qdap Chaining

Description

%&% - Chain qdap_dfs to qdap functions with a text.var argument. Saves typing of an explicit text.var argument and supplying a data.frame.

%>% - The magrittr "then" chain operator imported by dplyr. Imported for convenience. See https://github.com/tidyverse/magrittr for details.

Usage

qdap_df.object %&% qdap.fun

lhs %>% rhs

Arguments

References

Inspired by magrittr's %>% functionality.

See Also

%>%, qdap_df

Examples

## Not run: 
dat <- qdap_df(DATA, state)
dat %&% trans_cloud(grouping.var=person)
dat %&% trans_cloud(grouping.var=person, text.var=stemmer(DATA$state))
dat %&% termco(grouping.var=person, match.list=list("fun", "computer"))

## Various examples with qdap functions (sentSplit gives class "qdap_df")
dat <- sentSplit(DATA, "state")
dat %&% trans_cloud(grouping.var=person)
dat %&% termco(person, match.list=list("fun", "computer"))
dat %&% trans_venn(person)
dat %&% polarity(person)
dat %&% formality(person)
dat %&% automated_readability_index(person)
dat %&% Dissimilarity(person)
dat %&% gradient_cloud(sex)
dat %&% dispersion_plot(c("fun", "computer"))
dat %&% discourse_map(list(sex, adult))
dat %&% gantt_plot(person)
dat %&% word_list(adult)
dat %&% end_mark_by(person)
dat %&% end_mark()
dat %&% word_stats(person)
dat %&% wfm(person)
dat %&% word_cor(person, "i")
dat %&% sentCombine(person)
dat %&% question_type(person)
dat %&% word_network_plot()
dat %&% character_count()
dat %&% char_table(person)
dat %&% phrase_net(2, .1)
dat %&% boolean_search("it||!")
dat %&% trans_context(person, which(end_mark(DATA.SPLIT[, "state"]) == "?"))
dat %&% mgsub(c("it's", "I'm"), c("it is", "I am"))

## combine with magrittr/dplyr chaining
dat %&% wfm(person) %>% plot()
dat %&% polarity(person) %>% scores()
dat %&% polarity(person) %>% counts()
dat %&% polarity(person) %>% scores()
dat %&% polarity(person) %>% scores() %>% plot()
dat %&% polarity(person) %>% scores %>% plot

## Change text column in `qdap_df` (Example 1)
dat2 <- sentSplit(DATA, "state", stem.col = TRUE)
class(dat2)
dat2 %&% trans_cloud()
Text(dat2)
## change the `text.var` column
Text(dat2) <- "stem.text"
dat2 %&% trans_cloud()

## Change text column in `qdap_df` (Example 2)
(dat2$fake_dat <- paste(emoticon[1:11,2], dat2$state))
Text(dat2) <- "fake_dat"
(m <- dat2 %&% sub_holder(emoticon[,2]))
m$unhold(strip(m$output))

## End(Not run)

Add themes to a Network object.

Description

This operator allows you to add themes to a Network object.

Usage

## S3 method for class 'Network'
Network.obj + x

Arguments

Generic Animate Method

Description

Animate select qdap objects.

Usage

Animate(x, ...)

Arguments

Value

Returns a plot object.

See Also

scores, counts, preprocessed, proportions

Animate Character

Description

Animate.character - Animate a character object. Typically this function is useful in conjunction with other Animate objects to create complex animations with accompanying text.

Usage

## S3 method for class 'character'
Animate(
  x,
  wc.time = TRUE,
  time.constant = 2,
  width = 65,
  coord = c(0, 0.5),
  just = c(0, 0.5),
  size = 5,
  color = "black",
  border.color = NA,
  ...
)

Arguments

Details

character Method for Animate

See Also

theme

Examples

## Not run: 
Animate(DATA[["state"]])
Animate(DATA[["state"]], color="red")
Animate(DATA[["state"]], color=RColorBrewer::brewer.pal(11, "Set3"), size=10)
cls <- DATA[["person"]] %l% data.frame(levels(DATA[["person"]]), 
    RColorBrewer::brewer.pal(5, "Set3"))
Animate(DATA[["state"]], color=cls, size=10, width=30)
cls2 <- DATA[["sex"]] %l% data.frame(c("m", "f"),c("lightblue", "pink"))
Animate(DATA[["state"]], color=cls2, just=c(.5, .5), coord = c(.5, .5))

## Print method
print(Animate(DATA[["state"]], color=cls2, just=c(.5, .5), coord = c(.5, .5)), 
    pause=.25)
Animate(DATA[["state"]], color=sample(colors(), nrow(DATA)), 
    size=sample(4:13, nrow(DATA), TRUE), width=30,  just=c(.5, .5), coord = c(.5, .5))

## End(Not run)

Discourse Map

Description

Animate.discourse_map - Animate a discourse discourse_map.

Usage

## S3 method for class 'discourse_map'
Animate(
  x,
  edge.constant,
  sep = "_",
  current.color = "red",
  previous.color = "grey50",
  wc.time = TRUE,
  time.constant = 2,
  title = NULL,
  ...
)

Arguments

Details

discourse_map Method for Animate

Note

The width of edges is based on words counts on that edge until that moment divided by total number of words used until that moment. Thicker edges tend to thin as time passes. The actual duration the current edge stays as the current.color is based on word counts for that particular flow of dialogue divided by total dialogue (words) used.

Animate Formality

Description

Animate.formality - Animate a formality object.

Usage

## S3 method for class 'formality'
Animate(
  x,
  contextual = "yellow",
  formal = "red",
  edge.constant,
  wc.time = TRUE,
  time.constant = 2,
  title = NULL,
  digits = 3,
  current.color = "black",
  current.speaker.color = NULL,
  non.speaker.color = NA,
  missing.color = "purple",
  all.color.line = "red",
  plus.300.color = "grey40",
  under.300.color = "grey88",
  type = "network",
  width = 65,
  coord = c(0, 0.5),
  just = c(0, 0.5),
  ...
)

Arguments

Details

formality Method for Animate

Note

The width of edges is based on words counts on that edge until that moment divided by total number of words used until that moment. Thicker edges tend to thin as time passes. The actual duration the current edge stays as the current.color is based on word counts for that particular flow of dialogue divided by total dialogue (words) used. The edge label is the current formality for that turn of talk (an aggregation of the sub sentences of the current turn of talk). The coloring of the current edge formality is produced at th sentence level, therefor a label may indicate a positive current turn of talk, while the coloring may indicate a negative sentences. Coloring is based on percentage of formal parts of speech (i.e., noun, adjective, preposition, article).

Gantt Durations

Description

gantt - Animate discourse from gantt.

Usage

## S3 method for class 'gantt'
Animate(x, wc.time = TRUE, time.constant = 2, colors = NULL, ...)

Arguments

Details

gantt Method for Animate

Gantt Plot

Description

gantt_plot - Animate discourse from gantt_wrap, gantt_plot, or any other Gantt plotting method.

Usage

## S3 method for class 'gantt_plot'
Animate(x, wc.time = TRUE, time.constant = 2, colors = NULL, ...)

Arguments

Details

gantt_plot Method for Animate

Animate Formality

Description

Animate.lexical_classification - Animate a lexical_classification object.

Usage

## S3 method for class 'lexical_classification'
Animate(
  x,
  type = "network",
  content = "red",
  functional = "yellow",
  edge.constant,
  wc.time = TRUE,
  time.constant = 2,
  title = NULL,
  digits = 2,
  current.color = "black",
  current.speaker.color = NULL,
  non.speaker.color = NA,
  missing.color = "purple",
  all.color.line = "red",
  width = 65,
  function.words = qdapDictionaries::function.words,
  left = "<<",
  right = ">>",
  coord = c(0, 0.5),
  just = c(0, 0.5),
  ...
)

Arguments

Details

lexical_classification Method for Animate

Note

The width of edges is based on words counts on that edge until that moment divided by total number of words used until that moment. Thicker edges tend to thin as time passes. The actual duration the current edge stays as the current.color is based on word counts for that particular flow of dialogue divided by total dialogue (words) used. The edge label is the current content rate for that turn of talk (an aggregation of the sub sentences of the current turn of talk). The coloring of the current edge content rate is produced at th sentence level, therefor a label may indicate a more content laden current turn of talk, while the coloring may indicate a functional laden average of sentences. Coloring is based on percentage of conent words.

Animate Polarity

Description

Animate.polarity - Animate a polarity object.

Usage

## S3 method for class 'polarity'
Animate(
  x,
  negative = "blue",
  positive = "red",
  neutral = "yellow",
  edge.constant,
  wc.time = TRUE,
  time.constant = 2,
  title = NULL,
  digits = 3,
  width = 65,
  current.color = "black",
  current.speaker.color = NULL,
  non.speaker.color = NA,
  ave.color.line = "red",
  type = "network",
  coord = c(0, 0.5),
  just = c(0, 0.5),
  ...
)

Arguments

Details

polarity Method for Animate

Note

The width of edges is based on words counts on that edge until that moment divided by total number of words used until that moment. Thicker edges tend to thin as time passes. The actual duration the current edge stays as the current.color is based on word counts for that particular flow of dialogue divided by total dialogue (words) used. The edge label is the current polarity for that turn of talk (an aggregation of the sub sentences of the current turn of talk). The coloring of the current edge polarity is produced at th sentence level, therefor a label may indicate a positive current turn of talk, while the coloring may indicate a negative sentences.

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

Fictitious Split Sentence Classroom Dialogue

Description

A sentSplit version of the DATA dataset.

Usage

data(DATA.SPLIT)

Format

A data frame with 15 rows and 8 variables

Details

• person. Speaker

• tot. Turn of talk with sub sentences

• TOT. Turn of talk

• sex. Gender

• adult. Dummy coded adult (0-no; 1-yes)

• code. Dialogue coding scheme

• state. Statement (dialogue)

• stem.text. A stemmed version of the text.var

Fictitious Repeated Measures Classroom Dialogue

Description

A repeated measures version of the DATA dataset.

Usage

data(DATA2)

Format

A data frame with 74 rows and 7 variables

Details

• day. Day of observation

• class. Class period/subject of observation

• person. Speaker

• sex. Gender

• adult. Dummy coded adult (0-no; 1-yes)

• state. Statement (dialogue)

• code. Dialogue coding scheme

Dissimilarity Statistics

Description

Uses the distance function to calculate dissimilarity statistics by grouping variables.

Usage

Dissimilarity(
  text.var,
  grouping.var = NULL,
  method = "prop",
  diag = FALSE,
  upper = FALSE,
  p = 2,
  ...
)

Arguments

Value

Returns a matrix of dissimilarity values (the agreement between text).

See Also

dist

Examples

## Not run: 
with(DATA, Dissimilarity(state, list(sex, adult)))
with(DATA, Dissimilarity(state, person, diag = TRUE))

## Clustering: Dendrogram
(x <- with(pres_debates2012, Dissimilarity(dialogue, list(person, time))))
fit <- hclust(x)
plot(fit)
## draw dendrogram with red borders around the 3 clusters 
rect.hclust(fit, k=3, border=c("red", "purple", "seagreen"))

## Clustering: Dendrogram with p.values
library(pvclust)
wfm.mod <- with(pres_debates2012, wfm(dialogue, list(person, time)))
fit <- suppressMessages(pvclust(wfm.mod, method.hclust="ward",
    method.dist="euclidean"))
plot(fit) 
pvrect(fit, alpha=.95)

## Multidimentional Scaling
## Based on blog post from Bodong Chen
## http://bodongchen.com/blog/?p=301

## Fit it: 2-D
(diss <- with(pres_debates2012, Dissimilarity(dialogue, list(person, time), 
    method = "euclidean")))
fit <- cmdscale(diss, eig = TRUE, k = 2)

## Plot it 2-D
points <- data.frame(x = fit$points[, 1], y = fit$points[, 2])
ggplot(points, aes(x = x, y = y)) + 
    geom_point(data = points, aes(x = x, y = y, color = rownames(points))) + 
    geom_text(data = points, aes(x = x, y = y - 0.2, label = row.names(points)))
    
## Fit it: 3-D
library(scatterplot3d)
fit <- cmdscale(diss, eig = TRUE, k = 3)

points <- data.frame(colSplit(names(fit$points[, 1])))
library(qdapTools)
points$colors <- points$X1 %l% data.frame(levels(points$X1), 
    qcv(yellow, yellow, blue, yellow, red, yellow))
points$shape <- points$X2 %l% data.frame(levels(points$X2), c(15, 17, 19))

## Plot it: 3-D
scatterplot3d(fit$points[, 1], fit$points[, 2], fit$points[, 3], 
    color = points$colors, pch = points$shape, 
    main = "Semantic Space Scaled to 3D", xlab = "x", ylab = "y", 
    zlab = "z", type = "h")

legend("bottomright", title="Person",
   qcv(Obama, Romney, Other), fill=qcv(blue, red, yellow))
legend("topleft",  paste("Time", 1:3), pch=c(15, 17, 19))

## Compare to Cosine Similarity
cos_sim <- function(x, y) x %*% y / sqrt(x%*%x * y%*%y)
mat <- matrix(rbinom(500, 0:1, .45), ncol=10)
v_outer(mat, cos_sim)

v_outer(with(DATA, wfm(state, person)), cos_sim)
with(DATA, Dissimilarity(state, person))

## End(Not run)

Filter

Description

Filter.all_words - Filter words from a all_words that meet max/min word length criteria.

Filter.TermDocumentMatrix - Filter words from a TermDocumentMatrix vector that meet max/min word length criteria.

Filter.DocumentTermMatrix - Filter words from a DocumentTermMatrix that meet max/min word length criteria.

Filter - Filter words from various objects that meet max/min word length criteria.

Filter.wfm - Filter words from a wfm that meet max/min word length criteria.

Filter.character - Filter words from a character vector that meet max/min word length criteria.

Filter.fwl - Filter words from a fwl that meet max/min word length criteria.

Filter.fswl - Filter words from a fswl that meet max/min word length criteria.

Filter.rfswl - Filter words from a rfswl that meet max/min word length criteria.

Usage

## S3 method for class 'all_words'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

## S3 method for class 'TermDocumentMatrix'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

## S3 method for class 'DocumentTermMatrix'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

## S3 method for class 'wfm'
Filter(x, min = 1, max = Inf, count.apostrophe = TRUE, stopwords = NULL, ...)

## S3 method for class 'character'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

## S3 method for class 'fwl'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

## S3 method for class 'fswl'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

## S3 method for class 'rfswl'
Filter(
  x,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  stopwords = NULL,
  ignore.case = TRUE,
  ...
)

Arguments

Details

all_words Method for Filter

TermDocumentMatrix Method for Filter

DocumentTermMatrix Method for Filter

character Method for Filter

fwl Method for Filter

fswl Method for Filter

rfswl Method for Filter

Value

Filter.all_words - Returns a matrix of the class "all_words".

Filter.TermDocumentMatrix - Returns a matrix of the class "TermDocumentMatrix".

Filter.DocumentTermMatrix - Returns a matrix of the class "DocumentTermMatrix".

Filter - Returns a matrix of the class "wfm".

Filter.character - Returns a vector of the class "character".

Filter.wfm - Returns a matrix of the class "wfm".

Filter.fwl - Returns a matrix of the class "fwl".

Filter.fswl - Returns a matrix of the class "fswl".

Filter.rfswl - Returns a matrix of the class "rfswl".

Note

The name and idea behind this function is inspired by the dplyr package's filter function and has a similar meaning in that you are grabbing rows (or elements) meeting a particular criteria.

Examples

## Not run: 
Filter(with(DATA, wfm(state, list(sex, adult))), 5)
with(DATA, wfm(state, list(sex, adult)))

## Filter particular words based on max/min values in wfm
v <- with(DATA, wfm(state, list(sex, adult)))
Filter(v, 5)
Filter(v, 5, count.apostrophe = FALSE)
Filter(v, 5, 7)
Filter(v, 4, 4)
Filter(v, 3, 4)
Filter(v, 3, 4, stopwords = Top25Words)

## Filter works on character strings too...
x <- c("Raptors don't like robots!",  "I'd pay $500.00 to rid them.")
Filter(x, 3)
Filter(x, 4)
Filter(x, 4, count.apostrophe = FALSE)
Filter(x, 4, count.apostrophe = FALSE, stopwords="raptors")
Filter(x, 4, stopwords="raptors")
Filter(x, 4, stopwords="raptors", ignore.case = FALSE)

DATA[, "state"] <- Filter(DATA[, "state"], 4)
DATA <- qdap::DATA

## Filter `all_words`
head(all_words(raj$dialogue))
Filter(head(all_words(raj$dialogue)), min = 3)

## End(Not run)

Replace Missing Values (NA)

Description

Replace missing values (NA) in a vector or dataframe.

Usage

NAer(x, replace = 0)

Arguments

Value

Returns a vector or dataframe with missing values replaced.

Examples

## Not run: 
set.seed(10)              
(x <- sample(c(rep(NA, 4), 1:10), 20, rep=T))
NAer(x)

set.seed(10)
(y <- data.frame(matrix(x, 5, 4))                           )
NAer(y)
NAer(y, "MISSING")  

## End(Not run)

Generic Network Method

Description

Create a network plot for select qdap outputs.

Usage

Network(x, ...)

Arguments

Value

Returns a network plot.

Network Formality

Description

Network.formality - Network a formality object.

Usage

## S3 method for class 'formality'
Network(
  x,
  contextual = "yellow",
  formal = "red",
  edge.constant,
  title = NULL,
  digits = 3,
  plus.300.color = "grey40",
  under.300.color = "grey88",
  missing.color = "purple",
  ...
)

Arguments

Details

formality Method for Network

Network Lexical Classification

Description

Network.lexical_classification - Network a lexical_classification object.

Usage

## S3 method for class 'lexical_classification'
Network(
  x,
  functional = "yellow",
  content = "red",
  edge.constant,
  title = NULL,
  digits = 2,
  ...
)

Arguments

Details

lexical_classification Method for Network

Network Polarity

Description

Network.polarity - Network a polarity object.

Usage

## S3 method for class 'polarity'
Network(
  x,
  negative = "blue",
  positive = "red",
  neutral = "yellow",
  edge.constant,
  title = NULL,
  digits = 3,
  ...
)

Arguments

Details

polarity Method for Network

Search Columns of a Data Frame

Description

Search - Find terms located in columns of a data frame.

boolean_search - Conducts a Boolean search for terms/strings within a character vector.

%bs% - Binary operator version of boolean_search .

Usage

Search(dataframe, term, column.name = NULL, max.distance = 0.02, ...)

boolean_search(
  text.var,
  terms,
  ignore.case = TRUE,
  values = FALSE,
  exclude = NULL,
  apostrophe.remove = FALSE,
  char.keep = NULL,
  digit.remove = FALSE
)

text.var %bs% terms

Arguments

Details

The terms string is first split by the OR separators into a list. Next the list of vectors is split on the AND separator to produce a list of vectors of search terms. Each sentence is matched against the terms. For a sentence to be counted it must fit all of the terms in an AND Boolean or one of the conditions in an OR Boolean.

Value

Search - Returns the rows of the data frame that match the search term.

boolean_search - Returns the values (or indices) of a vector of strings that match given terms.

See Also

trans_context

termco

Examples

## Not run: 
## Dataframe search:
(SampDF <- data.frame("islands"=names(islands)[1:32],mtcars, row.names=NULL))

Search(SampDF, "Cuba", "islands")
Search(SampDF, "New", "islands")
Search(SampDF, "Ho")
Search(SampDF, "Ho", max.distance = 0)
Search(SampDF, "Axel Heiberg")
Search(SampDF, 19) #too much tolerance in max.distance
Search(SampDF, 19, max.distance = 0)
Search(SampDF, 19, "qsec", max.distance = 0)

##Boolean search:
boolean_search(DATA$state, " I ORliar&&stinks")
boolean_search(DATA$state, " I &&.", values=TRUE)
boolean_search(DATA$state, " I OR.", values=TRUE)
boolean_search(DATA$state, " I &&.")

## Exclusion:
boolean_search(DATA$state, " I ||.", values=TRUE)
boolean_search(DATA$state, " I ||.", exclude = c("way", "truth"), values=TRUE)

## From stackoverflow: http://stackoverflow.com/q/19640562/1000343
dat <- data.frame(x = c("Doggy", "Hello", "Hi Dog", "Zebra"), y = 1:4)
z <- data.frame(z =c("Hello", "Dog"))

dat[boolean_search(dat$x, paste(z$z, collapse = "OR")), ]

## Binary operator version
dat[dat$x %bs% paste(z$z, collapse = "OR"), ]

## Passing to `trans_context`
inds <- boolean_search(DATA.SPLIT$state, " I&&.|| I&&!", ignore.case = FALSE)
with(DATA.SPLIT, trans_context(state, person, inds=inds))

(inds2 <- boolean_search(raj$dialogue, spaste(paste(negation.words, 
    collapse = " || "))))
trans_context(raj$dialogue, raj$person, inds2)

## End(Not run)

Add Title to Select qdap Plots

Description

Add title to select qdap objects that store a plot.

Usage

Title(object)

Title(object) <- value

Arguments

Remove Leading/Trailing White Space

Description

Remove leading/trailing white space.

Usage

Trim(x)

Arguments

Value

Returns a vector with the leading/trailing white spaces removed.

Examples

## Not run: 
(x <- c("  talkstats.com ", "   really? ", " yeah"))
Trim(x)

## End(Not run)

Detect Incomplete Sentences; Add | Endmark

Description

Automatically detect missing endmarks and replace with the | endmark symbol to indicate an incomplete sentence.

Usage

add_incomplete(text.var, endmarks = "[.?|!]+$", silent = FALSE)

Arguments

Value

Returns a vector with missing endmarks replaced with |.

Examples

add_incomplete(
    c(
        "This in a", 
        "I am funny!", 
        "An ending of sorts%", 
        "What do you want?"
    )
)

Make Plural (or Verb to Singular) Versions of Words

Description

Add -s, -es, or -ies to words.

Usage

add_s(x, keep.original = TRUE)

Arguments

Value

Returns a vector of plural words.

Examples

set.seed(10)
add_s(sample(GradyAugmented, 10))
set.seed(10)
add_s(sample(GradyAugmented, 10), FALSE)

Takes a Matrix and Generates an Adjacency Matrix

Description

Takes a matrix (wfm) or termco object and generates an adjacency matrix for use with the igraph package.

