# Introduction

This tutorial introduces how to extract concordances and keyword-in-context (KWIC) displays with R.

This tutorial is aimed at beginners and intermediate users of R with the aim of showcasing how to extract keywords and key phrases from textual data and how to process the resulting concordances using R. The aim is not to provide a fully-fledged analysis but rather to show and exemplify selected useful methods associated with concordancing.

The entire R Notebook for the tutorial can be downloaded here. If you want to render the R Notebook on your machine, i.e. knitting the document to html or a pdf, you need to make sure that you have R and RStudio installed and you also need to download the bibliography file and store it in the same folder where you store the Rmd file.

Click this link to open an interactive version of this tutorial on MyBinder.org.
This interactive Jupyter notebook allows you to execute code yourself and you can also change and edit the notebook, e.g. you can change code and upload your own data.

In the language sciences, concordancing refers to the extraction of words from a given text or texts . Commonly, concordances are displayed in the form of keyword-in-context displays (KWICs) where the search term is shown in context, i.e. with preceding and following words. Concordancing are central to analyses of text and they often represents the first step in more sophisticated analyses of language data . The play such a key role in the language sciences because concordances are extremely valuable for understanding how a word or phrase is used, how often it is used, and in which contexts is used. As concordances allow us to analyze the context in which a word or phrase occurs and provide frequency information about word use, they also enable us to analyze collocations or the collocational profiles of words and phrases . Finally, concordances can also be used to extract examples and it is a very common procedure.

There are various very good software packages that can be used to create concordances - both for offline use (e.g. AntConc , SketchEngine, MONOCONC, and ParaConc) and online use (see e.g. here).

In addition, many corpora that are available such as the BYU corpora can be accessed via a web interface that have in-built concordancing functions.

While these packages are very user-friendly, offer various additional functionalities, and almost everyone who is engaged in analyzing language has used concordance software, they all suffer from shortcomings that render R a viable alternative. Such issues include that these applications

• are black boxes that researchers do not have full control over or do not know what is going on within the software

• they are not open source

• they hinder replication because the replications is more time consuming compared to analyses based on Notebooks.

• they are commonly not free-of charge or have other restrictions on use (a notable exception is AntConc)

• is extremely flexible and enables researchers to perform their entire analysis in a single environment

• allows full transparency and documentation as analyses can be based on Notebooks

• offer version control measures (this means that the specific versions of the involved software are traceable)

• makes research more replicable as entire analyses can be reproduced by simply running the Notebooks that the research is based on

Especially the aspect that R enables full transparency and replicability is relevant given the ongoing Replication Crisis . The Replication Crisis is a ongoing methodological crisis primarily affecting parts of the social and life sciences beginning in the early 2010s (see also Fanelli 2009). Replication is important so that other researchers, or the public for that matter, can see or, indeed, reproduce, exactly what you have done. Fortunately, R allows you to document your entire workflow as you can store everything you do in what is called a script or a notebook (in fact, this document was originally a R notebook). If someone is then interested in how you conducted your analysis, you can simply share this notebook or the script you have written with that person.

Preparation and session set up

This tutorial is based on R. If you have not installed R or are new to it, you will find an introduction to and more information how to use R here. For this tutorials, we need to install certain packages from an R library so that the scripts shown below are executed without errors. Before turning to the code below, please install the packages by running the code below this paragraph. If you have already installed the packages mentioned below, then you can skip ahead and ignore this section. To install the necessary packages, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the libraries so you do not need to worry if it takes some time).

# install packages
install.packages("quanteda")
install.packages("dplyr")
install.packages("stringr")
install.packages("flextable")
# install klippy for copy-to-clipboard button in code chunks
install.packages("remotes")
remotes::install_github("rlesur/klippy")

Now that we have installed the packages, we activate them as shown below.

# activate packages
library(quanteda)
library(dplyr)
library(stringr)
library(flextable)
# activate klippy for copy-to-clipboard button
klippy::klippy()

Once you have installed R and RStudio and also initiated the session by executing the code shown above, you are good to go.

For this tutorial, we will use Lewis Caroll’s Alice’s Adventures in Wonderland. You can use the code below to load this text into R (but you have to have access to the internet to do so).

text <- base::readRDS(url("https://slcladal.github.io/data/alice.rda", "rb"))
 . Alice’s Adventures in Wonderland by Lewis Carroll CHAPTER I. Down the Rabbit-Hole Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, “and what is the use of a book,” thought Alice “without pictures or conversations?” So she was considering in her own mind (as well as she could, for the

The table above shows that the example text requires formatting so that we can use it. Therefore, we collapse it into a single object (or text) and remove superfluous white spaces.

text <- text %>%
# collapse lines into a single  text
paste0(collapse = " ") %>%
# remove superfluous white spaces
str_squish()
 . Alice’s Adventures in Wonderland by Lewis Carroll CHAPTER I. Down the Rabbit-Hole Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, “and what is the use of a book,” thought Alice “without pictures or conversations?” So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her. There was nothing so _very_ remarkable in that; nor did Alice think it so _very_ much out of the way to hear the Rabbit say to itself, “Oh dear! Oh dear! I shall be late!” (when she thought it over afterwards, it occurred to her that she ought to have wondered at this, but at the time it all seemed quite natural); but when

The result confirms that the entire text is now combined into a single character object.

