review for their constructive contributions, and to the international organizations. He urged them to continue to show solidarity with C™te dÕIvoire. In closing, he said that, more than ever, C™te dÕIvoire needed their support in order to attain its objectives ahead of 2020.
*II. Conclusions and/or recommendations*
127. The recommendations listed below enjoy the support of C™te dÕIvoire:
127.1 Consider the accession to core human rights instruments (Lesotho); and to other main international human rights treaties that it is not yet a party to (Philippines);
127.2 Make efforts towards the ratification of the OP-CAT (Chile);
127.3 Ratify the OP-CAT (Ghana, Tunisia), as recommended previously in 2009 (Czech Republic) and take policy measures to prevent torture and ill-treatment (Estonia);"
Use this:
substring(text, regexpr("II.", text))
Or by giving start position as an integer:
substring(text, 300)
Related
In advance: Sorry for all the Norwegian references, but I hope I've explained my problem good enough for them to still make sense...
So, in 2005 Norway got a new criminal law. The old one was somewhat unstructured (only three chapters), while the statutory provisions in the 2005 version have been structured into 31 chapters, depending on the area of the offense (can be seen here: https://lovdata.no/dokument/NL/lov/2005-05-20-28). I call these "areas of law". For example, in the 2005 version laws regarding sexual offenses are in chapter 26. Logically, then the statutory provisions that belong to this chapter are categorized as belonging to the area of law called "s
Some of the old laws have been structured into the new chapters, some new have been added, and some have been repealed. I have what is called a "law mirror" – a list where you can find where the old provision are in the new law, if it hasn't been repealed. The new law came into force for offenses committed from the 1st of October in 2015.
An example of a law mirror: https://no.wikipedia.org/wiki/Straffeloven_(lovspeil). I've pivoted the list longer, such that it looks like this:
Law Mirror: "Seksuallovbrud" means sexual offense, "kap_2005" says which chapter in the 2005 law that the statutory provision (norwegian: "paragraf") falls under, and "straffelov" specifies whether the provison comes from the 2005 or 1902 version of the law.
The data I have consist of two separate data frames. Df1 is the law mirror. Df2 consists of cases in the Norwegian court of appeals from between 1993 and 2019, where the criminal law was the basis of the verdict. I've made a dummy (strl1902) in Df2 for whether the verdict in the case came before or after the new law came into force. Equal to 1 if it's the old one. I've also extracted the number of the statutory provision.
On the basis of this I want to categorize the cases using statutory provisions from the old criminal law into the areas of law from the new law.
This is where I need help:
Do any of you have any idea of how I can distinguish between the provisions from the old and the new law, such that I also can make dummies for the provisions from the 1902 law, such that they are separated into the areas of the law of the 2005 law?
Hope this makes sense.
I have a quanteda corpus of hundreds of documents. How do I remove specific sections - like the abstract and footnotes etc. Otherwise, I am faced with doing it manually. Thanks
As requested, here is a text example. It is from a regular journal article. It shows the Meta data, then the abstract, then keywords, then introduction, then author contact details, then body of article, then Note, then Disclosure statement, then Notes on contributors, then references. I would like to remove everything apart from the introduction and body of the article. I would also like to remove the author name and Journal title - which are repeated throughout
" Behavioral Sciences of Terrorism and Political Aggression
ISSN: 1943-4472 (Print) 1943-4480 (Online) Journal homepage: http://www.tandfonline.com/loi/rirt20
Sometimes they come back: responding to
American foreign fighter returnees and other
Elusive threats
Christopher J. Wright
To cite this article: Christopher J. Wright (2018): Sometimes they come back: responding to
American foreign fighter returnees and other Elusive threats, Behavioral Sciences of Terrorism and
Political Aggression, DOI: 10.1080/19434472.2018.1464493
To link to this article: https://doi.org/10.1080/19434472.2018.1464493
Published online: 23 Apr 2018.
