Package 'textshape' reference manual

Title:	Tools for Reshaping Text
Description:	Tools that can be used to reshape and restructure text data.
Authors:	Tyler Rinker [aut, cre], Joran Elias [ctb], Matthew Flickinger [ctb], Paul Foster [ctb]
Maintainer:	Tyler Rinker <[email protected]>
License:	GPL-2
Version:	1.7.6
Built:	2024-11-06 05:27:48 UTC
Source:	https://github.com/trinker/textshape

Row Bind a List of Named Dataframes or Vectors

Description

Deprecated, use tidy_list instead.

Usage

bind_list(x, id.name = "id", content.name = "content", ...)
bind_list(x, id.name = "id", content.name = "content", ...)

Arguments

`x`	A named `list` of `data.frame`s or `vector`.
`id.name`	The name to use for the column created from the `list`.
`content.name`	The name to use for the column created from the `list` of `vector`s (only used if `x` is `vector`).
`...`	ignored.

Value

Returns a data.table with the names from the list as an id column.

Examples

## Not run: 
bind_list(list(p=1:500, r=letters))
bind_list(list(p=mtcars, r=mtcars, z=mtcars, d=mtcars))

## 2015 Vice-Presidential Debates Example
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, magrittr, xml2)

debates <- c(
    wisconsin = "110908",
    boulder = "110906",
    california = "110756",
    ohio = "110489"
)

lapply(debates, function(x){
    xml2::read_html(paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x)) %>%
        rvest::html_nodes("p") %>%
        rvest::html_text() %>%
        textshape::split_index(grep("^[A-Z]+:", .)) %>%
        textshape::combine() %>%
        textshape::split_transcript() %>%
        textshape::split_sentence()
}) %>%
    textshape::bind_list("location")

## End(Not run)
## Not run: 
bind_list(list(p=1:500, r=letters))
bind_list(list(p=mtcars, r=mtcars, z=mtcars, d=mtcars))

## 2015 Vice-Presidential Debates Example
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, magrittr, xml2)

debates <- c(
    wisconsin = "110908",
    boulder = "110906",
    california = "110756",
    ohio = "110489"
)

lapply(debates, function(x){
    xml2::read_html(paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x)) %>%
        rvest::html_nodes("p") %>%
        rvest::html_text() %>%
        textshape::split_index(grep("^[A-Z]+:", .)) %>%
        textshape::combine() %>%
        textshape::split_transcript() %>%
        textshape::split_sentence()
}) %>%
    textshape::bind_list("location")

## End(Not run)

Column Bind a Table's Values with Its Names

Description

Deprecated, use tidy_table instead.

Usage

bind_table(x, id.name = "id", content.name = "content", ...)
bind_table(x, id.name = "id", content.name = "content", ...)

Arguments

`x`	A `table`.
`id.name`	The name to use for the column created from the `table` `names`.
`content.name`	The name to use for the column created from the `table` values.
`...`	ignored.

Value

Returns a data.table with the names from the table as an id column.

Examples

## Not run: 
x <- table(sample(LETTERS[1:6], 1000, TRUE))
bind_table(x)

## End(Not run)
## Not run: 
x <- table(sample(LETTERS[1:6], 1000, TRUE))
bind_table(x)

## End(Not run)

Column Bind an Atomic Vector's Values with Its Names

Description

Deprecated, use tidy_vector instead.

Usage

bind_vector(x, id.name = "id", content.name = "content", ...)
bind_vector(x, id.name = "id", content.name = "content", ...)

Arguments

`x`	A named atomic `vector`.
`id.name`	The name to use for the column created from the `vector` `names`.
`content.name`	The name to use for the column created from the `vector` values.
`...`	ignored.

Value

Returns a data.table with the names from the vector as an id column.

Examples

## Not run: 
x <- setNames(sample(LETTERS[1:6], 1000, TRUE), sample(state.name[1:5], 1000, TRUE))
bind_vector(x)

## End(Not run)
## Not run: 
x <- setNames(sample(LETTERS[1:6], 1000, TRUE), sample(state.name[1:5], 1000, TRUE))
bind_vector(x)

## End(Not run)

Indexing of Changes in Runs

Description

Find the indices of changes in runs in a vector. This function pairs well with split_index and is the default for the indices in all split_index functions that act on atomic vectors.

Usage

change_index(x, ...)
change_index(x, ...)

Arguments

`x`	A vector.
`...`	ignored.

Value

Returns a vector of integer indices of where a vector initially changes.

Examples

set.seed(10)
(x <- sample(0:1, 20, TRUE))
change_index(x)
split_index(x, change_index(x))


(p_chng <- change_index(CO2[["Plant"]]))
split_index(CO2[["Plant"]], p_chng)
set.seed(10)
(x <- sample(0:1, 20, TRUE))
change_index(x)
split_index(x, change_index(x))


(p_chng <- change_index(CO2[["Plant"]]))
split_index(CO2[["Plant"]], p_chng)

Reorder a Matrix Based on Hierarchical Clustering

Description

Reorder matrix rows, columns, or both via hierarchical clustering.

Usage

cluster_matrix(x, dim = "both", method = "ward.D2", ...)
cluster_matrix(x, dim = "both", method = "ward.D2", ...)

Arguments

`x`	A matrix.
`dim`	The dimension to reorder (cluster); must be set to "row", "col", or "both".
`method`	The agglomeration method to be used (see `hclust`).
`...`	ignored.

Value

Returns a reordered matrix.

Examples

cluster_matrix(mtcars)
cluster_matrix(mtcars, dim = 'row')
cluster_matrix(mtcars, dim = 'col')

## Not run: 
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, viridis, gridExtra)

## plot heatmap w/o clustering
wo <- mtcars %>%
    cor() %>%
    tidy_matrix('car', 'var') %>%
    ggplot(aes(var, car, fill = value)) +
         geom_tile() +
         scale_fill_viridis(name = expression(r[xy])) +
         theme(
             axis.text.y = element_text(size = 8)   ,
             axis.text.x = element_text(
                 size = 8, 
                 hjust = 1, 
                 vjust = 1, 
                 angle = 45
             ),   
             legend.position = 'bottom',
             legend.key.height = grid::unit(.1, 'cm'),
             legend.key.width = grid::unit(.5, 'cm')
         ) +
         labs(subtitle = "With Out Clustering")

## plot heatmap w clustering
w <- mtcars %>%
    cor() %>%
    cluster_matrix() %>%
    tidy_matrix('car', 'var') %>%
    mutate(
        var = factor(var, levels = unique(var)),
        car = factor(car, levels = unique(car))        
    ) %>%
    group_by(var) %>%
    ggplot(aes(var, car, fill = value)) +
         geom_tile() +
         scale_fill_viridis(name = expression(r[xy])) +
         theme(
             axis.text.y = element_text(size = 8)   ,
             axis.text.x = element_text(
                 size = 8, 
                 hjust = 1, 
                 vjust = 1, 
                 angle = 45
             ),   
             legend.position = 'bottom',
             legend.key.height = grid::unit(.1, 'cm'),
             legend.key.width = grid::unit(.5, 'cm')               
         ) +
         labs(subtitle = "With Clustering")

gridExtra::grid.arrange(wo, w, ncol = 2)

## End(Not run)
cluster_matrix(mtcars)
cluster_matrix(mtcars, dim = 'row')
cluster_matrix(mtcars, dim = 'col')

## Not run: 
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, viridis, gridExtra)

## plot heatmap w/o clustering
wo <- mtcars %>%
    cor() %>%
    tidy_matrix('car', 'var') %>%
    ggplot(aes(var, car, fill = value)) +
         geom_tile() +
         scale_fill_viridis(name = expression(r[xy])) +
         theme(
             axis.text.y = element_text(size = 8)   ,
             axis.text.x = element_text(
                 size = 8, 
                 hjust = 1, 
                 vjust = 1, 
                 angle = 45
             ),   
             legend.position = 'bottom',
             legend.key.height = grid::unit(.1, 'cm'),
             legend.key.width = grid::unit(.5, 'cm')
         ) +
         labs(subtitle = "With Out Clustering")

## plot heatmap w clustering
w <- mtcars %>%
    cor() %>%
    cluster_matrix() %>%
    tidy_matrix('car', 'var') %>%
    mutate(
        var = factor(var, levels = unique(var)),
        car = factor(car, levels = unique(car))        
    ) %>%
    group_by(var) %>%
    ggplot(aes(var, car, fill = value)) +
         geom_tile() +
         scale_fill_viridis(name = expression(r[xy])) +
         theme(
             axis.text.y = element_text(size = 8)   ,
             axis.text.x = element_text(
                 size = 8, 
                 hjust = 1, 
                 vjust = 1, 
                 angle = 45
             ),   
             legend.position = 'bottom',
             legend.key.height = grid::unit(.1, 'cm'),
             legend.key.width = grid::unit(.5, 'cm')               
         ) +
         labs(subtitle = "With Clustering")

gridExtra::grid.arrange(wo, w, ncol = 2)

## End(Not run)

Takes an existing column and uses it as rownames instead. This is useful when turning a data.frame into a matrix. Inspired by the tibble package's column_to_row which is now deprecated if done on a tibble object. By coercing to a data.frame this problem is avoided.