Usage

adjacency_matrix(matrix.obj)

adjmat(matrix.obj)

Arguments

Value

Returns list:

See Also

dist

Examples

## Not run: 
words <- c(" you", " the", "it", "oo")
Terms <- with(DATA, termco(state, list(sex, adult), words))
Terms
adjacency_matrix(Terms)

wordLIST <- c(" montague", " capulet", " court", " marry")
raj.termco <- with(raj.act.1, termco(dialogue, person, wordLIST))
raj.adjmat <- adjmat(raj.termco)
names(raj.adjmat)  #see what's available from the adjacency_matrix object
library(igraph)
g <- graph.adjacency(raj.adjmat$adjacency, weighted=TRUE, mode ="undirected")
g <- simplify(g)
V(g)$label <- V(g)$name
V(g)$degree <- degree(g)
plot(g, layout=layout.auto(g))

## End(Not run)

Searches Text Column for Words

Description

A convenience function to find words that begin with or contain a letter chunk and returns the frequency counts of the number of occurrences of each word.

Usage

all_words(
  text.var,
  begins.with = NULL,
  contains = NULL,
  alphabetical = TRUE,
  apostrophe.remove = FALSE,
  char.keep = char2space,
  char2space = "~~",
  ...
)

Arguments

Value

Returns a dataframe with frequency counts of words that begin with or contain the provided word chunk.

Note

Cannot provide both begins.with and contains arguments at once. If both begins.with and contains are NULL. all_words returns a frequency count for all words.

See Also

term_match

Examples

## Not run: 
x1 <- all_words(raj$dialogue, begins.with="re")
head(x1, 10)
x2 <- all_words(raj$dialogue, "q")
head(x2, 10)
all_words(raj$dialogue, contains="conc")
x3 <- all_words(raj$dialogue)
head(x3, 10)
x4 <- all_words(raj$dialogue, contains="the")
head(x4)
x5 <- all_words(raj$dialogue, contains="read")
head(x5)

## Filter by nchar and stopwords
Filter(head(x3), min = 3)

## Keep spaces
all_words(space_fill(DATA$state, c("are you", "can be")))

## End(Not run)

tm Package Compatibility Tools: Apply to or Convert to/from Term Document Matrix or Document Term Matrix

Description

as.tdm - Create term document matrices from raw text or wfm for use with other text analysis packages.

as.TermDocumentMatrix - Create document term matrices from raw text or wfm for use with other text analysis packages.

as.dtm - Create document term matrices from raw text or wfm for use with other text analysis packages.

as.DocumentTermMatrix - Create document term matrices from raw text or wfm for use with other text analysis packages.

as.data.frame - Convert a tm package Corpus to a qdap data.frame.

as.Corpus - Attempts to convert its argument into a tm package Corpus.

apply_as_tm - Apply functions intended to be used on the tm package's TermDocumentMatrix to a wfm object.

apply_as_df - Apply a tm Corpus as a qdap dataframe. apply_as_df - Apply functions intended to be used on the qdap package's data.frame + sentSplit to a tm Corpus object.

Usage

as.tdm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

as.TermDocumentMatrix(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

as.dtm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

as.DocumentTermMatrix(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'Corpus'
as.tdm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## Default S3 method:
as.tdm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'character'
as.tdm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'Corpus'
as.dtm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## Default S3 method:
as.dtm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'character'
as.dtm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'wfm'
as.tdm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'wfm'
as.dtm(text.var, grouping.var = NULL, vowel.check = TRUE, ...)

## S3 method for class 'Corpus'
as.data.frame(
  x,
  row.names,
  optional,
  ...,
  doc = "doc_id",
  text = "text",
  sent.split = FALSE
)

as.Corpus(text.var, grouping.var = NULL, demographic.vars, ...)

## S3 method for class 'sent_split'
as.Corpus(text.var, grouping.var = NULL, demographic.vars, ...)

## Default S3 method:
as.Corpus(text.var, grouping.var = NULL, demographic.vars, ...)

apply_as_tm(wfm.obj, tmfun, ..., to.qdap = TRUE)

apply_as_df(
  tm.corpus,
  qdapfun,
  ...,
  stopwords = NULL,
  min = 1,
  max = Inf,
  count.apostrophe = TRUE,
  ignore.case = TRUE
)

## S3 method for class 'TermDocumentMatrix'
as.Corpus(text.var, ...)

## S3 method for class 'DocumentTermMatrix'
as.Corpus(text.var, ...)

## S3 method for class 'wfm'
as.Corpus(text.var, ...)

Arguments

Details

Produces output that is identical to the tm package's TermDocumentMatrix, DocumentTermMatrix, Corpus or allows convenient interface between the qdap and tm packages.

Value

as.tdm - Returns a TermDocumentMatrix.

as.TermDocumentMatrix - Returns a TermDocumentMatrix.

as.dtm - Returns a DocumentTermMatrix.

as.DocumentTermMatrix - Returns a TermDocumentMatrix.

as.data.frame - Converts a Corpus and returns a qdap oriented data.frame.

as.Corpus - Converts a qdap oriented dataframe and returns a Corpus.

apply_as_tm - Applies a tm oriented function to a wfm and attempts to simplify back to a wfm or weight format.

apply_as_df - Returns the output typical of the applied qdap function.

Note

aply_as_df coerces to a dataframe with columns named 'docs' and the other named 'text'.

See Also

DocumentTermMatrix, Corpus, TermDocumentMatrix, as.wfm

Filter

Examples

## Not run: 
as.dtm(DATA$state, DATA$person)
as.tdm(DATA$state, DATA$person)

x <- wfm(DATA$state, DATA$person)
as.tdm(x)
as.dtm(x)
library(tm)
plot(as.tdm(x))

pres <- as.tdm(pres_debates2012$dialogue, pres_debates2012$person)
plot(pres, corThreshold = 0.8)
pres
(pres2 <- removeSparseTerms(pres, .3))
plot(pres2, corThreshold = 0.95)

shorts <- all_words(pres_debates2012)[,1][nchar(all_words(
    pres_debates2012)[,1]) < 4]

SW <- c(shorts, qdapDictionaries::contractions[, 1],
    qdapDictionaries::Top200Words,
    "governor", "president", "mister", "obama","romney")

DocTermMat2 <- with(pres_debates2012, as.dtm(dialogue, list(person, time), stopwords = SW))
DocTermMat2 <- removeSparseTerms(DocTermMat2,0.95)
(DocTermMat2 <- DocTermMat2[rowSums(as.matrix(DocTermMat2))> 0,])
plot(DocTermMat2)
    
## Correspondence Analysis
library(ca)

dat <- pres_debates2012
dat <- dat[dat$person %in% qcv(ROMNEY, OBAMA), ]

speech <- stemmer(dat$dialogue)
mytable1 <- with(dat, as.tdm(speech, list(person, time), stopwords = Top25Words))

fit <- ca(as.matrix(mytable1))
summary(fit)
plot(fit)
plot3d.ca(fit, labels=1)


mytable2 <- with(dat, as.tdm(speech, list(person, time), stopwords = Top200Words))

fit2 <- ca(as.matrix(mytable2))
summary(fit2)
plot(fit2)
plot3d.ca(fit2, labels=1)

## Topic Models
# Example 1 #
library(topicmodels); library(tm)

# Generate stop words based on short words, frequent words and contractions
shorts <- all_words(pres_debates2012)[,1][nchar(all_words(
    pres_debates2012)[,1]) < 4]
    
SW <- c(shorts, qdapDictionaries::contractions[, 1], 
    qdapDictionaries::Top200Words, 
    "governor", "president", "mister", "obama","romney")
    
DocTermMat <- with(pres_debates2012, as.dtm(dialogue, person, stopwords = SW))
DocTermMat <- removeSparseTerms(DocTermMat,0.999)
DocTermMat <- DocTermMat[rowSums(as.matrix(DocTermMat))> 0,]

lda.model <- LDA(DocTermMat, 5)

(topics <- posterior(lda.model, DocTermMat)$topics)
terms(lda.model,20)

# Plot the Topics Per Person
topic.dat <- matrix2df(topics, "Person")
colnames(topic.dat)[-1] <- paste2(t(terms(lda.model,20)), sep=", ")

library(reshape2)
mtopic <- melt(topic.dat, variable="Topic", value.name="Proportion")
ggplot(mtopic, aes(weight=Proportion, x=Topic, fill=Topic)) + 
    geom_bar() + 
    coord_flip() +
    facet_grid(Person~.) +
    guides(fill=FALSE)

# Example 2 #
DocTermMat2 <- with(pres_debates2012, as.dtm(dialogue, list(person, time), stopwords = SW))
DocTermMat2 <- removeSparseTerms(DocTermMat2,0.95)
DocTermMat2 <- DocTermMat2[rowSums(as.matrix(DocTermMat2))> 0,]

lda.model2 <- LDA(DocTermMat2, 6)

(topics2 <- posterior(lda.model2, DocTermMat2)$topics)
terms(lda.model2,20)
qheat(topics2, high="blue", low="yellow", by.col=FALSE)

# Example 3 #
lda.model3 <- LDA(DocTermMat2, 10)

(topics3 <- posterior(lda.model3, DocTermMat2)$topics)
terms(lda.model3, 20)
qheat(topics3, high="blue", low="yellow", by.col=FALSE)

# Plot the Topics Per Person
topic.dat3 <- matrix2df(topics3, "Person&Time")
colnames(topic.dat3)[-1] <- paste2(t(terms(lda.model3, 10)), sep=", ")
topic.dat3 <- colsplit2df(topic.dat3)

library(reshape2)
library(scales)
mtopic3 <- melt(topic.dat3, variable="Topic", value.name="Proportion")
(p1 <- ggplot(mtopic3, aes(weight=Proportion, x=Topic, fill=Topic)) +
    geom_bar() +
    coord_flip() +
    facet_grid(Person~Time) +
    guides(fill=FALSE) +
    scale_y_continuous(labels = percent) +
    theme(plot.margin = unit(c(1, 0, 0.5, .5), "lines")) +
    ylab("Proportion")) 

mtopic3.b <- mtopic3
mtopic3.b[, "Topic"] <- factor(as.numeric(mtopic3.b[, "Topic"]), levels = 1:10)
mtopic3.b[, "Time"] <- factor(gsub("time ", "", mtopic3.b[, "Time"]))

p2 <- ggplot(mtopic3.b, aes(x=Time, y=Topic, fill=Proportion)) +
    geom_tile(color = "white") +
    scale_fill_gradient(low = "grey70", high = "red") +
    facet_grid(Person~Time, scales = "free") +
    theme(axis.title.y = element_blank(),
        axis.text.x= element_text(colour="white"),
        axis.ticks.x= element_line(colour="white"),
        axis.ticks.y = element_blank(),
        axis.text.y= element_blank(),
        plot.margin = unit(c(1, -.5, .5, -.9), "lines")
) 

library(gridExtra)
grid.arrange(p1, p2, nrow=1, widths = grid::unit(c(.85, .15), "native")) 
    
## tm Matrices to wfm
library(tm)
data(crude)

## A Term Document Matrix Conversion
(tm_in <- TermDocumentMatrix(crude, control = list(stopwords = TRUE)))
converted <- as.wfm(tm_in)
head(converted)
summary(converted)

## A Document Term Matrix Conversion
(dtm_in <- DocumentTermMatrix(crude, control = list(stopwords = TRUE)))
summary(as.wfm(dtm_in))

## `apply_as_tm` Examples
## Create a wfm
a <- with(DATA, wfm(state, list(sex, adult)))
summary(a)

## Apply functions meant for a tm TermDocumentMatrix
out <- apply_as_tm(a, tm:::removeSparseTerms, sparse=0.6)
summary(out)

apply_as_tm(a, tm:::findAssocs, "computer", .8)
apply_as_tm(a, tm:::findFreqTerms, 2, 3)
apply_as_tm(a, tm:::Zipf_plot)
apply_as_tm(a, tm:::Heaps_plot)
apply_as_tm(a, tm:::plot.TermDocumentMatrix, corThreshold = 0.4)

library(proxy)
apply_as_tm(a, tm:::weightBin)
apply_as_tm(a, tm:::weightBin, to.qdap = FALSE)
apply_as_tm(a, tm:::weightSMART)
apply_as_tm(a, tm:::weightTfIdf)

## Convert tm Corpus to Dataframe
## A tm Corpus
library(tm)
reut21578 <- system.file("texts", "crude", package = "tm")
reuters <- Corpus(DirSource(reut21578),
    readerControl = list(reader = readReut21578XML))

## Convert to dataframe
corp_df <- as.data.frame(reuters)
htruncdf(corp_df)

z <- as.Corpus(DATA$state, DATA$person, 
       demographic=DATA[, qcv(sex, adult, code)])
as.data.frame(z)

## Apply a qdap function
out <- formality(corp_df$text, corp_df$docs)
plot(out)

## Convert a qdap dataframe to tm package Corpus
(x <- with(DATA2, as.Corpus(state, list(person, class, day))))
library(tm)
inspect(x)
inspect_text(x)
class(x)

(y <- with(pres_debates2012, as.Corpus(dialogue, list(person, time))))

## Add demographic info to DMetaData of Corpus
z <- as.Corpus(DATA$state, DATA$person, 
    demographic=DATA[, qcv(sex, adult, code)])
lview(z)

lview(as.Corpus(DATA$state, DATA$person,
    demographic=DATA$sex))

lview(as.Corpus(DATA$state, DATA$person,
    demographic=list(DATA$sex, DATA$adult)))

## Apply qdap functions meant for dataframes from sentSplit to tm Corpus
library(tm)
reut21578 <- system.file("texts", "crude", package = "tm")
reuters <- Corpus(DirSource(reut21578),
    readerControl = list(reader = readReut21578XML))

matches <- list(
    oil = qcv(oil, crude),
    money = c("economic", "money")
)

apply_as_df(reuters, word_stats)
apply_as_df(reuters, formality)
apply_as_df(reuters, word_list)
apply_as_df(reuters, polarity)
apply_as_df(reuters, Dissimilarity)
apply_as_df(reuters, diversity)
apply_as_df(reuters, pos_by)
apply_as_df(reuters, flesch_kincaid)
apply_as_df(reuters, trans_venn)
apply_as_df(reuters, gantt_plot)
apply_as_df(reuters, rank_freq_mplot)
apply_as_df(reuters, character_table)

(termco_out <- apply_as_df(reuters, termco, match.list = matches))
plot(termco_out, values = TRUE, high="red")

(wordcor_out <- apply_as_df(reuters, word_cor, word = unlist(matches)))
plot(wordcor_out)

(f_terms <- apply_as_df(reuters, freq_terms, at.least = 3))
plot(f_terms)

apply_as_df(reuters, trans_cloud)
## To use "all" rather than "docs" as "grouping.var"...
apply_as_df(reuters, trans_cloud, grouping.var=NULL, 
    target.words=matches, cloud.colors = c("red", "blue", "grey75"))

finds <- apply_as_df(reuters, freq_terms, at.least = 5,
    top = 5, stopwords = Top100Words)
apply_as_df(reuters, dispersion_plot, match.terms = finds[, 1],
    total.color = NULL)
    
## Filter for Term Document Matrix/Document Term Matrix
library(tm)
data(crude)

(tdm_in <- TermDocumentMatrix(crude, control = list(stopwords = TRUE)))
Filter(tdm_in, 5)

(dtm_in <- DocumentTermMatrix(crude, control = list(stopwords = TRUE)))
Filter(dtm_in, 5)

## Filter particular words based on max/min values
Filter(dtm_in, 5, 7)
Filter(dtm_in, 4, 4)
Filter(tdm_in, 3, 4)
Filter(tdm_in, 3, 4, stopwords = Top200Words)

## SPECIAL REMOVAL OF TERMS (more flexible consideration of words than wfm)
dat <- data.frame(
    person = paste0("person_", 1:5),
    tweets = c("test one two", "two apples","hashtag #apple", 
        "#apple #tree", "http://microsoft.com")
)

## remove specialty items
dat[[2]] <- rm_default(dat[[2]], pattern=pastex("@rm_url", "#apple\\b"))


myCorp <- tm::tm_map(crude, tm::removeWords, Top200Words)
myCorp %>% as.dtm() %>% tm::inspect()

## End(Not run)

Readability Measures

Description

automated_readability_index - Apply Automated Readability Index to transcript(s) by zero or more grouping variable(s).

coleman_liau - Apply Coleman Liau Index to transcript(s) by zero or more grouping variable(s).

SMOG - Apply SMOG Readability to transcript(s) by zero or more grouping variable(s).

flesch_kincaid - Flesch-Kincaid Readability to transcript(s) by zero or more grouping variable(s).

fry - Apply Fry Readability to transcript(s) by zero or more grouping variable(s).

linsear_write - Apply Linsear Write Readability to transcript(s) by zero or more grouping variable(s).

Usage

automated_readability_index(
  text.var,
  grouping.var = NULL,
  rm.incomplete = FALSE,
  ...
)

coleman_liau(text.var, grouping.var = NULL, rm.incomplete = FALSE, ...)

SMOG(
  text.var,
  grouping.var = NULL,
  output = "valid",
  rm.incomplete = FALSE,
  ...
)

flesch_kincaid(text.var, grouping.var = NULL, rm.incomplete = FALSE, ...)

fry(
  text.var,
  grouping.var = NULL,
  rm.incomplete = FALSE,
  auto.label = TRUE,
  grid = FALSE,
  div.col = "grey85",
  plot = TRUE,
  ...
)

linsear_write(text.var, grouping.var = NULL, rm.incomplete = FALSE, ...)

Arguments

Value

Returns a list of 2 dataframes: (1) Counts and (2) Readability. Counts are the raw scores used to calculate readability score and can be accessed via counts. Readability is the dataframe with the selected readability statistic by grouping variable(s) and can be access via scores. The fry function returns a graphic representation of the readability as the scores returns the information for graphing but not a readability score.

Warning

Many of the indices (e.g., Automated Readability Index) are derived from word difficulty (letters per word) and sentence difficulty (words per sentence). If you have not run the sentSplit function on your data the results may not be accurate.

Fry

The fry function is based on Fry's formula that randomly samples 3 100 word length passages. If a group(s) in does not contain 300+ words they will not be included in the output.

References

Coleman, M., & Liau, T. L. (1975). A computer readability formula designed for machine scoring. Journal of Applied Psychology, Vol. 60, pp. 283-284.

Fry, E. B. (1968). A readability formula that saves time. Journal of Reading, 11(7), 513-516, 575-578.

Fry, E. B. (1969). The readability graph validated at primary levels. The Reading Teacher, 22(6), 534-538.

Flesch R. (1948). A new readability yardstick. Journal of Applied Psychology. Vol. 32(3), pp. 221-233. doi: 10.1037/h0057532.

Gunning, T. G. (2003). Building Literacy in the Content Areas. Boston: Allyn & Bacon.

McLaughlin, G. H. (1969). SMOG Grading: A New Readability Formula. Journal of Reading, Vol. 12(8), pp. 639-646.

Smith, E. A. & Senter, R. J. (1967) Automated readability index. Technical Report AMRLTR-66-220, University of Cincinnati, Cincinnati, Ohio.

Examples

## Not run: 
AR1 <- with(rajSPLIT, automated_readability_index(dialogue, list(person, act)))
ltruncdf(AR1,, 15)
scores(AR1)
counts(AR1)
plot(AR1)
plot(counts(AR1))

AR2 <- with(rajSPLIT, automated_readability_index(dialogue, list(sex, fam.aff)))
ltruncdf(AR2,, 15)
scores(AR2)
counts(AR2)
plot(AR2)
plot(counts(AR2))

AR3 <- with(rajSPLIT, automated_readability_index(dialogue, person))
ltruncdf(AR3,, 15)
scores(AR3)
head(counts(AR3))
plot(AR3)
plot(counts(AR3))

CL1 <- with(rajSPLIT, coleman_liau(dialogue, list(person, act)))
ltruncdf(CL1, 20)
head(counts(CL1))
plot(CL1)

CL2 <- with(rajSPLIT, coleman_liau(dialogue, list(sex, fam.aff)))
ltruncdf(CL2)
plot(counts(CL2))

(SM1 <- with(rajSPLIT, SMOG(dialogue, list(person, act))))
plot(counts(SM1))
plot(SM1)

(SM2 <- with(rajSPLIT, SMOG(dialogue, list(sex, fam.aff))))

(FL1 <- with(rajSPLIT, flesch_kincaid(dialogue, list(person, act))))
plot(scores(FL1))
plot(counts(FL1))

(FL2 <-  with(rajSPLIT, flesch_kincaid(dialogue, list(sex, fam.aff))))
plot(scores(FL2))
plot(counts(FL2))

FR1 <- with(rajSPLIT, fry(dialogue, list(sex, fam.aff)))
scores(FR1)
plot(scores(FR1))
counts(FR1)
plot(counts(FR1))

FR2 <- with(rajSPLIT, fry(dialogue, person))
scores(FR2)
plot(scores(FR2))
counts(FR2)
plot(counts(FR2))

FR3 <- with(pres_debates2012, fry(dialogue, list(time, person)))
colsplit2df(scores(FR3))
plot(scores(FR3), auto.label = FALSE)
counts(FR3)
plot(counts(FR3))

library(ggplot2)
ggplot(colsplit2df(counts(FR3)), aes(sent.per.100.wrds, 
    syllables.per.100.wrds)) +
    geom_point(aes(fill=person), shape=21, size=3) +
    facet_grid(person~time)
    
LW1 <- with(rajSPLIT, linsear_write(dialogue, list(person, act)))
plot(scores(LW1))
plot(counts(LW1))

LW2 <- with(rajSPLIT, linsear_write(dialogue, list(sex, fam.aff)))
plot(scores(LW2), method="lm")
plot(counts(LW2))

## End(Not run)

Bag of Words

Description

bag_o_words - Reduces a text column to a bag of words.

unbag - Wrapper for paste(collapse=" ") to glue words back into strings.

breaker - Reduces a text column to a bag of words and qdap recognized end marks.

word_split - Reduces a text column to a list of vectors of bag of words and qdap recognized end marks (i.e., ".", "!", "?", "*", "-").

