Running examples in 'diffrprojects-Ex.R' failed
The error most likely occurred in:

> ### Name: diffrproject
> ### Title: class for diffrproject
> ### Aliases: diffrproject
> ### Keywords: data
> 
> ### ** Examples
> 
> 
> 
> 
> 
> ## Creating a Diffrprojects Instance
> 
> # To create a diffrproject we use the diffrproject creator object -
> # its simply an object with an function that knows how to create a project.
> 
> # Creating a project looks like this:
> 
> library(diffrprojects)
> dp <- diffrproject$new()
> 
> 
> # Et viola - we created a first, for now empty, project that we will
> # use throughout the tutorial.
> 
> 
> 
> 
> 
> ## Some Help Please
> 
> # To get a better idea about what this thing called *diffrproject* really is
> # you can consult its help page which gives a broad overview over its
> # capabilities:
> 
> ?diffrproject
diffrproject           package:diffrprojects           R Documentation

_c_l_a_s_s _f_o_r _d_i_f_f_r_p_r_o_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     class for diffrproject

_U_s_a_g_e:

     diffrproject
     
_F_o_r_m_a_t:

     'R6Class' creator object.

_V_a_l_u_e:

     Object of 'diffrproject'

_T_h_e _d_i_f_f_r_p_r_o_j_e_c_t_s _c_l_a_s_s _f_a_m_i_l_y:

     Diffrproject consists of an set of R6 classes that are conencted
     by inheritance. Each class handles a different set of
     functionalities that are modular.

     R6_rtext_extended A class that has nothing to do per se with
          diffrprojects. It merely adds some basic features to the base
          R6 class (debugging, hashing, getting fields and handling
          warnings and messages as well as listing content). This class
          is imported from rtext package

     dp_base [inherits from rtext::R6_rtext_extended] This class forms
          the foundation of all diffrpojects (dp_xxx) classes by
          implementing data fields for meta data, texts, data on texts,
          links between texts, alignment of text tokens, and data on
          the alignment of text tokens. Furthermore it implements
          methods add, delete, code, and link texts or to aggregate
          text data on text token level.

     dp_loadsave [inherits from dp_base] This class allows for loading
          and saving diffrprojects from and to Rdata files.

     dp_export [inherits from dp_loadsave] This class provides methods
          for exporting and importing to and from RSQLite.

     dp_align [inherits from dp_export] This is one of the workhorses
          of diffrprojects. The methods of this class allow for adding,
          deleting or computing alignments between text tokens (e.g.
          words or lines or sentences or characters or paragraphs, or
          some other way to split text into chunks).  Furthermore it
          allows to also assign data to individual alignments (a
          connection beween two token of text from different text
          versions).

     dp_inherit [inherits from dp_align] The text_data_inherit method
          added by this class allows to copy text data from one token
          of a text version to another token of another text version
          channeled through aligments with zero distance. Conflicting
          codings (a text might have multiple codings stemming from
          several links and from direct coding of the text) are
          resolved by the fact that text codings are accompanied by a
          hierarchy level that defaults to zero and gets decreased by
          one every time the coding is inherited by a token.

     diffrproject [inherits from dp_inherit] Just a wrapper inheriting
          from dp_inherit to have a less technical name at the end of
          the inheritance chain.

_E_x_a_m_p_l_e_s:

     ## Creating a Diffrprojects Instance
     
     # To create a diffrproject we use the diffrproject creator object -
     # its simply an object with an function that knows how to create a project.
     
     # Creating a project looks like this:
     
     library(diffrprojects)
     dp <- diffrproject$new()
     
     
     # Et viola - we created a first, for now empty, project that we will
     # use throughout the tutorial.
     
     
     
     
     
     ## Some Help Please
     
     # To get a better idea about what this thing called *diffrproject* really is
     # you can consult its help page which gives a broad overview over its
     # capabilities:
     
     ?diffrproject
     
     
     # Another way is to call the ls() method. This will present us with a
     # data frame listing all fields where data is stored and all the methods
     # (aka object specific functions) of our diffrprojects instance.
     # Those methods and fields located in *private* are not for the user
     # to mess around with while non-private (*self* aka public) data fields
     # can be read by the user and public methods can be triggered by the
     # user to manipulate the data or retrieve data in a specific format.
     
     
     dp$ls()
     
     
     # The base R class() function furthermore reveals from which classes the
     # diffrproject class inherits:
     
     class(dp)
     
     
     
     ## Adding Texts to Projects
     
     # Our diffrproject (`dp`) has one method called `text_add()` that allows to
     # add texts to the project. Basically the method can be used in three
     # different flavors: adding character vectors, adding texts stored on disk,
     # or by adding rtext objects (see rtext package:
     # https://CRAN.R-project.org/package=rtext; rtext objects are the way
     # individual texts are represented within diffrprojects).
     # For each of these used cases there is one option:
     # `text`, `text_file`, `rtext`; respectively.
     