Submit your article to this journal
Article views: 57
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=rirt20
"
"BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION, 2018
https://doi.org/10.1080/19434472.2018.1464493
Sometimes they come back: responding to American foreign
fighter returnees and other Elusive threats
Christopher J. Wright
Department of Criminal Justice, Austin Peay State University, Clarksville, TN, USA
ABSTRACT ARTICLE HISTORY
Much has been made of the threat of battle hardened jihadis from Received 8 January 2018
Islamist insurgencies, especially Syria. But do Americans who Accepted 10 April 2018
return home after gaining experience fighting abroad pose a
KEYWORDS
greater risk than homegrown jihadi militants with no such Terrorism; foreign fighters;
experience? Using updated data covering 1990–2017, this study domestic terrorism;
shows that the presence of a returnee decreases the likelihood homegrown terrorism;
that an executed plot will cause mass casualties. Plots carried out lone-wolf; homeland security
Introduction: being afraid. Being a little afraid
How great of a threat do would-be jihadis pose to their home country? And do those who
return home after gaining experience fighting abroad in Islamist insurgencies or attending
terror training camps pose a greater risk than other jihadi militants? The fear, as first outlined
by Hegghammer (2013), is two-fold. First, individuals that have gone abroad to fight might
CONTACT Christopher J. Wright wrightc#apsu.edu Department of Criminal Justice, Austin Peay State University,
Clarksville, TN 37043, USA
© 2018 Society for Terrorism Research
"
"2 C. J. WRIGHT
Many of the earliest studies on Western foreign fighters suggested that those who
returned were in fact more deadly than those with no experience fighting in Islamist insur-
gencies. Hegghammer’s (2013) analysis suggested that these foreign fighter returnees
were a greater danger than when they left. Likewise, Byman (2015), Nilson (2015),
Kenney (2015), and Vidno (2011) came to similar conclusions while offering key insights
into the various mechanisms linking foreign fighting with successful plot execution and
greater mass casualties.
Other studies came to either mixed conclusions or directly contradicted the earlier find-
ings. Adding several years of data to Hegghammer’s (2013) earlier study, Hegghammer
"
" BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION 3
for them to form the types of large, local networks that would be necessary to carry out a
large-scale attack without attracting the attention of security services’ (p. 92).
"
Note
1. Charges were brought against Noor Zahi Salman, the widow of the Omar Mateen who carried
out the June, 2016 attack against the Pulse Nightclub in Orlando, Florida (US Department of
Justice., 2017a, January 17). However, in March of 2018 a jury acquitted her of the charges that
she had foreknowledge of the attack.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Christopher J. Wright, Ph.D., is an Assistant Professor at Austin Peay State University where he
teaches in the Homeland Security Concentration.
ORCID
Christopher J. Wright http://orcid.org/0000-0003-0043-6616
References
Byman, D. (2015). The homecomings: What happens when Arab foreign fighters in Iraq and Syria
return? Studies in Conflict & Terrorism, 38(8), 581–602.
Byman, D. (2016). The Jihadist returnee threat: Just how dangerous? Political Science Quarterly, 131(1),
69–99.
Byman, D., & Shapiro, J. (2014). Be afraid. Be a little afraid: The threat of terrorism from Western foreign
fighters in Syria and Iraq. Foreign Policy at Brookings. Washington, DC: Brookings. Retrieved from
https://www.brookings.edu/wp-content/uploads/2016/06/Be-Afraid-web.pdf
The approach
The key here is to determine the regular markers that precede each section, and then to use them as tags in a call to corpus_segment(). It's the tags that will need tweaking, based on their degree of regularity across documents.
Based on what you supplied above, I pasted that into a plain text file that I named example.txt. This code extracted the Introduction and what I think is the body of the article, but for that I had to decide a tag that marked its ending. Below, I used "Disclosure Statement". So:
library("quanteda")
crp <- readtext::readtext("~/tmp/example.txt") %>%
corpus()
pat <- c("\nIntroduction?", "\nCONTACT", "©", "\nDisclosure statement")
crpextracted <- corpus_segment(crp, pattern = pat)
summary(crpextracted)
## Corpus consisting of 4 documents:
##
## Text Types Tokens Sentences pattern
## example.txt.1 62 74 5 Introduction:
## example.txt.2 18 21 2 CONTACT
## example.txt.3 156 253 11 ©
## example.txt.4 101 180 19 Disclosure statement
##
## Source: /Users/kbenoit/Dropbox (Personal)/GitHub/quanteda/quanteda/* on x86_64 by kbenoit
## Created: Fri Jul 6 19:51:01 2018
## Notes: corpus_segment.corpus(crp, pattern = pat)
When you examine the text in the "Introduction:" tagged segment, you can see that everything from that string until the next tag was extracted to that as a new document:
corpus_subset(crpextracted, pattern == "\nIntroduction:") %>%
texts() %>% cat()
## being afraid. Being a little afraid
##
## How great of a threat do would-be jihadis pose to their home country? And do those who
##
## return home after gaining experience fighting abroad in Islamist insurgencies or attending
##
## terror training camps pose a greater risk than other jihadi militants? The fear, as first outlined
##
## by Hegghammer (2013), is two-fold. First, individuals that have gone abroad to fight might
How to remove pdf junk
All pdf conversions produce unwanted junk such as running headers, footers, etc. Here's how to remove them. (Note: You will want to do this before the step above.) How to construct the toreplace pattern? You will need to understand something about regular expressions, and use some experimentation.
