tosca (version 0.3-4)

plotTopicWord: Plotting Counts of Topics-Words-Combination over Time (Relative to Words)

Description

Creates a plot of the counts/proportion of specified combination of topics and words. It is important to keep in mind that the baseline for proportions are the sums of words, not sums of topics. See also plotWordpt. There is an option to plot all curves in one plot or to create one plot for every curve (see pages). In addition the plots can be written to a pdf by setting file.

Usage

plotTopicWord(
  object,
  docs,
  ldaresult,
  ldaID,
  wordlist = lda::top.topic.words(ldaresult$topics, 1),
  link = c("and", "or"),
  select = 1:nrow(ldaresult$document_sums),
  tnames,
  wnames,
  rel = FALSE,
  mark = TRUE,
  unit = "month",
  curves = c("exact", "smooth", "both"),
  smooth = 0.05,
  legend = ifelse(pages, "onlyLast:topright", "topright"),
  pages = FALSE,
  natozero = TRUE,
  file,
  main,
  xlab,
  ylab,
  ylim,
  both.lwd,
  both.lty,
  col,
  ...
)

Value

A plot. Invisible: A dataframe with columns date and tnames: wnames

with the counts/proportion of the selected combination of topics and words.

Arguments

object

textmeta object with strictly tokenized text component (Character vectors) - such as a result of cleanTexts

docs

Object as a result of LDAprep which was handed over to LDAgen

ldaresult

The result of a function call LDAgen with docs as argument

ldaID

Character vector of IDs of the documents in ldaresult

wordlist

List of Ccharacter vectors. Every list element is an 'or' link, every character string in a vector is linked by the argument link. If wordlist is only a character vector it will be coerced to a list of the same length as the vector (see as.list), so that the argument link has no effect. Each character vector as a list element represents one curve in the emerging plot.

link

Character: Should the (inner) character vectors of each list element be linked by an "and" or an "or" (default: "and")?

select

List of integer vectors: Which topics - linked by an "or" every time - should be take into account for plotting the word counts/proportion (default: all topics as simple integer vector)?

tnames

Character vector of same length as select - labels for the topics (default are the first returned words of

wnames

Character vector of same length as wordlist - labels for every group of 'and' linked words top.topic.words from the lda package for each topic)

rel

Logical: Should counts (FALSE) or proportion (TRUE) be plotted (default: FALSE)?

mark

Logical: Should years be marked by vertical lines (default: TRUE)?

unit

Character: To which unit should dates be floored (default: "month")? Other possible units are "bimonth", "quarter", "season", "halfyear", "year", for more units see round_date

curves

Character: Should "exact", "smooth" curve or "both" be plotted (default: "exact")?

smooth

Numeric: Smoothing parameter which is handed over to lowess as f (default: 0.05)

legend

Character: Value(s) to specify the legend coordinates (default: "topright", "onlyLast:topright" for pages = TRUE respectively). If "none" no legend is plotted.

pages

Logical: Should all curves be plotted in a single plot (default: FALSE)? In addition you could set legend = "onlyLast:<argument>" with <argument> as a character legend argument for only plotting a legend on the last plot of set.

natozero

Logical: Should NAs be coerced to zeros (default: TRUE)?

file

Character: File path if a pdf should be created

main

Character: Graphical parameter

xlab

Character: Graphical parameter

ylab

Character: Graphical parameter

ylim

Graphical parameter

both.lwd

Graphical parameter for smoothed values if curves = "both"

both.lty

Graphical parameter for smoothed values if curves = "both"

col

Graphical parameter, could be a vector. If curves = "both" the function will for every wordgroup plot at first the exact and then the smoothed curve - this is important for your col order.

...

Additional graphical parameters

Examples

if (FALSE) {
data(politics)
poliClean <- cleanTexts(politics)
words10 <- makeWordlist(text=poliClean$text)
words10 <- words10$words[words10$wordtable > 10]
poliLDA <- LDAprep(text=poliClean$text, vocab=words10)
LDAresult <- LDAgen(documents=poliLDA, K=10, vocab=words10)

# plot topwords from each topic
plotTopicWord(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA))
plotTopicWord(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA), rel=TRUE)

# plot one word in different topics
plotTopicWord(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA),
              select=c(1,3,8), wordlist=c("bush"))

# Differences between plotTopicWord and plotWordpt
par(mfrow=c(2,2))
plotTopicWord(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA),
              select=c(1,3,8), wordlist=c("bush"), rel=FALSE)
plotWordpt(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA),
           select=c(1,3,8), wordlist=c("bush"), rel=FALSE)
plotTopicWord(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA),
              select=c(1,3,8), wordlist=c("bush"), rel=TRUE)
plotWordpt(object=poliClean, docs=poliLDA, ldaresult=LDAresult, ldaID=names(poliLDA),
           select=c(1,3,8), wordlist=c("bush"), rel=TRUE)
}