     # Below are shown examples using each of these methods:
     
     
     #  **adding text files**
     
     test_file1 <- stringb:::test_file("rc_1_ch1.txt")
     test_file2 <- stringb:::test_file("rc_2_ch1.txt")
     dp$text_add(text_file = c(test_file1, test_file2) )
     
     
     # **adding rtext objects**
     
     test_file <- stringb:::test_file("rc_1_ch1.txt")
     rt <- rtext$new( text_file = test_file)
     dp$text_add(rtext = rt)
     
     
     # **adding character vectors**
     
     test_file1 <- stringb:::test_file("rc_1_ch1.txt")
     test_file2 <- stringb:::test_file("rc_2_ch1.txt")
     cv <- ""
     cv[1] <- text_read(test_file1, NULL)
     cv[2] <- text_read(test_file2, NULL)
     dp$text_add(text = cv)
     
     # In the last case make sure to put each text in one separate line.
     # Functions like readLines() or text_read() read in texts such that
     # each line corresponds to one element in a character vector. With e.g.
     # text_read()'s tokenize parameter to NULL the text will be read in as one
     # long string.
     
     
     
     
     ## Piping Methods
     
     # Now is a good time to mention a feature of diffrprojects that comes in
     # handy: All functions that do not explicitly extract data
     # (those usually have some 'get' as part of their name) do return the
     # object itself so that one can pipe together a series of method calls.
     
     # Consider the following example where we initiate a new diffrprojects
     # instance and add two texts in just one pipe:
     
     dp <-
       diffrproject$
       new()$
       text_add(text_version_1, name = "version1")$
       text_add(text_version_2, name = "version2")
     
     length(dp$text)
     
     
     
     
     
     
     ## Getting Infos About Texts
     
     # If we want to get some general overview about the texts gathered in our
     # project, we can use the text_meta_data() method to do so.
     # The method has no parameters and returns a data.frame with several
     # variables informing us about its source, length, encoding used for
     # storage, and its name.
     
     
     dp$text_meta_data()
     
     
     
     
     ## Showing Text
     
     # If you want to have a look at your texts you may do so by using the
     # text's own text_show methods. Per default this method only shows the
     # first 500 characters, but it can be set to higher numbers as well.
     
     
     dp$text$version1$text_show(length=1000)
     dp$text$version2$text_show(length=1000)
     
     
     
     
     ## Getting And Setting Infos About the Project
     
     # Similar to the text_meta_data() method we can access the projects
     # meta data via data fields meta and options. But contrary to the
     # text_meta_data() method that gathers data from all the texts within the
     # project and does not allow for manipulation of the data, the data
     # fields allow reading and writing.
     
     # First let us have a look and thereafter turn off the message
     # notification service:
     
     # **getting data fields**
     
     dp$options
     
     
     # **setting data fields**
     
     dp$options$verbose <- FALSE
     
     
     # (note, ask is deprecated and only remains for compatibility
     # reasons but has no function anymore)
     
     # Now it's time to have a look at the projects meta data.
     # It tells us when the project was created, which path to use for
     # SQLite exports, which path to use for saving data as in RData
     # format and what is the projects id. The id is a hash of a time stamp
     # as well as session information which should ensure uniqueness across
     # space and time.
     
     # All these values can be manipulated by the user to her liking.
     
     
     dp$meta
     
     dp$meta$file_path = "./diffrproject.RData"
     
     
     
     
     
     
     
     
     ## Deleting Texts
     
     # Of cause we can not only add texts but delete them from the project as
     # well. For this purpose there is the text_delete() method.
     
     # Let's just add two texts and delete one by providing its index number and
     # the second by providing its name to the text_delete() method.
     
     
     dp$text_add(text = "nonesense", "n1")
     dp$text_add(text = "nonesense", "n2")
     
     dp$text_delete(3)
     dp$text_delete("n2")
     
     length(dp$text)
     names(dp$text)
     
     
     
     
     
     
     
     
     ## Defining Relationships Between Texts: Linking
     
     # The purpose of diffrprojects is to enable data collection on the
     # difference of texts. Having filled a project with various texts,
     # there are endless possibilities to form pairs of text for comparison
     # and change measurement - where endless actually is equal to: $n^2-n$.
     
