tosca (version 0.3-4)

plotHeat: Plotting Topics over Time relative to Corpus

Description

Creates a pdf showing a heat map. For each topic, the heat map shows the deviation of its current share from its mean share. Shares can be calculated on corpus level or on subcorpus level concerning LDA vocabulary. Shares can be calculated in absolute deviation from the mean or relative to the mean of the topic to account for different topic strengths.

Usage

plotHeat(
  object,
  ldaresult,
  ldaID,
  select = 1:nrow(ldaresult$document_sums),
  tnames,
  norm = FALSE,
  file,
  unit = "year",
  date_breaks = 1,
  margins = c(5, 0),
  ...
)

Value

A pdf. Invisible: A dataframe.

Arguments

object

textmeta object with strictly tokenized text component (calculation of proportion on document lengths) or textmeta object which contains only the meta component (calculation of proportion on count of words out of the LDA vocabulary in each document)

ldaresult

LDA result object.

ldaID

Character vector containing IDs of the texts.

select

Numeric vector containing the numbers of the topics to be plotted. Defaults to all topics.

tnames

Character vector with labels for the topics.

norm

Logical: Should the values be normalized by the mean topic share to account for differently sized topics (default: FALSE)?

file

Character vector containing the path and name for the pdf output file.

unit

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

date_breaks

How many labels should be shown on the x axis (default: 1)? If data_breaks is 5 every fifth label is drawn.

margins

See heatmap

...

Additional graphical parameters passed to heatmap, for example distfun or hclustfun. details The function is useful to search for peaks in the coverage of topics.

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)
plotHeat(object=poliClean, ldaresult=LDAresult, ldaID=names(poliLDA))
}