question_type: Count of Question Type

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,
  ...
)

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.

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.

Examples

Run this code

if (FALSE) {
## 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)
}