     # Linking can be done via the text_link method which accepts either
     # index numbers or text names for its from and to arguments
     # (a third argument delete will delete a specified link if set to TRUE).
     
     
     dp$text_link(from = 1, to = 2)
     dp$text_link(from = 1, to = 2, delete = TRUE)
     
     
     # If no arguments are specified, text_link will link the first text to
     # the second, the third to the fourth, the fourth to the fifths and so on.
     
     
     dp$text_link()
     
     
     
     # To get an idea of what links are currently specified, we can
     # directly access the link data field or/and ask R to transform the
     # list found there into a data.frame.
     
     
     dp$link
     
     dp$link %>% as.data.frame()
     
     
     
     
     
     
     
     
     
     ## Aligning Texts and Measuring Change
     
     # At the heart of each diffrproject lies the text_align method.
     # This method compares two texts and tries to align parts
     # of one text with parts of the other text. The first two
     # arguments (`t1` and `t2`) are for specifying which pair
     # of texts to compare - if left as-is, all text pairs that
     # are specified within the link data field will be aligned.
     
     # Text parts are arbitrary character spans defined by the
     # `tokenizer` argument. This argument expects a function splitting
     # text into a token data.frame. If the tokenizer argument
     # is left as-is, it will default to text_tokenize_lines function
     # from the stringb package.
     
     # Text tokens can be pre-processed before alignment. The `clean`
     # argument allows to hand over a function tranforming a charactr
     # vector of text tokens into their clean counterparts.
     
     # The `ignore` arguments expects a function that is able to
     # transform a character vector of tokens into a logical vector
     # of same length, indicating which tokens to ignore throughout
     # the alignment process and which to consider.
     
     # The next argument - `distance` - specifies which distance
     # metrics to use to calculate distances between strings.
     
     # Since the text_align method basically is a wrapper around
     # diff_align you can get more information via `?diff_align`
     # and since again diff_align is a wrapper around stringdist
     # from the stringdist package `?stringdist::stringdist` and
     # also ``?stringdist::`stringdist-metrics` `` will provide
     # further insights about possible metrics and how to use the
     # rest of the arguments to text_align (these are passed through
     # to stringdist).
     
     # Let's have an example using the Levenshtein distance to
     # calculate distances between tokens (lines per default).
     # Furthermore we allow the distance between two aligned tokens
     # to be as large as 15. Tokens which do not find a partner
     # below that distance are considered to have been deleted
     # or respectively inserted. Tokens which find a partner with
     # a non-zero distance which is not above the threshhold are
     # considered changes - transformations of one token into the other.
     
     # The following shows the resulting list of alignment data.frames.
     
     
     dp$text_align(distance = "lv", maxDist = 15)
     
     dp$alignment
     
     
     
     # To measure the change between those two texts we can e.g. aggregate
     # the distances by change type:
     
     
     sum_up_changes <- function(x){
     x %>%
     dplyr::group_by(type) %>%
     dplyr::summarise(sum_of_change = sum(distance))
     }
     
     lapply( dp$alignment, sum_up_changes)
     
     
     
     
     
     
     ## Coding Texts
     
     # Now let us put some data into our diffrproject.
     
     # The most basic method to do so is simply called text_code.
     # Text_code takes up
     # to five arguments (the first three are mandatory), where one
     # specifies the text to be coded (`text`, either by index
     # number or by name), how the variable to store the information
     # is called (`x`), and the index number or a vector of those
     # indicating which characters of the text should be coded.
     # The last two parameters are optional and specify which value
     # the variable should hold (`val`) and at which hierarchy
     # level the coding is placed (`hl`, higher or equal hierarchy
     # levels will overwrite existing codings of lower hierarchy
     # level for the same text, character span, and variable).
     
     
     dp$text_code(text = 1, x = "start", i=1:5, val = TRUE, hl = 0)
     dp$text_code(text = "version2", x = "start", i=1:5, val = TRUE, hl = 0)
     
     
     # The text_code method is quite verbose and in most cases more suited
     # to be accessed by a machine or algorithm than by a human.
     # Therefore, there are three other methods to code text:
     # text_code_regex, text_code_alignment_token,
     # text_code_alignment_token_regex.
     
     # The text_code_regex method allows to search for text patterns and
     # code a whole pattern instead of assigning codes character by
     # character - the `i` argument of text_code gets replaced by a
     # `pattern` argument. The in addition further arguments can be
     # passed to the pattern search functions via `...` - see e.g.
     # `?grep` for possible further arguments and
     # https://stat.ethz.ch/R-manual/R-devel/library/base/html/regex.html for a
     # description of regular expressions in R.
     
