question_type: Count of Question Type

In trinker/qdap: Bridging the Gap Between Qualitative Data and Quantitative Analysis

Description

Transcript apply question counts.

Usage

question_type(
  text.var,
  grouping.var = NULL,
  neg.cont = FALSE,
  percent = TRUE,
  zero.replace = 0,
  digits = 2,
  contraction = qdapDictionaries::contractions,
  bracket = "all",
  amplifiers = qdapDictionaries::amplification.words,
  ...
)

Arguments

text.var	The text variable
grouping.var	The grouping variables. Default NULL generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.
neg.cont	logical. If TRUE provides separate counts for the negative contraction forms of the interrogative words.
percent	logical. If TRUE output given as percent. If FALSE the output is proportion.
zero.replace	Value to replace 0 values with.
digits	Integer; number of decimal places to round when printing.
contraction	A two column key of contractions (column 1) and expanded form replacements (column 2) or a vector of contractions. Default is to use qdapDictionaries's contractions data set.
bracket	The type of bracket (and encased text) to remove. This is one or more of the strings "curly", "square", "round", "angle" and "all". These strings correspond to: {, [, (, < or all four types.
amplifiers	A character vector of terms that increase the intensity of a positive or negative word. Default is to use qdapDictionaries's amplification.words data set.
...	Other arguments passed to bracketX.

Details

The algorithm searches for the following interrogative words (and optionally, their negative contraction form as well):

1) whose 2) whom 3) who 4) where 5) what 6) which 7) why 8) when 9) were* 10) was* 11) does* 12) did* 13) do* 14) is 15) are* 16) will* 17) how 18) should 19) could 20) would* 21) shall 22) may 23) might* 24) must* 25) can* 26) has 27) have* 28) had* 29) ok 30) right 31) correct 32) implied do/does/did

The interrogative word that is found first (with the exception of "ok", "right"/"alright", and "correct") in the question determines the sentence type. "ok", "right"/"alright", and "correct" sentence types are determined if the sentence is a question with no other interrogative words found and "ok", "right"/"alright", or "correct" is the last word of the sentence. Those interrogative sentences beginning with the word "you", "wanna", or "want" are categorized as implying do/does/did question type, though the use of do/does/did is not explicit. Those sentence beginning with "you" followed by a select interrogative word (and or their negative counter parts) above (marked with *) or 1-2 amplifier(s) followed by the select interrogative word are categorized by the select word rather than an implied do/does/did question type. A sentence that is marked "ok" over rides an implied do/does/did label. Those with undetermined sentence type are labeled unknown.

Value

Returns a list of:

raw	A dataframe of the questions used in the transcript and their type.
count	A dataframe of total questions (tot.quest) and counts of question types (initial interrogative word) by grouping variable(s).
rnp	Dataframe of the frequency and proportions of question types by grouping variable.
inds	The indices of the original text variable that contain questions.
missing	The row numbers of the missing data (excluded from analysis).
percent	The value of percent used for plotting purposes.
zero.replace	The value of zero.replace used for plotting purposes.

See Also

colcomb2class, bracketX

Examples

## Not run: 
## Inspect the algorithm classification
x <- c("Kate's got no appetite doesn't she?",
    "Wanna tell Daddy what you did today?",
    "You helped getting out a book?", "umm hum?",
    "Do you know what it is?", "What do you want?",
    "Who's there?", "Whose?", "Why do you want it?",
    "Want some?", "Where did it go?", "Was it fun?")

left_just(preprocessed(question_type(x))[, c(2, 6)])

## Transcript/dialogue examples
(x <- question_type(DATA.SPLIT$state, DATA.SPLIT$person))

## methods
scores(x)
plot(scores(x))
counts(x)
plot(counts(x))
proportions(x)
plot(proportions(x))
truncdf(preprocessed(x), 15)
plot(preprocessed(x))

plot(x)
plot(x, label = TRUE)
plot(x, label = TRUE, text.color = "red")
question_type(DATA.SPLIT$state, DATA.SPLIT$person, percent = FALSE)
DATA[8, 4] <- "Won't I distrust you?"
question_type(DATA.SPLIT$state, DATA.SPLIT$person)
DATA <- qdap::DATA
with(DATA.SPLIT, question_type(state, list(sex, adult)))

out1 <- with(mraja1spl, question_type(dialogue, person))
## out1
out2 <- with(mraja1spl, question_type(dialogue, list(sex, fam.aff)))
## out2
out3 <- with(mraja1spl, question_type(dialogue, list(sex, fam.aff),
   percent = FALSE))
plot(out3, label = TRUE, lab.digits = 3)

## End(Not run)

trinker/qdap documentation built on Sept. 30, 2020, 6:28 p.m.