## Creating simple concordances

Now that we have loaded the data, we can easily extract concordances using the kwic function from the quanteda package. The kwic function takes the text (x) and the search pattern (pattern) as it main arguments but it also allows the specification of the context window, i.e. how many words/elements are show to the left and right of the key word (we will go over this later on).

kwic_alice <- kwic(
# define text
text,
# define search pattern
pattern = "Alice")
 docname from to pre keyword post pattern text1 14 14 I . Down the Rabbit-Hole Alice was beginning to get very Alice text1 73 73 a book , " thought Alice " without pictures or conversations Alice text1 153 153 in that ; nor did Alice think it so _very_ much Alice text1 239 239 and then hurried on , Alice started to her feet , Alice text1 309 309 In another moment down went Alice after it , never once Alice text1 348 348 down , so suddenly that Alice had not a moment to Alice text1 531 531 " Well ! " thought Alice to herself , " after Alice text1 657 657 for , you see , Alice had learnt several things of Alice text1 731 731 got to ? " ( Alice had no idea what Latitude Alice text1 924 924 else to do , so Alice soon began talking again . Alice

You will see that you get a warning stating that you should use token before extracting concordances. This can be done as shown below. Also, we can specify the package from which we want to use a function by adding the package name plus :: before the function (see below)

kwic_alice <- quanteda::kwic(
# define and tokenize text
quanteda::tokens(text),
# define search pattern
pattern = "alice")
 docname from to pre keyword post pattern text1 14 14 I . Down the Rabbit-Hole Alice was beginning to get very alice text1 73 73 a book , " thought Alice " without pictures or conversations alice text1 153 153 in that ; nor did Alice think it so _very_ much alice text1 239 239 and then hurried on , Alice started to her feet , alice text1 309 309 In another moment down went Alice after it , never once alice text1 348 348 down , so suddenly that Alice had not a moment to alice text1 531 531 " Well ! " thought Alice to herself , " after alice text1 657 657 for , you see , Alice had learnt several things of alice text1 731 731 got to ? " ( Alice had no idea what Latitude alice text1 924 924 else to do , so Alice soon began talking again . alice

We can easily extract the frequency of the search term (alice) using the nrow or the length functions which provide the number of rows of a tables (nrow) or the length of a vector (length).

nrow(kwic_alice)
## [1] 386
length(kwic_alice$keyword) ## [1] 386 The results show that there are 414 instances of the search term (alice) but we can also find out how often different variants (lower case versus upper case) of the search term were found using the table function. This is especially useful when searches involve many different search terms (while it is, admittedly, less useful in the present example). table(kwic_alice$keyword)
##
## Alice
##   386

To get a better understanding of the use of a word, it is often useful to extract more context. This is easily done by increasing size of the context window. To do this, we specify the window argument of the kwic function. In the example below, we set the context window size to 10 words/elements rather than using the default (which is 5 word/elements).

kwic_alice_longer <- kwic(
# define text
text,
# define search pattern
pattern = "alice",
# define context window size
window = 10)
 docname from to pre keyword post pattern text1 14 14 Wonderland by Lewis Carroll CHAPTER I . Down the Rabbit-Hole Alice was beginning to get very tired of sitting by her alice text1 73 73 what is the use of a book , " thought Alice " without pictures or conversations ? " So she was alice text1 153 153 was nothing so _very_ remarkable in that ; nor did Alice think it so _very_ much out of the way to alice text1 239 239 and looked at it , and then hurried on , Alice started to her feet , for it flashed across her alice text1 309 309 rabbit-hole under the hedge . In another moment down went Alice after it , never once considering how in the world alice text1 348 348 , and then dipped suddenly down , so suddenly that Alice had not a moment to think about stopping herself before alice text1 531 531 she fell past it . " Well ! " thought Alice to herself , " after such a fall as this alice text1 657 657 I think - " ( for , you see , Alice had learnt several things of this sort in her lessons alice text1 731 731 what Latitude or Longitude I've got to ? " ( Alice had no idea what Latitude was , or Longitude either alice text1 924 924 down . There was nothing else to do , so Alice soon began talking again . " Dinah'll miss me very alice

EXERCISE TIME!



1. Extract the first 10 concordances for the word confused.
  kwic_confused <- kwic(x = text, pattern = "confused")
  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.
  # inspect
kwic_alice %>%
as.data.frame() %>%
head(10)
  ##    docname from  to                         pre keyword
## 1    text1   14  14    I . Down the Rabbit-Hole   Alice
## 2    text1   73  73          a book , " thought   Alice
## 3    text1  153 153           in that ; nor did   Alice
## 4    text1  239 239       and then hurried on ,   Alice
## 5    text1  309 309 In another moment down went   Alice
## 6    text1  348 348     down , so suddenly that   Alice
## 7    text1  531 531          " Well ! " thought   Alice
## 8    text1  657 657             for , you see ,   Alice
## 9    text1  731 731                got to ? " (   Alice
## 10   text1  924 924             else to do , so   Alice
##                                   post pattern
## 1            was beginning to get very   alice
## 2  " without pictures or conversations   alice
## 3              think it so _very_ much   alice
## 4                started to her feet ,   alice
## 5                after it , never once   alice
## 6                  had not a moment to   alice
## 7                 to herself , " after   alice
## 8         had learnt several things of   alice
## 9            had no idea what Latitude   alice
## 10          soon began talking again .   alice
1. How many instances are there of the word wondering?
  kwic(x = text, pattern = "wondering") %>%
as.data.frame() %>%
nrow()
  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.
  ## [1] 7
1. Extract concordances for the word strange and show the first 5 concordance lines.
  kwic_strange <- kwic(x = text, pattern = "strange")
  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.
  # inspect
kwic_strange %>%
as.data.frame() %>%
head(5)
  ##   docname  from    to                          pre keyword
## 1   text1  3547  3547 her voice sounded hoarse and strange
## 2   text1 13273 13273         , that it felt quite strange
## 3   text1 33247 33247    remember them , all these strange
## 4   text1 33458 33458    her became alive with the strange
## 5   text1 33784 33784        and eager with many a strange
##                               post pattern
## 1              , and the words did strange
## 2               at first ; but she strange
## 3      Adventures of hers that you strange
## 4 creatures of her little sister's strange
## 5         tale , perhaps even with strange