library("stringr")
toreplace <- '\\n*\" \" BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION,{0,1} \\d+\\n*'
texts(crp) <- str_replace_all(texts(crp), regex(toreplace), "")
cat(texts(crp))
To demonstrate this on a section from your example:
# demonstration
x <- '
" " BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION 3
'
str_replace_all(x, regex(toreplace), "")
## [1] ""
I have one page story (i.e. text data), I need to use Bayesian network on that story and analyse the same. Could someone tell me whether it is possible in R? If yes, that how to proceed?
The objective of the analysis is - Extract Action Descriptions from
Narrative Text.
The data considered for analysis -
Krishna’s Dharam-shasthra to Arjuna:
The Gita is the conversation between Krishna and Arjuna leading up to the battle.
Krishna emphasised on two terms: Karma and Dharma. He told Arjun that this was a righteous war; a war of Dharma. Dharma is the way of righteousness or a set of rules and laws laid down. The Kauravas were on the side of Adharma and had broken rules and laws and hence Arjun would have to do his Karma to uphold Dharma.
Arjuna doesn't want to fight. He doesn't understand why he has to shed his family's blood for a kingdom that he doesn't even necessarily want. In his eyes, killing his evil and killing his family is the greatest sin of all. He casts down his weapons and tells Krishna he will not fight. Krishna, then, begins the systematic process of explaining why it is Arjuna's dharmic duty to fight and how he must fight in order to restore his karma.
Krishna first explains the samsaric cycle of birth and death. He says there is no true death of the soul simply a sloughing of the body at the end of each round of birth and death. The purpose of this cycle is to allow a person to work off their karma, accumulated through lifetimes of action. If a person completes action selflessly, in service to God, then they can work off their karma, eventually leading to a dissolution of the soul, the achievement of enlightenment and vijnana, and an end to the samsaric cycle. If they act selfishly, then they keep accumulating debt, putting them further and further into karmic debt.
What I want is - post tagger to separate verbs, nouns etc. and then create a meaningful network using that.
The steps that should be followed in pre-processing are:
syntactic processing (post tagger)
SRL algorithm (semantic role labelling of characters of the story)
conference resolution
Using all of the above I need to create a knowledge database and create a Bayesian network.
This is what I have tried so far to get post tagger:
txt <- c("As the years went by, they remained isolated in their city. Their numbers increased by freeing women from slavery.
Doom would come to the world in the form of Ares the god of war and the Son of Zeus. Ares was unhappy with the gods as he wanted to prove just how foul his father’s creation was. Hence, he decided to corrupt the mortal men created by Zeus. Fearing his wrath upon the world Zeus decided to create the God killer in order to stop Ares. He then commanded Hippolyta to mould a baby from the sand and clay of the island. Then the five goddesses went back into the Underworld, drawing out the last soul that remained in the Well and giving it incredible powers. The soul was merged with the clay and became flesh. Hippolyta had her daughter and named her Diana, Princess of the Amazons, the first child born on Paradise Island.
Each of the six members of the Greek Pantheon granted Diana a gift: Demeter, great strength; Athena, wisdom and courage; Artemis, a hunter's heart and a communion with animals; Aphrodite, beauty and a loving heart; Hestia, sisterhood with fire; Hermes, speed and the power of flight. Diana was also gifted with a sword, the Lasso of truth and the bracelets of penance as weapons to defeat Ares.
The time arrived when Diana, protector of the Amazons and mankind was sent to the Man's World to defeat Ares and rid the mortal men off his corruption. Diana believed that only love could truly rid the world of his influence. Diana was successfully able to complete the task she was sent out by defeating Ares and saving the world.
")
writeLines(txt, tf <- tempfile())
library(stringi)
library(cleanNLP)
cnlp_init_tokenizers()
anno <- cnlp_annotate(tf)
names(anno)
get_token(anno)
cnlp_init_spacy()
anno <- cnlp_annotate(tf)
get_token(anno)
cnlp_init_corenlp()
I have a data frame with a column of "text." Each row of this column is filled with text from media articles.
