This commit is contained in:
Martin Chan 2023-08-17 23:25:43 +01:00
Родитель dd2e83d6e0
Коммит 99d3cd8618
10 изменённых файлов: 20 добавлений и 10 удалений

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@ -40,6 +40,7 @@
#' @family Time-series
#'
#' @examples
#' \donttest{
#' # Returns a data frame
#' sq_data %>%
#' IV_by_period(
@ -48,7 +49,7 @@
#' after_start = "2020-01-05",
#' after_end = "2020-01-26"
#' )
#'
#' }
#' @export
IV_by_period <-

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@ -64,8 +64,8 @@
#'
#'
#' @examples
#' \donttest{
#' # Returns summary table
#'
#' create_ITSA(
#' data = sq_data,
#' before_start = "12/15/2019",
@ -89,6 +89,7 @@
#'
#' # Extract a plot as an example
#' plot_list$Workweek_span
#' }
#'
#' @export

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@ -50,6 +50,7 @@
#' @import ggplot2
#'
#' @examples
#' \donttest{
#' # return a heatmap table for words
#' mt_data %>% subject_scan(hrvar = "Organizer_Organization")
#'
@ -68,7 +69,7 @@
#'
#' # grouped by days
#' mt_data %>% subject_scan(mode = "days")
#'
#' }
#' @export
subject_scan <- function(data,
hrvar,

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@ -41,8 +41,10 @@
#'
#'
#' @examples
#' \donttest{
#' tm_freq(mt_data, token = "words")
#' tm_freq(mt_data, token = "ngrams")
#' }
#'
#' @family Text-mining
#'

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@ -68,16 +68,16 @@
#' @importFrom tidyr replace_na
#'
#' @examples
#' \donttest{
#' # Run clusters, returning plot
#' workpatterns_hclust(em_data, k = 5, return = "plot")
#'
#' # Run clusters, return raw data
#' workpatterns_hclust(em_data, k = 4, return = "data") %>% head()
#'
#'
#' # Run clusters for instant messages only, return hclust object
#' workpatterns_hclust(em_data, k = 4, return = "hclust", signals = c("IM"))
#'
#' }
#'
#' @family Clustering
#' @family Working Patterns

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@ -48,6 +48,7 @@ This function uses the Information Value algorithm to predict
which Workplace Analytics metrics are most explained by the change in dates.
}
\examples{
\donttest{
# Returns a data frame
sq_data \%>\%
IV_by_period(
@ -56,7 +57,7 @@ sq_data \%>\%
after_start = "2020-01-05",
after_end = "2020-01-26"
)
}
}
\seealso{
Other Variable Association:

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@ -72,8 +72,8 @@ has since been removed and dependent functions \code{Ljungbox()} incorporated in
the \strong{wpa} package.
}
\examples{
\donttest{
# Returns summary table
create_ITSA(
data = sq_data,
before_start = "12/15/2019",
@ -97,6 +97,7 @@ plot_list <-
# Extract a plot as an example
plot_list$Workweek_span
}
}
\seealso{

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@ -87,6 +87,7 @@ grouped by a specified attribute such as organisational attribute, day of the
week, or hours of the day.
}
\examples{
\donttest{
# return a heatmap table for words
mt_data \%>\% subject_scan(hrvar = "Organizer_Organization")
@ -105,5 +106,5 @@ mt_data \%>\% subject_scan(mode = "hours")
# grouped by days
mt_data \%>\% subject_scan(mode = "days")
}
}

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@ -45,8 +45,10 @@ There is an option to remove stopwords by passing a data frame into the
\code{stopwords} argument.
}
\examples{
\donttest{
tm_freq(mt_data, token = "words")
tm_freq(mt_data, token = "ngrams")
}
}
\seealso{

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@ -80,16 +80,16 @@ In other words, the clustering is applied on a dataset where the collaboration h
are averaged by person and calculated as \% of total daily collaboration.
}
\examples{
\donttest{
# Run clusters, returning plot
workpatterns_hclust(em_data, k = 5, return = "plot")
# Run clusters, return raw data
workpatterns_hclust(em_data, k = 4, return = "data") \%>\% head()
# Run clusters for instant messages only, return hclust object
workpatterns_hclust(em_data, k = 4, return = "hclust", signals = c("IM"))
}
}
\seealso{