## Extracting more than single words

While extracting single words is very common, you may want to extract more than just one word. To extract phrases, all you need to so is to specify that the pattern you are looking for is a phrase, as shown below.

kwic_pooralice <- kwic(text, pattern = phrase("poor alice"))
 docname from to pre keyword post pattern text1 1,555 1,556 go through , " thought poor Alice , " it would be poor alice text1 2,144 2,145 ; but , alas for poor Alice ! when she got to poor alice text1 2,346 2,347 use now , " thought poor Alice , " to pretend to poor alice text1 2,901 2,902 to the garden door . Poor Alice ! It was as much poor alice text1 3,624 3,625 right words , " said poor Alice , and her eyes filled poor alice text1 6,926 6,927 mean it ! " pleaded poor Alice . " But you're so poor alice text1 7,340 7,341 more ! " And here poor Alice began to cry again , poor alice text1 8,299 8,300 at home , " thought poor Alice , " when one wasn't poor alice text1 11,910 11,911 to it ! " pleaded poor Alice in a piteous tone . poor alice text1 19,287 19,288 " This answer so confused poor Alice , that she let the poor alice

You may also want to extract more or less fixed patterns rather than exact words or phrases. To search for patterns that allow variation rather than specific, exactly-defined words, you need to include regular expressions in your search pattern.

EXERCISE TIME!



1. Extract the first 10 concordances for the phrase the hatter.
  kwic_thehatter <- kwic(x = text, pattern = phrase("the hatter"))
  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.
  # inspect
kwic_thehatter %>%
as.data.frame() %>%
head(10)
  ##    docname  from    to                    pre    keyword
## 1    text1 16710 16711   wish I'd gone to see the Hatter
## 2    text1 16741 16742 and the March Hare and the Hatter
## 3    text1 16993 16994 wants cutting , " said the Hatter
## 4    text1 17039 17040     it's very rude . " The Hatter
## 5    text1 17187 17188         a bit ! " said the Hatter
## 6    text1 17312 17313      with you , " said the Hatter
## 7    text1 17347 17348  , which wasn't much . The Hatter
## 8    text1 17425 17426  days wrong ! " sighed the Hatter
## 9    text1 17476 17477         in as well , " the Hatter
## 10   text1 17591 17592 should it ? " muttered the Hatter
##                           post    pattern
## 1      instead ! " CHAPTER VII the hatter
## 2        were having tea at it the hatter
## 3        . He had been looking the hatter
## 4    opened his eyes very wide the hatter
## 5           . " You might just the hatter
## 6  , and here the conversation the hatter
## 7       was the first to break the hatter
## 8               . " I told you the hatter
## 9   grumbled : " you shouldn't the hatter
## 10       . " Does _your_ watch the hatter
1. How many instances are there of the phrase the hatter?
  kwic_thehatter %>%
as.data.frame() %>%
nrow()
  ## [1] 51
1. Extract concordances for the phrase the cat and show the first 5 concordance lines.
  kwic_thecat <- kwic(x = text, pattern = phrase("the cat"))
  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.
  # inspect
kwic_thecat %>%
as.data.frame() %>%
head(5)
  ##   docname  from    to               pre keyword                     post
## 1   text1   946   947   ! " ( Dinah was the cat             . ) " I hope
## 2   text1 15756 15757 a few yards off . The Cat only grinned when it saw
## 3   text1 15881 15882   get to , " said the Cat         . " I don't much
## 4   text1 15907 15908   you go , " said the Cat            . " - so long
## 5   text1 15937 15938  do that , " said the Cat          , " if you only
##   pattern
## 1 the cat
## 2 the cat
## 3 the cat
## 4 the cat
## 5 the cat

## Searches using regular expressions

Regular expressions allow you to search for abstract patterns rather than concrete words or phrases which provides you with an extreme flexibility in what you can retrieve. A regular expression (in short also called regex or regexp) is a special sequence of characters that stand for are that describe a pattern. You can think of regular expressions as very powerful combinations of wildcards or as wildcards on steroids. For example, the sequence [a-z]{1,3} is a regular expression that stands for one up to three lower case characters and if you searched for this regular expression, you would get, for instance, is, a, an, of, the, my, our, etc, and many other short words as results.

There are three basic types of regular expressions:

• regular expressions that stand for individual symbols and determine frequencies

• regular expressions that stand for classes of symbols

• regular expressions that stand for structural properties

The regular expressions below show the first type of regular expressions, i.e. regular expressions that stand for individual symbols and determine frequencies.

 RegEx Symbol/Sequence Explanation Example ? The preceding item is optional and will be matched at most once walk[a-z]? = walk, walks * The preceding item will be matched zero or more times walk[a-z]* = walk, walks, walked, walking + The preceding item will be matched one or more times walk[a-z]+ = walks, walked, walking {n} The preceding item is matched exactly n times walk[a-z]{2} = walked {n,} The preceding item is matched n or more times walk[a-z]{2,} = walked, walking {n,m} The preceding item is matched at least n times, but not more than m times walk[a-z]{2,3} = walked, walking

The regular expressions below show the second type of regular expressions, i.e. regular expressions that stand for classes of symbols.

 RegEx Symbol/Sequence Explanation [ab] lower case a and b [AB] upper case a and b [12] digits 1 and 2 [:digit:] digits: 0 1 2 3 4 5 6 7 8 9 [:lower:] lower case characters: a–z [:upper:] upper case characters: A–Z [:alpha:] alphabetic characters: a–z and A–Z [:alnum:] digits and alphabetic characters [:punct:] punctuation characters: . , ; etc. [:graph:] graphical characters: [:alnum:] and [:punct:] [:blank:] blank characters: Space and tab [:space:] space characters: Space, tab, newline, and other space characters [:print:] printable characters: [:alnum:], [:punct:] and [:space:]

The regular expressions that denote classes of symbols are enclosed in [] and :. The last type of regular expressions, i.e. regular expressions that stand for structural properties are shown below.