I am trying to extract a string that occurs like this: "term" (including the double quotes around the term). I tried the following regular expression to capture instances where a word is sandwiched between two double quotes:
stri_extract_all_regex(df$text, '"(.+?)"')
This seems to capture some instances of what I am looking for, but in other cases - where I know the criteria are met - it does not. It also captures what seem to be just quotes of longer text (and not other instances of quoted text). Here are the results of using the above:
[[19]]
[1] "\"play a constructive and positive role\""
[2] "\"active and hectic reception\""
[3] "\"[term]\""
I would like to have only exactly "term" as the output (including the double quotes). I am trying to find instances when the term is used alone in quotes.
Example in R:
test <- c(df$text[12], df$text[18])
res <- stri_extract_all_regex(test, '"\\S+"')
unlist(res)
[1] "\"Rohingya\"" "\"Bengali\"" NA
print(test)
[1] "Former UN general secretary Kofi Annan will advise Myanmar's government on resolving conflicts in Rakhine State, the office of the state counsellor announced today.Former UN Secretary General Kofi Annan speaks at the opening of the Consciouness Summit on climate change in Paris, France on July 21, 2015. Photo: EPARakhine State, one of the poorest in the Union, was wracked by sectarian violence in 2012 that forced more than 100,000 – mostly Muslims who ethnically identify as Rohingya – into squalid displacement camps where they face severe restrictions on movement as well as access to health care, education, and other other basic services.Addressing the ongoing crises has posed one of the most troubling challenges to Daw Aung San Suu Kyi's National League for Democracy-led government.Earlier today, the government announced the formation of an advisory panel that will be chaired by former UN chief, and focus on \"finding lasting solutions to the complex and delicate issues in the Rakhine State\".The board will submit recommendations to the government on \"conflict prevention, humanitarian assistance, rights and reconciliation, institution-building and promotion of development of Rakhine State,\" a statement from the state counsellor's office said.The statement did not use the word \"Rohingya\". Daw Aung San Suu Kyi has come under fire both at home and from international rights groups for failing prioritise to address the group's plight and seeking to placate hardline Buddhist nationalists by avoiding the politically-charged term. The government has already requested that the US Embassy and other diplomatic groups avoid the term Rohingya, and in June, she proposed \"Muslim community of Rakhine State\".The proposed neutral terminology, which the state counsellor ordered government officials to adopt, sparked mass protests in Rakhine State and in Yangon by hardline nationalists, who insist on use of the term \"Bengali\" that was also preferred by the previous government's to suggest the group's origins in neighbouring Bangladesh.In July UN special rapporteur for human rights Yanghee Lee urged the government to make ending \"institutionalised discrimination\" against the Rohingya and other Muslims in Rakhine an urgent priority.Myanmar also announced this week that current UN Secretary General Ban Ki-moon will attend the highly-anticipated 21st Century Panglong conference at the end of the month.The five-day talks, aimed at ending a host of complicated border ethnic conflicts that have lasted for decades, will begin on August 31."
[2] "Thousands of Kaman Muslims from the Rakhine State capital Sittwe obtained identity cards this week, some two years after they applied for the documents.“Now the problem is solved. The Kaman got national ID cards. We had proposed to the government and immigration office to work on the process of giving ID cards to Kaman,” said U Tin Hlaing Win, general secretary of the Kaman National Development Party (KNDP).The Kaman, as one of 135 officially recognised ethnic groups in Myanmar entitled to full citizenship, had struggled to get authorities to grant them the IDs due to the complex ethnic demographics and fraught identity politics of Rakhine State.Complicating the process for the Kaman applicants has been the population of more than 1 million Muslims in Rakhine State who self-identify as Rohingya, most of whom are stateless. “Citizenship scrutiny” programs to issue some form of identification to the minority group by the previous and current governments have been met with resistance by Rakhine nationalists.The shared Muslim faith of the Kaman and Rohingya became just one aspect of a contentious debate over terminology earlier this year, when State Counsellor Daw Aung San Suu Kyi put forward the phrase “the Muslim community in Rakhine State” to refer to self-identifying Rohingya in an attempt to chart a middle course on the issue of lexicon. “Rohingya” stirs passions among Buddhist nationalists, who insist that they be called “Bengalis” to imply that they are illegal immigrants from neighbouring Bangladesh, despite many tracing familial lineage in Rakhine State back generations.Some Kaman have since viewed the state counsellor’s edict warily, concerned that the Kaman identity might be conflated with that of the Rohingya – who are not entitled to citizenship – and could jeopardise Kaman prospects for ID cards and full rights under the law.Violence in 2012 between Buddhists and Muslims in Rakhine State affected Rakhine Buddhists, Kaman and Rohingya, but the latter suffered the brunt of casualties and displacement.U Tin Hlaing Win told The Myanmar Times last week that some of the Kaman Muslims displaced by the conflict in the island town of Rambre had also recently received national ID cards.