Usage

bag_o_words(text.var, apostrophe.remove = FALSE, ...)

unbag(text.var, na.rm = TRUE)

breaker(text.var)

word_split(text.var)

Arguments

Value

Returns a vector of stripped words.

unbag - Returns a string.

breaker - Returns a vector of striped words and qdap recognized endmarks (i.e., ".", "!", "?", "*", "-").

Examples

## Not run: 
bag_o_words("I'm going home!")
bag_o_words("I'm going home!", apostrophe.remove = TRUE)
unbag(bag_o_words("I'm going home!"))

bag_o_words(DATA$state)
by(DATA$state, DATA$person, bag_o_words)
lapply(DATA$state,  bag_o_words)

breaker(DATA$state)
by(DATA$state, DATA$person, breaker)
lapply(DATA$state,  breaker)
unbag(breaker(DATA$state))

word_split(c(NA, DATA$state))
unbag(word_split(c(NA, DATA$state)))

## End(Not run)

Grab Begin/End of String to Character

Description

beg2char - Grab from beginning of string to a character(s).

char2end - Grab from character(s) to end of string.

Usage

beg2char(text.var, char = " ", noc = 1, include = FALSE)

char2end(text.var, char = " ", noc = 1, include = FALSE)

Arguments

Value

returns a vector of text with char on/forward removed.

Author(s)

Josh O'Brien, Justin Haynes and Tyler Rinker <tyler.rinker@gmail.com>.

References

https://stackoverflow.com/q/15909626/1000343

Examples

## Not run: 
x <- c("a_b_c_d", "1_2_3_4", "<_?_._:")
beg2char(x, "_")
beg2char(x, "_", 2)
beg2char(x, "_", 3)
beg2char(x, "_", 4)
beg2char(x, "_", 3, include=TRUE)

char2end(x, "_")
char2end(x, "_", 2)
char2end(x, "_", 3)
char2end(x, "_", 4)
char2end(x, "_", 3, include=TRUE)

x2 <- gsub("_", " ", x)
char2end(x2, " ", 2)
beg2char(x2, " ", 2)

x3 <- gsub("_", "\\^", x)
char2end(x3, "^", 2)
beg2char(x3, "^", 2)

## End(Not run)

Replace Blanks in a dataframe

Description

Replaces blank (empty) cells in a dataframe. Generally, for internal use.

Usage

blank2NA(dataframe, missing = NA)

Arguments

Value

Returns a data frame with blank spaces replaced.

See Also

rm_row

Examples

## Not run: 
set.seed(15)
dat <- data.frame(matrix(sample(c(month.abb[1:4], ""), 50, TRUE), 
    10, byrow = TRUE), stringsAsFactors = FALSE)

dat
blank2NA(dat)

## End(Not run)

Bracket Parsing

Description

bracketX - Apply bracket removal to character vectors.

bracketXtract - Apply bracket extraction to character vectors.

genX - Apply general chunk removal to character vectors. A generalized version of bracketX.

genXtract - Apply general chunk extraction to character vectors. A generalized version of bracketXtract.

Usage

bracketX(
  text.var,
  bracket = "all",
  missing = NULL,
  names = FALSE,
  fix.space = TRUE,
  scrub = fix.space
)

bracketXtract(text.var, bracket = "all", with = FALSE, merge = TRUE)

genX(
  text.var,
  left,
  right,
  missing = NULL,
  names = FALSE,
  fix.space = TRUE,
  scrub = TRUE
)

genXtract(text.var, left, right, with = FALSE, merge = TRUE)

Arguments

Value

bracketX - returns a vector of text with brackets removed.

bracketXtract - returns a list of vectors of bracketed text.

genXtract - returns a vector of text with chunks removed.

genX - returns a list of vectors of removed text.

Author(s)

Martin Morgan and Tyler Rinker <tyler.rinker@gmail.com>.

References

https://stackoverflow.com/q/8621066/1000343

See Also

regex

Examples

## Not run: 
examp <- structure(list(person = structure(c(1L, 2L, 1L, 3L), 
    .Label = c("bob", "greg", "sue"), class = "factor"), text = 
    c("I love chicken [unintelligible]!", 
    "Me too! (laughter) It's so good.[interrupting]", 
    "Yep it's awesome {reading}.", "Agreed. {is so much fun}")), .Names = 
    c("person", "text"), row.names = c(NA, -4L), class = "data.frame")    

examp                                                              
bracketX(examp$text, "square")  
bracketX(examp$text, "curly") 
bracketX(examp$text, c("square", "round")) 
bracketX(examp$text)  
                                              
                                              
bracketXtract(examp$text, "square")  
bracketXtract(examp$text, "curly")  
bracketXtract(examp$text, c("square", "round")) 
bracketXtract(examp$text, c("square", "round"), merge = FALSE)  
bracketXtract(examp$text)  
bracketXtract(examp$text, with = TRUE)

paste2(bracketXtract(examp$text, "curly"), " ")

x <- c("Where is the /big dog#?", 
    "I think he's @arunning@b with /little cat#.")
genXtract(x, c("/", "@a"), c("#", "@b"))

x <- c("Where is the L1big dogL2?", 
    "I think he's 98running99 with L1little catL2.")
genXtract(x, c("L1", 98), c("L2", 99))

DATA$state  #notice number 1 and 10
genX(DATA$state, c("is", "we"), c("too", "on"))

## End(Not run)

Replace Temporary Introduction to qdap Vignette

Description

Replaces the temporary (place holder) Introduction to qdap Vignette with the actual vignette.

Usage

build_qdap_vignette(download.html = FALSE)

Arguments

Value

Places the (1) HTML, (2) source, & (3) R code for the Introduction to qdap Vignette in the user's ‘R-VERSION/library/qdap/doc’.

Note

The knitr built HTML approach above takes about 4 minutes. The user may choose the faster approach (< 30 seconds) that downloads the HTML file directly from the Internet (this is for the latest CRAN release of qdap). This choice is controlled via the download.html argument. The function will ask for the user's permission before writing the documents. Once the user has run this function browseVignettes(package = 'qdap') will allow access to the new vignette files.

Capitalize Select Words

Description

A helper function for word_list that allows the user to supply vectors of words to be capitalized.

Usage

capitalizer(text, caps.list = NULL, I.list = TRUE, apostrophe.remove = FALSE)

Arguments

Value

Returns a vector of capitalized words based on supplied capitalization arguments.

Note

Not intended for general use. Acts as a helper function to several qdap functions.

Examples

## Not run: 
capitalizer(bag_o_words("i like it but i'm not certain"), "like")
capitalizer(bag_o_words("i like it but i'm not certain"), "like", FALSE)

## End(Not run)

Check Spelling

Description

check_spelling - Check the spelling for an vector of strings. The function use the following technique:

• Separate the words from a string into a bag of words.

• Look those words up in a dictionary to find words not recognized/found (considered possibly misspelled).

• These misses (possible misspellings) will be what is looked up for suggested replacements.

• Optionally, reduce dictionary by assuming the first letter of the misspelled word is correct (dictionary for this letter only).

• Reduce dictionary by eliminating words outside of the range of number of characters of the misspelled word.

• Use stringdist to find string distances between possible replacements and the misspelled term.

• Select n (n.suggests) terms from dictionary that are closest to the misspelled term.

which_misspelled - Check the spelling for a string.

check_spelling_interactive - Interactively check spelling.

correct - Access the spell corrector function from a "check_spelling_interactive" object for subsequent text character vector spelling corrections.

Usage

check_spelling(
  text.var,
  range = 2,
  assume.first.correct = TRUE,
  method = "jw",
  dictionary = qdapDictionaries::GradyAugmented,
  parallel = TRUE,
  cores = parallel::detectCores()/2,
  n.suggests = 8
)

which_misspelled(
  x,
  suggest = FALSE,
  range = 2,
  assume.first.correct = TRUE,
  dictionary = qdapDictionaries::GradyAugmented,
  method = "jw",
  nchar.dictionary = nchar(dictionary),
  first.char.dictionary = substring(dictionary, 1, 1),
  n.suggests = 8
)

check_spelling_interactive(
  text.var,
  range = 2,
  assume.first.correct = TRUE,
  click = TRUE,
  method = "jw",
  dictionary = qdapDictionaries::GradyAugmented,
  parallel = TRUE,
  cores = parallel::detectCores()/2,
  n.suggests = 8,
  ...
)

correct(x, ...)

Arguments

Value

check_spelling - Returns a data.frame with row (row number), not.found word.no (number of misspelled word), not.found (a word not found in the dictionary), suggestion (the most likely replacement for the word), and more.suggestions (A list of vectors of up to 10 most likely replacements).

which_misspelled - Returns either a named vector (names are the word number) of possible misspelled words (ifsuggestions = FALSE) or a data.frame with word.no (number of misspelled word), not.found (a word not found in the dictionary), suggestion (the most likely replacement for the word), and more.suggestions (A list of vectors of up to 10 most likely replacements).

check_spelling_interactive - Returns a character vector with the corrected text, the replacement list (via an attribute to the character vector), and a function to correct the same spelling errors in subsequent text character vectors.

correct - Returns a function for correcting spelling errors.

Note

A possible misspelled word is defined as not found in the dictionary.

check_spelling_interactive - The user may go back (undo) by pressing "TYPE MY OWN" entering either "!" (not) or "0" (similar to a phone system). The second choice in the "SELECT REPLACEMNT:" will be the original word and is prefixed with "IGNORE:". Press this to keep the original word.

References

https://stackoverflow.com/a/24454727/1000343
https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Hornik+Murdoch.pdf

See Also

stringdist

Examples

## Not run: 
x <- "Robots are evl creatres and deserv exterimanitation."
which_misspelled(x, suggest=FALSE)
which_misspelled(x, suggest=TRUE)

check_spelling(DATA$state)

## browseURL("http://stackoverflow.com/a/24454727/1000343")
terms <- c("accounts", "account", "accounting", "acounting", "acount", "acounts", "accounnt")

set.seed(10)
(fake_text <- unlist(lapply(terms, function(x) {
    unbag(sample(c(x, sample(DICTIONARY[[1]], sample(1:5, 1)))))
})))

check_spelling(fake_text)

##============================##
## INTERACTIVE SPELL CHECKING ##
##============================##

## No misspellings found
check_spelling_interactive(DATA$state)

## character method approach (minimal example)
dat <- DATA$state; dat[1] <- "I likedd the cokie icekream"
(o <- check_spelling_interactive(dat))
preprocessed(o)
fixit <- attributes(o)$correct
fixit(dat)

## character method approach (larger example)
m <- check_spelling_interactive(mraja1spl$dialogue[1:75])
preprocessed(m)
fixit <- attributes(m)$correct
fixit(mraja1spl$dialogue[1:75])

## check_spelling method approach
out <- check_spelling(mraja1spl$dialogue[1:75])
(x <- check_spelling_interactive(out))
preprocessed(x)
correct(x)(mraja1spl$dialogue[1:75])
(y <- check_spelling_interactive(out, click=FALSE))
preprocessed(y)

## Examine Methods (?stringdist::stringdist)
strings <- c(
    "Robots are evl creatres and deserv exterimanitation kream.",
    "I gots me a biggert measrue, tommorrow"
)

meths <- c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw")

stats::setNames(lapply(meths, function(x) check_spelling(strings, method=x)), meths)

## End(Not run)

Check Spelling

Description

View character check_spelling_interactive.

Usage

## S3 method for class 'character'
check_spelling_interactive(
  text.var,
  range = 2,
  assume.first.correct = TRUE,
  click = TRUE,
  method = "jw",
  dictionary = qdapDictionaries::GradyAugmented,
  parallel = TRUE,
  cores = parallel::detectCores()/2,
  n.suggests = 8,
  ...
)

Arguments

Details

character Method for check_spelling_interactive

Check Spelling

Description

View check_spelling check_spelling_interactive.

Usage

## S3 method for class 'check_spelling'
check_spelling_interactive(
  text.var,
  range = 2,
  assume.first.correct = TRUE,
  click = TRUE,
  method = "jw",
  dictionary = qdapDictionaries::GradyAugmented,
  parallel = TRUE,
  cores = parallel::detectCores()/2,
  n.suggests = 8,
  ...
)

Arguments

Details

check_spelling Method for check_spelling_interactive

Check Spelling

Description

View factor check_spelling_interactive.

Usage

## S3 method for class 'factor'
check_spelling_interactive(
  text.var,
  range = 2,
  assume.first.correct = TRUE,
  click = TRUE,
  method = "jw",
  dictionary = qdapDictionaries::GradyAugmented,
  parallel = TRUE,
  cores = parallel::detectCores()/2,
  n.suggests = 8,
  ...
)

Arguments

Details

factor Method for check_spelling_interactive

Check Text For Potential Problems

Description

Uncleaned text may result in errors, warnings, and incorrect results in subsequent analysis. check_text checks text for potential problems and suggests possible fixes. Potential text anomalies that are detected include: factors, missing ending punctuation, empty cells, double punctuation, non-space after comma, no alphabetic characters, non-ascii, missing value, and potentially misspelled words.

Usage

check_text(text.var, file = NULL)

Arguments

Value

Returns a list with the following potential text faults reports:

• non_character- Text that is non-character.

• missing_ending_punctuation- Text with no endmark at the end of the string.

• empty- Text that contains an empty element (i.e., "").

• double_punctuation- Text that contains two qdap punctuation marks in the same string.

• non_space_after_comma- Text that contains commas with no space after them.

• no_alpha- Text that contains string elements with no alphabetic characters.

• non_ascii- Text that contains non-ASCII characters.

• missing_value- Text that contains missing values (i.e., NA).

• containing_escaped- Text that contains escaped (see ?Quotes).

• containing_digits- Text that contains digits.

• indicating_incomplete- Text that contains endmarks that are indicative of incomplete/trailing sentences (e.g., ...).

• potentially_misspelled- Text that contains potentially misspelled words.

Note

The output is a list but prints as a pretty formatted output with potential problem elements, the accompanying text, and possible suggestions to fix the text.

See Also

check_spelling_interactive

Examples

## Not run: 
x <- c("i like", "i want. thet them .", "I am ! that|", "", NA, 
    "they,were there", ".", "   ", "?", "3;", "I like goud eggs!", 
    "i 4like...", "\\tgreat",  "She said \"yes\"")
check_text(x)
print(check_text(x), include.text=FALSE)

y <- c("A valid sentence.", "yet another!")
check_text(y)

## End(Not run)

Break Text Into Ordered Word Chunks

Description

Some visualizations and algorithms require text to be broken into chunks of ordered words. chunker 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

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

Arguments

Value

Returns a list of text chunks.

Examples

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

## Bigger data
with(hamlet, chunker(dialogue, person, n.chunks = 10))
with(hamlet, chunker(dialogue, person, n.words = 300))

## Not run: 
## with polarity hedonmetrics
dat <- with(pres_debates2012[pres_debates2012$person %in% qcv(OBAMA, ROMNEY), ], 
    chunker(dialogue, list(person, time), n.words = 300))

dat2 <- colsplit2df(list2df(dat, "dialogue", "person&time")[, 2:1])

dat3 <- split(dat2[, -2], dat2$time)
ltruncdf(dat3, 10, 50)

poldat <- lapply(dat3, function(x) with(x, polarity(dialogue, person, constrain = TRUE)))


m <- lapply(poldat, function(x) plot(cumulative(x)))
m <- Map(function(w, x, y, z) {
        w + ggtitle(x) + xlab(y) + ylab(z)
    }, 
        m, 
        paste("Debate", 1:3), 
        list(NULL, NULL, "Duration (300 Word Segment)"), 
        list(NULL, "Cumulative Average Polarity", NULL)
)

library(gridExtra)
do.call(grid.arrange, m)

## By person
## By person
poldat2 <- Map(function(x, x2){

    scores <- with(counts(x), split(polarity, person))
    setNames(lapply(scores, function(y) {
        y <- list(cumulative_average_polarity = y)
        attributes(y)[["constrained"]] <- TRUE
        qdap:::plot.cumulative_polarity(y) + xlab(NULL) + ylab(x2)
    }), names(scores))

}, poldat, paste("Debate", 1:3))

poldat2 <- lapply(poldat2, function(x) {
    x[[2]] <- x[[2]] + ylab(NULL)
    x
})

poldat2[[1]] <- Map(function(x, y) {
        x + ggtitle(y)
    },
        poldat2[[1]], qcv(Obama, Romney)
)

library(gridExtra)
do.call(grid.arrange, unlist(poldat2, recursive=FALSE))

## End(Not run)

Remove Escaped Characters

Description

Preprocess data to remove escaped characters

Usage

clean(text.var)

Arguments

Value

Returns a vector of character strings with escaped characters removed.

Examples

## Not run: 
x <- "I go \\r
    to the \\tnext line"
x
clean(x)

## End(Not run)

A Generic to Long Function

Description

A wrapper for cm_df2long, cm_range2long, and cm_time2long that automatically detects the objects being read and outputs the correct form and class.

Usage

cm_2long(..., v.name = "variable", list.var = TRUE, debug = TRUE)

Arguments

Value

Returns a long data.frame of the correct cm_XXX classes.

See Also

cm_df2long, cm_range2long, cm_time2long

Examples

## Not run: 
## cm_range2long use: 
foo <- list(
    person_greg = qcv(terms='7:11, 20:24, 30:33, 49:56'),
    person_researcher = qcv(terms='42:48'),
    person_sally = qcv(terms='25:29, 37:41'),
    person_sam = qcv(terms='1:6, 16:19, 34:36'),
    person_teacher = qcv(terms='12:15'),
    adult_0 = qcv(terms='1:11, 16:41, 49:56'),
    adult_1 = qcv(terms='12:15, 42:48'),
    AA = qcv(terms="1"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:9, 100:150")
)

foo2  <- list(
    person_greg = qcv(terms='7:11, 20:24, 30:33, 49:56'),
    person_researcher = qcv(terms='42:48'),
    person_sally = qcv(terms='25:29, 37:41'),
    person_sam = qcv(terms='1:6, 16:19, 34:36'),
    person_teacher = qcv(terms='12:15'),
    adult_0 = qcv(terms='1:11, 16:41, 49:56'),
    adult_1 = qcv(terms='12:15, 42:48'),
    AA = qcv(terms="40"),
    BB = qcv(terms="50:90"),
    CC = qcv(terms="60:90, 100:120, 150"),
    DD = qcv(terms="")
)

cm_2long(foo, foo2, v.name = "time")

## cm_time2long use: 
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 
        9.00, 1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)
cm_2long(x)

## cm_df2long use:
codes <- qcv(dc, sf, wes, pol, rejk, lk, azx, mmm)
x1 <- cm_df.temp(DATA, "state", codes)
#fill it randomly
x1[, 7:14] <- lapply(7:14,  function(i) sample(0:1, nrow(x1), TRUE))
out2 <- cm_2long(x1)
head(out2, 15)
plot(out2)

## End(Not run)

Blank Code Transformation

Description

Transform codes with any binary operator combination.

Usage

cm_code.blank(x2long.obj, combine.code.list, rm.var = NULL, overlap = TRUE)

Arguments

Value

Returns a dataframe with transformed occurrences of supplied overlapping codes added.

Note

For most jobs cm_code.transform will work. This adds a bit of flexibility in exclusion and partial matching. The code column must be named "code" and your start and end columns must be named "start" and "end".

See Also

cm_range2long, cm_time2long, cm_df2long, cm_code.overlap, cm_code.combine, cm_code.exclude, cm_code.transform

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)

## Single occurrence version
(x <- cm_range2long(foo))

cm_code.blank(x, combine.code.list = list(ABC=qcv(AA, BB, CC)),
    overlap = "!=1")

## Repeated measures version
(z <- cm_range2long(foo, foo2, v.name="time"))

cm_code.blank(z, combine.code.list = list(ABC=qcv(AA, BB, CC)),
    rm.var = "time", overlap = "!=1")

cm_code.blank(z, combine.code.list = list(AB=qcv(AA, BB)),
    rm.var = "time", overlap = TRUE)

cm_code.blank(z, combine.code.list = list(AB=qcv(AA, BB)),
    rm.var = "time", overlap = FALSE)

cm_code.blank(z, combine.code.list = list(AB=qcv(AA, BB)),
    rm.var = "time", overlap = ">1")

cm_code.blank(z, combine.code.list = list(AB=qcv(AA, BB)),
    rm.var = "time", overlap = "==2")

## Notice `overlap = "==2"` above is identical to `cm_code.overlap`
cm_code.overlap(z, overlap.code.list = list(AB=qcv(AA, BB)),
    rm.var = "time")


#WITH cm_time2long
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00,
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

y <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00,
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

dat <- cm_time2long(x, y, v.name="time")
head(dat, 10)
out <- cm_code.blank(dat, combine.code.list = list(ABC=qcv(A, B, C)),
    rm.var = "time", overlap = "!=1")

head(out)
plot(out)

## End(Not run)

Combine Codes

Description

Combine all occurrences of codes into a new code.

Usage

cm_code.combine(x2long.obj, combine.code.list, rm.var = NULL)

Arguments

Value

Returns a dataframe with combined occurrences of supplied overlapping codes added.

Note

The code column must be named "code" and your start and end columns must be named "start" and "end".

See Also

cm_range2long, cm_time2long, cm_df2long, cm_code.blank, cm_code.exclude, cm_code.overlap, cm_code.transform

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)

(x <- cm_range2long(foo))
(z <- cm_range2long(foo, foo2, v.name="time"))
cm_code.combine(x, list(AB=qcv(AA, BB)))
cm_code.combine(x, list(ALL=qcv(AA, BB, CC)))
combines <- list(AB=qcv(AA, BB), ABC=qcv(AA, BB, CC))
cm_code.combine(z, combines, rm.var = "time")

#WITH cm_time2long
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00, 
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

y <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00, 
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

dat <- cm_time2long(x, y)
head(dat, 12)
cm_code.combine(dat, list(P=qcv(A, B), Q=qcv(B, C), R=qcv(A, B, C)), "variable")

## End(Not run)

Exclude Codes

Description

Find the occurrences of n codes excluding the nth code. For example you have times/words coded for a teacher and you also have times/words coded for happiness. You can find all the happiness times excluding the teacher times or vice versa.

Usage

cm_code.exclude(x2long.obj, exclude.code.list, rm.var = NULL)

Arguments

Value

Returns a dataframe with n codes excluding the nth code.

Note

The code column must be named "code" and your start and end columns must be named "start" and "end".