     # In this example we are searching for the word *"it"* in text 1 and code
     # each instance.
     
     
     dp$text_code_regex(text = 1, x = "it", pattern = "\\bit\\b", ignore.case=TRUE)
     
     
     # Another variant of coding text is by using alignment tokens.
     # Having alignment data availible, this allows for selecting:
     # link, alignment and text while the other arguments from above stay the
     # same.
     
     
     
     # having a look at alignment number 4
     
     dp$alignment[[1]][4,]
     
     
     # coding text connected by alignment number 4
     
     dp$text_code_alignment_token(
       link        = 1,
       alignment_i = 4,
       text1       = TRUE,
       text2       = TRUE,
       x   = "token_coding",
       val = 4,
       hl  = 0
     )
     
     
     
     
     
     
     
     
     ## Getting Text Codings
     
     # The most basic way to get text data is to use the text_data method.
     # This method will go through all or only selected texts, gather all
     # the data stored there and put it into a neat data.frame where name
     # identifies the text from which the data comes per name, char informs
     # us about the character that was coded, and i refers to the characters
     # position within the text. All other variables hold the data we added
     # during the examples above.
     
     
     dp$text_data(text = 1) %>% head()
     
     
     
     
     
     
     ## Aggregating Text Codings
     
     # The usage of text_data has its merits but often one is more
     # interested in text data aggregated to a specific level.
     # The following three aggregation functions offer a solution
     # to this problem: tokenize_text_data_lines, tokenize_text_data_words,
     # and tokenize_text_data_regex. These three methods make use
     # of the similiary named methods provided by the rtext package.
     
     # One important thing to keep in mind is that using these methods
     # implies aggregating several data values on character level
     # into one data value at token level. Therefore there has
     # to be some aggregation function to be involved. The default
     # is to use the value that occurs most often on character
     # level, if more than one distinct values occur more than
     # once the first is choosen.
     
     # The aggregation function can be changed to whatever function the
     # user seems appropriate by passing it to `aggregate_function`
     # - as long as it
     # reduces a vector of values into a vector with only one value.
     
     # The `join` argument allows to decide how text and data are
     # joined
     # into the resulting data.frame - left: all token, right: all data, full:
     # token with or without data and data with or without token.
     
     
     dp$tokenize_text_data_lines(
     text = 1,
     join = "right",
     aggregate_function =
     function(x){
     paste(x[1:3], collapse = ",")
     }
     )
     
     
     
     
     
     
     
     ## Text Coding Inheritence
     
     # Having aligned two texts via token pairs another functionality of
     # diffrprojects becomes availible: text coding inheritance via no-change
     # tokens. This means that text codings can get copied to those tokens they
     # are aligned with, given that they are considered the same - i.e. the
     # distance equals zero and the change type therefore is no-change.
     
     # To show this feature we use the text_inherit method and we will
     # start with a fresh example. A new project with two texts. The first text
     # gets some codings, then they are aligned, and in a last step codings are
     # transfered from one text to the other via the text_data_inherit method.
     
     
     
     dp <-
     diffrproject$new()$
     text_add(text_version_1)$
     text_add(text_version_2)$
     text_code_regex(
     text    = 1,
     x       = "test1",
     pattern = "This part.*?change",
     val = "inherited"
     )$
     text_code_regex(
     text    = 1,
     x       = "test2",
     pattern = "This part.*?change",
     val = "inherited"
     )
     
     dp$tokenize_text_data_lines(1)
     
     
     dp$
     text_link()$
     text_align()$
     text_data_inherit(
     link      = 1,
     direction = "forward"
     )
     
     dp$tokenize_text_data_lines(2)
     
     
     
     
     ## Saving and Loading Projects
     
     # Diffrprojects also allow for storing and loading project to and
     # from disk.
     
     # note, uncomment code lines to run
     
     # save to file
     # dp$save(file = "dp_save.RData")
     
     # remove object
     # rm(dp)
     
     # create new object and load saved data into new object
     # dp <- diffrproject$new()
     # dp$load("dp_save.RData")
     # dp$tokenize_text_data_lines(2)
     