Usage

column_to_rownames(x, loc = 1)
column_to_rownames(x, loc = 1)

Arguments

`x`	An object that can be coerced to a `data.frame`.
`loc`	The column location as either an integer or string index location. Must be unique row names.

Value

Returns a data.frame with the specified column moved to rownames.

Examples

state_dat <- data.frame(state.name, state.area, state.center, state.division)
column_to_rownames(state_dat)
column_to_rownames(state_dat, 'state.name')
state_dat <- data.frame(state.name, state.area, state.center, state.division)
column_to_rownames(state_dat)
column_to_rownames(state_dat, 'state.name')

Combine Elements

Description

Combine (paste) elements (vectors, lists, or data.frames) together with collapse = TRUE.

Usage

combine(x, ...)

## Default S3 method:
combine(x, fix.punctuation = TRUE, ...)

## S3 method for class 'data.frame'
combine(x, text.var = TRUE, ...)
combine(x, ...)

## Default S3 method:
combine(x, fix.punctuation = TRUE, ...)

## S3 method for class 'data.frame'
combine(x, text.var = TRUE, ...)

Arguments

`x`	A `data.frame` or character vector with runs.
`fix.punctuation`	logical If `TRUE` spaces before/after punctuation that should not be are a removed (regex used: `"(\s+(?=[,.?!;:%-]))\|((?<=[$-])\s+)"`).
`text.var`	The name of the text variable.
`...`	Ignored.

Value

Returns a vector (if given a list/vector) or an expanded data.table with elements pasted together.

Examples

(x <- split_token(DATA[["state"]][1], FALSE))
combine(x)

(x2 <- split_token(DATA[["state"]], FALSE))
combine(x2)

(x3 <- split_sentence(DATA))

## without dropping the non-group variable column
combine(x3)

## Dropping the non-group variable column
combine(x3[, 1:5, with=FALSE])
(x <- split_token(DATA[["state"]][1], FALSE))
combine(x)

(x2 <- split_token(DATA[["state"]], FALSE))
combine(x2)

(x3 <- split_sentence(DATA))

## without dropping the non-group variable column
combine(x3)

## Dropping the non-group variable column
combine(x3[, 1:5, with=FALSE])

Fictitious Classroom Dialogue

Description

A fictitious dataset useful for small demonstrations.

Usage

data(DATA)
data(DATA)

Format

A data frame with 11 rows and 5 variables

Details

person. Speaker
sex. Gender
adult. Dummy coded adult (0-no; 1-yes)
state. Statement (dialogue)
code. Dialogue coding scheme

Duration of Turns of Talk

Description

duration - Calculate duration (start and end times) for duration of turns of talk measured in words.

startss - Calculate start times from a numeric vector.

ends - Calculate end times from a numeric vector.

Usage

duration(x, ...)

## Default S3 method:
duration(x, grouping.var = NULL, ...)

## S3 method for class 'data.frame'
duration(x, text.var = TRUE, ...)

## S3 method for class 'numeric'
duration(x, ...)

starts(x, ...)

ends(x, ...)
duration(x, ...)

## Default S3 method:
duration(x, grouping.var = NULL, ...)

## S3 method for class 'data.frame'
duration(x, text.var = TRUE, ...)

## S3 method for class 'numeric'
duration(x, ...)

starts(x, ...)

ends(x, ...)

Arguments

`x`	A `data.frame` or character vector with a text variable or a numeric vector.
`grouping.var`	The grouping variables. Default `NULL` generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.
`text.var`	The name of the text variable. If `TRUE` `duration` tries to detect the text column.
`...`	Ignored.

Value

Returns a vector or data frame of starts and/or ends.

Examples

(x <- c(
    "Mr. Brown comes! He says hello. i give him coffee.",
    "I'll go at 5 p. m. eastern time.  Or somewhere in between!",
    "go there"
))
duration(x)
group <- c("A", "B", "A")
duration(x, group)

groups <- list(group1 = c("A", "B", "A"), group2 = c("red", "red", "green"))
duration(x, groups)

data(DATA)
duration(DATA)

## Larger data set
duration(hamlet)

## Integer values
x <- sample(1:10, 10)
duration(x)
starts(x)
ends(x)
(x <- c(
    "Mr. Brown comes! He says hello. i give him coffee.",
    "I'll go at 5 p. m. eastern time.  Or somewhere in between!",
    "go there"
))
duration(x)
group <- c("A", "B", "A")
duration(x, group)

groups <- list(group1 = c("A", "B", "A"), group2 = c("red", "red", "green"))
duration(x, groups)

data(DATA)
duration(DATA)

## Larger data set
duration(hamlet)

## Integer values
x <- sample(1:10, 10)
duration(x)
starts(x)
ends(x)

Flatten a Nested List of Vectors Into a Single Tier List of Vectors

Description

Flatten a named, nested list of atomic vectors to a single level using the concatenated list/atomic vector names as the names of the single tiered list.

Usage

flatten(x, sep = "_", ...)
flatten(x, sep = "_", ...)

Arguments

`x`	A nested, named list of vectors.
`sep`	A separator to use for the concatenation of the names from the nested list.
`...`	ignored.

Value

Returns a flattened list.

Note

The order of the list is sorted alphabetically. Pull requests for the option to return the original order would be appreciated.

Author(s)

StackOverflow user @Michael and Paul Foster and Tyler Rinker <[email protected]>.

References

https://stackoverflow.com/a/41882883/1000343
https://stackoverflow.com/a/48357114/1000343

Examples

x <- list(
    urban = list(
        cars = c('volvo', 'ford'),
        food.dining = list(
            local.business = c('carls'),
            chain.business = c('dennys', 'panera')
        )
    ),
    rural = list(
        land.use = list(
            farming =list(
                dairy = c('cows'),
                vegie.plan = c('carrots')
            )
        ),
        social.rec = list(
            community.center = c('town.square')
        ),
        people.type = c('good', 'bad', 'in.between')
    ),
    other.locales = c('suburban'),
    missing = list(
        unknown = c(),
        known = c()
    ),
    end = c('wow')
)

x

flatten(x)
flatten(x, ' -> ')
x <- list(
    urban = list(
        cars = c('volvo', 'ford'),
        food.dining = list(
            local.business = c('carls'),
            chain.business = c('dennys', 'panera')
        )
    ),
    rural = list(
        land.use = list(
            farming =list(
                dairy = c('cows'),
                vegie.plan = c('carrots')
            )
        ),
        social.rec = list(
            community.center = c('town.square')
        ),
        people.type = c('good', 'bad', 'in.between')
    ),
    other.locales = c('suburban'),
    missing = list(
        unknown = c(),
        known = c()
    ),
    end = c('wow')
)

x

flatten(x)
flatten(x, ' -> ')

Prepare Discourse Data for Network Plotting

Description

from_to - Add the next speaker as the from variable in a to/from network data structure. Assumes that the flow of discourse is coming from person A to person B, or at the very least the talk is taken up by person B. Works by taking the vector of speakers and shifting everything down one and then adding a closing element.

from_to_summarize - A wrapper for from_to.data.frame that adds a word.count column and then combines duplicate rows.