 RegEx Symbol/Sequence Explanation \\w Word characters: [[:alnum:]_] \\W No word characters: [^[:alnum:]_] \\s Space characters: [[:blank:]] \\S No space characters: [^[:blank:]] \\d Digits: [[:digit:]] \\D No digits: [^[:digit:]] \\b Word edge \\B No word edge < Word beginning > Word end ^ Beginning of a string $End of a string To include regular expressions in your KWIC searches, you include them in your search pattern and set the argument valuetype to "regex". The search pattern "\\balic.*|\\bhatt.*" retrieves elements that contain alic and hatt followed by any characters and where the a in alic and the h in hatt are at a word boundary, i.e. where they are the first letters of a word. Hence, our search would not retrieve words like malice or shatter. The | is an operator (like +, -, or *) that stands for or. # define search patterns patterns <- c("\\balic.*|\\bhatt.*") kwic_regex <- kwic( # define text text, # define search pattern patterns, # define valuetype valuetype = "regex")  docname from to pre keyword post pattern text1 1 1 Alice's Adventures in Wonderland by Lewis \balic.*|\bhatt.* text1 14 14 I . Down the Rabbit-Hole Alice was beginning to get very \balic.*|\bhatt.* text1 73 73 a book , " thought Alice " without pictures or conversations \balic.*|\bhatt.* text1 153 153 in that ; nor did Alice think it so _very_ much \balic.*|\bhatt.* text1 239 239 and then hurried on , Alice started to her feet , \balic.*|\bhatt.* text1 309 309 In another moment down went Alice after it , never once \balic.*|\bhatt.* text1 348 348 down , so suddenly that Alice had not a moment to \balic.*|\bhatt.* text1 531 531 " Well ! " thought Alice to herself , " after \balic.*|\bhatt.* text1 657 657 for , you see , Alice had learnt several things of \balic.*|\bhatt.* text1 731 731 got to ? " ( Alice had no idea what Latitude \balic.*|\bhatt.* EXERCISE TIME!  1. Extract the first 10 concordances for words containing exu. Answer  kwic_exu <- kwic(x = text, pattern = ".*exu.*", valuetype = "regex")  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.  # inspect kwic_exu %>% as.data.frame() %>% head(10)  ## [1] docname from to pre keyword post pattern ## <0 Zeilen> (oder row.names mit Länge 0) 1. How many instances are there of words beginning with pit? Answer  kwic(x = text, pattern = "\\bpit.*", valuetype = "regex") %>% as.data.frame() %>% nrow()  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.  ## [1] 5 1. Extract concordances for words ending with ption and show the first 5 concordance lines. Answer  kwic(x = text, pattern = "ption\\b", valuetype = "regex") %>% as.data.frame() %>% head(5)  ## Warning: 'kwic.character()' is deprecated. Use 'tokens()' first.  ## docname from to pre keyword ## 1 text1 5823 5823 adjourn , for the immediate adoption ## post pattern ## 1 of more energetic remedies - ption\\b  ## Piping concordances Quite often, we only want to retrieve patterns if they occur in a certain context. For instance, we might be interested in instances of selection but only if the preceding word is natural. Such conditional concordances could be extracted using regular expressions but they are easier to retrieve by piping. Piping is done using the %>% function from the dplyr package and the piping sequence can be translated as and then. We can then filter those concordances that contain natural using the filter function from the dplyr package. Note the the $ stands for the end of a string so that natural$means that natural is the last element in the string that is preceding the keyword. kwic_pipe <- kwic(x = text, pattern = "alice") %>% dplyr::filter(stringr::str_detect(pre, "poor$|little$"))  docname from to pre keyword post pattern text1 1,556 1,556 through , " thought poor Alice , " it would be alice text1 1,739 1,739 " but the wise little Alice was not going to do alice text1 2,145 2,145 but , alas for poor Alice ! when she got to alice text1 2,347 2,347 now , " thought poor Alice , " to pretend to alice text1 3,625 3,625 words , " said poor Alice , and her eyes filled alice text1 6,927 6,927 it ! " pleaded poor Alice . " But you're so alice text1 7,341 7,341 ! " And here poor Alice began to cry again , alice text1 8,300 8,300 home , " thought poor Alice , " when one wasn't alice text1 11,911 11,911 it ! " pleaded poor Alice in a piteous tone . alice text1 19,288 19,288 This answer so confused poor Alice , that she let the alice Piping is a very useful helper function and it is very frequently used in R - not only in the context of text processing but in all data science related domains. ## Arranging concordances and adding frequency information When inspecting concordances, it is useful to re-order the concordances so that they do not appear in the order that they appeared in the text or texts but by the context. To reorder concordances, we can use the arrange function from the dplyr package which takes the column according to which we want to re-arrange the data as it main argument. In the example below, we extract all instances of alice and then arrange the instances according to the content of the post column in alphabetical. kwic_ordered <- kwic(x = text, pattern = "alice") %>% dplyr::arrange(post)  docname from to pre keyword post pattern text1 31,131 31,131 voice , the name " Alice ! " CHAPTER XII . alice text1 8,497 8,497 " Oh , you foolish Alice ! " she answered herself alice text1 7,808 7,808 happen : " ' Miss Alice ! Come here directly , alice text1 2,902 2,902 the garden door . Poor Alice ! It was as much alice text1 2,145 2,145 but , alas for poor Alice ! when she got to alice text1 73 73 a book , " thought Alice " without pictures or conversations alice text1 2,620 2,620 and curiouser ! " cried Alice ( she was so much alice text1 26,047 26,047 I haven't , " said Alice ) - " and perhaps alice text1 2,959 2,959 of yourself , " said Alice , " a great girl alice text1 2,424 2,424 eat it , " said Alice , " and if it alice Arranging concordances according to alphabetical properties may, however, not be the most useful option. A more useful option may be to arrange concordances according to the frequency of co-occurring terms or collocates. In order to do this, we need to extract the co-occurring words and calculate their frequency. We can do this by combining the mutate, group_by, n() functions from the dplyr package with the str_remove_all function from the stringr package. Then, we arrange the concordances by the frequency of the collocates in descending order (that is why we put a - in the arrange function). In order to do this, we need to 1. create a new variable or column which represents the word that co-occurs with, or, as in the example below, immediately follows the search term. In the example below, we use the mutate function to create a new column called post_word. We then use the str_remove_all function to remove everything except for the word that immediately follows the search term (we simply remove everything and including a white space). 2. group the data by the word that immediately follows the search term. 3. create a new column called post_word_freq which represents the frequencies of all the words that immediately follow the search term. 4. arrange the concordances by the frequency of the collocates in descending order. kwic_ordered_coll <- kwic( # define text x = text, # define search pattern pattern = "alice") %>% # extract word following the keyword dplyr::mutate(post_word = str_remove_all(post, " .