See Also

cm_range2long, cm_time2long, cm_df2long, cm_code.blank, cm_code.combine, cm_code.overlap, cm_code.transform

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)


(x <- cm_range2long(foo))
(z <- cm_range2long(foo, foo2, v.name="time"))
cm_code.exclude(x, list(ABnoC=qcv(AA, BB, CC)))
cm_code.exclude(z, list(ABnoC=qcv(AA, BB, CC)), rm.var="time")
excludes <- list(AnoB=qcv(AA, BB), ABnoC=qcv(AA, BB, CC))
(a <- cm_code.exclude(z, excludes, rm.var="time"))
plot(a)

#WITH cm_time2long
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00, 
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

y <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00, 
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

dat <- cm_time2long(x, y)
head(dat, 10)
cm_code.exclude(dat, list(P=qcv(A, B), Q=qcv(B, C), R=qcv(A, B, C)), 
    rm.var = "variable")

## End(Not run)

Find Co-occurrence Between Codes

Description

Combine co-occurrences of codes into a new code.

Usage

cm_code.overlap(x2long.obj, overlap.code.list, rm.var = NULL)

Arguments

Value

Returns a dataframe with co-occurrences of supplied overlapping codes added.

Note

The code column must be named code and your start and end columns must be named "start" and "end".

See Also

cm_range2long, cm_time2long, cm_df2long, cm_code.combine, cm_code.transform

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)


(x <- cm_range2long(foo))
(z <- cm_range2long(foo, foo2, v.name="time"))
cm_code.overlap(x, list(AB=qcv(AA, BB)))
cm_code.overlap(x, list(ALL=qcv(AA, BB, CC)))
combines <- list(AB=qcv(AA, BB), ABC=qcv(AA, BB, CC))
(a <- cm_code.overlap(z, combines, "time"))
plot(a)

#WITH cm_time2long
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00, 
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

y <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00, 
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

dat <- cm_time2long(x, y)
head(dat, 10)
out <- cm_code.overlap(dat, list(P=qcv(A, B), Q=qcv(B, C), R=qcv(A, B, C)), 
    rm.var="variable")
head(out, 10)

## End(Not run)

Transform Codes

Description

Transform co-occurrences and/or combinations of codes into a new code(s).

Usage

cm_code.transform(
  x2long.obj,
  overlap.code.list = NULL,
  combine.code.list = NULL,
  exclude.code.list = NULL,
  rm.var = NULL
)

Arguments

Value

Returns a dataframe with overlapping, combined occurrences, and/or exclusion of supplied overlapping codes added.

Note

The code column must be named "code" and your start and end columns must be named "start" and "end".

See Also

cm_range2long, cm_time2long, cm_df2long, cm_code.blank, cm_code.combine, cm_code.exclude, cm_code.overlap

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)

bar1 <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "0.00:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00,
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 16.25:17.01")
)

(x <- cm_range2long(foo))
(z <- cm_range2long(foo, foo2, v.name="time"))
(dat <- cm_time2long(bar1))

cm_code.transform(x, 
    overlap.code.list = list(ABC=qcv(AA, BB, CC)),
    combine.code.list = list(oABC=qcv(AA, BB, CC)), 
    exclude.code.list = list(ABnoC=qcv(AA, BB, CC))
)

cm_code.transform(z, 
    overlap.code.list = list(ABC=qcv(AA, BB, CC)),
    combine.code.list = list(oABC=qcv(AA, BB, CC)), 
    exclude.code.list = list(ABnoC=qcv(AA, BB, CC)), "time"
)

cm_code.transform(dat, 
    overlap.code.list = list(ABC=qcv(A, B, C)),
    combine.code.list = list(oABC=qcv(A, B, C)), 
    exclude.code.list = list(ABnoC=qcv(A, B, C))
)

## End(Not run)

Find Co-occurrence Between Dummy Codes

Description

Combine code columns where they co-occur.

Usage

cm_combine.dummy(cm.l2d.obj, combine.code, rm.var = "time", overlap = TRUE)

Arguments

Value

Returns a dataframe with co-occurrences of provided code columns.

See Also

cm_long2dummy

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)


(x <- cm_range2long(foo))
(D1 <- cm_long2dummy(x))

(z <- cm_range2long(foo, foo2, v.name="time"))
(D2 <- cm_long2dummy(z, "time"))
cm_combine.dummy(D1, combine.code = list(AB=qcv(AA, BB)))
cm_combine.dummy(D1, combine.code = list(AB=qcv(AA, BB)), overlap="==1")
cm_combine.dummy(D1, combine.code = list(AB=qcv(AA, BB)), overlap="!=1")
D1 <- cm_combine.dummy(D1, combine.code = list(AB=qcv(AA, BB)), overlap=0)
D1 <- cm_combine.dummy(D1, combine.code = list(CAB=qcv(AB, CC)), overlap=FALSE)

combines <- list(AB=qcv(AA, BB), ABC=qcv(AA, BB, CC))
cm_combine.dummy(D1, combine.code = combines)
cm_combine.dummy(D2, combine.code = combines)

## End(Not run)

Range Coding

Description

Allows range coding of words for efficient coding.

Usage

cm_df.fill(
  dataframe,
  ranges,
  value = 1,
  text.var = NULL,
  code.vars = NULL,
  transform = FALSE
)

Arguments

Details

After ranging coding transcripts via (cm_df.temp) or the blank code matrix via (cm_df.transcript),cm_df.fill is used to create a matrix of what codes occurred at what words (a filled code matrix). A list of range codes (word number spans) is fed to cm_df.fill. A single number indicates a single word with that coding scheme whereas the colon is used as a separator that indicates the range of words from x to y are that particular code.

Value

Generates a dummy coded dataframe.

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_df.temp, cm_df.transcript, cm_df2long

Examples

## Not run: 
codes <- qcv(dc, sf, wes, pol, rejk, lk, azx, mmm)
X <- cm_df.temp(DATA, "state", codes)
head(X, 10)

#recommended structure
cds1 <- list(
     dc=c(1:3, 5), 
     sf=c(4, 6:9, 11), 
     wes=0, 
     pol=0, 
     rejk=0,
     lk=0, 
     azx=1:30, 
     mmm=5
)

out1 <- cm_df.fill(X, cds1)
head(out1)

#recommended structure
cds2 <- list(
    sf=c(4, 6:9, 11), 
    dc=c(1:3, 5), 
    azx=1:30, 
    mmm=5
)
out2 <- cm_df.fill(X, cds2)
head(out2)

## End(Not run)

Break Transcript Dialogue into Blank Code Matrix

Description

Breaks transcript dialogue into words while retaining the demographic factors associate with each word. The codes argument provides a matrix of zeros that can serve as a dummy coded matrix of codes per word.

Usage

cm_df.temp(
  dataframe,
  text.var,
  codes = NULL,
  file = NULL,
  transpose = FALSE,
  strip = FALSE,
  ...
)

Arguments

Value

Generates a dataframe, and optional csv file, of individual words while maintaining demographic information. If a vector of codes is provided the outcome is a matrix of words used by codes filled with zeros. This dataframe is useful for dummy coded (1-yes code exists; 0-no it does not) representation of data and can be used for visualizations and statistical analysis.

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_range2long, cm_df.transcript, cm_df.fill

Examples

## Not run: 
codes <- qcv(dc, sf, wes, pol, rejk, lk, azx, mmm)
out1 <- cm_df.temp(DATA, "state", codes)
head(out1, 15)
out2 <- cm_df.temp(DATA, "state", codes, transpose = TRUE)
out2[, 1:10]
out3 <- cm_df.temp(raj.act.1, "dialogue", codes)
head(out3, 15)
out4 <- cm_df.temp(raj.act.1, "dialogue", codes, transpose = TRUE)
out4 [, 1:8]

## End(Not run)

Transcript With Word Number

Description

Output a transcript with word number/index above for easy input back into qdap after coding.

Usage

cm_df.transcript(
  text.var,
  grouping.var,
  file = NULL,
  indent = 4,
  width = 70,
  space = 2,
  ...
)

Arguments

Value

Returns a transcript by grouping variable with word number above each word. This makes use with cm_df2long transfer/usage easier because the researcher has coded on a transcript with the numeric word index already.

Note

It is recommended that the researcher actually codes on the output from this file. The codes can then be transferred to via a list. If a file already exists cm_df.transcript will append to that file.

Author(s)

BondedDust (stackoverflow.com), Gavin Simpson and Tyler Rinker <tyler.rinker@gmail.com>

See Also

cm_df2long, cm_df.temp

Examples

## Not run: 
with(DATA, cm_df.transcript(state, person))
with(DATA, cm_df.transcript(state, list(sex, adult)))
#use it with nested variables just to keep track of demographic info
with(DATA, cm_df.transcript(state, list(person, sex, adult)))

#use double tilde "~~" to keep word group as one word
DATA$state <- mgsub("be certain", "be~~certain", DATA$state, fixed = TRUE)
with(DATA, cm_df.transcript(state, person))
DATA <- qdap::DATA

##  with(mraja1spl, cm_df.transcript(dialogue, list(person)))
##  with(mraja1spl, cm_df.transcript(dialogue, list(sex, fam.aff, died)))
##  with(mraja1spl, cm_df.transcript(dialogue, list(person), file="foo.doc"))
##  delete("foo.doc")   #delete the file just created

## End(Not run)

Transform Codes to Start-End Durations

Description

Transforms the range coding structure(s) from cm_df.temp (in list format) into a data frame of start and end durations in long format.

Usage

cm_df2long(
  df.temp.obj,
  v.name = "variable",
  list.var = TRUE,
  code.vars = NULL,
  no.code = NA,
  add.start.end = TRUE,
  repeat.vars = NULL,
  rev.code = FALSE
)

Arguments

Value

Generates a data frame of start and end times for each code.

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_time2long, cm_range2long, cm_df.temp

Examples

## Not run: 
codes <- qcv(dc, sf, wes, pol, rejk, lk, azx, mmm)
x1 <- cm_df.temp(DATA, "state", codes)
head(x1)

#empty code matrix
out1 <- cm_df2long(x1,  code.vars = codes)
head(out1, 15)

#fill it randomly
x1[, 7:14] <- lapply(7:14,  function(i) sample(0:1, nrow(x1), TRUE))
out2 <- cm_df2long(x1,  code.vars = codes)
head(out2, 15)
plot(out2)

## End(Not run)

Distance Matrix Between Codes

Description

Generate distance measures to ascertain a mean distance measure between codes.

Usage

cm_distance(
  dataframe,
  pvals = c(TRUE, FALSE),
  replications = 1000,
  parallel = TRUE,
  extended.output = TRUE,
  time.var = TRUE,
  code.var = "code",
  causal = FALSE,
  start.var = "start",
  end.var = "end",
  cores = detectCores()/2
)

Arguments

Details

Note that row names are the first code and column names are the second comparison code. The values for Code A compared to Code B will not be the same as Code B compared to Code A. This is because, unlike a true distance measure, cm_distance's matrix is asymmetrical. cm_distance computes the distance by taking each span (start and end) for Code A and comparing it to the nearest start or end for Code B.

Value

An object of the class "cm_distance". This is a list with the following components:

Within the lists of extended.output and list of the main.output are the following items:

Warning

p-values are estimated and thus subject to error. More replications decreases the error. Use:

p \pm \left ( 1.96 \cdot \sqrt{\frac{\alpha(1-\alpha)}{n}}\right )

to adjust the confidence in the estimated p-values based on the number of replications.

References

https://stats.stackexchange.com/a/22333/7482

See Also

print.cm_distance

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="02:03, 05"),
    BB = qcv(terms="1:2, 3:10"),
    CC = qcv(terms="1:9, 100:150")
)

foo2  <- list(
    AA = qcv(terms="40"),
    BB = qcv(terms="50:90"),
    CC = qcv(terms="60:90, 100:120, 150"),
    DD = qcv(terms="")
)

(dat <- cm_2long(foo, foo2, v.name = "time"))
plot(dat)
(out <- cm_distance(dat, replications=100))
names(out)
names(out$main.output)
out$main.output
out$extended.output
print(out, new.order = c(3, 2, 1))
print(out, new.order = 3:2)
#========================================
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 6.32:7.00, 9.00,
        10.00:11.00, 59.56"),
    B = qcv(terms = "3.01:3.02, 5.01,  19.00, 1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.32:7.00, 9.00, 17.01")
)
(dat <- cm_2long(x))
plot(dat)
(a <- cm_distance(dat, causal=TRUE, replications=100))

## Plotting as a network graph
datA <- list(
    A = qcv(terms="02:03, 05"),
    B = qcv(terms="1:2, 3:10, 45, 60, 200:206, 250, 289:299, 330"),
    C = qcv(terms="1:9, 47, 62, 100:150, 202, 260, 292:299, 332"),
    D = qcv(terms="10:20, 30, 38:44, 138:145"),
    E = qcv(terms="10:15, 32, 36:43, 132:140"),
    F = qcv(terms="1:2, 3:9, 10:15, 32, 36:43, 45, 60, 132:140, 250, 289:299"),
    G = qcv(terms="1:2, 3:9, 10:15, 32, 36:43, 45, 60, 132:140, 250, 289:299"),
    H = qcv(terms="20, 40, 60, 150, 190, 222, 255, 277"),
    I = qcv(terms="20, 40, 60, 150, 190, 222, 255, 277")
)

datB  <- list(
    A = qcv(terms="40"),
    B = qcv(terms="50:90, 110, 148, 177, 200:206, 250, 289:299"),
    C = qcv(terms="60:90, 100:120, 150, 201, 244, 292"),
    D = qcv(terms="10:20, 30, 38:44, 138:145"),
    E = qcv(terms="10:15, 32, 36:43, 132:140"),
    F = qcv(terms="10:15, 32, 36:43, 132:140, 148, 177, 200:206, 250, 289:299"),
    G = qcv(terms="10:15, 32, 36:43, 132:140, 148, 177, 200:206, 250, 289:299"),
    I = qcv(terms="20, 40, 60, 150, 190, 222, 255, 277")  
)

(datC <- cm_2long(datA, datB, v.name = "time"))
plot(datC)
(out2 <- cm_distance(datC, replications=1250))

plot(out2)
plot(out2, label.cex=2, label.dist=TRUE, digits=5)

## End(Not run)

Convert cm_combine.dummy Back to Long

Description

cm_combine.dummy back to long.

Usage

cm_dummy2long(cm_long2dummy_obj, rm.var = "time")

Arguments

Value

Returns a dataframe with co-occurrences of provided code columns.

See Also

cm_long2dummy, cm_combine.dummy

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)

(x <- cm_range2long(foo))
(out1 <- cm_long2dummy(x))

(z <- cm_range2long(foo, foo2, v.name="time"))
out2 <- cm_long2dummy(z, "time")
lapply(out2, head)
cm_combine.dummy(out1, combine.code = list(AB=qcv(AA, BB)))

combines <- list(AB=qcv(AA, BB), ABC=qcv(AA, BB, CC))
A <- cm_combine.dummy(out2, combine.code = combines)
head(A, 10)
B <- cm_combine.dummy(out1, combine.code = combines)
head(B, 10)

cm_dummy2long(A)
cm_dummy2long(B)
plot(cm_dummy2long(A))

## End(Not run)

Stretch and Dummy Code cm_xxx2long

Description

Stretches and dummy codes a cm_xxx2long dataframe to allow for combining columns.

Usage

cm_long2dummy(
  dataframe,
  rm.var = NULL,
  code = "code",
  start = "start",
  end = "end"
)

Arguments

Value

Returns a dataframe or a list of stretched and dummy coded dataframe(s).

See Also

cm_range2long, cm_time2long, cm_df2long

Examples

## Not run: 
foo <- list(
    AA = qcv(terms="1:10"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:3, 5:6")
)

foo2  <- list(
    AA = qcv(terms="4:8"),
    BB = qcv(terms="1:4, 10:12"),
    CC = qcv(terms="1, 11, 15:20"),
    DD = qcv(terms="")
)

(x <- cm_range2long(foo))
cm_long2dummy(x)

(z <- cm_range2long(foo, foo2, v.name="time"))
out <- cm_long2dummy(z, "time")
ltruncdf(out)

## End(Not run)

Range Code Sheet

Description

Generates a range coding sheet for coding words.

Usage

cm_range.temp(codes, text.var = NULL, grouping.var = NULL, file = NULL)

Arguments

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_time.temp

Examples

## Not run: 
cm_range.temp(qcv(AA, BB, CC))
with(DATA, cm_range.temp(qcv(AA, BB, CC), state, list(person, adult)))
## cm_range.temp(qcv(AA, BB, CC), file = "foo.txt")
## delete("foo.txt")

## End(Not run)

Transform Codes to Start-End Durations

Description

Transforms the range coding structure(s) from cm_range.temp (in list format) into a data frame of start and end durations in long format.

Usage

cm_range2long(
  ...,
  v.name = "variable",
  list.var = TRUE,
  debug = TRUE,
  object = NULL
)

Arguments

Value

Generates a data frame of start and end spans for each code.

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_df2long, cm_time.temp, cm_df.transcript

Examples

## Not run: 
foo <- list(
    person_greg = qcv(terms='7:11, 20:24, 30:33, 49:56'),
    person_researcher = qcv(terms='42:48'),
    person_sally = qcv(terms='25:29, 37:41'),
    person_sam = qcv(terms='1:6, 16:19, 34:36'),
    person_teacher = qcv(terms='12:15'),
    adult_0 = qcv(terms='1:11, 16:41, 49:56'),
    adult_1 = qcv(terms='12:15, 42:48'),
    AA = qcv(terms="1"),
    BB = qcv(terms="1:2, 3:10, 19"),
    CC = qcv(terms="1:9, 100:150")
)

foo2  <- list(
    person_greg = qcv(terms='7:11, 20:24, 30:33, 49:56'),
    person_researcher = qcv(terms='42:48'),
    person_sally = qcv(terms='25:29, 37:41'),
    person_sam = qcv(terms='1:6, 16:19, 34:36'),
    person_teacher = qcv(terms='12:15'),
    adult_0 = qcv(terms='1:11, 16:41, 49:56'),
    adult_1 = qcv(terms='12:15, 42:48'),
    AA = qcv(terms="40"),
    BB = qcv(terms="50:90"),
    CC = qcv(terms="60:90, 100:120, 150"),
    DD = qcv(terms="")
)

## General ldots Approach
(dat <- cm_range2long(foo, foo2, v.name = "time"))
plot(dat)

## Specify `object` Approach
cm_range2long(object=list(foo=foo))
cm_range2long(object=list(foo=foo, foo2=foo2), v.name="time")
cm_range2long(object=list(a=foo, b=foo2), v.name="time")

## End(Not run)

Time Span Code Sheet

Description

Generates a time span coding sheet and coding format sheet.

Usage

cm_time.temp(
  codes,
  grouping.var = NULL,
  start = ":00",
  end = NULL,
  file = NULL,
  coding = FALSE,
  print = TRUE
)

Arguments

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_range.temp,

Examples

## Not run: 
## cm_time.temp(qcv(AA, BB, CC), ":30", "7:40", file = "foo.txt")
## delete("foo.txt")
cm_time.temp(qcv(AA, BB, CC), ":30", "7:40")

x <- list(
    transcript_time_span = qcv(terms="00:00 - 1:12:00"),
    A = qcv(terms="2.40:3.00, 5.01, 6.52:7.00, 9.00"),
    B = qcv(terms="2.40, 3.01:3.02, 5.01, 6.52:7.00, 9.00, 1.12.00:1.19.01"),
    C = qcv(terms="2.40:3.00, 5.01, 6.52:7.00, 9.00, 17.01")
)
cm_time2long(x)
cm_time.temp(qcv(AA, BB, CC))

## End(Not run)

Transform Codes to Start-End Times

Description

Transforms the range coding structure(s) from cm_time.temp (in list format) into a data frame of start and end times in long format.

Usage

cm_time2long(
  ...,
  v.name = "variable",
  list.var = TRUE,
  debug = TRUE,
  object = NULL
)

Arguments

Value

Generates a dataframe of start and end times for each code.

References

Miles, M. B. & Huberman, A. M. (1994). An expanded sourcebook: Qualitative data analysis. 2nd ed. Thousand Oaks, CA: SAGE Publications.

See Also

cm_df2long, cm_time.temp

Examples

## Not run: 
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 
        9.00, 1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)
(dat <- cm_time2long(x))
plot(dat)

bar1 <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00,
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 16.25:17.01")
)

bar2 <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00"),
    B = qcv(terms = "2.40, 3.01:3.02, 5.01, 6.02:7.00, 9.00,
        1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.02:7.00, 9.00, 17.01")
)

## General ldots Approach
cm_time2long(bar1)
cm_time2long(bar1, bar2, v.name="time")

## Specify `object` Approach
cm_time2long(object=list(bar1=bar1))
cm_time2long(object=list(bar1=bar1, bar2=bar2), v.name="time")
cm_time2long(object=list(a=bar1, b=bar2), v.name="time")

## End(Not run)

Separate a Column Pasted by paste2

Description

Separates a paste2 column into separate columns.

Usage

colSplit(column, col.sep = ".", name.sep = "&")

Arguments

Value

Returns a dataframe of split columns.

See Also

colsplit2df, paste2

Examples

## Not run: 
foo1 <- paste2(CO2[, 1:3])
head(foo1, 12)
bar1 <- colSplit(foo1)
head(bar1, 10)

foo2  <- paste2(mtcars[, 1:3], sep="|")
head(foo2, 12)
bar2 <- colSplit(foo2, col.sep = "|")
head(bar2, 10)

## End(Not run)

Combine Columns to Class

Description

Combine columns from qdap classes or a data.frame.

Usage

colcomb2class(
  dataframe,
  combined.columns,
  class = "list",
  percent = TRUE,
  digits = 2,
  elim.old = TRUE,
  zero.replace = 0,
  override = FALSE
)

Arguments

Value

Returns a list with raw counts, percents and combined raw and percents.