“The Kaman are ethnics belonging to Myanmar,” said U Than Htun Aung, a senior immigration officer for Rakhine State. “Township immigration officers will examine [legitimate Kaman claims to citizenship] according to the process and they will ensure they get their rights.”Around 2000 Kaman applied for national ID cards in 2014, but only 38 people were issued the documents.The others were told they had not received IDs because of the purported existence of “fake Kaman”.More than 100,000 people are thought to hold government-issued national ID cards identifying them as Kaman, but KNDP research in 2013 estimated the actual ethnic Kaman population to be about 50,000.U Tin Hlaing Win said sorting out the “fake Kaman” issue was not solely the responsibility of Kaman people, adding that immigration officers through the years, and generations of ethnically mixed marriages and the offspring they produced, were also to blame for the confusion.“According to our research and knowledge tracing family trees, some Kaman identity-card holders were Rakhine plus Bengali or Rakhine plus Indian, not Kaman. It [identity problems] should be solved by three groups – we Kaman, the Rakhine and immigration authorities,” U Tin Hlaing Win told The Myanmar Times last week.What most seem to agree on is that “real Kaman” deserve the documentation they need to enjoy the full rights of citizenship.Ethnic Rakhine youth leader Ko Khine Lamin said, “The Rakhine objected to national ID cards for Kaman because of the controversy over fake Kaman. But there are real Kaman who have lived in Rakhine State since a long, long time ago. They should get their ethnic rights through careful examination by immigration officers.”Kaman politicians are not satisfied with their victory this week and are trying to meet with Rakhine State Chief Minister U Nyi Pu to raise other difficulties Kaman people face, such as transportation barriers. They also intend to ask the chief minister for rehabilitation programs for Kaman internally displaced people, as well as education and health support for the broader Kaman community."
The above code can only return terms in [1].
The "(.+?)" pattern matches ", then any char other than line break chars, as few as possible, up to the closest (leftmost) ". It means it can match whitespaces, too, and thus matches "play a constructive and positive role" and "active and hectic reception".
To match a streak of non-whitespace chars in-between double quotes, you need to use
stri_extract_all_regex(df$text, '"\\S+"')
The "\S+" pattern matches ", then 1 or more non-whitespace chars, and then a closing ".
See the regex demo.
If you only want to match word chars (letters, digits, _) in between double quotes, use
'"\\w+"'
See another regex demo.
To match curly quotes, use '["“]\\S+["”]' regex:
> res <- stri_extract_all_regex(test, '["“]\\S+["”]')
> unlist(res)
[1] "\"Rohingya\"" "\"Bengali\"" "\u0093Rohingya\u0094"
[4] "\u0093Bengalis\u0094"
And if you need to "normalize" the double quotes, use
> gsub("[“”]", '"', unlist(res))
[1] "\"Rohingya\"" "\"Bengali\"" "\"Rohingya\"" "\"Bengalis\""
It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center.
Closed 9 years ago.
I'm doing some basic text analysis in R and want to count the number of lines for a transcript from a .txt file that I load into R. With the example below to yield a count in which each speaker gets a new line attached to the linecount such that Mr. Smith = 4, Mr. Gordon = 6, Mr. Catalano = 3.
[71] "\"511\"\t\"MR Smith: Mr Speaker, I like the spirit in which we are agreeing on this. The administration of FUFA is present here. FUFA could be used as a conduit, but the intention of what hon. Beti Kamya brought up and what hon. Rose Namayanja has said was okufuwa - just giving a token of appreciation to the players who achieved this.\""
[72] "\"513\"\t\"MR Gordon: Thank you very much, Mr Speaker. FUFA is an organisation and the players are the ones who got the cup for us. To promote motivation in all activities, not only football, you should remunerate people who have done well. In this case, we have heard about FUFA with their problems. They have not paid water bills and they can take this money to pay the water bills. If we agree that this money is supposed to go to the players and the coaches, then when it goes there they would know the amount and they will sit among themselves and distribute according to what we will have given. (Applause) I thank you.\""
[73] "\"515\"\t\"MR Catalano: Mr Speaker, I want to give information to my dear colleagues. The spirit is very good but you must be mindful that the administration of FUFA is what has made this happen. The money to the players. That indicates to you that FUFA is very trustworthy. This is not the old FUFA we are talking about.\""
The function countLine() doesn't work since it requires a connection - these are just .txt imported into R. I realize that the line count is subject to the formatting of what the text is opened up in, but any general help on if this is feasible would help. Thanks.