> 
> 
> # Another way is to call the ls() method. This will present us with a
> # data frame listing all fields where data is stored and all the methods
> # (aka object specific functions) of our diffrprojects instance.
> # Those methods and fields located in *private* are not for the user
> # to mess around with while non-private (*self* aka public) data fields
> # can be read by the user and public methods can be triggered by the
> # user to manipulate the data or retrieve data in a specific format.
> 
> 
> dp$ls()
                        name   where                     class
1               execute_load private                  function
2                       hash private                  function
3                     hashed private                  function
5               prepare_save private                  function
4                     hashes private                      list
9             alignment_data    self alignment_data_list, list
6                  alignment    self      alignment_list, list
21                      link    self      alignment_list, list
7              alignment_add    self                  function
8             alignment_code    self                  function
10       alignment_data_full    self                  function
11        alignment_data_set    self                  function
12          alignment_delete    self                  function
13                     clone    self                  function
14                     debug    self                  function
15                export_csv    self                  function
16             export_sqlite    self                  function
17                       get    self                  function
18                import_csv    self                  function
19             import_sqlite    self                  function
20                initialize    self                  function
22                      load    self                  function
23                        ls    self                  function
24                   message    self                  function
27                      save    self                  function
29                  text_add    self                  function
30                text_align    self                  function
31                 text_code    self                  function
32 text_code_alignment_token    self                  function
33           text_code_regex    self                  function
34                 text_data    self                  function
35         text_data_inherit    self                  function
36               text_delete    self                  function
37                 text_link    self                  function
38            text_meta_data    self                  function
39  tokenize_text_data_lines    self                  function
40  tokenize_text_data_regex    self                  function
41  tokenize_text_data_words    self                  function
42                   warning    self                  function
25                      meta    self                      list
26                   options    self                      list
28                      text    self                      list
> 
> 
> # The base R class() function furthermore reveals from which classes the
> # diffrproject class inherits:
> 
> class(dp)
[1] "diffrproject"      "dp_inherit"        "dp_align"         
[4] "dp_export"         "rtext_loadsave"    "dp_base"          
[7] "R6_rtext_extended" "R6"               
> 
> 
> 
> ## Adding Texts to Projects
> 
> # Our diffrproject (`dp`) has one method called `text_add()` that allows to
> # add texts to the project. Basically the method can be used in three
> # different flavors: adding character vectors, adding texts stored on disk,
> # or by adding rtext objects (see rtext package:
> # https://CRAN.R-project.org/package=rtext; rtext objects are the way
> # individual texts are represented within diffrprojects).
> # For each of these used cases there is one option:
> # `text`, `text_file`, `rtext`; respectively.
> 
> # Below are shown examples using each of these methods:
> 
> 
> #  **adding text files**
> 
> test_file1 <- stringb:::test_file("rc_1_ch1.txt")
> test_file2 <- stringb:::test_file("rc_2_ch1.txt")
> dp$text_add(text_file = c(test_file1, test_file2) )
rtext : initializing
rtext : initializing
> 
> 
> # **adding rtext objects**
> 
> test_file <- stringb:::test_file("rc_1_ch1.txt")
> rt <- rtext$new( text_file = test_file)
rtext : initializing
> dp$text_add(rtext = rt)
> 
> 
> # **adding character vectors**
> 
> test_file1 <- stringb:::test_file("rc_1_ch1.txt")
> test_file2 <- stringb:::test_file("rc_2_ch1.txt")
> cv <- ""
> cv[1] <- text_read(test_file1, NULL)
> cv[2] <- text_read(test_file2, NULL)
> dp$text_add(text = cv)
rtext : initializing
rtext : initializing
> 
> # In the last case make sure to put each text in one separate line.
> # Functions like readLines() or text_read() read in texts such that
> # each line corresponds to one element in a character vector. With e.g.
> # text_read()'s tokenize parameter to NULL the text will be read in as one
> # long string.
> 
> 
> 
> 
> ## Piping Methods
> 
> # Now is a good time to mention a feature of diffrprojects that comes in
> # handy: All functions that do not explicitly extract data
> # (those usually have some 'get' as part of their name) do return the
> # object itself so that one can pipe together a series of method calls.
> 
> # Consider the following example where we initiate a new diffrprojects
> # instance and add two texts in just one pipe:
> 
> dp <-
+   diffrproject$
+   new()$
+   text_add(text_version_1, name = "version1")$
+   text_add(text_version_2, name = "version2")
rtext : initializing
rtext : initializing
> 
> length(dp$text)
[1] 2
> 
> 
> 
> 
> 
> 
> ## Getting Infos About Texts
> 
> # If we want to get some general overview about the texts gathered in our
> # project, we can use the text_meta_data() method to do so.
> # The method has no parameters and returns a data.frame with several
> # variables informing us about its source, length, encoding used for
> # storage, and its name.
> 
> 
> dp$text_meta_data()
  text_file character encoding sourcetype     name
1      <NA>       479    UTF-8       text version1
2      <NA>       539    UTF-8       text version2
> 
> 
> 
> 
> ## Showing Text
> 
> # If you want to have a look at your texts you may do so by using the
> # text's own text_show methods. Per default this method only shows the
> # first 500 characters, but it can be set to higher numbers as well.
> 
> 
> dp$text$version1$text_show(length=1000)
This part of the
document has stayed the
same from version to
version.  It shouldn't
be shown if it doesn't
change.  Otherwise, that
would not be helping to
compress the size of the
changes.