*")) %>% # group following words dplyr::group_by(post_word) %>% # extract frequencies of the following words dplyr::mutate(post_word_freq = n()) %>% # arrange/order by the frequency of the following word dplyr::arrange(-post_word_freq)  docname from to pre keyword post pattern post_word post_word_freq text1 348 348 down , so suddenly that Alice had not a moment to alice had 2 text1 657 657 for , you see , Alice had learnt several things of alice had 2 text1 14 14 I . Down the Rabbit-Hole Alice was beginning to get very alice was 1 text1 73 73 a book , " thought Alice " without pictures or conversations alice " 1 text1 153 153 in that ; nor did Alice think it so _very_ much alice think 1 text1 239 239 and then hurried on , Alice started to her feet , alice started 1 text1 309 309 In another moment down went Alice after it , never once alice after 1 text1 531 531 " Well ! " thought Alice to herself , " after alice to 1 We add more columns according to which we could arrange the concordance following the same schema. For example, we could add another column that represented the frequency of words that immediately preceded the search term and then arrange according to this column. ## Ordering by subsequent elements In this section, we will extract the three words following the keyword (alice) and organize the concordances by the frequencies of the following words. We begin by inspecting the first 6 lines of the concordance of selection. head(kwic_alice) ## Keyword-in-context with 6 matches. ## [text1, 14] I. Down the Rabbit-Hole | Alice | ## [text1, 73] a book," thought | Alice | ## [text1, 153] in that; nor did | Alice | ## [text1, 239] and then hurried on, | Alice | ## [text1, 309] In another moment down went | Alice | ## [text1, 348] down, so suddenly that | Alice | ## ## was beginning to get very ## " without pictures or conversations ## think it so _very_ much ## started to her feet, ## after it, never once ## had not a moment to Next, we take the concordances and create a clean post column that is all in lower case and that does not contain any punctuation. kwic_alice %>% # convert to data frame as.data.frame() %>% # create new CleanPost dplyr::mutate(CleanPost = stringr::str_remove_all(post, "[:punct:]"), CleanPost = stringr::str_squish(CleanPost), CleanPost = tolower(CleanPost))-> kwic_alice_following # inspect head(kwic_alice_following) ## docname from to pre keyword ## 1 text1 14 14 I . Down the Rabbit-Hole Alice ## 2 text1 73 73 a book , " thought Alice ## 3 text1 153 153 in that ; nor did Alice ## 4 text1 239 239 and then hurried on , Alice ## 5 text1 309 309 In another moment down went Alice ## 6 text1 348 348 down , so suddenly that Alice ## post pattern CleanPost ## 1 was beginning to get very alice was beginning to get very ## 2 " without pictures or conversations alice without pictures or conversations ## 3 think it so _very_ much alice think it so very much ## 4 started to her feet , alice started to her feet ## 5 after it , never once alice after it never once ## 6 had not a moment to alice had not a moment to In a next step, we extract the 1st, 2nd, and 3rd words following the keyword. kwic_alice_following %>% # extract first element after keyword dplyr::mutate(FirstWord = stringr::str_remove_all(CleanPost, " .*")) %>% # extract second element after keyword dplyr::mutate(SecWord = stringr::str_remove(CleanPost, ".*? "), SecWord = stringr::str_remove_all(SecWord, " .*")) %>% # extract third element after keyword dplyr::mutate(ThirdWord = stringr::str_remove(CleanPost, ".*? "), ThirdWord = stringr::str_remove(ThirdWord, ".*? "), ThirdWord = stringr::str_remove_all(ThirdWord, " .*")) -> kwic_alice_following # inspect head(kwic_alice_following) ## docname from to pre keyword ## 1 text1 14 14 I . Down the Rabbit-Hole Alice ## 2 text1 73 73 a book , " thought Alice ## 3 text1 153 153 in that ; nor did Alice ## 4 text1 239 239 and then hurried on , Alice ## 5 text1 309 309 In another moment down went Alice ## 6 text1 348 348 down , so suddenly that Alice ## post pattern CleanPost ## 1 was beginning to get very alice was beginning to get very ## 2 " without pictures or conversations alice without pictures or conversations ## 3 think it so _very_ much alice think it so very much ## 4 started to her feet , alice started to her feet ## 5 after it , never once alice after it never once ## 6 had not a moment to alice had not a moment to ## FirstWord SecWord ThirdWord ## 1 was beginning to ## 2 without pictures or ## 3 think it so ## 4 started to her ## 5 after it never ## 6 had not a Next, we calculate the frequencies of the subsequent words and order in descending order from the 1st to the 3rd word following the keyword. kwic_alice_following %>% # calculate frequency of following words # 1st word dplyr::group_by(FirstWord) %>% dplyr::mutate(FreqW1 = n()) %>% # 2nd word dplyr::group_by(SecWord) %>% dplyr::mutate(FreqW2 = n()) %>% # 3rd word dplyr::group_by(ThirdWord) %>% dplyr::mutate(FreqW3 = n()) %>% # ungroup dplyr::ungroup() %>% # arrange by following words dplyr::arrange(-FreqW1, -FreqW2, -FreqW3) -> kwic_alice_following # inspect results head(kwic_alice_following, 10) ## # A tibble: 10 × 14 ## docname from to pre keyword post pattern Clean…¹ First…² SecWord ## <chr> <int> <int> <chr> <chr> <chr> <fct> <chr> <chr> <chr> ## 1 text1 15840 15840 "so far , … Alice ", a… alice and sh… and she ## 2 text1 20942 20942 "be behead… Alice ", a… alice and sh… and she ## 3 text1 25847 25847 "quite a n… Alice ", a… alice and sh… and she ## 4 text1 33229 33229 "curious d… Alice ", a… alice and sh… and she ## 5 text1 33350 33350 ", and thi… Alice "and… alice and al… and all ## 6 text1 16498 16498 "said pig … Alice "; \… alice and i … and i ## 7 text1 3625 3625 "words , \… Alice ", a… alice and he… and her ## 8 text1 1692 1692 "here befo… Alice ", )… alice and ro… and round ## 9 text1 25955 25955 "eyes . He… Alice ", a… alice and tr… and tried ## 10 text1 6573 6573 "you know … Alice ", \… alice and wh… and why ## # … with 4 more variables: ThirdWord <chr>, FreqW1 <int>, FreqW2 <int>, ## # FreqW3 <int>, and abbreviated variable names ¹​CleanPost, ²​FirstWord The results now show the concordance arranged by the frequency of the words following the keyword. ## Concordances from transcriptions As many analyses use transcripts as their primary data and because transcripts have features that require additional processing, we will now perform concordancing based on on transcripts. As a first step, we load five example transcripts that represent the first five files from the Irish component of the International Corpus of English. # define corpus files files <- paste("https://slcladal.github.io/data/ICEIrelandSample/S1A-00", 1:5, ".txt", sep = "") # load corpus files transcripts <- sapply(files, function(x){ x <- readLines(x) })  . <#> Well how did the riding go tonight <#> It was good so it was <#> Just I I couldn't believe that she was going to let me jump <,> that was only the fourth time you know <#> It was great <&> laughter <#> What did you call your horse <#> I can't remember <#> Oh Mary 's Town <,> oh <#> And how did Mabel do <#> Did you not see her whenever she was going over the jumps <#> There was one time her horse refused and it refused three times <#> And then <,> she got it round and she just lined it up straight and she just kicked it and she hit it with the whip <,> and over it went the last time you know <#> And Stephanie told her she was very determined and very well-ridden <&> laughter because it had refused the other times you know <#> But Stephanie wouldn't let her give up on it <#> She made her keep coming back and keep coming back <,> until <,> it jumped it you know <#> It was good <#> Yeah I 'm not so sure her jumping 's improving that much <#> She uh <,> seemed to be holding the reins very tight The first ten lines shown above let us know that, after the header (<S1A-001 Riding>) and the symbol which indicates the start of the transcript (<I>), each utterance is preceded by a sequence which indicates the section, file, and speaker (e.g. <S1A-001$A>). The first utterance is thus uttered by speaker A in file 001 of section S1A. In addition, there are several sequences that provide meta-linguistic information which indicate the beginning of a speech unit (<#>), pauses (<,>), and laughter (<&> laughter </&>).