Examples

## Not run: 
## `termco` example
ml <- list(
    cat1 = c(" the ", " a ", " an "),
    cat2 = c(" I'" ),
    "good",
    the = c("the", " the ", " the", "the")
)
dat1 <- with(raj.act.1,  termco(dialogue, person, ml))
colcomb2class(dat1, list(cats = c("cat1", "cat2")))

## `question_type` example
dat2 <- question_type(DATA.SPLIT$state, DATA.SPLIT$person)
combs <- list(
    `wh/how` = c("what", "how"), 
    oth = c("shall", "implied_do/does/did")
)
colcomb2class(dat2, combs)

## `pos_by` example
dat3 <- with(DATA, pos_by(state, list(adult, sex)))
colcomb2class(dat3, qcv(DT, EX, FW))


## data.frame example
dat4 <- data.frame(X=LETTERS[1:5], matrix(sample(0:5, 20, TRUE), ncol = 4))
colcomb2class(dat4, list(new = c("X1", "X4")))

## End(Not run)

Wrapper for colSplit that Returns Dataframe(s)

Description

colsplit2df - Wrapper for colSplit that returns a dataframe.

lcolsplit2df - Wrapper for colsplit2df designed for qdap lists that returns a list dataframes.

Usage

colsplit2df(
  dataframe,
  splitcols = 1,
  new.names = NULL,
  sep = ".",
  keep.orig = FALSE,
  name.sep = "&",
  index.names = FALSE
)

lcolsplit2df(qdap.list, keep.orig = FALSE)

Arguments

Value

colsplit2df - returns a dataframe with the paste2 column split into new columns.

lcolsplit2df - returns a list of dataframes with the paste2 column split into new columns.

Warning

This will strip the class of the qdap object.

Note

lcolsplit2df is a convenience function that is less flexible than colsplit2df but operates on multiple dataframes at once.

See Also

colSplit, colpaste2df paste2

Examples

## Not run: 
CO2$`Plant&Type&Treatment` <- paste2(CO2[, 1:3])
CO2 <- CO2[, -c(1:3)]
head(CO2)
head(colsplit2df(CO2, 3))
head(colsplit2df(CO2, 3, qcv(A, B, C)))
head(colsplit2df(CO2, 3, qcv(A, B, C), keep.orig=TRUE))
head(colsplit2df(CO2, "Plant&Type&Treatment"))
CO2 <- datasets::CO2

(dat <- colpaste2df(head(mtcars), list(1:3), sep = "|"))
colsplit2df(dat, 12, sep = "|")

## Multiple split example
E <- list(
    c(1, 2, 3, 4, 5),
    qcv(mpg, hp),
    c("disp", "am")
)

(dat2 <- colpaste2df(head(mtcars), E, sep ="|"))
cols <- c("mpg&cyl&disp&hp&drat", "mpg&hp", "disp&am")
colsplit2df(dat2, cols, sep = "|")

## lcolsplit2df example
(x <- with(DATA.SPLIT, question_type(state, list(sex, adult))))
ltruncdf(x)
z <- lcolsplit2df(x)
ltruncdf(z)

## End(Not run)

Ensure Space After Comma

Description

Adds a space after a comma as strip and many other functions may consider a comma separated string as one word (i.e., "one,two,three" becomes "onetwothree" rather than "one two three").

Usage

comma_spacer(text.var)

Arguments

Value

Returns a vector of strings with commas that have a space after them.

Examples

## Not run: 
x <- c("the,  dog,went", "I,like,it", "where are you", NA, "why", ",", ",f")
comma_spacer(x)

## End(Not run)

Find Common Words Between Groups

Description

Find common words between grouping variables (e.g., people).

Usage

common(word.list, overlap = "all", equal.or = "more", ...)

Arguments

Value

Returns a dataframe of all words that match the criteria set by overlap and equal.or.

list Method for common

Description

list Method for common

Usage

## S3 method for class 'list'
common(word.list, overlap = "all", equal.or = "more", ...)

Arguments

Condense Dataframe Columns

Description

Condense dataframe columns that are a list of vectors to a single vector of strings.

Usage

condense(dataframe, sep = ", ")

Arguments

Value

Returns a dataframe with condensed columns that can be wrote to csv/xlsx.

See Also

mcsv_w

Examples

## Not run: 
library(qdap)
poldat <- with(DATA.SPLIT, polarity(state, person))
write.csv(x = condense(counts(poldat)), file = "foo.csv")

## End(Not run)

Generic Counts Method

Description

Access the count dataframes from select qdap outputs.

Usage

counts(x, ...)

Arguments

Value

Returns a data.frame of counts.

See Also

scores, proportions, preprocessed, visual

Readability Measures

Description

counts.SMOG - View counts from SMOG.

Usage

## S3 method for class 'SMOG'
counts(x, ...)

Arguments

Details

SMOG Method for counts.

Readability Measures

Description

counts.automated_readability_index - View counts from automated_readability_index.

Usage

## S3 method for class 'automated_readability_index'
counts(x, ...)

Arguments

Term Counts

Description

View character_table counts.

Usage

## S3 method for class 'character_table'
counts(x, ...)

Arguments

Details

character_table Method for counts

Readability Measures

Description

counts.coleman_liau - View counts from coleman_liau.

Usage

## S3 method for class 'coleman_liau'
counts(x, ...)

Arguments

Details

coleman_liau Method for counts.

Question Counts

Description

View end_mark_by counts.

Usage

## S3 method for class 'end_mark_by'
counts(x, ...)

Arguments

Details

end_mark_by Method for counts

Readability Measures

Description

counts.flesch_kincaid - View counts from flesch_kincaid.

Usage

## S3 method for class 'flesch_kincaid'
counts(x, ...)

Arguments

Details

flesch_kincaid Method for counts.

Formality

Description

View formality counts.

Usage

## S3 method for class 'formality'
counts(x, ...)

Arguments

Details

formality Method for counts

Readability Measures

Description

counts.fry - View counts from fry.

Usage

## S3 method for class 'fry'
counts(x, ...)

Arguments

Details

fry Method for counts.

Readability Measures

Description

counts.linsear_write - View counts from linsear_write.

Usage

## S3 method for class 'linsear_write'
counts(x, ...)

Arguments

Details

linsear_write Method for counts.

Question Counts

Description

View object_pronoun_type counts.

Usage

## S3 method for class 'object_pronoun_type'
counts(x, ...)

Arguments

Details

object_pronoun_type Method for counts

Polarity

Description

counts.polarity - View counts from polarity.

Usage

## S3 method for class 'polarity'
counts(x, ...)

Arguments

Details

polarity Method for counts.

Parts of Speech

Description

View pos counts.

Usage

## S3 method for class 'pos'
counts(x, ...)

Arguments

Details

pos Method for counts

Parts of Speech

Description

View pos_by counts.

Usage

## S3 method for class 'pos_by'
counts(x, ...)

Arguments

Details

pos_by Method for counts

Question Counts

Description

View pronoun_type counts.

Usage

## S3 method for class 'pronoun_type'
counts(x, ...)

Arguments

Details

pronoun_type Method for counts

Question Counts

Description

View question_type counts.

Usage

## S3 method for class 'question_type'
counts(x, ...)

Arguments

Details

question_type Method for counts

Question Counts

Description

View subject_pronoun_type counts.

Usage

## S3 method for class 'subject_pronoun_type'
counts(x, ...)

Arguments

Details

subject_pronoun_type Method for counts

Term Counts

Description

View termco counts.

Usage

## S3 method for class 'termco'
counts(x, ...)

Arguments

Details

termco Method for counts

Word Length Counts

Description

View word_length counts.

Usage

## S3 method for class 'word_length'
counts(x, ...)

Arguments

Details

word_length Method for counts

Word Position

Description

View word_position counts.

Usage

## S3 method for class 'word_position'
counts(x, ...)

Arguments

Details

word_position Method for counts

Word Stats

Description

View word_stats counts.

Usage

## S3 method for class 'word_stats'
counts(x, ...)

Arguments

Details

word_stats Method for counts

Cumulative Scores

Description

cumulative - Generate rolling/cumulative scores for select qdap objects.

Usage

cumulative(x, ...)

## S3 method for class 'end_mark'
cumulative(x, ...)

## S3 method for class 'formality'
cumulative(x, ...)

## S3 method for class 'pos'
cumulative(x, ...)

## S3 method for class 'pos_by'
cumulative(x, ...)

## S3 method for class 'animated_formality'
cumulative(x, ...)

## S3 method for class 'lexical_classification'
cumulative(x, ...)

## S3 method for class 'animated_lexical_classification'
cumulative(x, ...)

## S3 method for class 'polarity'
cumulative(x, ...)

## S3 method for class 'animated_polarity'
cumulative(x, ...)

## S3 method for class 'syllable_freq'
cumulative(x, ...)

## S3 method for class 'combo_syllable_sum'
cumulative(x, ...)

Arguments

Easy File Handling

Description

delete - Deletes files and directories.

folder - Create a folder/directory.

Usage

delete(file = NULL)

folder(..., folder.name = NULL)

Arguments

Value

delete permanently removes a file/directory.

folder creates a folder/directory.

See Also

unlink, file.remove, dir.create

Examples

## Not run: 
(x <- folder("DELETE.ME"))
which(dir() == "DELETE.ME")
delete("DELETE.ME")
which(dir() == "DELETE.ME")

folder("the/next/big/thing", "hello world", "now/is/the/time")

folder(cat, dog)
lapply(c("cat", "dog"), delete)

## End(Not run)

Map Transcript Files from a Directory to a Script

Description

Generate script text (and optionally output it to the clipboard and/or an external file) that can be used to individually read in every file in a directory and assign it to an object.

Usage

dir_map(
  loc = "DATA/TRANSCRIPTS/CLEANED_TRANSCRIPTS",
  obj.prefix = "dat",
  use.path = TRUE,
  col.names = c("person", "dialogue"),
  file = NULL,
  copy2clip = interactive()
)

Arguments

Details

Generally, the researcher will want to read in and parse every transcript document separately. The task of writing the script for multiple transcript documents can be tedious. This function is designed to make the process more efficient and less prone to errors.

Value

Prints a read in script text to the console, optionally copies the wrapped text to the clipboard on a Mac or Windows machine and optionally prints to an outside file.

Note

skip is set to 0, however, it is likely that this value will need to be changed for each transcript.

See Also

read.transcript

Examples

## Not run: 
(DIR <- system.file("extdata/transcripts", package = "qdap"))
dir_map(DIR)

## End(Not run)

Discourse Mapping

Description

View the flow of discourse from social actors.

Usage

discourse_map(
  text.var,
  grouping.var,
  edge.constant,
  sep = "_",
  condense = TRUE,
  ...
)

Arguments

Details

For an example of the video generated from the Animate output of discourse_map see: https://www.youtube.com/watch?v=7LcqFZODXNo&feature=youtu.be. An HTML output can be viewed: http://trinker.github.io/qdap_examples/animation_dialogue/.

Value

Returns a list:

Examples

## Not run: 
discourse_map(DATA$state, list(DATA$person, DATA$sex))
x <- with(mraja1, discourse_map(dialogue, person))
x
lview(x)
library(igraph)
plot(visual(x), edge.curved=FALSE)

## Quickly add/remove a title
Title(x) <- "Act 1"
x
Title(x) <- NULL
x

## Augmenting the plot
library(qdapTools)
mygraph <- visual(x)

plot(mygraph, edge.curved=TRUE)

V(mygraph)$sex <- V(mygraph)$name %lc% raj.demographics[, 1:2]
V(mygraph)$color <- ifelse(V(mygraph)$sex=="f", "pink", "lightblue")

plot(mygraph, edge.curved=TRUE)

V(mygraph)$family <- V(mygraph)$name %l+% raj.demographics[, c(1, 3)]
cols <- qcv(blue, red, brown, darkgreen, grey10)
V(mygraph)$label.color <- lookup(V(mygraph)$family, 
    unique(V(mygraph)$family), cols)

plot(mygraph, edge.curved=TRUE)

## Community detection
x <- with(mraja1, discourse_map(dialogue, person))
wc <- walktrap.community(visual(x))
colors <- grDevices::rainbow(max(membership(wc)))
plot(x, vertex.color=colors[membership(wc)])

## Repeated Measures (BASIC EXAMPLE)
##------------------------------

## First merge data and map to discourse per act 
## to separate networks

dat <- key_merge(raj, raj.demographics)
list_dat <- split(dat, dat$act)
plot_dat <- lapply(list_dat, function(x) with(x, discourse_map(dialogue, person)))

opar <- par()$mar
par(mfrow=c(3, 2), mar=c(0, 0, 3, 0))

lapply(seq_along(plot_dat), function(i){
    plot(plot_dat[[i]])
    graphics::mtext(paste("Act", names(plot_dat)[i]), side=3)
})


## Repeated Measures (EXTENDED EXAMPLE)
##------------------------------
fam_key <- data.frame(fam=unique(raj.demographics$fam.aff),
    cols=qcv(blue, grey10, red, orange), 
    stringsAsFactors = FALSE)

par(mfrow=c(3, 2), mar=c(0, 1, 3, 1))
lapply(seq_along(plot_dat), function(i){

    THE_PLOT <- visual(plot_dat[[i]])

    V(THE_PLOT)$sex <- V(THE_PLOT)$name %l% raj.demographics[, 1:2]
    V(THE_PLOT)$color <- ifelse(V(THE_PLOT)$sex=="f", "pink", "lightblue")
    V(THE_PLOT)$family <- V(THE_PLOT)$name %lc+% raj.demographics[, c(1, 3)]
    V(THE_PLOT)$label.color <- lookup(V(THE_PLOT)$family, fam_key)

    plot(THE_PLOT, edge.curved=TRUE)
    graphics::mtext(paste("Act", names(plot_dat)[i]), side=3)
})
frame()
bords <- rep("black", 7)
bords[3] <- "white"
legend(.29, .95, c("Female", "Male", NA, as.character(fam_key[, 1])), 
    fill=c("pink", "lightblue", NA, fam_key[, 2]), border=bords, cex=1.5) 

## Reset graphics margins
par(mar=opar)

## ANIMATION
#===========
test <- discourse_map(DATA$state, list(DATA$person))

## Very quick, hard to see
Animate(test)

pdf("test.pdf")
    par(mar=c(0, 0, 1, 0))
    Animate(test, title="Test Plot")
dev.off()

## Animate it
##-----------
library(animation)
library(igraph)

loc <- folder(animation_dialogue)
ans <- Animate(test)

## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)

FUN <- function() {
    lapply(seq_along(ans), function(i) {
        par(mar=c(0, 0, 1, 0))
        set.seed(10)
        plot.igraph(ans[[i]], edge.curved=TRUE, layout=layout.circle)
        graphics::mtext("Discourse Map", side=3)
        animation::ani.pause()
    })
}

## Detect OS
type <- if(.Platform$OS.type == "windows") shell else system
saveGIF(FUN(), interval = 0.1, outdir = loc, cmd.fun = type)

saveVideo(FUN(), video.name = "discourse_map.avi", interval = 0.1, outdir = loc)

saveLatex(FUN(), autoplay = TRUE, loop = FALSE, latex.filename = "tester.tex", 
    caption = "animated dialogue", outdir = loc, ani.type = "pdf", 
    ani.dev = "pdf", ani.width = 5, ani.height = 5.5, interval = 0.1)

saveHTML(FUN(), autoplay = FALSE, loop = TRUE, verbose = FALSE, 
    outdir = file.path(loc, "new"), single.opts = 
    "'controls': ['first', 'previous', 'play', 'next', 'last', 'loop', 'speed'], 'delayMin': 0")
    
    
## More Elaborate Layout
test2 <- with(mraja1, discourse_map(dialogue, person))

loc2 <- folder(animation_dialogue2)
ans2 <- Animate(test2)
## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)

FUN3 <- function() {
    lapply(seq_along(ans2), function(i) {
        par(mar=c(0, 0, 1, 0))
        set.seed(10)
        plot.igraph(ans2[[i]], edge.curved=TRUE, layout=layout.auto)
        graphics::mtext("Discourse Map\nRomeo and Juliet: Act 1", side=3)
        animation::ani.pause()
    })
}

saveHTML(FUN3(), autoplay = FALSE, loop = FALSE, verbose = FALSE,
    outdir = file.path(loc2, "new"), single.opts =
    "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")
    
saveVideo(FUN3(), video.name = "discourse_map.avi", interval = 0.2, 
    outdir = loc2)    

## End(Not run)

Lexical Dispersion Plot

Description

Generate a lexical dispersion plot of terms.

Usage

dispersion_plot(
  text.var,
  match.terms,
  grouping.var = NULL,
  rm.vars = NULL,
  color = "blue",
  bg.color = "grey90",
  horiz.color = "grey85",
  total.color = "black",
  symbol = "|",
  title = "Lexical Dispersion Plot",
  rev.factor = TRUE,
  wrap = "'",
  xlab = "Dialogue (Words)",
  ylab = NULL,
  size = 4,
  plot = TRUE,
  char2space = "~~",
  apostrophe.remove = FALSE,
  scales = "free",
  space = "free",
  ...
)

Arguments

Value

Plots a dispersion plot and invisibly returns the ggplot2 object.

Note

The match.terms is character sensitive. Spacing is an important way to grab specific words and requires careful thought. Using "read" will find the words "bread", "read" "reading", and "ready". If you want to search for just the word "read" you'd supply a vector of c(" read ", " reads", " reading", " reader").

See Also

term_match

Examples

## Not run: 
term_match(raj$dialogue, c(" love ", "love", " night ", "night"))
dispersion_plot(raj$dialogue, c(" love ", "love", " night ", "night"))
dispersion_plot(raj$dialogue, c("love", "night"), rm.vars = raj$act)
with(rajSPLIT , dispersion_plot(dialogue, c("love", "night"), 
    grouping.var = list(fam.aff, sex), rm.vars = act))

## With grouping variables
with(rajSPLIT , dispersion_plot(dialogue, c("love", "night"), 
     grouping.var = sex, rm.vars = act))

## Drop total with `total.color = NULL`
with(rajSPLIT , dispersion_plot(dialogue, c("love", "night"), 
     grouping.var = sex, rm.vars = act, total.color = NULL))

## Change color scheme
with(rajSPLIT, dispersion_plot(dialogue, c("love", "night"), 
    bg.color = "black", grouping.var = list(fam.aff, sex), 
    color = "yellow", total.color = "white", horiz.color="grey20"))
    
## Use `word_list`
## Presidential debates by all
wrds <- word_list(pres_debates2012$dialogue, stopwords = Top200Words)
wrds2 <- spaste(wrds[["rfswl"]][["all"]][, "WORD"])
wrds2 <- c(" governor~~romney ", wrds2[-c(3, 12)])
with(pres_debates2012 , dispersion_plot(dialogue, wrds2, rm.vars = time))

## Presidential debates by person
dat <- pres_debates2012
dat <- dat[dat$person %in% qcv(ROMNEY, OBAMA), ]

wordlist <- c(" tax", " health", " rich ", "america", " truth", 
    " money", "cost", " governnor", " president", " we ", 
    " job", " i ", " you ", " because ", " our ", " years ")

with(dat, dispersion_plot(dialogue, wordlist, total.color = NULL, 
    bg.color = "white", grouping.var = person, rm.vars = time,
    color = "black", horiz.color="grey80"))

wordlist2 <- c(" i'd ", " i'll ", " i'm ", " i've ", " i ", 
    " we'd ", " we'll ", " we're ", " we've ", " we ", 
    " you'd ",  " you'll ", " you're ", " you've ", " you ", " your ",
    " he'd ", " he'll ", " he's ", " he ")

with(dat, dispersion_plot(dialogue, wordlist2, 
    bg.color = "black", grouping.var = person, rm.vars = time,
    color = "yellow", total.color = NULL, horiz.color="grey20"))
   
with(dat, dispersion_plot(dialogue, wordlist2, 
    bg.color = "black", grouping.var = person, rm.vars = time,
    color = "red", total.color = "white", horiz.color="grey20"))

## `match.terms` as a named list        
wordlist3 <- list(
    I = c(" i'd ", " i'll ", " i'm ", " i've ", " i "),
    we = c(" we'd ", " we'll ", " we're ", " we've ", " we "),
    you = c(" you'd ",  " you'll ", " you're ", " you've ", " you ", " your "),
    he = c(" he'd ", " he'll ", " he's ", " he ")
)

with(dat, dispersion_plot(dialogue, wordlist3,
    bg.color = "grey60", grouping.var = person, rm.vars = time,
    color = "blue", total.color = "grey40", horiz.color="grey20"))

colsplit2df(scores(with(dat, termco(dialogue, list(time, person), wordlist3))))

## Extras:
## Reverse facets

x <- with(pres_debates2012 , dispersion_plot(dialogue, wrds2, rm.vars = time))

## function to reverse ggplot2 facets
rev_facet <- function(x) {
    names(x$facet)[1:2] <- names(x$facet)[2:1]
    print(x)
}

rev_facet(x)

## Discourse Markers: See...
## Schiffrin, D. (2001). Discourse markers: Language, meaning, and context. 
##    In D. Schiffrin, D. Tannen, & H. E. Hamilton (Eds.), The handbook of 
##    discourse analysis (pp. 54-75). Malden, MA: Blackwell Publishing.

discoure_markers <- list(
    response_cries = c(" oh ", " ah ", " aha ", " ouch ", " yuk "),
    back_channels = c(" uh-huh ", " uhuh ", " yeah "), 
    summons = " hey ", 
    justification = " because "
)

(markers <- with(pres_debates2012, 
    termco(dialogue, list(person, time), discoure_markers)
))
plot(markers, high="red")

with(pres_debates2012, 
    termco(dialogue, list(person, time), discoure_markers, elim.old = FALSE)
)

with(pres_debates2012, 
    dispersion_plot(dialogue, unlist(discoure_markers), person, time)
)

## End(Not run)

SPSS Style Frequency Tables

Description

Generates a distribution table for vectors, matrices and dataframes.

Usage

dist_tab(dataframe, breaks = NULL, digits = 2, ...)

Arguments

Value

Returns a list of data frames (or singular data frame for a vector) of frequencies, cumulative frequencies, percentages and cumulative percentages for each interval.

See Also

cut

Examples

## Not run: 
dist_tab(rnorm(10000), 10)
dist_tab(sample(c("red", "blue", "gray"), 100, T), right = FALSE)
dist_tab(CO2, 4)

out1 <- dist_tab(mtcars[, 1:3])
ltruncdf(out1, 4)

out2 <- dist_tab(mtcars[, 1:3], 4)
ltruncdf(out2, 4)

wdst <- with(mraja1spl, word_stats(dialogue, list(sex, fam.aff, died)))
out3 <- dist_tab(wdst$gts[1:4])
ltruncdf(out3, 4)

## End(Not run)

Diversity Statistics

Description

Transcript apply diversity/richness indices.