This paragraph contains
text that is outdated.
It will be deleted in the
near future.

It is important to spell
check this dokument. On
the other hand, a
misspelled word isn't
the end of the world.
Nothing in the rest of
this paragraph needs to
be changed. Things can
be added after it.
 > dp$text$version2$text_show(length=1000)
This is an important
notice! It should
therefore be located at
the beginning of this
document!

This part of the
document has stayed the
same from version to
version.  It shouldn't
be shown if it doesn't
change.  Otherwise, that
would not be helping to
compress anything.

It is important to spell
check this document. On
the other hand, a
misspelled word isn't
the end of the world.
Nothing in the rest of
this paragraph needs to
be changed. Things can
be added after it.

This paragraph contains
important new additions
to this document. > 
> 
> 
> 
> ## Getting And Setting Infos About the Project
> 
> # Similar to the text_meta_data() method we can access the projects
> # meta data via data fields meta and options. But contrary to the
> # text_meta_data() method that gathers data from all the texts within the
> # project and does not allow for manipulation of the data, the data
> # fields allow reading and writing.
> 
> # First let us have a look and thereafter turn off the message
> # notification service:
> 
> # **getting data fields**
> 
> dp$options
$verbose
[1] TRUE

$warning
[1] TRUE

$ask
[1] TRUE

> 
> 
> # **setting data fields**
> 
> dp$options$verbose <- FALSE
> 
> 
> # (note, ask is deprecated and only remains for compatibility
> # reasons but has no function anymore)
> 
> # Now it's time to have a look at the projects meta data.
> # It tells us when the project was created, which path to use for
> # SQLite exports, which path to use for saving data as in RData
> # format and what is the projects id. The id is a hash of a time stamp
> # as well as session information which should ensure uniqueness across
> # space and time.
> 
> # All these values can be manipulated by the user to her liking.
> 
> 
> dp$meta
$ts_created
[1] "2019-11-26 11:54:19 UTC"

$db_path
[1] "./diffrproject.db"

$file_path
[1] ""

$project_id
[1] "fd5e0af1da1301281d42743c3b8aee22"

> 
> dp$meta$file_path = "./diffrproject.RData"
> 
> 
> 
> 
> 
> 
> 
> 
> ## Deleting Texts
> 
> # Of cause we can not only add texts but delete them from the project as
> # well. For this purpose there is the text_delete() method.
> 
> # Let's just add two texts and delete one by providing its index number and
> # the second by providing its name to the text_delete() method.
> 
> 
> dp$text_add(text = "nonesense", "n1")
> dp$text_add(text = "nonesense", "n2")
> 
> dp$text_delete(3)
> dp$text_delete("n2")
> 
> length(dp$text)
[1] 2
> names(dp$text)
[1] "version1" "version2"
> 
> 
> 
> 
> 
> 
> 
> 
> ## Defining Relationships Between Texts: Linking
> 
> # The purpose of diffrprojects is to enable data collection on the
> # difference of texts. Having filled a project with various texts,
> # there are endless possibilities to form pairs of text for comparison
> # and change measurement - where endless actually is equal to: $n^2-n$.
> 
> # Linking can be done via the text_link method which accepts either
> # index numbers or text names for its from and to arguments
> # (a third argument delete will delete a specified link if set to TRUE).
> 
> 
> dp$text_link(from = 1, to = 2)
> dp$text_link(from = 1, to = 2, delete = TRUE)
> 
> 
> # If no arguments are specified, text_link will link the first text to
> # the second, the third to the fourth, the fourth to the fifths and so on.
> 
> 
> dp$text_link()
> 
> 
> 
> # To get an idea of what links are currently specified, we can
> # directly access the link data field or/and ask R to transform the
> # list found there into a data.frame.
> 
> 
> dp$link
$`version1~version2`
$`version1~version2`$from
[1] "version1"