To perform the concordancing, we need to change the format of the transcripts because the kwic function only works on character, corpus, tokens object- in their present form, the transcripts represent a list which contains vectors of strings. To change the format, we collapse the individual utterances into a single character vector for each transcript.

transcripts_collapsed <- sapply(files, function(x){
# paste all lines together
x <- paste0(x, collapse = " ")
# remove superfluous white spaces
x <- str_squish(x)
})
 . <#> Well how did the riding go tonight <#> It was good so it was <#> Just I I couldn't believe that she was going to let me jump <,> that was only the fourth time you know <#> It was great <&> laughter <#> What did you call your horse <#> I can't remember <#> Oh Mary 's Town <,> oh <#> And how did Mabel do <#> Did you not see her whenever she was going over the jumps <#> There was one time her horse <#> He 's been married for three years and is now <{> <[> getting divorced <#> <[> No no he 's got married last year and he 's getting <{> <[> divorced <#> <[> He 's now getting divorced <#> Just right <#> A wee girl of her age like <#> Well there was a guy <#> How long did she try it for <#> An hour a a year <#> Mhm <{> <[> mhm <#> I <.> wa I want to go to Peru but uh <#> Do you <#> Oh aye <#> I 'd love to go to Peru <#> I want I want to go up the Machu Picchu before it falls off the edge of the mountain <#> Lima 's supposed to be a bit dodgy <#> Mm <#> Bet it would be <#> Mm <#> But I I just I I would like <,> Machu Picchu is collapsing <#> I don't know wh <#> Honest to God <,> I think the young ones <#> Sure they 're flying on Monday in I think it 's Shannon <#> This is from Texas <#> This English girl <#> The youngest one <,> the dentist <,> she 's married to the dentist <#> Herself and her husband <,> three children and she 's six months pregnant <#> Oh God <#> And where are they going <#> Coming to Dublin to the mother <{> <[> or 3 sy <#> Right shall we risk another beer or shall we try and <,> <{> <[> ride the bikes down there or do something like that <#> <[> Well <,> what about the provisions <#> What time <{> <[> 4 sylls <#> <[> Is is your man coming here <#> <{> <[> Yeah <#> <[> He said he would meet us here <#> Just the boat 's arriving you know a few minutes ' wa