Usage

from_to(x, ...)

## Default S3 method:
from_to(x, final = "End", ...)

## S3 method for class 'character'
from_to(x, final = "End", ...)

## S3 method for class 'factor'
from_to(x, final = "End", ...)

## S3 method for class 'numeric'
from_to(x, final = "End", ...)

## S3 method for class 'data.frame'
from_to(x, from.var, final = "End", ...)

from_to_summarize(x, from.var, id.vars = NULL, text.var = TRUE, ...)
from_to(x, ...)

## Default S3 method:
from_to(x, final = "End", ...)

## S3 method for class 'character'
from_to(x, final = "End", ...)

## S3 method for class 'factor'
from_to(x, final = "End", ...)

## S3 method for class 'numeric'
from_to(x, final = "End", ...)

## S3 method for class 'data.frame'
from_to(x, from.var, final = "End", ...)

from_to_summarize(x, from.var, id.vars = NULL, text.var = TRUE, ...)

Arguments

`x`	A data form `vector` or `data.frame`.
`final`	The name of the closing element or node.
`from.var`	A character string naming the column to be considered the origin of the talk.
`id.vars`	The variables that correspond to the speaker or are attributes of the speaker (from variable).
`text.var`	The name of the text variable. If `TRUE` `duration` tries to detect the text column.
`...`	Ignored.

Value

Returns a vector (if given a vector) or an augmented data.table.

Examples

from_to(DATA, 'person')
from_to_summarize(DATA, 'person')
from_to_summarize(DATA, 'person', c('sex', 'adult'))
## Not run: 
if (!require("pacman")) install.packages("pacman"); library(pacman)
p_load(dplyr, geomnet, qdap, stringi, scales)
p_load_current_gh('trinker/textsahpe')

dat <- from_to_summarize(DATA, 'person', c('sex', 'adult')) %>%
    mutate(words = rescale(word.count, c(.5, 1.5)))

dat %>%
    ggplot(aes(from_id = from, to_id = to)) +
        geom_net(
            aes(linewidth = words),
            layout.alg = "fruchtermanreingold",
            directed = TRUE,
            labelon = TRUE,
            size = 1,
            labelcolour = 'black',
            ecolour = "grey70",
            arrowsize = 1,
            curvature = .1
        ) +
        theme_net() +
        xlim(c(-0.05, 1.05))

## End(Not run)
from_to(DATA, 'person')
from_to_summarize(DATA, 'person')
from_to_summarize(DATA, 'person', c('sex', 'adult'))
## Not run: 
if (!require("pacman")) install.packages("pacman"); library(pacman)
p_load(dplyr, geomnet, qdap, stringi, scales)
p_load_current_gh('trinker/textsahpe')

dat <- from_to_summarize(DATA, 'person', c('sex', 'adult')) %>%
    mutate(words = rescale(word.count, c(.5, 1.5)))

dat %>%
    ggplot(aes(from_id = from, to_id = to)) +
        geom_net(
            aes(linewidth = words),
            layout.alg = "fruchtermanreingold",
            directed = TRUE,
            labelon = TRUE,
            size = 1,
            labelcolour = 'black',
            ecolour = "grey70",
            arrowsize = 1,
            curvature = .1
        ) +
        theme_net() +
        xlim(c(-0.05, 1.05))

## End(Not run)

Sentence Boundary Disambiguation Edge Cases

Description

A slightly filtered dataset containing Dias's sentence boundary disambiguation edge cases. This is a nested data set with the outcome column as a nested list of desired splits. The non-ASCII cases and spaced ellipsis examples have been removed.

Usage

data(golden_rules)
data(golden_rules)

Format

A data frame with 45 rows and 3 variables

Details

Rule. The name of the rule to test
Text. The testing text
Outcome. The desired outcome of the sentence disambiguation

References

Dias, Kevin S. 2015. Golden Rules (English). Retrieved: https://s3.amazonaws.com/tm-town-nlp-resources/golden_rules.txt

Get Elements Matching Between 2 Points

Description

Use regexes to get all the elements between two points.

Usage

grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'character'
grab_index(x, from = NULL, to = NULL, ...)

## Default S3 method:
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'list'
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'data.frame'
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'matrix'
grab_index(x, from = NULL, to = NULL, ...)
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'character'
grab_index(x, from = NULL, to = NULL, ...)

## Default S3 method:
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'list'
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'data.frame'
grab_index(x, from = NULL, to = NULL, ...)

## S3 method for class 'matrix'
grab_index(x, from = NULL, to = NULL, ...)

Arguments

`x`	A character vector, `data.frame`, or list.
`from`	An integer to start from (if `NULL` defaults to the first element/row).
`to`	A integer to get up to (if `NULL` defaults to the last element/row).
`...`	ignored.

Value

Returns a subset of the original data set.

Examples

grab_index(DATA, from = 2, to = 4)
grab_index(DATA$state, from = 2, to = 4)
grab_index(DATA$state, from = 2)
grab_index(DATA$state, to = 4)
grab_index(matrix(1:100, nrow = 10), 2, 4)
grab_index(DATA, from = 2, to = 4)
grab_index(DATA$state, from = 2, to = 4)
grab_index(DATA$state, from = 2)
grab_index(DATA$state, to = 4)
grab_index(matrix(1:100, nrow = 10), 2, 4)

Get Elements Matching Between 2 Points

Description

Use regexes to get all the elements between two points.

Usage

grab_match(x, from = NULL, to = NULL, from.n = 1, to.n = 1, ...)

## S3 method for class 'character'
grab_match(x, from = NULL, to = NULL, from.n = 1, to.n = 1, ...)

## S3 method for class 'list'
grab_match(x, from = NULL, to = NULL, from.n = 1, to.n = 1, ...)

## S3 method for class 'data.frame'
grab_match(
  x,
  from = NULL,
  to = NULL,
  from.n = 1,
  to.n = 1,
  text.var = TRUE,
  ...
)
grab_match(x, from = NULL, to = NULL, from.n = 1, to.n = 1, ...)

## S3 method for class 'character'
grab_match(x, from = NULL, to = NULL, from.n = 1, to.n = 1, ...)

## S3 method for class 'list'
grab_match(x, from = NULL, to = NULL, from.n = 1, to.n = 1, ...)

## S3 method for class 'data.frame'
grab_match(
  x,
  from = NULL,
  to = NULL,
  from.n = 1,
  to.n = 1,
  text.var = TRUE,
  ...
)

Arguments

`x`	A character vector, `data.frame`, or list.
`from`	A regex to start getting from (if `NULL` defaults to the first element/row).
`to`	A regex to get up to (if `NULL` defaults to the last element/row).
`from.n`	If more than one element matches `from` this dictates which one should be used. Must be an integer up to the number of possible matches, `'first'` (equal to `1`), `'last'` (the last match possible), or `'n'` (the same as `'last'`).
`to.n`	If more than one element matches `to` this dictates which one should be used. Must be an integer up to the number of possible matches, `'first'` (equal to `1`), `'last'` (the last match possible), or `'n'` (the same as `'last'`).
`text.var`	The name of the text variable with matches. If `TRUE` `grab_match` tries to detect the text column.
`...`	Other arguments passed to `grep`, most notable is `ignore.case`.

Value

Returns a subset of the original data set.

Examples

grab_match(DATA$state, from = 'dumb', to = 'liar')
grab_match(DATA$state, from = 'dumb')
grab_match(DATA$state, to = 'liar')
grab_match(DATA$state, from = 'no', to = 'the', ignore.case = TRUE)
grab_match(DATA$state, from = 'no', to = 'the', ignore.case = TRUE, 
    from.n = 'first', to.n = 'last')
grab_match(as.list(DATA$state), from = 'dumb', to = 'liar')

## Data.frame: attempts to find text.var
grab_match(DATA, from = 'dumb', to = 'liar')
grab_match(DATA$state, from = 'dumb', to = 'liar')
grab_match(DATA$state, from = 'dumb')
grab_match(DATA$state, to = 'liar')
grab_match(DATA$state, from = 'no', to = 'the', ignore.case = TRUE)
grab_match(DATA$state, from = 'no', to = 'the', ignore.case = TRUE, 
    from.n = 'first', to.n = 'last')
grab_match(as.list(DATA$state), from = 'dumb', to = 'liar')

## Data.frame: attempts to find text.var
grab_match(DATA, from = 'dumb', to = 'liar')

Hamlet (Complete & Split by Sentence)

Description

A dataset containing the complete dialogue of Hamlet with turns of talk split into sentences.