$`version1~version2`$to
[1] "version2"


attr(,"class")
[1] "alignment_list" "list"          
> 
> dp$link %>% as.data.frame()
      from       to              link
1 version1 version2 version1~version2
> 
> 
> 
> 
> 
> 
> 
> 
> 
> ## Aligning Texts and Measuring Change
> 
> # At the heart of each diffrproject lies the text_align method.
> # This method compares two texts and tries to align parts
> # of one text with parts of the other text. The first two
> # arguments (`t1` and `t2`) are for specifying which pair
> # of texts to compare - if left as-is, all text pairs that
> # are specified within the link data field will be aligned.
> 
> # Text parts are arbitrary character spans defined by the
> # `tokenizer` argument. This argument expects a function splitting
> # text into a token data.frame. If the tokenizer argument
> # is left as-is, it will default to text_tokenize_lines function
> # from the stringb package.
> 
> # Text tokens can be pre-processed before alignment. The `clean`
> # argument allows to hand over a function tranforming a charactr
> # vector of text tokens into their clean counterparts.
> 
> # The `ignore` arguments expects a function that is able to
> # transform a character vector of tokens into a logical vector
> # of same length, indicating which tokens to ignore throughout
> # the alignment process and which to consider.
> 
> # The next argument - `distance` - specifies which distance
> # metrics to use to calculate distances between strings.
> 
> # Since the text_align method basically is a wrapper around
> # diff_align you can get more information via `?diff_align`
> # and since again diff_align is a wrapper around stringdist
> # from the stringdist package `?stringdist::stringdist` and
> # also ``?stringdist::`stringdist-metrics` `` will provide
> # further insights about possible metrics and how to use the
> # rest of the arguments to text_align (these are passed through
> # to stringdist).
> 
> # Let's have an example using the Levenshtein distance to
> # calculate distances between tokens (lines per default).
> # Furthermore we allow the distance between two aligned tokens
> # to be as large as 15. Tokens which do not find a partner
> # below that distance are considered to have been deleted
> # or respectively inserted. Tokens which find a partner with
> # a non-zero distance which is not above the threshhold are
> # considered changes - transformations of one token into the other.
> 
> # The following shows the resulting list of alignment data.frames.
> 
> 
> dp$text_align(distance = "lv", maxDist = 15)
> 
> dp$alignment
$`version1~version2`
   alignment_i token_i_1 token_i_2 distance      type from_1 to_1 from_2 to_2
1            1         1         6        0 no-change      1   16     97  112
2            2         2         7        0 no-change     18   40    114  136
3            3         3         8        0 no-change     42   61    138  157
4            4         4         9        0 no-change     63   84    159  180
5            5         5        10        0 no-change     86  107    182  203
6            6         6        11        0 no-change    109  132    205  228
7            7         7        12        0 no-change    134  156    230  252
8            8         8        13       14    change    158  181    254  271
9            9         9         5        8    change    183  190     86   94
11          10        10        23        0 no-change    193  215    475  497
12          11        11        25       13    change    217  238    523  539
13          12        12        NA       25  deletion    240  264     NA   NA
14          13        13         5       11    change    266  277     86   94
16          14        14        14        0 no-change    280  303    274  297
17          15        15        15        1    change    305  327    299  321
18          16        16        16        0 no-change    329  345    323  339
19          17        17        17        0 no-change    347  367    341  361
20          18        18        18        0 no-change    369  389    363  383
21          19        19        19        0 no-change    391  412    385  406
22          20        20        20        0 no-change    414  436    408  430
23          21        21        21        0 no-change    438  459    432  453
24          22        22        22        0 no-change    461  478    455  472
15          23        NA         1       20 insertion     NA   NA      1   20
25          24        NA         2       17 insertion     NA   NA     22   38
31          25        NA         3       23 insertion     NA   NA     40   62
41          26        NA         4       21 insertion     NA   NA     64   84
27          27        NA        24       23 insertion     NA   NA    499  521

attr(,"class")
[1] "alignment_list" "list"          
> 
> 
> 
> # To measure the change between those two texts we can e.g. aggregate
> # the distances by change type:
> 
> 
> sum_up_changes <- function(x){
+ x %>%
+ dplyr::group_by(type) %>%
+ dplyr::summarise(sum_of_change = sum(distance))
+ }
> 
> lapply( dp$alignment, sum_up_changes)
$`version1~version2`
# A tibble: 4 x 2
  type      sum_of_change
  <chr>             <dbl>
1 change               47
2 deletion             25
3 insertion           104
4 no-change             0