We can now extract the concordances.

kwic_trans <- quanteda::kwic(
# tokenize transcripts
quanteda::tokens(transcripts_collapsed),
# define search pattern
pattern = phrase("you know"))
 docname from to pre keyword post pattern https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 62 63 was only the fourth time you know < # > It was you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 204 205 it went the last time you know < # > And Stephanie you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 235 236 had refused the other times you know < # > But Stephanie you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 272 273 , > it jumped it you know < # > It was you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 602 603 that one < , > you know and starting anew fresh < you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 665 666 { > < [ > you know < / [ > < you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 736 737 > We didn't discuss it you know < S1A-001 $A > you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 922 923 on Tuesday < , > you know < # > But I you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 1,126 1,127 that she could take her you know the wee shoulder bag she you know https://slcladal.github.io/data/ICEIrelandSample/S1A-001.txt 1,257 1,258 around < , > uhm you know their timetable and < , you know The results show that each non-alphanumeric character is counted as a single word which reduces the context of the keyword substantially. Also, the docname column contains the full path to the data which make it hard to parse the content of the table. To address the first issue, we specify the tokenizer that we will use to not disrupt the annotation too much. In addition, we clean the docname column and extract only the file name. Lastly, we will expand the context window to 10 so that we have a better understanding of the context in which the phrase was used. kwic_trans <- quanteda::kwic( # tokenize transcripts quanteda::tokens(transcripts_collapsed, what = "fasterword"), # define search pattern = phrase("you know"), # extend context window = 10) %>% # clean docnames dplyr::mutate(docname = str_replace_all(docname, ".*/([A-Z][0-9][A-Z]-[0-9]{1,3}).txt", "\\1"))  docname from to pre keyword post pattern S1A-001 42 43 let me jump <,> that was only the fourth time you know <#> It was great <&> laughter <#> What you know S1A-001 140 141 the whip <,> and over it went the last time you know <#> And Stephanie told her she was very determined and you know S1A-001 164 165 <&> laughter because it had refused the other times you know <#> But Stephanie wouldn't let her give up on it you know S1A-001 193 194 and keep coming back <,> until <,> it jumped it you know <#> It was good <#> Yeah I 'm not you know S1A-001 402 403 'd be far better waiting <,> for that one <,> you know and starting anew fresh <#> Yeah but I mean you know S1A-001 443 444 the best goes top of the league <,> <{> <[> you know <#> <[> So it 's like you know S1A-001 484 485 I 'm not sure now <#> We didn't discuss it you know <#> Well it sounds like more money <#> you know Extending the context can also be used to identify the speaker that has uttered the search pattern that we are interested in. We will do just that as this is a common task in linguistics analyses. To extract speakers, we need to follow these steps: 1. Create normal concordances of the pattern that we are interested in. 2. Generate concordances of the pattern that we are interested in with a substantially enlarged context window size. 3. Extract the speakers from the enlarged context window size. 4. Add the speakers to the normal concordances using the left-join function from the dplyr package. kwic_normal <- quanteda::kwic( # tokenize transcripts quanteda::tokens(transcripts_collapsed, what = "fasterword"), # define search pattern = phrase("you know")) %>% as.data.frame() kwic_speaker <- quanteda::kwic( # tokenize transcripts quanteda::tokens(transcripts_collapsed, what = "fasterword"), # define search pattern = phrase("you know"), # extend search window window = 500) %>% # convert to data frame as.data.frame() %>% # extract speaker (comes after$ and before >)
dplyr::mutate(speaker = stringr::str_replace_all(pre, ".*\\\$(.*?)>.*", "\\1")) %>%
# extract speaker
dplyr::pull(speaker)
# add speaker to normal kwic
kwic_combined <- kwic_normal %>%
dplyr::mutate(speaker = kwic_speaker) %>%
# simplify docname
dplyr::mutate(docname = stringr::str_replace_all(docname, ".*/([A-Z][0-9][A-Z]-[0-9]{1,3}).txt", "\\1")) %>%
# remove superfluous columns
dplyr::select(-to, -from, -pattern)
 docname pre keyword post speaker S1A-001 was only the fourth time you know <#> It was great <&> B S1A-001 it went the last time you know <#> And Stephanie told her B S1A-001 had refused the other times you know <#> But Stephanie wouldn't let B S1A-001 until <,> it jumped it you know <#> It was good B S1A-001 <,> for that one <,> you know and starting anew fresh B S1A-001 the league <,> <{> <[> you know <#> <[> So B S1A-001 <#> We didn't discuss it you know <#> Well it sounds B S1A-001 her lesson on Tuesday <,> you know <#> But I was keeping B S1A-001 that she could take her you know the wee shoulder bag she B S1A-001 show them around <,> uhm you know their timetable and <,> give B

The resulting table shows that we have successfully extracted the speakers (identified by the letters in the speaker column) and cleaned the file names (in the docnames column).

