зеркало из https://github.com/microsoft/wpa.git
78 строки
2.4 KiB
R
78 строки
2.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/personas_hclust.R
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\name{personas_hclust}
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\alias{personas_hclust}
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\title{Create hierarchical clusters of selected metrics using a Person query}
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\usage{
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personas_hclust(data, metrics, k = 4, return = "plot")
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}
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\arguments{
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\item{data}{A data frame containing \code{PersonId} and selected metrics for
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clustering.}
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\item{metrics}{Character vector containing names of metrics to use for
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clustering. See examples section.}
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\item{k}{Numeric vector to specify the \code{k} number of clusters to cut by.}
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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{"data"}
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\item \code{"table"}
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\item \code{"hclust"}
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}
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See \code{Value} for more information.}
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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' object. A heatmap plot comparing the key metric averages
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of the clusters as per \code{keymetrics_scan()}.
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\item \code{"data"}: data frame. Raw data with clusters appended
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\item \code{"table"}: data frame. Summary table for identified clusters
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\item \code{"hclust"}: 'hclust' object. hierarchical model generated by the function.
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}
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}
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\description{
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\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#questioning}{\figure{lifecycle-questioning.svg}{options: alt='[Questioning]'}}}{\strong{[Questioning]}}
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Apply hierarchical clustering to selected metrics. Person averages are computed prior to clustering.
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The hierarchical clustering uses cosine distance and the ward.D method
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of agglomeration.
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}
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\examples{
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# Return plot
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personas_hclust(sq_data,
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metrics = c("Collaboration_hours", "Workweek_span"),
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k = 4)
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# Return summary table
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personas_hclust(sq_data,
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metrics = c("Collaboration_hours", "Workweek_span"),
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k = 4,
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return = "table")
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\donttest{
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# Return data with clusters appended
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personas_hclust(sq_data,
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metrics = c("Collaboration_hours", "Workweek_span"),
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k = 4,
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return = "data")
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}
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}
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\seealso{
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Other Clustering:
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\code{\link{workpatterns_classify}()},
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\code{\link{workpatterns_hclust}()}
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}
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\author{
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Ainize Cidoncha \href{mailto:ainize.cidoncha@microsoft.com}{ainize.cidoncha@microsoft.com}
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}
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\concept{Clustering}
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