> 
> 
> 
> 
> 
> 
> ## Coding Texts
> 
> # Now let us put some data into our diffrproject.
> 
> # The most basic method to do so is simply called text_code.
> # Text_code takes up
> # to five arguments (the first three are mandatory), where one
> # specifies the text to be coded (`text`, either by index
> # number or by name), how the variable to store the information
> # is called (`x`), and the index number or a vector of those
> # indicating which characters of the text should be coded.
> # The last two parameters are optional and specify which value
> # the variable should hold (`val`) and at which hierarchy
> # level the coding is placed (`hl`, higher or equal hierarchy
> # levels will overwrite existing codings of lower hierarchy
> # level for the same text, character span, and variable).
> 
> 
> dp$text_code(text = 1, x = "start", i=1:5, val = TRUE, hl = 0)
> dp$text_code(text = "version2", x = "start", i=1:5, val = TRUE, hl = 0)
> 
> 
> # The text_code method is quite verbose and in most cases more suited
> # to be accessed by a machine or algorithm than by a human.
> # Therefore, there are three other methods to code text:
> # text_code_regex, text_code_alignment_token,
> # text_code_alignment_token_regex.
> 
> # The text_code_regex method allows to search for text patterns and
> # code a whole pattern instead of assigning codes character by
> # character - the `i` argument of text_code gets replaced by a
> # `pattern` argument. The in addition further arguments can be
> # passed to the pattern search functions via `...` - see e.g.
> # `?grep` for possible further arguments and
> # https://stat.ethz.ch/R-manual/R-devel/library/base/html/regex.html for a
> # description of regular expressions in R.
> 
> # In this example we are searching for the word *"it"* in text 1 and code
> # each instance.
> 
> 
> dp$text_code_regex(text = 1, x = "it", pattern = "\\bit\\b", ignore.case=TRUE)
> 
> 
> # Another variant of coding text is by using alignment tokens.
> # Having alignment data availible, this allows for selecting:
> # link, alignment and text while the other arguments from above stay the
> # same.
> 
> 
> 
> # having a look at alignment number 4
> 
> dp$alignment[[1]][4,]
  alignment_i token_i_1 token_i_2 distance      type from_1 to_1 from_2 to_2
4           4         4         9        0 no-change     63   84    159  180
> 
> 
> # coding text connected by alignment number 4
> 
> dp$text_code_alignment_token(
+   link        = 1,
+   alignment_i = 4,
+   text1       = TRUE,
+   text2       = TRUE,
+   x   = "token_coding",
+   val = 4,
+   hl  = 0
+ )
> 
> 
> 
> 
> 
> 
> 
> 
> ## Getting Text Codings
> 
> # The most basic way to get text data is to use the text_data method.
> # This method will go through all or only selected texts, gather all
> # the data stored there and put it into a neat data.frame where name
> # identifies the text from which the data comes per name, char informs
> # us about the character that was coded, and i refers to the characters
> # position within the text. All other variables hold the data we added
> # during the examples above.
> 
> 
> dp$text_data(text = 1) %>% head()
   i char start it token_coding     name
1  1    T  TRUE NA           NA version1
2  2    h  TRUE NA           NA version1
3  3    i  TRUE NA           NA version1
4  4    s  TRUE NA           NA version1
5  5       TRUE NA           NA version1
6 63    v    NA NA            4 version1
> 
> 
> 
> 
> 
> 
> ## Aggregating Text Codings
> 
> # The usage of text_data has its merits but often one is more
> # interested in text data aggregated to a specific level.
> # The following three aggregation functions offer a solution
> # to this problem: tokenize_text_data_lines, tokenize_text_data_words,
> # and tokenize_text_data_regex. These three methods make use
> # of the similiary named methods provided by the rtext package.
> 
> # One important thing to keep in mind is that using these methods
> # implies aggregating several data values on character level
> # into one data value at token level. Therefore there has
> # to be some aggregation function to be involved. The default
> # is to use the value that occurs most often on character
> # level, if more than one distinct values occur more than
> # once the first is choosen.
> 
> # The aggregation function can be changed to whatever function the
> # user seems appropriate by passing it to `aggregate_function`
> # - as long as it
> # reduces a vector of values into a vector with only one value.
> 
> # The `join` argument allows to decide how text and data are
> # joined
> # into the resulting data.frame - left: all token, right: all data, full:
> # token with or without data and data with or without token.
> 
> 
> dp$tokenize_text_data_lines(
+ text = 1,
+ join = "right",
+ aggregate_function =
+ function(x){
+ paste(x[1:3], collapse = ",")
+ }
+ )
Error in aggregate.data.frame(., by = list(token_i = token_i), FUN = aggregate_function,  : 
  arguments must have same length
Calls: <Anonymous> ... withVisible -> <Anonymous> -> <Anonymous> -> aggregate.data.frame
Execution halted