plotHeat: Plotting Topics over Time relative to Corpus

In tosca: Tools for Statistical Content Analysis

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),
  ...
)

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.

Value

A pdf. Invisible: A dataframe.

Examples

## Not run: 
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))

## End(Not run)

tosca documentation built on Oct. 28, 2021, 5:07 p.m.