Fictitious Classroom Dialogue

Description

A fictitious dataset useful for small demonstrations.

Usage

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

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", ...)

Arguments

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)

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", ...)

Arguments

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)

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", ...)

Arguments

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)

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, ...)

Arguments

Value

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

See Also

split_index

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)

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", ...)

Arguments

Value

Returns a reordered matrix.

See Also

hclust

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)

Add a Column as Rownames

Description

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)

Arguments

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')

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, ...)

Arguments

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])

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, ...)

Arguments

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)

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 = "_", ...)

Arguments

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 <tyler.rinker@gmail.com>.

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, ' -> ')

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, ...)

Arguments

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)

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)

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, ...)

Arguments

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)

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,
  ...
)

Arguments

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')

Hamlet (Complete & Split by Sentence)

Description

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

Usage

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))

Arguments

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 <tyler.rinker@gmail.com>.

References

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

See Also

tabulate

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)))

Simple DocumentTermMatrix

Description

A dataset containing a simple DocumentTermMatrix.

Usage

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, ...)

Arguments

Value

Returns of list of data forms broken at the indices.

Note

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

See Also

change_index

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))

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, ...)

Arguments

Value

Returns a list of vectors.

Author(s)

Matthew Flickinger and Tyler Rinker <tyler.rinker@gmail.com>.

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)

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,
  ...
)

Arguments

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))

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, ...)

Arguments

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")

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, ...)

Arguments

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)

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, ...)

Arguments

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)

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", "&", ","), ...)

Arguments

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)

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, ...)

Arguments

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)

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,
  ...
)

Arguments

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 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, ...)

Arguments

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)

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, ...)

Arguments

Value

Returns a tidied data.frame.

See Also

unique_pairs

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)

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, ...)

Arguments

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)

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",
  ...
)

Arguments

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)

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, ...)

Arguments

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)))

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", ...)

Arguments

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)

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", ...)

Arguments

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)

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", ...)

Arguments

Value

Returns a filtered data.frame.

See Also

tidy_colo_dtm

Examples

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, ...)

Arguments

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)