Usage

data(hamlet)
data(hamlet)

Format

A data frame with 2007 rows and 7 variables

Details

act. The act (akin to repeated measures)
tot. The turn of talk
scene. The scene (nested within an act)
location. Location of the scene
person. Character in the play
died. Logical coded death variable if yes the character dies in the play
dialogue. The spoken dialogue

References

http://www.gutenberg.org

Tabulate Frequency Counts for Multiple Vectors

Description

mtabulate - Similar to tabulate that works on multiple vectors.

as_list - Convert a count matrix to a named list of elements. The semantic inverse of mtabulate.

Usage

mtabulate(vects)

as_list(mat, nm = rownames(mat))
mtabulate(vects)

as_list(mat, nm = rownames(mat))

Arguments

`vects`	A `vector`, `list`, or `data.frame` of named/unnamed vectors.
`mat`	A matrix of counts.
`nm`	A character vector of names to assign to the list.

Value

mtabulate - Returns a data.frame with columns equal to number of unique elements and the number of rows equal to the the original length of the vector, list, or data.frame (length equals number of columns in data.frame). If list of vectors is named these will be the rownames of the dataframe.

as_list - Returns a list of elements.

Author(s)

Joran Elias and Tyler Rinker <[email protected]>.

References

https://stackoverflow.com/a/9961324/1000343

Examples

mtabulate(list(w=letters[1:10], x=letters[1:5], z=letters))
mtabulate(list(mtcars$cyl[1:10]))

## Dummy coding
mtabulate(mtcars$cyl[1:10])
mtabulate(CO2[, "Plant"])

dat <- data.frame(matrix(sample(c("A", "B"), 30, TRUE), ncol=3))
mtabulate(dat)
as_list(mtabulate(dat))
t(mtabulate(dat))
as_list(t(mtabulate(dat)))
mtabulate(list(w=letters[1:10], x=letters[1:5], z=letters))
mtabulate(list(mtcars$cyl[1:10]))

## Dummy coding
mtabulate(mtcars$cyl[1:10])
mtabulate(CO2[, "Plant"])

dat <- data.frame(matrix(sample(c("A", "B"), 30, TRUE), ncol=3))
mtabulate(dat)
as_list(mtabulate(dat))
t(mtabulate(dat))
as_list(t(mtabulate(dat)))

Simple `DocumentTermMatrix`

Description

A dataset containing a simple DocumentTermMatrix.

Usage

data(simple_dtm)
data(simple_dtm)

Format

A list with 6 elements

Details

i: The document locations
j: The term locations
v: The count of terms for that particular element position
nrow: The number of rows
ncol: The number of columns
dimnames: document and terms

Split Data Forms at Specified Indices

Description

Split data forms at specified integer indices.

Usage

split_index(
  x,
  indices = if (is.atomic(x)) {
     NULL
 } else {
     change_index(x)
 },
  names = NULL,
  ...
)

## S3 method for class 'list'
split_index(x, indices, names = NULL, ...)

## S3 method for class 'data.frame'
split_index(x, indices, names = NULL, ...)

## S3 method for class 'matrix'
split_index(x, indices, names = NULL, ...)

## S3 method for class 'numeric'
split_index(x, indices = change_index(x), names = NULL, ...)

## S3 method for class 'factor'
split_index(x, indices = change_index(x), names = NULL, ...)

## S3 method for class 'character'
split_index(x, indices = change_index(x), names = NULL, ...)

## Default S3 method:
split_index(x, indices = change_index(x), names = NULL, ...)
split_index(
  x,
  indices = if (is.atomic(x)) {
     NULL
 } else {
     change_index(x)
 },
  names = NULL,
  ...
)

## S3 method for class 'list'
split_index(x, indices, names = NULL, ...)

## S3 method for class 'data.frame'
split_index(x, indices, names = NULL, ...)

## S3 method for class 'matrix'
split_index(x, indices, names = NULL, ...)

## S3 method for class 'numeric'
split_index(x, indices = change_index(x), names = NULL, ...)

## S3 method for class 'factor'
split_index(x, indices = change_index(x), names = NULL, ...)

## S3 method for class 'character'
split_index(x, indices = change_index(x), names = NULL, ...)

## Default S3 method:
split_index(x, indices = change_index(x), names = NULL, ...)

Arguments

`x`	A data form (`list`, `vector`, `data.frame`, `matrix`).
`indices`	A vector of integer indices to split at. If `indices` contains the index 1, it will be silently dropped. The default value when `x` evaluates to `TRUE` for `is.atomic` is to use `change_index(x)`.
`names`	Optional vector of names to give to the list elements.
`...`	Ignored.

Value

Returns of list of data forms broken at the indices.

Note

Two dimensional object will retain dimension (i.e., drop = FALSE is used).

Examples

## character
split_index(LETTERS, c(4, 10, 16))
split_index(LETTERS, c(4, 10, 16), c("dog", "cat", "chicken", "rabbit"))

## numeric
split_index(1:100, c(33, 66))

## factor
(p_chng <- change_index(CO2[["Plant"]]))
split_index(CO2[["Plant"]], p_chng)
#`change_index` was unnecessary as it is the default of atomic vectors
split_index(CO2[["Plant"]])

## list
split_index(as.list(LETTERS), c(4, 10, 16))

## data.frame
(vs_change <- change_index(mtcars[["vs"]]))
split_index(mtcars, vs_change)

## matrix
(mat <- matrix(1:50, nrow=10))
split_index(mat, c(3, 6, 10))
## character
split_index(LETTERS, c(4, 10, 16))
split_index(LETTERS, c(4, 10, 16), c("dog", "cat", "chicken", "rabbit"))

## numeric
split_index(1:100, c(33, 66))

## factor
(p_chng <- change_index(CO2[["Plant"]]))
split_index(CO2[["Plant"]], p_chng)
#`change_index` was unnecessary as it is the default of atomic vectors
split_index(CO2[["Plant"]])

## list
split_index(as.list(LETTERS), c(4, 10, 16))

## data.frame
(vs_change <- change_index(mtcars[["vs"]]))
split_index(mtcars, vs_change)

## matrix
(mat <- matrix(1:50, nrow=10))
split_index(mat, c(3, 6, 10))

Split a Vector By Split Points

Description

split_match - Splits a vector into a list of vectors based on split points.

split_match_regex - split_match with regex = TRUE.

Usage

split_match(x, split = "", include = FALSE, regex = FALSE, ...)

split_match_regex(x, split = "", include = FALSE, ...)
split_match(x, split = "", include = FALSE, regex = FALSE, ...)

split_match_regex(x, split = "", include = FALSE, ...)

Arguments

`x`	A vector with split points.
`split`	A vector of places (elements) to split on or a regular expression if `regex` argument is `TRUE`.
`include`	An integer of `1` (`split` character(s) are not included in the output), `2` (`split` character(s) are included at the beginning of the output), or `3` (`split` character(s) are included at the end of the output).
`regex`	logical. If `TRUE` regular expressions will be enabled for `split` argument.
`...`	other arguments passed to `grep` and `grepl`.

Value

Returns a list of vectors.

Author(s)

Matthew Flickinger and Tyler Rinker <[email protected]>.

References

https://stackoverflow.com/a/24319217/1000343

Examples

set.seed(15)
x <- sample(c("", LETTERS[1:10]), 25, TRUE, prob=c(.2, rep(.08, 10)))

split_match(x)
split_match(x, "C")
split_match(x, c("", "C"))

split_match(x, include = 0)
split_match(x, include = 1)
split_match(x, include = 2)

set.seed(15)
x <- sample(1:11, 25, TRUE, prob=c(.2, rep(.08, 10)))
split_match(x, 1)
set.seed(15)
x <- sample(c("", LETTERS[1:10]), 25, TRUE, prob=c(.2, rep(.08, 10)))

split_match(x)
split_match(x, "C")
split_match(x, c("", "C"))

split_match(x, include = 0)
split_match(x, include = 1)
split_match(x, include = 2)

set.seed(15)
x <- sample(1:11, 25, TRUE, prob=c(.2, rep(.08, 10)))
split_match(x, 1)

Break Text Into Ordered Word Chunks

Description

Some visualizations and algorithms require text to be broken into chunks of ordered words. split_portion breaks text, optionally by grouping variables, into equal chunks. The chunk size can be specified by giving number of words to be in each chunk or the number of chunks.

