# NOT RUN {
#termco examples:
term <- c("the ", "she", " wh")
(out <- with(raj.act.1, termco(dialogue, person, term)))
plot(out)
scores(out)
plot(scores(out))
counts(out)
plot(counts(out))
proportions(out)
plot(proportions(out))
# General form for match.list as themes
#
# ml <- list(
# cat1 = c(),
# cat2 = c(),
# catn = c()
# )
ml <- list(
cat1 = c(" the ", " a ", " an "),
cat2 = c(" I'" ),
"good",
the = c("the", " the ", " the", "the")
)
(dat <- with(raj.act.1, termco(dialogue, person, ml)))
scores(dat) #useful for presenting in tables
counts(dat) #prop and raw counts are useful for performing calculations
proportions(dat)
datb <- with(raj.act.1, termco(dialogue, person, ml,
short.term = FALSE, elim.old=FALSE))
ltruncdf(datb, 20, 6)
(dat2 <- data.frame(dialogue=c("@bryan is bryan good @br",
"indeed", "@ brian"), person=qcv(A, B, A)))
ml2 <- list(wrds=c("bryan", "indeed"), "@", bryan=c("bryan", "@ br", "@br"))
with(dat2, termco(dialogue, person, match.list=ml2))
with(dat2, termco(dialogue, person, match.list=ml2, percent = FALSE))
DATA$state[1] <- "12 4 rgfr r0ffrg0"
termco(DATA$state, DATA$person, '0', digit.remove=FALSE)
DATA <- qdap::DATA
#Using with term_match and exclude
exclude(term_match(DATA$state, qcv(th), FALSE), "truth")
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)
syns <- synonyms("doubt")
syns[1]
termco(DATA$state, DATA$person, unlist(syns[1]))
synonyms("doubt", FALSE)
termco(DATA$state, DATA$person, list(doubt = synonyms("doubt", FALSE)))
termco(DATA$state, DATA$person, syns)
#termco_d examples:
termco_d(DATA$state, DATA$person, c(" the", " i'"))
termco_d(DATA$state, DATA$person, c(" the", " i'"), ignore.case=FALSE)
termco_d(DATA$state, DATA$person, c(" the ", " i'"))
# termco2mat example:
MTCH.LST <- exclude(term_match(DATA$state, qcv(a, i)), qcv(is, it, am, shall))
termco_obj <- termco(DATA$state, DATA$person, MTCH.LST)
termco2mat(termco_obj)
plot(termco_obj)
plot(termco_obj, label = TRUE)
plot(termco_obj, label = TRUE, text.color = "red")
plot(termco_obj, label = TRUE, text.color="red", lab.digits=3)
## REVERSE TERMCO (return raw words found per variable)
df <- data.frame(x=1:6,
y = c("the fluffy little bat" , "the man was round like a ball",
"the fluffy little bat" , "the man was round like a ball",
"he ate the chair" , "cough, cough"),
stringsAsFactors=FALSE)
l <- list("bat" ,"man", "ball", "heavy")
z <- counts(termco(df$y, qdapTools::id(df), l))[, -2]
counts2list(z[, -1], z[, 1])
## politness
politness <- c("please", "excuse me", "thank you", "you welcome",
"you're welcome", "i'm sorry", "forgive me", "pardon me")
with(pres_debates2012, termco(dialogue, person, politness))
with(hamlet, termco(dialogue, person, politness))
## Term Use Percentage per N Words
dat <- with(raj, chunker(dialogue, person, n.words = 100, rm.unequal = TRUE))
dat2 <- list2df(dat, "Dialogue", "Person")
dat2[["Duration"]] <- unlist(lapply(dat, id, pad=FALSE))
dat2 <- qdap_df(dat2, "Dialogue")
Top5 <- sapply(split(raj$dialogue, raj$person), wc, FALSE) %>%
sort(decreasing=TRUE) %>%
list2df("wordcount", "person") %>%
`[`(1:5, 2)
propdat <- dat2 %&%
termco(list(Person, Duration), as.list(Top25Words[1:5]), percent = FALSE) %>%
proportions %>%
colsplit2df %>%
reshape2::melt(id=c("Person", "Duration", "word.count"), variable="Word") %>%
dplyr::filter(Person %in% Top5)
head(propdat)
ggplot(propdat, aes(y=value, x=Duration, group=Person, color=Person)) +
geom_line(size=1.25) +
facet_grid(Word~., scales="free_y") +
ylab("Percent of Word Use") +
xlab("Per 100 Words") +
scale_y_continuous(labels = percent)
ggplot(propdat, aes(y=value, x=Duration, group=Word, color=Word)) +
geom_line(size=1.25) +
facet_grid(Person~.) +
ylab("Percent of Word Use") +
xlab("Per 100 Words") +
scale_y_continuous(labels = percent)
ggplot(propdat, aes(y=value, x=Duration, group=Word)) +
geom_line() +
facet_grid(Word~Person, scales="free_y") +
ylab("Percent of Word Use") +
xlab("Per 100 Words") +
scale_y_continuous(labels = percent) +
ggthemes::theme_few()
## Discourse Markers: See...
## Schffrin, 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)
)
# }
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