Usage

diversity(text.var, grouping.var = NULL)

Arguments

Details

These are the formulas used to calculate the indices:

Shannon index:

H_1(X)=-\sum\limits_{i=1}^R{p_i};log;p_i

Shannon, C. E. (1948). A mathematical theory of communication. Bell System

Simpson index:

D=\frac{\sum_{i=1}^R{p_i};n_i(n_i -1)}{N(N-1))}

Simpson, E. H. (1949). Measurement of diversity. Nature 163, p. 688

Collision entropy:

H_2(X)=-log\sum_{i=1}^n{p_i}^2

Renyi, A. (1961). On measures of information and entropy. Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics and Probability, 1960. pp. 547-5661.

Berger Parker index:

D_{BP}=\frac{N_{max}}{N}

Berger, W. H., & Parker, F. L.(1970). Diversity of planktonic Foramenifera in deep sea sediments. Science 168, pp. 1345-1347.

Brillouin index:

H_B=\frac{ln(N!)-\sum{ln(n_1)!}}{N}

Magurran, A. E. (2004). Measuring biological diversity. Blackwell.

Value

Returns a dataframe of various diversity related indices for Shannon, collision, Berger Parker and Brillouin.

References

https://arxiv.org/abs/physics/0512106

Examples

## Not run: 
div.mod <- with(mraja1spl, diversity(dialogue, list(sex, died, fam.aff)))
colsplit2df(div.mod)
plot(div.mod, high = "red", low = "yellow")
plot(div.mod, high = "red", low = "yellow", values = TRUE)

## End(Not run)

Find Duplicated Words in a Text String

Description

Find duplicated word/word chunks in a string. Intended for internal use.

Usage

duplicates(string, threshold = 1)

Arguments

Value

Returns a vector of all duplicated words/chunks.

Examples

## Not run: 
duplicates(DATA$state)
duplicates(DATA$state[1])

## End(Not run)

Test for Incomplete Sentences

Description

Test for incomplete sentences and optionally remove them.

Usage

end_inc(dataframe, text.var, warning.report = TRUE, which.mode = FALSE)

Arguments

Value

Generates a dataframe with incomplete sentences removed.

Examples

## Not run: 
dat <- sentSplit(DATA, "state", stem.col = FALSE)
dat$state[c(2, 5)] <- paste(strip(dat$state[c(2, 5)]), "|")
end_inc(dat, "state")
end_inc(dat, "state", warning.report = FALSE)
end_inc(dat, "state", which.mode = TRUE)

## End(Not run)

Sentence End Marks

Description

end_mark - Grab the sentence end marks for a transcript. This can be useful to categorize based on sentence type.

end_mark_by - Grab the sentence end marks for a transcript by grouping variable(s).

Usage

end_mark(
  text.var,
  missing.end.mark = "_",
  missing.text = NA,
  other.endmarks = NULL
)

end_mark_by(
  text.var,
  grouping.var,
  digits = 3,
  percent = FALSE,
  zero.replace = 0,
  ...
)

Arguments

Value

Returns a character vector of qdap end marks for each sentence. End marks include:

Examples

## Not run: 
end_mark(DATA.SPLIT$state)
end_mark(mraja1spl$dialogue)
table(end_mark(mraja1spl$dialogue))
plot(end_mark(mraja1spl$dialogue))
ques <- mraja1spl[end_mark(mraja1spl$dialogue) == "?", ] #grab questions
htruncdf(ques)
non.ques <- mraja1spl[end_mark(mraja1spl$dialogue) != "?", ] #non questions
htruncdf(non.ques, 20)
ques.per <- mraja1spl[end_mark(mraja1spl$dialogue) %in% c(".", "?"), ] #grab ? and .
htruncdf(ques.per, 20)

(x_by <- end_mark_by(DATA.SPLIT$state, DATA.SPLIT$person))
scores(x_by)
counts(x_by)
proportions(x_by)
preprocessed(x_by)
plot(scores(x_by))
plot(counts(x_by))
plot(proportions(x_by))
plot(preprocessed(x_by))

#================================#
## End Marks Over Time Examples ##
#================================#
##EXAMPLE 1
sentpres <- lapply(with(pres_debates2012, split(dialogue, time)), function(x) {
    end_mark(x)
})

sentplots <- lapply(seq_along(sentpres), function(i) {
    m <- plot(cumulative(sentpres[[i]]))
    if (i != 2) m <- m + ylab("")
    if (i != 3) m <- m + xlab(NULL)
    m + ggtitle(paste("Debate", i))
})

library(grid)
library(gridExtra)
do.call(grid.arrange, sentplots)

##EXAMPLE 2
sentraj <- lapply(with(rajSPLIT, split(dialogue, act)), function(x) {
    end_mark(x)
})
 
sentplots2 <- lapply(seq_along(sentraj), function(i) {
    m <- plot(cumulative(sentraj[[i]]))
    if (i != 2) m <- m + ylab("")
    if (i != 3) m <- m + xlab(NULL)
    act <- qcv(I, II, III, IV, V)
    m + ggtitle(paste("Act", act[i]))
})

## ggplot2 function to extract legend
g_legend <- function(a.gplot){ 
    tmp <- ggplot_gtable(ggplot_build(a.gplot)) 
    leg <- which(sapply(tmp[["grobs"]], function(x) x[["name"]]) == "guide-box") 
    legend <- tmp[["grobs"]][[leg]] 
    legend
} 

## remove legends from plots
sentplots3 <- lapply(sentplots2, function(x){
    x + theme(legend.position="none") + xlab(NULL) + ylab(NULL)
})

sentplots3[[6]] <- g_legend(sentplots2[[1]]) 

do.call(grid.arrange, sentplots3)

## End(Not run)

Syllable Lookup Environment

Description

A dataset containing a syllable lookup environment (see DICTIONARY).

Usage

data(env.syl)

Format

A environment with the DICTIONARY data set.

Details

For internal use.

References

UCI Machine Learning Repository website

Exclude Elements From a Vector

Description

exclude - Quickly exclude words from a word list

%ex% - Binary operator version of exclude .

Usage

exclude(word.list, ...)

## S3 method for class 'TermDocumentMatrix'
exclude(word.list, ...)

## S3 method for class 'DocumentTermMatrix'
exclude(word.list, ...)

## S3 method for class 'wfm'
exclude(word.list, ...)

## S3 method for class 'list'
exclude(word.list, ...)

## Default S3 method:
exclude(word.list, ...)

word.list %ex% ...

Arguments

Value

Returns a vector with the excluded terms removed.

Examples

## Not run: 
exclude(1:10, 3, 4)
exclude(1:10, 3:4)
Top25Words
exclude(Top25Words, qcv(the, of, and))
exclude(Top25Words, "the", "of", "an")

#Using with term_match and termco 
terms <- term_match(DATA$state, qcv(th), FALSE) 
exclude(terms, "truth")  
#all together
termco(DATA$state, DATA$person, exclude(term_match(DATA$state, qcv(th), 
    FALSE), "truth"))

MTCH.LST <- exclude(term_match(DATA$state, qcv(th, i)), qcv(truth, stinks))
termco(DATA$state, DATA$person, MTCH.LST)

## Works with wfm
dat <- wfm(DATA$state, DATA$person)
the.no <- term_match(DATA$state, c("the", "no"))
exclude(dat, unlist(the.no))

## Works with tm's TermDocumentMatrix/DocumentTermMatrix
dat2 <- as.dtm(DATA$state, DATA$person)
out.dtm <- exclude(dat2, unlist(the.no))
tm::inspect(out.dtm)

dat3 <- as.tdm(DATA$state, DATA$person)
out.tdm <- exclude(dat3, unlist(the.no))
tm::inspect(out.tdm)

## End(Not run)

Formality Score

Description

Transcript apply formality score by grouping variable(s) and optionally plot the breakdown of the model.

Usage

formality(
  text.var,
  grouping.var = NULL,
  order.by.formality = TRUE,
  digits = 2,
  ...
)

Arguments

Details

Heylighen & Dewaele(2002)'s formality score is calculated as:

F = 50(\frac{n_{f}-n_{c}}{N} + 1)

Where:

f = \left \{noun, \;adjective, \;preposition, \;article\right \}

c = \left \{pronoun, \;verb, \;adverb, \;interjection\right \}

N = \sum{(f \;+ \;c \;+ \;conjunctions)}

Value

A list containing at the following components:

Warning

Heylighen & Dewaele (2002) state, "At present, a sample would probably need to contain a few hundred words for the measure to be minimally reliable. For single sentences, the F-value should only be computed for purposes of illustration" (p. 24).

References

Heylighen, F., & Dewaele, J.M. (2002). Variation in the contextuality of language: An empirical measure. Context in Context, Special issue of Foundations of Science, 7 (3), 293-340.

Examples

## Not run: 
with(DATA, formality(state, person))
(x1 <- with(DATA, formality(state, list(sex, adult))))
plot(x1)
plot(x1, short.names = FALSE)

scores(x1)
counts(x1)
proportions(x1)
preprocessed(x1)

plot(scores(x1))
plot(counts(x1))
plot(proportions(x1), high="darkgreen")
plot(preprocessed(x1))

data(rajPOS) #A data set consisting of a pos list object
x2 <- with(raj, formality(rajPOS, act))
plot(x2)
cumulative(x2)
x3 <- with(raj, formality(rajPOS, person))
plot(x3, bar.colors="Dark2")
plot(x3, bar.colors=c("Dark2", "Set1"))
x4 <- with(raj, formality(rajPOS, list(person, act)))
plot(x4, bar.colors=c("Dark2", "Set1"))

rajDEM <- key_merge(raj, raj.demographics) #merge demographics with transcript.
x5 <- with(rajDEM, formality(rajPOS, sex))
plot(x5, bar.colors="RdBu")
x6 <- with(rajDEM, formality(rajPOS, list(fam.aff, sex)))
plot(x6, bar.colors="RdBu")
x7 <- with(rajDEM, formality(rajPOS, list(died, fam.aff)))
plot(x7, bar.colors="RdBu",  point.cex=2, point.pch = 3)
x8 <- with(rajDEM, formality(rajPOS, list(died, sex)))
plot(x8, bar.colors="RdBu",  point.cex=2, point.pch = "|")

names(x8)
colsplit2df(x8$formality)

#pass an object from pos or pos_by
ltruncdf(with(raj, formality(x8 , list(act, person))), 6, 4)

#=============#
## ANIMATION ##
#=============#
## EXAMPLE 1
form_ani <- formality(DATA.SPLIT$state, DATA.SPLIT$person)
forma <- Animate(form_ani, contextual="white", formal="blue", 
    current.color = "yellow", current.speaker.color="grey70")

bgb <- vertex_apply(forma, label.color="grey80", size=20, color="grey40")
bgb <- edge_apply(bgb, label.color="yellow")

print(bgb, bg="black", net.legend.color ="white", pause=1)

## EXAMPLE 2
form_ani2 <- formality(raj.act.1POS, mraja1spl$person)
forma2 <- Animate(form_ani2, contextual="white", formal="blue",
    current.color = "yellow", current.speaker.color="grey70")

bgb2 <- vertex_apply(forma2, label.color="grey80", size=17, color="grey40")
bgb2 <- edge_apply(bgb2, label.color="yellow")
print(bgb2, bg="black", pause=.75, net.legend.color = "white")

## EXAMPLE 3 (bar plot)
Animate(form_ani2, as.network=FALSE)

#=====================#
## Complex Animation ##
#=====================#
library(animation)
library(grid)
library(gridBase)
library(qdap)
library(igraph)
library(plotrix)

form_ani2 <- formality(raj.act.1POS, mraja1spl$person)

## Set up the network version
form_net <- Animate(form_ani2, contextual="white", formal="blue",
    current.color = "yellow", current.speaker.color="grey70")
bgb <- vertex_apply(form_net, label.color="grey80", size=17, color="grey40")
bgb <- edge_apply(bgb, label.color="yellow")


## Set up the bar version
form_bar <- Animate(form_ani2, as.network=FALSE)

## Generate a folder
loc <- folder(animation_formality)

## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)


FUN <- function(follow=FALSE, theseq = seq_along(bgb)) {

    Title <- "Animated Formality: Romeo and Juliet Act 1"
    Legend <- c(.2, -1, 1.5, -.95)
    Legend.cex <- 1

    lapply(theseq, function(i) {
        if (follow) {
            png(file=sprintf("%s/images/Rplot%s.png", loc, i),
                width=650, height=725)
        }
        ## Set up the layout
        layout(matrix(c(rep(1, 9), rep(2, 4)), 13, 1, byrow = TRUE))

        ## Plot 1
        par(mar=c(2, 0, 2, 0), bg="black")
        #par(mar=c(2, 0, 2, 0))
        set.seed(22)
        plot.igraph(bgb[[i]], edge.curved=TRUE)
        graphics::mtext(Title, side=3, col="white")
        color.legend(Legend[1], Legend[2], Legend[3], Legend[4],
              c("Contextual", "Formal"), attributes(bgb)[["legend"]],
              cex = Legend.cex, col="white")

        ## Plot2
        plot.new()
        vps <- baseViewports()

        uns <- unit(c(-1.3,.5,-.75,.25), "cm")
        p <- form_bar[[i]] +
            theme(plot.margin = uns,
                text=element_text(color="white"),
                legend.text=element_text(color="white"),
                legend.background = element_rect(fill = "black"),
                plot.background = element_rect(fill = "black",
                    color="black"))
        print(p,vp = vpStack(vps$figure,vps$plot))
        animation::ani.pause()

        if (follow) {
            dev.off()
        }
    })

}

FUN()

## Detect OS
type <- if(.Platform$OS.type == "windows") shell else system

saveHTML(FUN(, 1:20), autoplay = FALSE, loop = TRUE, verbose = FALSE,
    ani.height = 1000, ani.width=650,
    outdir = loc, single.opts =
    "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")

FUN(TRUE)

#==================#
## Static Network ##
#==================#
(formdat <- with(sentSplit(DATA, 4), formality(state, person)))
m <- Network(formdat)
m
print(m, bg="grey97", vertex.color="grey75")

print(m, title="Formality Discourse Map", title.color="white", bg="black",
    legend.text.color="white", vertex.label.color = "grey70", 
    edge.label.color="yellow")
    
## or use themes:
dev.off()
m + qtheme()
m + theme_nightheat
dev.off()
m + theme_nightheat(title="Formality Discourse Map", 
    vertex.label.color = "grey50")
    
#===============================#
## Formality Over Time Example ##
#===============================#
formpres <- lapply(with( pres_debates2012, split(dialogue, time)), function(x) {
    formality(x)
})
formplots <- lapply(seq_along(formpres), function(i) {
    m <- plot(cumulative(formpres[[i]]))
    if (i != 2) m <- m + ylab("")
    if (i != 3) m <- m + xlab(NULL)
    m + ggtitle(paste("Debate", i))
})

library(grid)
library(gridExtra)
do.call(grid.arrange, formplots)

## End(Not run)

Find Frequent Terms

Description

Find the most frequently occurring terms in a text vector.

Usage

freq_terms(
  text.var,
  top = 20,
  at.least = 1,
  stopwords = NULL,
  extend = TRUE,
  ...
)

Arguments

Value

Returns a dataframe with the top occurring words.

See Also

word_list, all_words

Examples

## Not run: 
freq_terms(DATA$state, 5)
freq_terms(DATA$state)
freq_terms(DATA$state, extend = FALSE)
freq_terms(DATA$state, at.least = 4)
(out <- freq_terms(pres_debates2012$dialogue, stopwords = Top200Words))
plot(out)

## All words by sentence (row)
library(qdapTools)
x <- raj$dialogue
list_df2df(setNames(lapply(x, freq_terms, top=Inf), seq_along(x)), "row")
list_df2df(setNames(lapply(x, freq_terms, top=10, stopwords = Dolch), 
    seq_along(x)), "Title")


## All words by person
FUN <- function(x, n=Inf) freq_terms(paste(x, collapse=" "), top=n)
list_df2df(lapply(split(x, raj$person), FUN), "person")

## Plot it
out <- lapply(split(x, raj$person), FUN, n=10)
pdf("Freq Terms by Person.pdf", width=13) 
lapply(seq_along(out), function(i) {
    ## dev.new()
    plot(out[[i]], plot=FALSE) + ggtitle(names(out)[i])
})
dev.off()

## Keep spaces
freq_terms(space_fill(DATA$state, "are you"), 500, char.keep="~~")

## End(Not run)

Gantt Durations

Description

gantt - Generates start and end times of supplied text selections (i.e., text selections are determined by any number of grouping variables).

plot_gantt_base - For internal use.

Usage

gantt(text.var, grouping.var, units = "words", sums = FALSE, col.sep = "_")

plot_gantt_base(
  x,
  sums = NULL,
  fill.colors = NULL,
  box.color = "white",
  title = NULL
)

Arguments

Value

Returns a data frame of start and end times by grouping variable(s) or optionally returns a list of two: (1) A data frame of the total units used by grouping variable(s) and (2) a data frame of start and end times by grouping variable(s).

Note

For non-repeated measures data use gantt. For more flexible plotting needs use gantt_wrap over the generic plotting method.

Author(s)

DigEmAll (stackoverflow.com) and Tyler Rinker <tyler.rinker@gmail.com>.

References

Clark, W. & Gantt, H. (1922) The Gantt chart, a working tool of management. New York, Ronald Press.

See Also

gantt_rep, gantt_wrap, gantt_plot

Examples

## Not run: 
(a <- gantt(DATA$state, DATA$person))  
plot(a)
plot(a, base = TRUE)

(b <- gantt(DATA$state, DATA$person, sums = TRUE)) 
plot(b)
plot(b, base = FALSE) 

(d <- gantt(DATA$state, list(DATA$sex, DATA$adult)))        
plot(d)

x <- gantt(mraja1$dialogue, mraja1$person) 
plot(x, base = TRUE)
plot(x, , base = TRUE, box.color = "black") 

z <- gantt(mraja1$dialogue, mraja1$sex)  
plot(z)  
                                                          
e <- with(mraja1, gantt(dialogue, list(fam.aff, sex, died), 
   units = "characters", sums = TRUE))
plot(e)  
     
f <- gantt(mraja1$dialogue, mraja1$person, units = "syllables",
    sums = TRUE)
plot(f, box.color = "red")
plot(f, base = FALSE)

dat <- gantt(mraja1$dialogue, list(mraja1$fam.aff, mraja1$sex),
    units = "sentences", col.sep = "_")
    
    
## Animate It
##=================
ani_gannt <- with(DATA.SPLIT, gantt(state, person))
Animate(ani_gannt)
Animate(plot(ani_gannt))

library(animation)
loc <- folder(animation_gantt)

## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)

FUN <- function() {
    out <- Animate(ani_gannt)
    lapply(out, function(x) {
        print(x)
        animation::ani.pause()
    })

}

type <- if(.Platform$OS.type == "windows") shell else system
saveGIF(FUN(), interval = 0.1, outdir = loc, cmd.fun = type)

## End(Not run)

Gantt Plot

Description

A convenience function that wraps gantt, gantt_rep and gantt_wrap into a single plotting function.

Usage

gantt_plot(
  text.var,
  grouping.var = NULL,
  rm.var = NULL,
  fill.var = NULL,
  xlab = "duration (in words)",
  units = "words",
  col.sep = "__",
  ...
)

Arguments

Value

Returns a Gantt style visualization. Invisibly returns the ggplot2 list object.

Note

For non-repeated measures data/plotting use gantt; for repeated measures data output use gantt_rep; and for a flexible gantt plot that words with code matrix functions (cm) use gantt_wrap.

References

Clark, W. & Gantt, H. (1922) The Gantt chart, a working tool of management. New York, Ronald Press.

See Also

gantt, gantt_rep, gantt_wrap

Examples

## Not run: 
with(rajSPLIT, gantt_plot(text.var = dialogue,
    grouping.var = person, size=4))

with(rajSPLIT, gantt_plot(text.var = dialogue,
    grouping.var = list(fam.aff, sex), rm.var  = act,
    title = "Romeo and Juliet's dialogue"))

with(rajSPLIT, gantt_plot(dialogue, list(fam.aff, sex), act,
    transform=T))

rajSPLIT2 <- rajSPLIT

rajSPLIT2$newb <- as.factor(sample(LETTERS[1:2], nrow(rajSPLIT2),
    replace=TRUE))

z <- with(rajSPLIT2, gantt_plot(dialogue, list(fam.aff, sex),
    list(act, newb), size = 4))

library(ggplot2); library(scales); library(RColorBrewer); library(grid)
z + theme(panel.spacing = unit(1, "lines")) + scale_colour_grey()
z + scale_colour_brewer(palette="Dark2")

## Fill Variable Example
dat <- rajSPLIT[rajSPLIT$act == 1, ]
dat$end_mark <- factor(end_mark(dat$dialogue))

with(dat, gantt_plot(text.var = dialogue, grouping.var = list(person, sex),
    fill.var=end_mark))

## Repeated Measures with Fill Example
rajSPLIT$end_mark <- end_mark(rajSPLIT$dialogue)

with(rajSPLIT, gantt_plot(text.var = dialogue,
    grouping.var = list(fam.aff), rm.var  = list(act),
    fill.var=end_mark, title = "Romeo and Juliet's dialogue"))

## Repeated Measures Sentence Type Example
with(rajSPLIT, gantt_plot(text.var = dialogue,
    grouping.var = list(fam.aff, sex), rm.var  = list(end_mark, act),
    title = "Romeo and Juliet's dialogue"))

## Reset rajSPLIT
rajSPLIT <- qdap::rajSPLIT

## Animate It
##=================
ani_gantt <- with(mraja1, gantt_plot(dialogue, person))

library(animation)
loc <- folder(animation_gantt)

## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)

FUN <- function() {
    out <- Animate(ani_gantt)
    lapply(out, function(x) {
        print(x)
        animation::ani.pause()
    })

}

type <- if(.Platform$OS.type == "windows") shell else system
saveVideo(FUN(), video.name = "animation.avi", interval = 0.1, outdir = loc)

saveLatex(FUN(), autoplay = TRUE, loop = FALSE, latex.filename = "tester.tex", 
    caption = "animated dialogue", outdir = loc, ani.type = "pdf", 
    ani.dev = "pdf", ani.width = 5, ani.height = 5.5, interval = 0.1)


saveHTML(FUN(), autoplay = FALSE, loop = TRUE, verbose = FALSE, 
    ani.width=600, ani.height=280,
    outdir = file.path(loc, "new"), single.opts = 
    "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")


## End(Not run)

Generate Unit Spans for Repeated Measures

Description

Produces start and end times for occurrences for each repeated measure condition.

