зеркало из https://github.com/microsoft/wpa.git
67 строки
1.9 KiB
R
67 строки
1.9 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/tm_cooc.R
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\name{tm_cooc}
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\alias{tm_cooc}
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\title{Analyse word co-occurrence in subject lines and return a network plot}
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\usage{
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tm_cooc(data, stopwords = NULL, seed = 100, return = "plot", lmult = 0.05)
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}
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\arguments{
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\item{data}{A Meeting Query dataset in the form of a data frame.}
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\item{stopwords}{A character vector OR a single-column data frame labelled
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\code{'word'} containing custom stopwords to remove.}
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\item{seed}{A numeric vector to set seed for random generation.}
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\item{return}{String specifying what to return. This must be one of the
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following strings:
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\itemize{
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\item \code{"plot"}
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\item \code{"table"}
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}
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See \code{Value} for more information.}
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\item{lmult}{A multiplier to adjust the line width in the output plot.
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Defaults to 0.05.}
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}
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\value{
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A different output is returned depending on the value passed to the \code{return}
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argument:
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\itemize{
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\item \code{"plot"}: 'ggplot' and 'ggraph' object. A network plot.
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\item \code{"table"}: data frame. A summary table.
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}
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}
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\description{
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This function generates a word co-occurrence network plot, with options to
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return a table. This function is used within \code{meeting_tm_report()}.
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}
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\details{
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This function uses \code{tm_clean()} as the underlying data wrangling function.
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There is an option to remove stopwords by passing a data frame into the
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\code{stopwords} argument.
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}
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\examples{
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# Demo using a subset of `mt_data`
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mt_data \%>\%
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dplyr::slice(1:20) \%>\%
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tm_cooc(lmult = 0.01)
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}
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\seealso{
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Other Text-mining:
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\code{\link{meeting_tm_report}()},
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\code{\link{pairwise_count}()},
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\code{\link{subject_validate_report}()},
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\code{\link{subject_validate}()},
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\code{\link{tm_clean}()},
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\code{\link{tm_freq}()},
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\code{\link{tm_wordcloud}()}
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}
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\author{
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Carlos Morales \href{mailto:carlos.morales@microsoft.com}{carlos.morales@microsoft.com}
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}
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\concept{Text-mining}
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