Usage

split_portion(
  text.var,
  grouping.var = NULL,
  n.words,
  n.chunks,
  as.string = TRUE,
  rm.unequal = FALSE,
  as.table = TRUE,
  ...
)
split_portion(
  text.var,
  grouping.var = NULL,
  n.words,
  n.chunks,
  as.string = TRUE,
  rm.unequal = FALSE,
  as.table = TRUE,
  ...
)

Arguments

`text.var`	The text variable
`grouping.var`	The grouping variables. Default `NULL` generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.
`n.words`	An integer specifying the number of words in each chunk (must specify n.chunks or n.words).
`n.chunks`	An integer specifying the number of chunks (must specify n.chunks or n.words).
`as.string`	logical. If `TRUE` the chunks are returned as a single string. If `FALSE` the chunks are returned as a vector of single words.
`rm.unequal`	logical. If `TRUE` final chunks that are unequal in length to the other chunks are removed.
`as.table`	logical. If `TRUE` the list output is coerced to `data.table` or tibble.
`...`	Ignored.

Value

Returns a list or data.table of text chunks.

Examples

with(DATA, split_portion(state, n.chunks = 10))
with(DATA, split_portion(state, n.words = 10))
with(DATA, split_portion(state, n.chunks = 10, as.string=FALSE))
with(DATA, split_portion(state, n.chunks = 10, rm.unequal=TRUE))
with(DATA, split_portion(state, person, n.chunks = 10))
with(DATA, split_portion(state, list(sex, adult), n.words = 10))
with(DATA, split_portion(state, person, n.words = 10, rm.unequal=TRUE))

## Bigger data
with(hamlet, split_portion(dialogue, person, n.chunks = 10))
with(hamlet, split_portion(dialogue, list(act, scene, person), n.chunks = 10))
with(hamlet, split_portion(dialogue, person, n.words = 300))
with(hamlet, split_portion(dialogue, list(act, scene, person), n.words = 300))
with(DATA, split_portion(state, n.chunks = 10))
with(DATA, split_portion(state, n.words = 10))
with(DATA, split_portion(state, n.chunks = 10, as.string=FALSE))
with(DATA, split_portion(state, n.chunks = 10, rm.unequal=TRUE))
with(DATA, split_portion(state, person, n.chunks = 10))
with(DATA, split_portion(state, list(sex, adult), n.words = 10))
with(DATA, split_portion(state, person, n.words = 10, rm.unequal=TRUE))

## Bigger data
with(hamlet, split_portion(dialogue, person, n.chunks = 10))
with(hamlet, split_portion(dialogue, list(act, scene, person), n.chunks = 10))
with(hamlet, split_portion(dialogue, person, n.words = 300))
with(hamlet, split_portion(dialogue, list(act, scene, person), n.words = 300))

Split Runs

Description

Split runs of consecutive characters.

Usage

split_run(x, ...)

## Default S3 method:
split_run(x, ...)

## S3 method for class 'data.frame'
split_run(x, text.var = TRUE, ...)
split_run(x, ...)

## Default S3 method:
split_run(x, ...)

## S3 method for class 'data.frame'
split_run(x, text.var = TRUE, ...)

Arguments

`x`	A `data.frame` or character vector with runs.
`text.var`	The name of the text variable with runs. If `TRUE` `split_word` tries to detect the text column with runs.
`...`	Ignored.

Value

Returns a list of vectors of runs or an expanded data.table with runs split apart.

Examples

x1 <- c(
     "122333444455555666666",
     NA,
     "abbcccddddeeeeeffffff",
     "sddfg",
     "11112222333"
)

x <- c(rep(x1, 2), ">>???,,,,....::::;[[")

split_run(x)


DATA[["run.col"]] <- x
split_run(DATA, "run.col")
x1 <- c(
     "122333444455555666666",
     NA,
     "abbcccddddeeeeeffffff",
     "sddfg",
     "11112222333"
)

x <- c(rep(x1, 2), ">>???,,,,....::::;[[")

split_run(x)


DATA[["run.col"]] <- x
split_run(DATA, "run.col")

Split Sentences

Description

Split sentences.

Usage

split_sentence(x, ...)

## Default S3 method:
split_sentence(x, ...)

## S3 method for class 'data.frame'
split_sentence(x, text.var = TRUE, ...)
split_sentence(x, ...)

## Default S3 method:
split_sentence(x, ...)

## S3 method for class 'data.frame'
split_sentence(x, text.var = TRUE, ...)

Arguments

`x`	A `data.frame` or character vector with sentences.
`text.var`	The name of the text variable. If `TRUE` `split_sentence` tries to detect the column with sentences.
`...`	Ignored.

Value

Returns a list of vectors of sentences or a expanded data.frame with sentences split apart.

Examples

(x <- c(paste0(
    "Mr. Brown comes! He says hello. i give him coffee.  i will ",
    "go at 5 p. m. eastern time.  Or somewhere in between!go there"
),
paste0(
    "Marvin K. Mooney Will You Please Go Now!", "The time has come.",
    "The time has come. The time is now. Just go. Go. GO!",
    "I don't care how."
)))
split_sentence(x)

data(DATA)
split_sentence(DATA)

## Not run: 
## Kevin S. Dias' sentence boundary disambiguation test set
data(golden_rules)
library(magrittr)

golden_rules %$%
    split_sentence(Text)

## End(Not run)
(x <- c(paste0(
    "Mr. Brown comes! He says hello. i give him coffee.  i will ",
    "go at 5 p. m. eastern time.  Or somewhere in between!go there"
),
paste0(
    "Marvin K. Mooney Will You Please Go Now!", "The time has come.",
    "The time has come. The time is now. Just go. Go. GO!",
    "I don't care how."
)))
split_sentence(x)

data(DATA)
split_sentence(DATA)

## Not run: 
## Kevin S. Dias' sentence boundary disambiguation test set
data(golden_rules)
library(magrittr)

golden_rules %$%
    split_sentence(Text)

## End(Not run)

Split Sentences & Tokens

Description

Split sentences and tokens.

Usage

split_sentence_token(x, ...)

## Default S3 method:
split_sentence_token(x, lower = TRUE, ...)

## S3 method for class 'data.frame'
split_sentence_token(x, text.var = TRUE, lower = TRUE, ...)
split_sentence_token(x, ...)

## Default S3 method:
split_sentence_token(x, lower = TRUE, ...)

## S3 method for class 'data.frame'
split_sentence_token(x, text.var = TRUE, lower = TRUE, ...)

Arguments

`x`	A `data.frame` or character vector with sentences.
`lower`	logical. If `TRUE` the words are converted to lower case.
`text.var`	The name of the text variable. If `TRUE` `split_sentence_token` tries to detect the column with sentences.
`...`	Ignored.

Value

Returns a list of vectors of sentences or a expanded data.frame with sentences split apart.

Examples

(x <- c(paste0(
    "Mr. Brown comes! He says hello. i give him coffee.  i will ",
    "go at 5 p. m. eastern time.  Or somewhere in between!go there"
),
paste0(
    "Marvin K. Mooney Will You Please Go Now!", "The time has come.",
    "The time has come. The time is now. Just go. Go. GO!",
    "I don't care how."
)))
split_sentence_token(x)

data(DATA)
split_sentence_token(DATA)

## Not run: 
## Kevin S. Dias' sentence boundary disambiguation test set
data(golden_rules)
library(magrittr)

golden_rules %$%
    split_sentence_token(Text)

## End(Not run)
(x <- c(paste0(
    "Mr. Brown comes! He says hello. i give him coffee.  i will ",
    "go at 5 p. m. eastern time.  Or somewhere in between!go there"
),
paste0(
    "Marvin K. Mooney Will You Please Go Now!", "The time has come.",
    "The time has come. The time is now. Just go. Go. GO!",
    "I don't care how."
)))
split_sentence_token(x)

data(DATA)
split_sentence_token(DATA)

## Not run: 
## Kevin S. Dias' sentence boundary disambiguation test set
data(golden_rules)
library(magrittr)

golden_rules %$%
    split_sentence_token(Text)

## End(Not run)

Break and Stretch if Multiple Persons per Cell

Description

Look for cells with multiple people and create separate rows for each person.