Usage

gantt_rep(
  rm.var,
  text.var,
  grouping.var = NULL,
  units = "words",
  col.sep = "_",
  name.sep = "_"
)

Arguments

Value

Returns a data frame of start and end times by repeated measure and grouping variable(s)

Note

For non-repeated measures data use gantt. For more flexible plotting needs use gantt_wrap over the generic plotting method.

References

Clark, W. & Gantt, H. (1922) The Gantt chart, a working tool of management. New York, Ronald Press.

See Also

gantt, gantt_wrap, gantt_plot

Examples

## Not run: 
dat <- with(rajSPLIT, gantt_rep(act, dialogue, list(fam.aff, sex),
    units = "words", col.sep = "_"))
head(dat, 20)
plot(dat)

gantt_wrap(dat,  "fam.aff_sex",  facet.vars = "act",
    title = "Repeated Measures Gantt Plot",
    minor.line.freq = 25, major.line.freq = 100)

## Two facets variables
dat2 <- with(DATA2, gantt_rep(list(day, class), state, person,
    units = "words", col.sep = "_"))
head(dat2, 20)
plot(dat2)

## End(Not run)

Gantt Plot

Description

A ggplot2 wrapper that produces a Gantt plot.

Usage

gantt_wrap(
  dataframe,
  plot.var,
  facet.vars = NULL,
  fill.var = NULL,
  title = NULL,
  ylab = plot.var,
  xlab = "duration.default",
  rev.factor = TRUE,
  transform = FALSE,
  ncol = NULL,
  minor.line.freq = NULL,
  major.line.freq = NULL,
  sig.dig.line.freq = 1,
  hms.scale = NULL,
  scale = NULL,
  space = NULL,
  size = 3,
  rm.horiz.lines = FALSE,
  x.ticks = TRUE,
  y.ticks = TRUE,
  legend.position = NULL,
  bar.color = NULL,
  border.color = NULL,
  border.size = 2,
  border.width = 0.1,
  constrain = TRUE,
  plot = TRUE
)

Arguments

Value

Returns a Gantt style visualization. Invisibly returns the ggplot2 list object.

Note

For non-repeated measures data/plotting use gantt; for repeated measures data output use gantt_rep; and for a convenient wrapper that takes text and generates plots use gantt_plot.

Author(s)

Andrie de Vries and Tyler Rinker <tyler.rinker@gmail.com>.

References

Clark, W. & Gantt, H. (1922) The Gantt chart, a working tool of management. New York, Ronald Press.

See Also

gantt, gantt_plot, gantt_rep, facet_grid, facet_wrap

Examples

## Not run: 
dat <- gantt(mraja1$dialogue, list(mraja1$fam.aff, mraja1$sex),
    units = "sentences", col.sep = "_")
htruncdf(dat)
gantt_wrap(dat, "fam.aff_sex", title = "Gantt Plot")
dat$codes <- sample(LETTERS[1:3], nrow(dat), TRUE)
gantt_wrap(dat, "fam.aff_sex", fill.var = "codes",
    legend.position = "bottom")

dat2 <- with(rajSPLIT, gantt_rep(act, dialogue,
    list(fam.aff, sex), units = "words", col.sep = "_"))
htruncdf(dat2)
x <- gantt_wrap(dat2, "fam.aff_sex", facet.vars = "act",
    title = "Repeated Measures Gantt Plot")

library(ggplot2); library(scales); library(RColorBrewer)
x + scale_color_manual(values=rep("black",
    length(levels(dat2$fam.aff_sex))))

## End(Not run)

Gradient Word Cloud

Description

Produces a gradient word cloud colored by a binary grouping variable.

Usage

gradient_cloud(
  text.var,
  bigroup.var,
  rev.binary = FALSE,
  X = "red",
  Y = "blue",
  stem = FALSE,
  stopwords = NULL,
  caps = TRUE,
  caps.list = NULL,
  I.list = TRUE,
  random.order = FALSE,
  rot.per = 0,
  min.freq = 1,
  max.word.size = NULL,
  min.word.size = 0.5,
  breaks = 10,
  cloud.font = NULL,
  title = NULL,
  title.font = NULL,
  title.color = "black",
  title.padj = 0.25,
  title.location = 3,
  title.cex = NULL,
  legend.cex = 0.8,
  legend.location = c(0.025, 0.025, 0.25, 0.04),
  char2space = "~~"
)

Arguments

Details

Breaking is done using quantile. This will ensure a certain percentage of words will be colored at each bin.

Value

Plots a gradient word cloud and invisibly returns the dataframe used to make the cloud.

See Also

trans_cloud, wordcloud, color.legend

Examples

## Not run: 
DATA$state <- space_fill(DATA$state, c("is fun", "too fun", "you liar"))

gradient_cloud(DATA$state, DATA$sex, title="fun")
gradient_cloud(DATA$state, DATA$sex, title="fun", rev.binary = TRUE)
gradient_cloud(DATA$state, DATA$sex, title="fun", max.word.size = 5,
    min.word.size = .025)
    
with(mraja1, gradient_cloud(dialogue, died, stopwords = Top25Words, 
    rot.per = .5, title="Heatcloud", title.color="orange", title.cex=1.75))    
x <- with(subset(mraja1, fam.aff %in% qcv(cap, mont)), 
    gradient_cloud(dialogue, fam.aff))
head(x) 

## 2012 U.S. Presidential Debates
invisible(lapply(split(pres_debates2012, pres_debates2012$time), function(x) {
    x <- x[x$person %in% qcv(ROMNEY, OBAMA), ]
    dev.new()
    gradient_cloud(x$dialogue, x$person, 
        title = paste("Debate", char2end(x$time[1])),
        stopwords = BuckleySaltonSWL,
        X = "blue", Y = "red", 
        max.word.size = 2.2, 
        min.word.size = 0.55
    )
}))
        

## End(Not run)

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

Dataframe Viewing

Description

htruncdf - Convenience function to view the head of a truncated dataframe.

truncdf - Convenience function to view a truncated dataframe.

ltruncdf - Convenience function to view the head of a list of truncated dataframes.

qview - Convenience function to view a summary and head of a dataframe.

lview - Convenience function to view the list (list view) of qdap objects that have print methods that print a single dataframe.

Usage

htruncdf(dataframe, n = 10, width = 10, ...)

truncdf(dataframe, end = 10, begin = 1)

ltruncdf(dat.list, n = 6, width = 10, ...)

qview(dataframe, ...)

lview(x, print = TRUE)

Arguments

Value

htrundf - returns n number of rows of a truncated dataframe.

trundf - returns a truncated dataframe.

ltruncdf - returns a list of n number of rows of a truncated dataframes.

qview - returns a dataframe head with summary statistics.

lview - prints a list of the qdap object and invisibly returns the unclassed object.

See Also

head

Examples

## Not run: 
truncdf(raj[1:10, ])
truncdf(raj[1:10, ], 40)
htruncdf(raj)
htruncdf(raj, 20)
htruncdf(raj, ,20)
ltruncdf(rajPOS, width = 4)
qview(raj)
qview(CO2)
lview(question_type(DATA.SPLIT$state, DATA.SPLIT$person))
lview(rajPOS)
lview(lm(mpg~hp, data = mtcars))

## End(Not run)

Intuitively Remark Sentences as Imperative

Description

Automatic imperative remarking.

Usage

imperative(
  dataframe,
  person.var,
  text.var,
  lock.incomplete = FALSE,
  additional.names = NULL,
  parallel = FALSE,
  warning = FALSE
)

Arguments

Value

Returns a dataframe with a text variable indicating imperative sentences. Imperative sentences are marked with * followed by the original end mark.

Warning

The algorithm used by imperative is sensitive to English language dialects and types. Commas can indicate a choppy sentence and may indicate a false positive. Sentences marked with 'AAVE' may be the use of African American Vernacular English and not an imperative sentence.

Examples

## Not run: 
dat <- data.frame(name=c("sue", rep(c("greg", "tyler", "phil", 
    "sue"), 2)), statement=c("go get it|", "I hate to read.", 
    "Stop running!", "I like it!", "You are terrible!", "Don't!", 
    "Greg, go to the red, brick office.", "Tyler go to the gym.", 
    "Alex don't run."), stringsAsFactors = FALSE)

imperative(dat, "name", "statement", , c("Alex"))
imperative(dat, "name", "statement", lock.incomplete = TRUE, c("Alex"))
imperative(dat, "name", "statement", , c("Alex"), warning=TRUE)
imperative(dat, "name", "statement", , c("Alex"), warning=TRUE,  
    parallel = TRUE)

## End(Not run)

Denote Incomplete End Marks With "|"

Description

Replaces incomplete sentence end marks (.., ..., .?, ..?, en & em dash etc.) with "|".

Usage

incomplete_replace(text.var, scan.mode = FALSE)

incomp(text.var, scan.mode = FALSE)

Arguments

Value

Returns a text variable (character sting) with incomplete sentence marks (.., ..., .?, ..?, en & em dash etc.) replaced with "|". If scan mode is TRUE returns a data frame with incomplete sentence location.

Examples

## Not run: 
x <- c("the...",  "I.?", "you.", "threw..", "we?")
incomplete_replace(x)
incomp(x)
incomp(x, scan.mode = TRUE)

## End(Not run)

Inspect Text Vectors

Description

inspect_text - Inspect a text vector with adjustable string wrapping; created a pretty printed named list.

Usage

inspect_text(text.var, grouping.var = NULL, ...)

## Default S3 method:
inspect_text(text.var, grouping.var = NULL, ...)

## S3 method for class 'Corpus'
inspect_text(text.var, ...)

Arguments

Value

Returns a named list (prints pretty).

Examples

## Not run: 
with(raj, inspect_text(dialogue))
with(raj, inspect_text(dialogue, person))
with(raj, inspect_text(dialogue, list(paste("Act", act), person)))

## With a tm Corpus object
library(tm)
data(crude)
inspect_text(crude)

## End(Not run)

Test If Environment is Global

Description

A logical test to determine if the current environment is the global environment.

Usage

is.global(n = 1)

Arguments

Value

A logical response.

Author(s)

Simon O'Hanlon and Tyler Rinker <tyler.rinker@gmail.com>

References

http://stackoverflow.com/questions/18637656/detect-if-environment-is-global-enviroment

See Also

globalenv, parent.frame

Examples

is.global()
lapply(1:3, function(i) is.global())
FUN <- function() is.global(); FUN()

FUN2 <- function(x = is.global(2)) x
FUN2()
FUN3 <- function() FUN2(); FUN3()

Merge Demographic Information with Person/Text Transcript

Description

Wrapper function (merge) for merging demographic information with a person/text transcript.

Usage

key_merge(transcript.df, key.df, common.column = NULL, defualt.arrange = TRUE)

Arguments

Value

Outputs a merged transcript dataframe with demographic information.

See Also

merge

Examples

## Not run: 
#First view transcript dataframe and demographics dataframe.
ltruncdf(list(raj, raj.demographics), 10, 50)
merged.raj <- key_merge(raj, raj.demographics)
htruncdf(merged.raj, 10, 40)

## End(Not run)

Kullback Leibler Statistic

Description

A proximity measure between two probability distributions applied to speech.

Usage

kullback_leibler(x, y = NULL)

Arguments

Details

Uses Kullback & Leibler's (1951) formula:

D_{KL}(P||Q)=\sum_i{ln\left ( \frac{P_{i}}{Q_{i}} \right )}P_{i}

Value

Returns a matrix of the Kullback Leibler measure between each vector of probabilities.

Note

The kullback_leibler function generally receives the output of either wfm or wfdf functions.

References

Kullback, S., & Leibler, R.A. (1951). On Information and sufficiency. Annals of Mathematical Statistics 22 (1): 79-86. doi:10.1214/aoms/1177729694

Examples

## Not run: 
p.df <- wfdf(DATA$state, DATA$person)
p.mat <- wfm(text.var = DATA$state, grouping.var = DATA$person)
kullback_leibler(p.mat)
(x <- kullback_leibler(p.df))
print(x, digits = 5)
kullback_leibler(p.df$greg, p.df$sam)

## p.df2 <- wfdf(raj$dialogue, raj$person)
## x <- kullback_leibler(p.df2)

## End(Not run)

Text Justification

Description

left_just - Left justifies a text/character column.

right_just - A means of undoing a left justification.

Usage

left_just(dataframe, column = NULL, keep.class = FALSE)

right_just(dataframe)

Arguments

Value

Returns a dataframe with selected text column left/right justified.

Note

left_just inserts spaces to achieve the justification. This could interfere with analysis and therefore the output from left_just should only be used for visualization purposes, not analysis.

Examples

## Not run: 
left_just(DATA)
left_just(DATA, "state")
left_just(CO2[1:15,])
right_just(left_just(CO2[1:15,]))

## End(Not run)

Lexical Classification Score

Description

Transcript apply lexical classification score (content to functional word proportion) by grouping variable(s) and optionally plot the breakdown of the model.

Usage

lexical_classification(
  text.var,
  grouping.var = NULL,
  order.by.lexical_classification = TRUE,
  function.words = qdapDictionaries::function.words,
  bracket = "all",
  ...
)

Arguments

Details

Content words (i.e., nouns, verbs, adjectives, and adverbs) tend to be the words speakers stresses in language use. Whereas, functional words are the "glue" that holds the content together. Speakers devote much less time and stress to these words (i.e., pronouns, articles, conjunctions, quantifiers, and prepositions).

Value

A list containing at the following components:

References

Chung, C. & Pennebaker, J. (2007). The Psychological Functions of Function Words. In K. Fiedler (Ed.) Social Communication (pp. 343-359). New York: Psychology Press.

Pulvermuller, F. (1999). Words in the brain's language. Behavioral and Brain Sciences, 22, pp. 253-279. doi:10.1017/S0140525X9900182X

Segalowitz, S. J. & Lane, K. (2004). Perceptual fluency and lexical access for function versus content words. Behavioral and Brain Sciences, 27, 307-308. doi:10.1017/S0140525X04310071

Bell, A., Brenier, J. M., Gregory, M., Girand, C. & Jurafsky, D. (2009). Predictability Effects on Durations of Content and Function Words in Conversational English. Journal of Memory and Language, 60(1), 92-111. doi:10.1016/j.jml.2008.06.003

Examples

## Not run: 
lexical_classification("I did not like the dog.")
lexical_classification(DATA.SPLIT$state, DATA.SPLIT$person)

(out <- with(pres_debates2012, lexical_classification(dialogue, list(person, time))))
plot(out)

scores(out)

out2 <- preprocessed(out)
htruncdf(out2)
plot(out2)

plot(out[["content"]])
dev.new()
plot(out[["functional"]])

## cloud of functional vs. content
## Highlight Content Words
set.seed(10)
par(mar = c(0,0,0,0))
list(
        content = out[["content"]],
        functional = out[["functional"]]
    ) %>%
    list_df2df("type") %>%
    dplyr::mutate(colors = ifelse(type == "functional", "gray80", "blue")) %>%
    with(., wordcloud::wordcloud(
        word, 
        freq, 
        min.freq = 8, 
        random.order=FALSE,
        ordered.colors = TRUE,
        colors = colors
    )) 
mtext("2012 Presidential Debates:\nFunctional vs. Content Word Use", padj=1.25)
legend(
    .05, .12, bty = "n",
    legend = c("functional", "content"), 
    fill = c("gray80", "blue"),  
    cex = .7
)

## Highlight Functional Words
set.seed(10)
par(mar = c(0,0,0,0))
list(
        content = out[["content"]],
        functional = out[["functional"]]
    ) %>%
    list_df2df("type") %>%
    dplyr::mutate(colors = ifelse(type == "functional", "red", "gray80")) %>%
    with(., wordcloud::wordcloud(
        word, 
        freq, 
        min.freq = 8, 
        random.order=FALSE,
        ordered.colors = TRUE,
        colors = colors
    )) 
mtext("2012 Presidential Debates:\nFunctional vs. Content Word Use", padj=1.25)
legend(
    .05, .12, bty = "n",
    legend = c("functional", "content"), 
    fill = c("red", "gray80"),  
    cex = .7
)

#=============#
## ANIMATION ##
#=============#
## EXAMPLE 1
lex_ani <- lexical_classification(DATA.SPLIT$state, DATA.SPLIT$person)
lexa <- Animate(lex_ani, content="white", functional="blue",
    current.color = "yellow", current.speaker.color="grey70")

bgb <- vertex_apply(lexa, label.color="grey80", size=20, color="grey40")
bgb <- edge_apply(bgb, label.color="yellow")

print(bgb, bg="black", net.legend.color ="white", pause=1)

## EXAMPLE 2
lex_ani2 <- lexical_classification(mraja1spl$dialogue, mraja1spl$person)
lexa2 <- Animate(lex_ani2, content="white", functional="blue",
    current.color = "yellow", current.speaker.color="grey70")

bgb2 <- vertex_apply(lexa2, label.color="grey80", size=17, color="grey40")
bgb2 <- edge_apply(bgb2, label.color="yellow")
print(bgb2, bg="black", pause=.75, net.legend.color = "white")

## EXAMPLE 3 (bar plot)
Animate(lex_ani2, type="bar")

## EXAMPLE 4 (text plot)
Animate(lex_ani2, type="text")

#======================#
## Complex Animations ##
#======================#
## EXAMPLE 1: Network + Text + Bar
 
library(animation)
library(grid)
library(gridBase)
library(qdap)
library(igraph)
library(plotrix)

lex_ani2 <- lexical_classification(mraja1spl$dialogue, mraja1spl$person)

## Set up the network version
lex_net <- Animate(lex_ani2, contextual="white", lexal="blue",
    current.color = "yellow", current.speaker.color="grey70")
bgb <- vertex_apply(lex_net, label.color="grey80", size=17, color="grey40")
bgb <- edge_apply(bgb, label.color="yellow")


## Set up the bar version
lex_bar <- Animate(lex_ani2, type="bar")

## Set up the text
lex_text <- Animate(lex_ani2, type="text", size = 3, width=125, color="white")

## Generate a folder
loc <- folder(animation_lexical_classification)
setwd(loc)

## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)


lex_text_bar <- Map(function(x, y){

    uns <- unit(c(-1.6,.5,-.2,.25), "cm")

    x <- x +
        theme(plot.margin = uns,
            text=element_text(color="white"),
            legend.text=element_text(color="white"),
            legend.background = element_rect(fill = "black"),
            panel.border = element_rect(color = "black"),
            panel.background = element_rect(fill = "black"),
            plot.background = element_rect(fill = "black",
                color="black"))

    uns2 <- unit(c(-.5,.5,-.45,.25), "cm")

    y <- y +
        theme(plot.margin = uns2,
            text=element_text(color="white"),
            legend.text=element_text(color="white"),
            legend.background = element_rect(fill = "black"),
            plot.background = element_rect(fill = "black",
                color="black"))

    gA <- ggplotGrob(x)
    gB <- ggplotGrob(y)
    maxWidth <- grid::unit.pmax(gA$widths[2:5], gB$widths[2:5])
    gA$widths[2:5] <- as.list(maxWidth)
    gB$widths[2:5] <- as.list(maxWidth)
    out <- arrangeGrob(gA, gB, ncol=1, heights = grid::unit(c(.3, .7), "native"))
    ## grid.draw(out)
    invisible(out)

}, lex_text, lex_bar)


FUN <- function(follow=FALSE, theseq = seq_along(bgb)) {

    Title <- "Animated Content Rate: Romeo and Juliet Act 1"
    Legend <- c(.2, -1, 1.5, -.95)
    Legend.cex <- 1

    lapply(theseq, function(i) {
        if (follow) {
            png(file=sprintf("%s/images/Rplot%s.png", loc, i),
                width=750, height=875)
        }
        ## Set up the layout
        layout(matrix(c(rep(1, 7), rep(2, 6)), 13, 1, byrow = TRUE))

        ## Plot 1
        par(mar=c(2, 0, 2, 0), bg="black")
        #par(mar=c(2, 0, 2, 0))
        set.seed(22)
        plot.igraph(bgb[[i]], edge.curved=TRUE)
        mtext(Title, side=3, col="white")
        color.legend(Legend[1], Legend[2], Legend[3], Legend[4],
              c("Functional", "Content"), attributes(bgb)[["legend"]],
              cex = Legend.cex, col="white")

        ## Plot2
        plot.new()
        vps <- baseViewports()

        print(lex_text_bar[[i]], vp = vpStack(vps$figure,vps$plot))

        animation::ani.pause()

        if (follow) {
            dev.off()
        }
    })

}

FUN()

## Detect OS
type <- if(.Platform$OS.type == "windows") shell else system


saveHTML(FUN(), autoplay = FALSE, loop = TRUE, verbose = FALSE,
    ani.height = 1000, ani.width=750,
    outdir = loc, single.opts =
    "'controls': ['first', 'previous', 'play', 'next', 'last', 'loop', 'speed'], 'delayMin': 0")

FUN(TRUE)

## EXAMPLE 2: Line + Text + Bar
## Generate a folder
loc2 <- folder(animation_lexical_classification2)
setwd(loc2)

lex_ani2 <- lexical_classification(mraja1spl$dialogue, mraja1spl$person)

## Set up the bar version
lex_bar <- Animate(lex_ani2, type="bar")
cumline <- cumulative(lex_bar)
lex_line <- plot(cumline)
ylims <- range(cumline[[1]][-c(1:100)]) + c(-.1, .1)

## Set up the text
lex_text <- Animate(lex_ani2, type="text", size = 4, width = 80)


lex_line_text_bar <- Map(function(x, y, z){

    mar <- theme(plot.margin = unit(c(0, .5, 0, .25), "cm"))

    gA <- ggplotGrob(x + mar + 
        theme(panel.background = element_rect(fill = NA, colour = NA), 
            panel.border = element_rect(fill = NA, colour = NA),
            plot.background = element_rect(fill = NA, colour = NA)))
    gB <- ggplotGrob(y + mar)
    gC <- ggplotGrob(z + mar + ylab("Average Content Rate") + 
        coord_cartesian(ylim = ylims) +
        ggtitle("Average Content Rate: Romeo & Juliet Act 1"))

    maxWidth <- grid::unit.pmax(gA$widths[2:5], gB$widths[2:5], gC$widths[2:5])
    gA$widths[2:5] <- as.list(maxWidth)
    gB$widths[2:5] <- as.list(maxWidth)
    gC$widths[2:5] <- as.list(maxWidth)
    out <- arrangeGrob(gC, gA, gB, ncol=1, heights = grid::unit(c(.38, .25, .37), "native"))
    ## grid.draw(out)
    invisible(out)

}, lex_text, lex_bar, lex_line)


FUN2 <- function(follow=FALSE, theseq = seq_along(lex_line_text_bar)) {


    lapply(theseq, function(i) {
        if (follow) {
            png(file=sprintf("%s/images/Rplot%s.png", loc2, i),
                width=750, height=875)
        }
 
        print(lex_line_text_bar[[i]])
        animation::ani.pause()

        if (follow) {
            dev.off()
        }
    })

}

FUN2()

## Detect OS
type <- if(.Platform$OS.type == "windows") shell else system

library(animation)
saveHTML(FUN2(), autoplay = FALSE, loop = TRUE, verbose = FALSE,
    ani.height = 1000, ani.width=750,
    outdir = loc2, single.opts =
    "'controls': ['first', 'previous', 'play', 'next', 'last', 'loop', 'speed'], 'delayMin': 0")

FUN2(TRUE)

#==================#
## Static Network ##
#==================#
(lexdat <- with(sentSplit(DATA, 4), lexical_classification(state, person)))
m <- Network(lexdat)
m
print(m, bg="grey97", vertex.color="grey75")

print(m, title="Lexical Content Discourse Map", title.color="white", 
    bg="black", legend.text.color="white", vertex.label.color = "grey70",
    edge.label.color="yellow")

## or use themes:
dev.off()
m + qtheme()
m + theme_nightheat
dev.off()
m + theme_nightheat(title="Lexical Content Discourse Map",
    vertex.label.color = "grey50")
    
#==================================#
## Content Rate Over Time Example ##
#==================================#
lexpres <- lapply(with( pres_debates2012, split(dialogue, time)), function(x) {
    lexical_classification(x)
})
lexplots <- lapply(seq_along(lexpres), function(i) {
    dat <- cumulative(lexpres[[i]])
    m <- plot(dat)
    if (i != 2) m <- m + ylab("")  
    if (i == 2) m <- m + ylab("Average Content Rate") 
    if (i != 3) m <- m + xlab(NULL)
    if (i != 1) m <- m + theme(plot.margin=unit(c(0, 1, 0, .5) + .1, "lines"))
    m + ggtitle(paste("Debate", i)) + 
        coord_cartesian(xlim = c(300, length(dat[[1]])),
            ylim = unlist(range(dat[[1]][-c(1:300)]) + c(-.25, .25)))
})

library(grid)
library(gridExtra)
do.call(grid.arrange, lexplots)

## End(Not run)

Read/Write Multiple csv Files at a Time

Description

mcsv_r - Read and assign multiple csv files at the same time.

mcsv_w - Write multiple csv files into a file at the same time.