## Customizing concordances

As R represents a fully-fledged programming environment, we can, of course, also write our own, customized concordance function. The code below shows how you could go about doing so. Note, however, that this function only works if you enter more than a single file.

mykwic <- function(txts, pattern, context) {
# activate packages
require(stringr)
# list files
txts <- txts[stringr::str_detect(txts, pattern)]
conc <- sapply(txts, function(x) {
# determine length of text
lngth <- as.vector(unlist(nchar(x)))
# determine position of hits
idx <- str_locate_all(x, pattern)
idx <- idx[[1]]
ifelse(nrow(idx) >= 1, idx <- idx, return(NA))
# define start position of hit
token.start <- idx[,1]
# define end position of hit
token.end <- idx[,2]
# define start position of preceding context
pre.start <- ifelse(token.start-context < 1, 1, token.start-context)
# define end position of preceding context
pre.end <- token.start-1
# define start position of subsequent context
post.start <- token.end+1
# define end position of subsequent context
post.end <- ifelse(token.end+context > lngth, lngth, token.end+context)
# extract the texts defined by the positions
PreceedingContext <- substring(x, pre.start, pre.end)
Token <- substring(x, token.start, token.end)
SubsequentContext <- substring(x, post.start, post.end)
Id <- 1:length(Token)
conc <- cbind(Id, PreceedingContext, Token, SubsequentContext)
# return concordance
return(conc)
})
concdf <- do.call(rbind, conc) %>%
as.data.frame()
return(concdf)
}

We can now try if this function works by searching for the sequence you know in the transcripts that we have loaded earlier. One difference between the kwic function provided by the quanteda package and the customized concordance function used here is that the kwic function uses the number of words to define the context window, while the mykwic function uses the number of characters or symbols instead (which is why we use a notably higher number to define the context window).

kwic_youknow <- mykwic(transcripts_collapsed, "you know", 50)
 Id PreceedingContext Token SubsequentContext 1 to let me jump <,> that was only the fourth time you know <#> It was great <&> laughter <# 2 with the whip <,> and over it went the last time you know <#> And Stephanie told her she was very determine 3 ghter because it had refused the other times you know <#> But Stephanie wouldn't let her give up on it 4 k and keep coming back <,> until <,> it jumped it you know <#> It was good <#> Yeah I 'm not so 5 she 'd be far better waiting <,> for that one <,> you know and starting anew fresh <#> Yeah but 6 er 's the best goes top of the league <,> <{> <[> you know <#> <[> So it 's like

As this concordance function only works for more than one text, we split the text into chapters and assign each section a name.

# read in text
text_split <- text %>%
stringr::str_squish() %>%
stringr::str_split("[CHAPTER]{7,7} [XVI]{1,7}\\. ") %>%
unlist()
text_split <- text_split[which(nchar(text_split) > 2000)]
names(text_split) <- paste0("text", 1:length(text_split))
# inspect data
nchar(text_split)
##  text1  text2  text3  text4  text5  text6  text7  text8  text9 text10 text11
##  11331  10888   9137  13830  11767  13730  12564  13585  12527  11287  10292
## text12
##  11518

Now that we have named elements, we can search for the pattern poor alice. We also need to clean the concordance as some sections do not contain any instances of the search pattern. To clean the data, we select only the columns File, PreceedingContext, Token, and SubsequentContext and then remove all rows where information is missing.

mykwic_pooralice <- mykwic(text_split, "poor Alice", 50)
 Id PreceedingContext Token SubsequentContext 1 ; “and even if my head would go through,” thought poor Alice , “it would be of very little use without my shoul 2 d on going into the garden at once; but, alas for poor Alice ! when she got to the door, she found she had forg 3 to be two people. “But it’s no use now,” thought poor Alice , “to pretend to be two people! Why, there’s hardl 1 !” “I’m sure those are not the right words,” said poor Alice , and her eyes filled with tears again as she went 1 lking such nonsense!” “I didn’t mean it!” pleaded poor Alice . “But you’re so easily offended, you know!” The M 2 onder if I shall ever see you any more!” And here poor Alice began to cry again, for she felt very lonely and

You can go ahead and modify the customized concordance function to suit your needs.

# Citation & Session Info

Schweinberger, Martin. 2022. Concordancing with R. Brisbane: The University of Queensland. url: https://ladal.edu.au/kwics.html (Version 2022.11.15).

@manual{schweinberger2022kwics,
author = {Schweinberger, Martin},
title = {Concordancing with R},
year = {2022},
organization = "The University of Queensland, Australia. School of Languages and Cultures},
edition = {2011.11.15}
}
sessionInfo()
## R version 4.2.1 RC (2022-06-17 r82510 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8
## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
## [5] LC_TIME=German_Germany.utf8
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
## [1] flextable_0.8.2 stringr_1.4.1   dplyr_1.0.10    quanteda_3.2.2
##
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.2   xfun_0.32          bslib_0.4.0        purrr_0.3.4
##  [5] lattice_0.20-45    vctrs_0.4.1        generics_0.1.3     htmltools_0.5.3
##  [9] yaml_2.3.5         base64enc_0.1-3    utf8_1.2.2         rlang_1.0.4
## [13] jquerylib_0.1.4    pillar_1.8.1       glue_1.6.2         DBI_1.1.3
## [17] gdtools_0.2.4      uuid_1.1-0         lifecycle_1.0.1    zip_2.2.0
## [21] evaluate_0.16      knitr_1.40         fastmap_1.1.0      fansi_1.0.3
## [25] highr_0.9          Rcpp_1.0.9         cachem_1.0.6       RcppParallel_5.1.5
## [29] jsonlite_1.8.0     systemfonts_1.0.4  fastmatch_1.1-3    stopwords_2.3
## [33] digest_0.6.29      stringi_1.7.8      grid_4.2.1         cli_3.3.0
## [37] tools_4.2.1        magrittr_2.0.3     sass_0.4.2         klippy_0.0.0.9500
## [41] tibble_3.1.8       pkgconfig_2.0.3    ellipsis_0.3.2     Matrix_1.5-1
## [45] data.table_1.14.2  xml2_1.3.3         assertthat_0.2.1   rmarkdown_2.16
## [49] officer_0.4.4      rstudioapi_0.14    R6_2.5.1           compiler_4.2.1

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