Usage

split_speaker(dataframe, speaker.var = 1, sep = c("and", "&", ","), ...)
split_speaker(dataframe, speaker.var = 1, sep = c("and", "&", ","), ...)

Arguments

`dataframe`	A dataframe that contains the person variable.
`speaker.var`	The person variable to be stretched.
`sep`	The separator(s) to search for and break on. Default is: c("and", "&", ",")
`...`	Ignored.

Value

Returns an expanded dataframe with person variable stretched and accompanying rows repeated.

Examples

## Not run: 
DATA$person <- as.character(DATA$person)
DATA$person[c(1, 4, 6)] <- c("greg, sally, & sam",
    "greg, sally", "sam and sally")

split_speaker(DATA)

DATA$person[c(1, 4, 6)] <- c("greg_sally_sam",
    "greg.sally", "sam; sally")

split_speaker(DATA, sep = c(".", "_", ";"))

DATA <- textshape::DATA  #reset DATA

## End(Not run)
## Not run: 
DATA$person <- as.character(DATA$person)
DATA$person[c(1, 4, 6)] <- c("greg, sally, & sam",
    "greg, sally", "sam and sally")

split_speaker(DATA)

DATA$person[c(1, 4, 6)] <- c("greg_sally_sam",
    "greg.sally", "sam; sally")

split_speaker(DATA, sep = c(".", "_", ";"))

DATA <- textshape::DATA  #reset DATA

## End(Not run)

Split Tokens

Description

Split tokens.

Usage

split_token(x, ...)

## Default S3 method:
split_token(x, lower = TRUE, ...)

## S3 method for class 'data.frame'
split_token(x, text.var = TRUE, lower = TRUE, ...)
split_token(x, ...)

## Default S3 method:
split_token(x, lower = TRUE, ...)

## S3 method for class 'data.frame'
split_token(x, text.var = TRUE, lower = TRUE, ...)

Arguments

`x`	A `data.frame` or character vector with tokens.
`lower`	logical. If `TRUE` the words are converted to lower case.
`text.var`	The name of the text variable. If `TRUE` `split_token` tries to detect the text column with tokens.
`...`	Ignored.

Value

Returns a list of vectors of tokens or an expanded data.table with tokens split apart.

Examples

(x <- c(
    "Mr. Brown comes! He says hello. i give him coffee.",
    "I'll go at 5 p. m. eastern time.  Or somewhere in between!",
    "go there"
))
split_token(x)
split_token(x, lower=FALSE)

data(DATA)
split_token(DATA)
split_token(DATA, lower=FALSE)

## Larger data set
split_token(hamlet)
(x <- c(
    "Mr. Brown comes! He says hello. i give him coffee.",
    "I'll go at 5 p. m. eastern time.  Or somewhere in between!",
    "go there"
))
split_token(x)
split_token(x, lower=FALSE)

data(DATA)
split_token(DATA)
split_token(DATA, lower=FALSE)

## Larger data set
split_token(hamlet)

Split a Transcript Style Vector on Delimiter & Coerce to Dataframe

Description

Split a transcript style vector (e.g., c("greg: Who me", "sarah: yes you!") into a name and dialogue vector that is coerced to a data.table. Leading/trailing white space in the columns is stripped out.

Usage

split_transcript(
  x,
  delim = ":",
  colnames = c("person", "dialogue"),
  max.delim = 15,
  ...
)
split_transcript(
  x,
  delim = ":",
  colnames = c("person", "dialogue"),
  max.delim = 15,
  ...
)

Arguments

`x`	A transcript style vector (e.g., `c("greg: Who me", "sarah: yes you!")`.
`delim`	The delimiter to split on.
`colnames`	The column names to use for the `data.table` output.
`max.delim`	An integer stating how many characters may come before a delimiter is found. This is useful for the case when a colon is the delimiter but time stamps are also found in the text.
`...`	Ignored.

Value

Returns a 2 column data.table.

Examples

split_transcript(c("greg: Who me", "sarah: yes you!"))

## Not run: 
## 2015 Vice-Presidential Debates Example
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, magrittr, xml2)

debates <- c(
    wisconsin = "110908",
    boulder = "110906",
    california = "110756",
    ohio = "110489"
)

lapply(debates, function(x){
    xml2::read_html(paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x)) %>%
        rvest::html_nodes("p") %>%
        rvest::html_text() %>%
        textshape::split_index(grep("^[A-Z]+:", .)) %>%
        textshape::combine() %>%
        textshape::split_transcript() %>%
        textshape::split_sentence()
})

## End(Not run)
split_transcript(c("greg: Who me", "sarah: yes you!"))

## Not run: 
## 2015 Vice-Presidential Debates Example
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, magrittr, xml2)

debates <- c(
    wisconsin = "110908",
    boulder = "110906",
    california = "110756",
    ohio = "110489"
)

lapply(debates, function(x){
    xml2::read_html(paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x)) %>%
        rvest::html_nodes("p") %>%
        rvest::html_text() %>%
        textshape::split_index(grep("^[A-Z]+:", .)) %>%
        textshape::combine() %>%
        textshape::split_transcript() %>%
        textshape::split_sentence()
})

## End(Not run)

Split Words

Description

Split words.

Usage

split_word(x, ...)

## Default S3 method:
split_word(x, lower = TRUE, ...)

## S3 method for class 'data.frame'
split_word(x, text.var = TRUE, lower = TRUE, ...)
split_word(x, ...)

## Default S3 method:
split_word(x, lower = TRUE, ...)

## S3 method for class 'data.frame'
split_word(x, text.var = TRUE, lower = TRUE, ...)

Arguments

`x`	A `data.frame` or character vector with words.
`lower`	logical. If `TRUE` the words are converted to lower case.
`text.var`	The name of the text variable. If `TRUE` `split_word` tries to detect the text column with words.
`...`	Ignored.

Value

Returns a list of vectors of words or an expanded data.table with words split apart.

Examples

(x <- c(
    "Mr. Brown comes! He says hello. i give him coffee.",
    "I'll go at 5 p. m. eastern time.  Or somewhere in between!",
    "go there"
))
split_word(x)
split_word(x, lower=FALSE)

data(DATA)
split_word(DATA)
split_word(DATA, lower=FALSE)

## Larger data set
split_word(hamlet)
(x <- c(
    "Mr. Brown comes! He says hello. i give him coffee.",
    "I'll go at 5 p. m. eastern time.  Or somewhere in between!",
    "go there"
))
split_word(x)
split_word(x, lower=FALSE)

data(DATA)
split_word(DATA)
split_word(DATA, lower=FALSE)

## Larger data set
split_word(hamlet)

Tools for Reshaping Text

Description

Tools that can be used to reshape and restructure text data.

Convert a `DocumentTermMatrix`/`TermDocumentMatrix` into Collocating Words in Tidy Form

Description

Converts non-zero elements of a DocumentTermMatrix/TermDocumentMatrix into a tidy data set made of collocating words.

Usage

tidy_colo_tdm(x, ...)

tidy_colo_dtm(x, ...)
tidy_colo_tdm(x, ...)

tidy_colo_dtm(x, ...)

Arguments

`x`	A `DocumentTermMatrix`/`TermDocumentMatrix`.
`...`	Ignored.

Value

Returns a tidied data.frame.