Usage

mcsv_r(
  files,
  a.names = NULL,
  l.name = NULL,
  list = TRUE,
  pos = 1,
  envir = as.environment(pos)
)

mcsv_w(
  ...,
  dir = NULL,
  open = FALSE,
  sep = ", ",
  dataframes = NULL,
  pos = 1,
  envir = as.environment(pos)
)

Arguments

Details

mcsv is short for "multiple csv" and the suffix c(_r, _w) stands for "read" (r) or "write" (w).

Value

mcsv_r - reads in multiple csv files at once.

mcsv_w - creates a directory with multiple csv files. Silently returns the path of the directory.

Note

mcsv_r is useful for reading in multiple csv files from cm_df.temp for interaction with cm_range2long.

See Also

cm_range2long, cm_df.temp, condense, assign

Examples

## Not run: 
## mcsv_r EXAMPLE:
mtcarsb <- mtcars[1:5, ]; CO2b <- CO2[1:5, ]
(a <- mcsv_w(mtcarsb, CO2b, dir="foo"))
rm("mtcarsb", "CO2b")  # gone from .GlobalEnv
(nms <- dir(a))
mcsv_r(file.path(a, nms))
mtcarsb; CO2b
rm("mtcarsb", "CO2b")  # gone from .GlobalEnv
mcsv_r(file.path(a, nms), paste0("foo.dat", 1:2))
foo.dat1; foo.dat2
rm("foo.dat1", "foo.dat2")  # gone from .GlobalEnv
delete("foo")

## mcsv_w EXAMPLES:
(a <- mcsv_w(mtcars, CO2, dir="foo"))
delete("foo")

## Write lists of dataframes as well
poldat <- with(DATA.SPLIT, polarity(state, person))
term <- c("the ", "she", " wh")
termdat <- with(raj.act.1,  termco(dialogue, person, term))
mcsv_w(poldat, termdat, mtcars, CO2, dir="foo2")
delete("foo2")

## End(Not run)

Romeo and Juliet: Act 1 Dialogue Merged with Demographics

Description

A dataset containing act 1 of Romeo and Juliet with demographic information.

Usage

data(mraja1)

Format

A data frame with 235 rows and 5 variables

Details

• person. Character in the play

• sex. Gender

• fam.aff. Family affiliation of character

• died. Dummy coded death variable (0-no; 1-yes); if yes the character dies in the play

• dialogue. The spoken dialogue

References

http://shakespeare.mit.edu/romeo_juliet/full.html

Romeo and Juliet: Act 1 Dialogue Merged with Demographics and Split

Description

A dataset containing act 1 of Romeo and Juliet with demographic information and turns of talk split into sentences.

Usage

data(mraja1spl)

Format

A data frame with 508 rows and 7 variables

Details

• person. Character in the play

• tot.

• sex. Gender

• fam.aff. Family affiliation of character

• died. Dummy coded death variable (0-no; 1-yes); if yes the character dies in the play

• dialogue. The spoken dialogue

• stem.text.

References

http://shakespeare.mit.edu/romeo_juliet/full.html

Multiple gsub

Description

multigsub - A wrapper for gsub that takes a vector of search terms and a vector or single value of replacements.

sub_holder - This function holds the place for particular character values, allowing the user to manipulate the vector and then revert the place holders back to the original values.

Usage

multigsub(
  pattern,
  replacement,
  text.var,
  leadspace = FALSE,
  trailspace = FALSE,
  fixed = TRUE,
  trim = TRUE,
  order.pattern = fixed,
  ...
)

mgsub(
  pattern,
  replacement,
  text.var,
  leadspace = FALSE,
  trailspace = FALSE,
  fixed = TRUE,
  trim = TRUE,
  order.pattern = fixed,
  ...
)

sub_holder(pattern, text.var, alpha.type = TRUE, ...)

Arguments

Value

multigsub - Returns a vector with the pattern replaced.

sub_holder - Returns a list with the following:

Note

The unhold function for sub_holder will only work on keys that have not been disturbed by subsequent alterations. The key follows the pattern of 'qdapplaceholder' followed by lower case letter keys followed by 'qdap'.

See Also

gsub

Examples

## Not run: 
## ======================
##    `mgsub` Function
## ======================

multigsub(c("it's", "I'm"), c("it is", "I am"), DATA$state)
mgsub(c("it's", "I'm"), c("it is", "I am"), DATA$state)
mgsub("[[:punct:]]", "PUNC", DATA$state, fixed = FALSE)

## ====================== 
## `sub_holder` Function
## ======================

## `alpha.type` as TRUE
(fake_dat <- paste(emoticon[1:11,2], DATA$state))
(m <- sub_holder(emoticon[,2], fake_dat))
m$unhold(strip(m$output))
# With Stemming
m$unhold(stemmer(strip(m$output), capitalize = FALSE))

## `alpha.type` as FALSE (numeric keys)
vowels <- LETTERS[c(1, 5, 9, 15, 21)]
(m2 <- sub_holder(vowels, toupper(DATA$state), alpha.type = FALSE))
m2$unhold(gsub("[^0-9]", "", m2$output))
mtabulate(strsplit(m2$unhold(gsub("[^0-9]", "", m2$output)), ""))

## End(Not run)

Nested Standardization

Description

Standardize within a subgroup and then within a group.

Usage

multiscale(numeric.var, grouping.var, original_order = TRUE, digits = 2)

Arguments

Value

Returns a list of two:

See Also

scale

Examples

## Not run: 
dat <- with(mraja1spl, word_stats(dialogue, list(person, sex, fam.aff)))
htruncdf(colsplit2df(dat$ts), ,4)
out1 <- with(colsplit2df(dat$ts), multiscale(word.count, person))
ltruncdf(out1, 10)
out2 <- with(colsplit2df(dat$ts), multiscale(word.count, 
    list(fam.aff, sex)))
ltruncdf(out2, 10)
out3 <- with(colsplit2df(dat$ts), multiscale(word.count, 
    list(fam.aff, sex), original_order = FALSE))
ltruncdf(out3, 10)

## End(Not run)

Names to Gender

Description

A wrapper for the gender function used to predict gender based on first name.

Usage

name2sex(names.list, USE.NAMES = FALSE, ...)

Arguments

Value

Returns a vector of predicted gender (M/F) based on first name.

See Also

gender

Examples

## Not run: 
name2sex(qcv(mary, jenn, linda, JAME, GABRIEL, OLIVA, 
    tyler, jamie, JAMES, tyrone, cheryl, drew))

## End(Not run)

Project Template

Description

Generate a project template to increase efficiency.

Usage

new_project(project = "new", path = getwd(), open = is.global(2), ...)

Arguments

Details

The project template includes these main directories and scripts:

• CODEBOOK - A directory to store coding conventions or demographics data:

  • KEY.csv - A blank template for demographic information

• CORRESPONDENCE - A directory to store correspondence and agreements with the client:

  • CONTACT_INFO.txt - A text file to put research team members' contact information

• DATA - A directory to store data:

  • CLEANED_TRANSCRIPTS - A directory to store the cleaned transcripts (If the transcripts are already cleaned you may choose to not utilize the RAW_TRANSCRIPTS directory)

  • CM_DATA - A directory to export/import scripts for cm_xxx family of functions

  • DATA_FOR_REVIEW - A directory to put data that may need to be altered or needs to be inspected more closely

  • RAW_DATA - A directory to store non-transcript data related to the project:

    • ANALYTIC_MEMOS - A directory to put audio files (or shortcuts)

    • AUDIO - A directory to put audio files (or shortcuts)

    • FIELD_NOTES - A directory to put audio files (or shortcuts)

    • PAPER_ARTIFACTS - A directory to put paper artifacts

    • PHOTOGRAPHS - A directory to put photographs

    • VIDEO - A directory to put video files (or shortcuts)

  • TRANSCRIPTS - A directory to put transcription data:

    • CLEANED_TRANSCRIPTS - A directory to store the cleaned transcripts (If the transcripts are already cleaned you may choose to not utilize the RAW_TRANSCRIPTS directory)

    • RAW_TRANSCRIPTS - A directory to store the raw transcripts

• DOCUMENTATION - A directory to store documents related to the project

• PLOTS - A directory to store plots

• REPORTS - A directory with report and presentation related tools.

• SCRIPTS - A directory to store scripts; already contains the following:

  • 01_clean_data.R - initial cleaning of raw transcripts

  • 02_analysis_I.R - initial analysis

  • 03_plots.R - plotting script

• TABLES - A directory to export tables to

• WORD_LISTS - A directory to store word lists that can be sourced and supplied to functions

• extra_functions.R - A script to store user made functions related to the project

  • email - A function to view, and optionally copy to the clipboard, emails for the client/lead researcher, analyst and/or other project members (information taking from ~/CORRESPONDENCE/CONTACT_INFO.txt file)

  • todo - A function to view, and optionally copy to the clipboard, non-completed tasks from the TO_DO.txt file

• LOG - A text file documenting project changes/needs etc.

• PROJECT_WORKFLOW_GUIDE.pdf - A pdf explaining the structure of the project template

• xxx.Rproj - A project file used by RRtudio; clicking this will open the project in RStudio.

• TO_DO - A text file documenting project tasks

The template comes with a .Rproj file. This makes operating in RStudio very easy. The file can be kept on the desktop or a git application such as github, bitbucket or dropbox, depending on what the client/research team is comfortable utilizing.

Value

Creates a project template.

Generate ngrams

Description

Transcript apply ngrams.

Usage

ngrams(text.var, grouping.var = NULL, n = 2, ...)

Arguments

Value

Returns a list of:

Examples

## Not run: 
ngrams(DATA$state, DATA$person, 2)
ngrams(DATA$state, DATA$person, 3)
ngrams(DATA$state, , 3)
with(mraja1, ngrams(dialogue, list(sex, fam.aff), 3))

## Alternative ngram analysis:
n_gram <- function(x, n = 2, sep = " "){

    m <- qdap::bag_o_words(x)
    if (length(m) < n) return(character(0))
    starts <- 1:(length(m) - (n - 1))
    ends <- n:length(m) 
    Map(function(x, y){
            paste(m[x:y], collapse=sep)
        }, starts, ends
    )
}

dat <- sentSplit(DATA, "state")

dat[["grams"]] <- sapply(dat[["state"]], function(x) { 
    unbag(n_gram(x, sep = "~~"))
})

m <- with(dat, as.tdm(grams, person))
rownames(m) <- gsub("~~", " ", rownames(m))
as.matrix(m)
rowSums(as.matrix(m))



dat2 <- sentSplit(raj, "dialogue")

dat2[["grams"]] <- sapply(dat2[["dialogue"]], function(x) { 
    unbag(n_gram(x, sep = "~~"))
})

m2 <- with(dat2, as.tdm(grams, person))
rownames(m2) <- gsub("~~", " ", rownames(m2))
qheat(t(as.matrix(tm:::weightTfIdf(tm::removeSparseTerms(m2, .7)))), high="red")

sort(rowSums(as.matrix(m2)))

## End(Not run)

Count Object Pronouns Per Grouping Variable

Description

Count the number of object pronouns per grouping variables.

Usage

object_pronoun_type(
  text.var,
  grouping.var = NULL,
  object.pronoun.list = NULL,
  ...
)

Arguments

Details

The following object pronoun categories are the default searched terms:

• me = c(" me ", " my ", " mine ")

• us = c(" us ", " our ", " ours ")

• you = c(" you'd ", " you'll ", " you're ", " you've ", " you ", " your ")

• him = c(" him ", " his ")

• her = c(" her ", " hers ")

• them = c(" them ")

• their = c(" their ", "theirs ")

• it = c(" it'd ", " it'll ", " it's ", " it ")

Value

Returns a list, of class "object_pronoun_type", of data frames regarding object pronoun word counts:

See Also

subject_pronoun_type, pronoun_type

Examples

## Not run: 
dat <- pres_debates2012
dat <- dat[dat$person %in% qcv(ROMNEY, OBAMA), ]
(out <- object_pronoun_type(dat$dialogue, dat$person))
plot(out)
plot(out, 2)
plot(out, 3)
plot(out, 3, ncol=2)

scores(out)
counts(out)
proportions(out)
preprocessed(out)

plot(scores(out))
plot(counts(out))
plot(proportions(out))

## End(Not run)

Detect Outliers in Text

Description

Locate possible outliers for text variables given numeric word function.

Usage

outlier_detect(
  text.var,
  grouping.var = NULL,
  FUN = word_count,
  scale.by = "grouping"
)

Arguments

Value

Returns a dataframe with possible outliers.

Examples

## Not run: 
with(DATA, outlier_detect(state))
with(DATA, outlier_detect(state, FUN = character_count))
with(DATA, outlier_detect(state, person, FUN = character_count))
with(DATA, outlier_detect(state, list(sex, adult), FUN = character_count))
with(DATA, outlier_detect(state, FUN = syllable_sum))
htruncdf(with(raj, outlier_detect(dialogue, person)), 15, 45)

## End(Not run)   

Locate Outliers in Numeric String

Description

Locate and label possible outliers in a string.

Usage

outlier_labeler(x, standardize = TRUE, ...)

Arguments

Value

Returns a matrix (one column) of possible outliers coded as "3sd", "2sd" and "1.5sd", corresponding to >= to 3, 2, or 1.5 standard deviations.

See Also

scale

Examples

## Not run: 
outlier_labeler(mtcars$hp)[20:32]
by(mtcars$mpg, mtcars$cyl, outlier_labeler)
tapply(mtcars$mpg, mtcars$cyl, outlier_labeler)

## End(Not run)

Paste an Unspecified Number Of Text Columns

Description

paste2 - Paste unspecified columns or a list of vectors together.

colpaste2df - Wrapper for paste2 that returns a dataframe with columns pasted together.

Usage

paste2(multi.columns, sep = ".", handle.na = TRUE, trim = TRUE)

colpaste2df(
  mat,
  combined.columns,
  sep = ".",
  name.sep = "&",
  keep.orig = TRUE,
  ...
)

Arguments

Value

paste2 - Returns a vector with row-wise elements pasted together.

colpaste2df - Returns a dataframe with pasted columns.

Note

paste differs from paste2 because paste does not allowed an unspecified number of columns to be pasted. This behavior can be convenient for inside of functions when the number of columns being pasted is unknown.

See Also

paste, colsplit2df

Examples

## Not run: 
## paste2 examples
v <- rep(list(state.abb[1:8],  month.abb[1:8]) , 5)
n <- sample(5:10, 1)
paste(v[1:n]) #odd looking return
paste2(v[1:n]) 
paste2(v[1:n], sep="|") 
paste2(mtcars[1:10,], sep="|") 
paste(mtcars[1:10,], sep="|") #odd looking return
paste2(CO2[1:10,], sep="|-|") 

## colpaste2df examples
A <- list(
    a = c(1, 2, 3),
    b = qcv(mpg, hp),
    c = c("disp", "am")
)
B <- list(
    c(1, 2, 3),
    new.col = qcv(mpg, hp),
    c("disp", "am")
)
E <- list(
    c(1, 2, 3, 4, 5),
    qcv(mpg, hp),
    c("disp", "am")
)

colpaste2df(head(mtcars), A)
colpaste2df(head(mtcars), B)
colpaste2df(head(mtcars), E)
colpaste2df(head(mtcars), qcv(am, disp, drat), sep ="_", name.sep = "|")
colpaste2df(head(CO2), list(c(1, 2, 3, 4, 5), qcv("conc", "uptake")))

## End(Not run)

Phrase Nets

Description

Create Many Eyes style phrase nets.

Usage

phrase_net(
  text.var,
  freq = 4,
  r = 0.35,
  edge.constant = 6,
  vertex.constant = 3,
  ...
)

Arguments

Value

Returns an igraph object.

Note

While Many Eyes phrase nets inspired this function the two outputs are not identical. The phrase_net function operates off of correlations between words in sentences.

References

http://trinker.github.io/many-eye/

Examples

## Not run: 
x <- "Questions must be at least 2 days old to be eligible for a bounty.
    There can only be 1 active bounty per question at any given time.
    Users must have at least 75 reputation to offer a bounty, and may
    only have a maximum of 3 active bounties at any given time. The
    bounty period lasts 7 days. Bounties must have a minimum duration of
    at least 1 day. After the bounty ends, there is a grace period of 24
    hours to manually award the bounty. If you do not award your bounty
    within 7 days (plus the grace period), the highest voted answer
    created after the bounty started with at least 2 upvotes will be
    awarded half the bounty amount. If there's no answer meeting that
    criteria, the bounty is not awarded to anyone. If the bounty was
    started by the question owner, and the question owner accepts an
    answer during the bounty period, and the bounty expires without an
    explicit award - we assume the bounty owner liked the answer they
    accepted and award it the full bounty amount at the time of bounty
    expiration. In any case, you will always give up the amount of
    reputation specified in the bounty, so if you start a bounty, be sure
    to follow up and award your bounty to the best answer! As an
    additional bonus, bounty awards are immune to the daily reputation
    cap and community wiki mode."

phrase_net(sent_detect(x), r=.5)
library(igraph)
plot(phrase_net(sent_detect(x), r=.5), edge.curved = FALSE)

## Declaration of Independence Example
y <- readLines("http://www.constitution.org/usdeclar.txt")
y <- paste(y[grep("When, in the", y):length(y)], collapse=" ")
phrase_net(sent_detect(y), r=.7)


## Multiple grouping variables
z <- lapply(split(raj.act.1$dialogue, raj.act.1$person), paste, collapse = " ")
par(mfrow=c(2, 5), mai = c(.05, 0.15, 0.15, 0.15))
lapply(seq_along(z), function(i) {
    x <- try(phrase_net(sent_detect(z[i]), r=.6))
    if (!inherits(x, "try-error")) {
        print(x)
        box()
        mtext(names(z)[i])
    }
}) 


lapply(seq_along(z), function(i) {
    x <- try(phrase_net(sent_detect(z[i]), r=.6))
    if (!inherits(x, "try-error")) {
        dev.new()
        print(x)
        mtext(names(z)[i], padj=-1, cex=1.7, col="red")
    }
}) 

## End(Not run)

Plots a Network Object

Description

Plots a Network object.

Usage

## S3 method for class 'Network'
plot(x, ...)

Arguments

Plots a SMOG Object

Description

Plots a SMOG object.

Usage

## S3 method for class 'SMOG'
plot(x, ...)

Arguments

Plots an animated_character Object

Description

Plots an animated_character object.

Usage

## S3 method for class 'animated_character'
plot(x, ...)

Arguments

Plots an animated_discourse_map Object

Description

Plots an animated_discourse_map object.

Usage

## S3 method for class 'animated_discourse_map'
plot(x, ...)

Arguments

Plots a animated_formality Object

Description

Plots a animated_formality object.

Usage

## S3 method for class 'animated_formality'
plot(x, ...)

Arguments

Plots an animated_lexical_classification Object

Description

Plots an animated_lexical_classification object.

Usage

## S3 method for class 'animated_lexical_classification'
plot(x, ...)

Arguments

Plots an animated_polarity Object

Description

Plots an animated_polarity object.

Usage

## S3 method for class 'animated_polarity'
plot(x, ...)

Arguments