I didn't think your example was reproducible, so I edited it to get it to contain what you posted but I do not know if the names will match:
txtvec <- structure(list(`'511' ` = "MR Smith: Mr Speaker, I like the spirit in which we are agreeing on this. The administration of FUFA is present here. FUFA could be used as a conduit, but the intention of what hon. Beti Kamya brought up and what hon. Rose Namayanja has said was okufuwa - just giving a token of appreciation to the players who achieved this.\"",
`'513' ` = "MR Gordon: Thank you very much, Mr Speaker. FUFA is an organisation and the players are the ones who got the cup for us. To promote motivation in all activities, not only football, you should remunerate people who have done well. In this case, we have heard about FUFA with their problems. They have not paid water bills and they can take this money to pay the water bills. If we agree that this money is supposed to go to the players and the coaches, then when it goes there they would know the amount and they will sit among themselves and distribute according to what we will have given. (Applause) I thank you.\"",
`'515' ` = "MR Catalano: Mr Speaker, I want to give information to my dear colleagues. The spirit is very good but you must be mindful that the administration of FUFA is what has made this happen. The money to the players. That indicates to you that FUFA is very trustworthy. This is not the old FUFA we are talking about.\""), .Names = c("'511'\t",
"'513'\t", "'515'\t"))
So it's only a matter or running a regex expression across it and tabling the results:
> table( sapply(txtvec, function(x) sub("(^MR.+)\\:.+", "\\1", x) ) )
#MR Catalano MR Gordon MR Smith
1 1 1
There was concern expressed that the names were not in the original structure. This is another version with unnamed vector and a slightly modified regex:
txtvec <- c("\"511\"\t\"\nMR Smith: Mr Speaker, I like the spirit in which we are agreeing on this. The administration of FUFA is present here. FUFA could be used as a conduit, but the intention of what hon. Beti Kamya brought up and what hon. Rose Namayanja has said was okufuwa - just giving a token of appreciation to the players who achieved this.\"",
"\"513\"\t\"\nMR Gordon: Thank you very much, Mr Speaker. FUFA is an organisation and the players are the ones who got the cup for us. To promote motivation in all activities, not only football, you should remunerate people who have done well. In this case, we have heard about FUFA with their problems. They have not paid water bills and they can take this money to pay the water bills. If we agree that this money is supposed to go to the players and the coaches, then when it goes there they would know the amount and they will sit among themselves and distribute according to what we will have given. (Applause) I thank you.\"",
"\"515\"\t\"\nMR Catalano: Mr Speaker, I want to give information to my dear colleagues. The spirit is very good but you must be mindful that the administration of FUFA is what has made this happen. The money to the players. That indicates to you that FUFA is very trustworthy. This is not the old FUFA we are talking about.\""
)
table( sapply(txtvec, function(x) sub(".+\\n(MR.+)\\:.+", "\\1", x) ) )
#MR Catalano MR Gordon MR Smith
# 1 1 1
To count the number of "lines" these would occupy on a wrapping device with 80 characters per line you could use this code (which could easily be converted to a function):
sapply(txtvec, function(tt) 1+nchar(tt) %/% 80)
#[1] 5 8 4
This is raised in the comments, but it really bares being it's own answer:
You cannot "count lines" without defining what a "line" is. A line is a very vague concept and can vary by the program being used.
Unless of course the data contains some indicator of a line break, such as \n. But even then, you would not be counting lines, you would be counting linebreaks. You would then have to ask yourself if the hardcoded line break is in accord with what you are hoping to analyze.
--
If your data does not contain linebreaks, but you still want to count the number of lines, then we're back to the question of "how do you define a line"? The most basic way, is as #flodel suggests, which is to use character length. For example, you can define a line as 76 characters long, and then take
ceiling(nchar(X) / 76))
This of course assumes that you can cut words. (If you need words to remain whole, then you have to get craftier)