Examples

data(simple_dtm)

tidied <- tidy_colo_dtm(simple_dtm)
tidied
unique_pairs(tidied)

## Not run: 
if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh('trinker/gofastr', 'trinker/lexicon')
pacman::p_load(tidyverse, magrittr, ggstance)

my_dtm <- with(
    presidential_debates_2012, 
    q_dtm(dialogue, paste(time, tot, sep = "_"))
)

tidy_colo_dtm(my_dtm) %>%
    tbl_df() %>%
    filter(!term_1 %in% c('i', lexicon::sw_onix) & 
        !term_2 %in% lexicon::sw_onix
    ) %>%
    filter(term_1 != term_2) %>%
    unique_pairs() %>%
    filter(n > 15) %>%
    complete(term_1, term_2, fill = list(n = 0)) %>%
    ggplot(aes(x = term_1, y = term_2, fill = n)) +
        geom_tile() +
        scale_fill_gradient(low= 'white', high = 'red') +
        theme(axis.text.x = element_text(angle = 45, hjust = 1))

## End(Not run)
data(simple_dtm)

tidied <- tidy_colo_dtm(simple_dtm)
tidied
unique_pairs(tidied)

## Not run: 
if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh('trinker/gofastr', 'trinker/lexicon')
pacman::p_load(tidyverse, magrittr, ggstance)

my_dtm <- with(
    presidential_debates_2012, 
    q_dtm(dialogue, paste(time, tot, sep = "_"))
)

tidy_colo_dtm(my_dtm) %>%
    tbl_df() %>%
    filter(!term_1 %in% c('i', lexicon::sw_onix) & 
        !term_2 %in% lexicon::sw_onix
    ) %>%
    filter(term_1 != term_2) %>%
    unique_pairs() %>%
    filter(n > 15) %>%
    complete(term_1, term_2, fill = list(n = 0)) %>%
    ggplot(aes(x = term_1, y = term_2, fill = n)) +
        geom_tile() +
        scale_fill_gradient(low= 'white', high = 'red') +
        theme(axis.text.x = element_text(angle = 45, hjust = 1))

## End(Not run)

Convert a `DocumentTermMatrix`/`TermDocumentMatrix` into Tidy Form

Description

Converts non-zero elements of a DocumentTermMatrix/TermDocumentMatrix into a tidy data set.

Usage

tidy_dtm(x, ...)

tidy_tdm(x, ...)
tidy_dtm(x, ...)

tidy_tdm(x, ...)

Arguments

`x`	A `DocumentTermMatrix`/`TermDocumentMatrix`.
`...`	ignored.

Value

Returns a tidied data.frame.

Examples

data(simple_dtm)

tidy_dtm(simple_dtm)

## Not run: 
if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh('trinker/gofastr')
pacman::p_load(tidyverse, magrittr, ggstance)

my_dtm <- with(
    presidential_debates_2012, 
    q_dtm(dialogue, paste(time, tot, sep = "_"))
)

tidy_dtm(my_dtm) %>%
    tidyr::extract(
        col = doc, 
        into = c("time", "turn", "sentence"), 
        regex = "(\\d)_(\\d+)\\.(\\d+)"
    ) %>%
    mutate(
        time = as.numeric(time),
        turn = as.numeric(turn),
        sentence = as.numeric(sentence)
    ) %>%
    tbl_df() %T>%
    print() %>%
    group_by(time, term) %>%
    summarize(n = sum(n)) %>%
    group_by(time) %>%
    arrange(desc(n)) %>%
    slice(1:10) %>%
    ungroup() %>%
    mutate(
        term = factor(paste(term, time, sep = "__"),
            levels = rev(paste(term, time, sep = "__")))
    ) %>%
    ggplot(aes(x = n, y = term)) +
        geom_barh(stat='identity') +
        facet_wrap(~time, ncol=2, scales = 'free_y') +
        scale_y_discrete(labels = function(x) gsub("__.+$", "", x))

## End(Not run)
data(simple_dtm)

tidy_dtm(simple_dtm)

## Not run: 
if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh('trinker/gofastr')
pacman::p_load(tidyverse, magrittr, ggstance)

my_dtm <- with(
    presidential_debates_2012, 
    q_dtm(dialogue, paste(time, tot, sep = "_"))
)

tidy_dtm(my_dtm) %>%
    tidyr::extract(
        col = doc, 
        into = c("time", "turn", "sentence"), 
        regex = "(\\d)_(\\d+)\\.(\\d+)"
    ) %>%
    mutate(
        time = as.numeric(time),
        turn = as.numeric(turn),
        sentence = as.numeric(sentence)
    ) %>%
    tbl_df() %T>%
    print() %>%
    group_by(time, term) %>%
    summarize(n = sum(n)) %>%
    group_by(time) %>%
    arrange(desc(n)) %>%
    slice(1:10) %>%
    ungroup() %>%
    mutate(
        term = factor(paste(term, time, sep = "__"),
            levels = rev(paste(term, time, sep = "__")))
    ) %>%
    ggplot(aes(x = n, y = term)) +
        geom_barh(stat='identity') +
        facet_wrap(~time, ncol=2, scales = 'free_y') +
        scale_y_discrete(labels = function(x) gsub("__.+$", "", x))

## End(Not run)

Tidy a List of Named Dataframes or Named Vectors or Vectors

Description

rbind a named list of data.frames or vectors to output a single data.frame with the names from the list as an id column.

Usage

tidy_list(
  x,
  id.name = "id",
  content.name = "content",
  content.attribute.name = "attribute",
  ...
)
tidy_list(
  x,
  id.name = "id",
  content.name = "content",
  content.attribute.name = "attribute",
  ...
)

Arguments

`x`	A named `list` of `data.frame`s or `vector`.
`id.name`	The name to use for the column created from the `list`.
`content.name`	The name to use for the column created from the `list` of `vector`s (only used if `x` is `vector`).
`content.attribute.name`	The name to use for the column created from the `list` of names given to the `vector`s (only used if `x` is named `vector`).
`...`	Ignored.

Value

Returns a data.table with the names from the list as an id column.

Examples

tidy_list(list(p=1:500, r=letters))
tidy_list(list(p=mtcars, r=mtcars, z=mtcars, d=mtcars))

x <- list(
    a = setNames(c(1:4), LETTERS[1:4]),
    b = setNames(c(7:9), LETTERS[7:9]),
    c = setNames(c(10:15), LETTERS[10:15]),
    d = c(x=4, y=6, 4),
    e = setNames(1:10, sample(state.abb, 10, TRUE)),
    f = setNames(1:10, sample(month.abb, 10, TRUE))
)

tidy_list(x)

## Not run: 
## 2015 Vice-Presidential Debates Example
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, magrittr, xml2)

debates <- c(
    wisconsin = "110908",
    boulder = "110906",
    california = "110756",
    ohio = "110489"
)

lapply(debates, function(x){
    paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x) %>%
        xml2::read_html() %>%
        rvest::html_nodes("p") %>%
        rvest::html_text() %>%
        textshape::split_index(grep("^[A-Z]+:", .)) %>%
        textshape::combine() %>%
        textshape::split_transcript() %>%
        textshape::split_sentence()
}) %>%
    textshape::tidy_list("location")

## End(Not run)
tidy_list(list(p=1:500, r=letters))
tidy_list(list(p=mtcars, r=mtcars, z=mtcars, d=mtcars))

x <- list(
    a = setNames(c(1:4), LETTERS[1:4]),
    b = setNames(c(7:9), LETTERS[7:9]),
    c = setNames(c(10:15), LETTERS[10:15]),
    d = c(x=4, y=6, 4),
    e = setNames(1:10, sample(state.abb, 10, TRUE)),
    f = setNames(1:10, sample(month.abb, 10, TRUE))
)

tidy_list(x)

## Not run: 
## 2015 Vice-Presidential Debates Example
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, magrittr, xml2)

debates <- c(
    wisconsin = "110908",
    boulder = "110906",
    california = "110756",
    ohio = "110489"
)

lapply(debates, function(x){
    paste0("http://www.presidency.ucsb.edu/ws/index.php?pid=", x) %>%
        xml2::read_html() %>%
        rvest::html_nodes("p") %>%
        rvest::html_text() %>%
        textshape::split_index(grep("^[A-Z]+:", .)) %>%
        textshape::combine() %>%
        textshape::split_transcript() %>%
        textshape::split_sentence()
}) %>%
    textshape::tidy_list("location")

## End(Not run)

Convert a Matrix into Tidy Form

Description

tidy_matrix - Converts matrices into a tidy data set. Essentially, a stacking of the matrix columns and repeating row/column names as necessary.

tidy_adjacency_matrix - A wrapper for tidy_matrix with the row.name, col.name, & value.name all set to "from","to", & "n", assuming preparation for network analysis.

Usage

tidy_matrix(x, row.name = "row", col.name = "col", value.name = "value", ...)

tidy_adjacency_matrix(x, ...)
tidy_matrix(x, row.name = "row", col.name = "col", value.name = "value", ...)

tidy_adjacency_matrix(x, ...)

Arguments

`x`	A matrix.
`row.name`	A string to use for the row names that are now a column.
`col.name`	A string to use for the column names that are now a column.
`value.name`	A string to use for the values that are now a column.
`...`	ignored.

Value

Returns a tidied data.frame.

Examples

mat <- matrix(1:16, nrow = 4,
    dimnames = list(LETTERS[1:4], LETTERS[23:26])
)

mat
tidy_matrix(mat)


data(simple_dtm)
tidy_matrix(as.matrix(simple_dtm), 'doc', 'term', 'n')

X <- as.matrix(simple_dtm[1:10, 1:10])
tidy_adjacency_matrix(crossprod(X))
tidy_adjacency_matrix(crossprod(t(X)))
mat <- matrix(1:16, nrow = 4,
    dimnames = list(LETTERS[1:4], LETTERS[23:26])
)

mat
tidy_matrix(mat)


data(simple_dtm)
tidy_matrix(as.matrix(simple_dtm), 'doc', 'term', 'n')

X <- as.matrix(simple_dtm[1:10, 1:10])
tidy_adjacency_matrix(crossprod(X))
tidy_adjacency_matrix(crossprod(t(X)))

Tidy a Table: Bind Its Values with Its Names

Description

cbind a table's values with its names to form id (from the names) and content columns.

Usage

tidy_table(x, id.name = "id", content.name = "content", ...)
tidy_table(x, id.name = "id", content.name = "content", ...)

Arguments

`x`	A `table`.
`id.name`	The name to use for the column created from the `table` `names`.
`content.name`	The name to use for the column created from the `table` values.
`...`	ignored.

Value

Returns a data.table with the names from the table as an id column.

Examples

x <- table(sample(LETTERS[1:6], 1000, TRUE))
tidy_table(x)
x <- table(sample(LETTERS[1:6], 1000, TRUE))
tidy_table(x)

Tidy a Named Atomic Vector: Bind Its Values with Its Names

Description

cbind a named atomic vector's values with its names to form id (from the names) and content columns.

Usage

tidy_vector(x, id.name = "id", content.name = "content", ...)
tidy_vector(x, id.name = "id", content.name = "content", ...)

Arguments

`x`	A named atomic `vector`.
`id.name`	The name to use for the column created from the `vector` `names`.
`content.name`	The name to use for the column created from the `vector` values.
`...`	ignored.

Value

Returns a data.table with the names from the vector as an id column.

Examples

x <- setNames(sample(LETTERS[1:6], 1000, TRUE), sample(state.name[1:5], 1000, TRUE))
tidy_vector(x)
x <- setNames(sample(LETTERS[1:6], 1000, TRUE), sample(state.name[1:5], 1000, TRUE))
tidy_vector(x)

Extract Only Unique Pairs of Collocating Words in `tidy_colo_dtm`

Description

tidy_colo_dtm utilizes the entire matrix to generate the tidied data.frame. This means that the upper and lower triangles are used redundantly. This function eliminates this redundancy by dropping one set of the pairs from a tidied data.frame.

Usage

unique_pairs(x, col1 = "term_1", col2 = "term_2", ...)

## Default S3 method:
unique_pairs(x, col1 = "term_1", col2 = "term_2", ...)

## S3 method for class 'data.table'
unique_pairs(x, col1 = "term_1", col2 = "term_2", ...)
unique_pairs(x, col1 = "term_1", col2 = "term_2", ...)

## Default S3 method:
unique_pairs(x, col1 = "term_1", col2 = "term_2", ...)

## S3 method for class 'data.table'
unique_pairs(x, col1 = "term_1", col2 = "term_2", ...)

Arguments

`x`	A `data.frame` with two columns that contain redundant pairs.
`col1`	A string naming column 1.
`col2`	A string naming column 2.
`...`	ignored.

Value

Returns a filtered data.frame.

Examples

dat <- data.frame(
    term_1 = LETTERS[1:10],
    term_2 = LETTERS[10:1],
    stringsAsFactors = FALSE
)

unique_pairs(dat)
dat <- data.frame(
    term_1 = LETTERS[1:10],
    term_2 = LETTERS[10:1],
    stringsAsFactors = FALSE
)

unique_pairs(dat)

Un-nest Nested Text Columns

Description

Un-nest nested text columns in a data.frame. Attempts to locate the nested text column without specifying.

Usage

unnest_text(dataframe, column, integer.rownames = TRUE, ...)
unnest_text(dataframe, column, integer.rownames = TRUE, ...)

Arguments

`dataframe`	A dataframe object.
`column`	Column name to search for markers/terms.
`integer.rownames`	logical. If `TRUE` then the rownames are numbered 1 through number of rows, otherwise the original row number is retained followed by a period and the element number from the list.
`...`	ignored.

Value

Returns an un-nested data.frame.

Examples

dat <- DATA

## Add a nested/list text column
dat$split <- lapply(dat$state, function(x) {
    unlist(strsplit(x, '(?<=[?!.])\\s+', perl = TRUE))
})

unnest_text(dat)
unnest_text(dat, integer.rownames = FALSE)

## Add a second nested integer column
dat$d <- lapply(dat$split, nchar)
## Not run: 
unnest_text(dat) # causes error, must supply column explicitly

## End(Not run)
unnest_text(dat, 'split')

## As a data.table
library(data.table)
dt_dat <- data.table::as.data.table(data.table::copy(dat))
unnest_text(dt_dat, 'split')
## Not run: 
unnest_text(dt_dat, 'd')

## End(Not run)

## Not run: 
## As a tibble
library(tibble)
t_dat <- tibble:::as_tibble(dat)
unnest_text(t_dat, 'split')

## End(Not run)
dat <- DATA

## Add a nested/list text column
dat$split <- lapply(dat$state, function(x) {
    unlist(strsplit(x, '(?<=[?!.])\\s+', perl = TRUE))
})

unnest_text(dat)
unnest_text(dat, integer.rownames = FALSE)

## Add a second nested integer column
dat$d <- lapply(dat$split, nchar)
## Not run: 
unnest_text(dat) # causes error, must supply column explicitly

## End(Not run)
unnest_text(dat, 'split')

## As a data.table
library(data.table)
dt_dat <- data.table::as.data.table(data.table::copy(dat))
unnest_text(dt_dat, 'split')
## Not run: 
unnest_text(dt_dat, 'd')

## End(Not run)

## Not run: 
## As a tibble
library(tibble)
t_dat <- tibble:::as_tibble(dat)
unnest_text(t_dat, 'split')

## End(Not run)

Package 'textshape'

Help Index

Row Bind a List of Named Dataframes or Vectors

Description

Usage

Arguments

Value

Examples

Column Bind a Table's Values with Its Names

Description

Usage

Arguments

Value

Examples

Column Bind an Atomic Vector's Values with Its Names

Description

Usage

Arguments

Value

Examples

Indexing of Changes in Runs

Description

Usage

Arguments

Value

See Also

Examples

Reorder a Matrix Based on Hierarchical Clustering

Description

Usage

Arguments

Value

See Also

Examples

Add a Column as Rownames

Description

Usage

Arguments

Value

Examples

Combine Elements

Description

Usage

Arguments

Value

Examples

Fictitious Classroom Dialogue

Description

Usage

Format

Details

Duration of Turns of Talk

Description

Usage

Arguments

Value

Examples

Flatten a Nested List of Vectors Into a Single Tier List of Vectors

Description

Usage

Arguments

Value

Note

Author(s)

References

Examples

Prepare Discourse Data for Network Plotting

Description

Usage

Arguments

Value

Examples

Sentence Boundary Disambiguation Edge Cases

Description

Usage

Format

Details

References

Get Elements Matching Between 2 Points

Description

Simple `DocumentTermMatrix`