docs: roxygen2 update

R/workpatterns_area.R

@@ -16,12 +16,13 @@
 #'
 #' @param mingroup Numeric value setting the privacy threshold / minimum group size, defaults to 5.
 #'
-#' @param signals Character vector to specify which collaboration metrics to use:
-#'   - "email" (default) for emails only
-#'   - "IM" for Teams messages only
-#'   - "unscheduled_calls" for Unscheduled Calls only
-#'   - "meetings" for Meetings only
-#'   - or a combination of signals, such as `c("email", "IM")`
+#' @param signals Character vector to specify which collaboration metrics to
+#'   use:
+#'   - a combination of signals, such as `c("email", "IM")` (default)
+#'   - `"email"` for emails only
+#'   - `"IM"` for Teams messages only
+#'   - `"unscheduled_calls"` for Unscheduled Calls only
+#'   - `"meetings"` for Meetings only
 #'
 #' @param return Character vector to specify what to return. Valid options include:
 #'   - "plot": returns an overlapping area plot (default)

R/workpatterns_classify_bw.R

@@ -23,9 +23,12 @@
 #'   over time
 #'
 #' @param signals Character vector to specify which collaboration metrics to
-#'   use: - "email" (default) for emails only - "IM" for Teams messages only, -
-#'   "unscheduled_calls" for Unscheduled Calls only - "meetings" for Meetings
-#'   only - or a combination of signals, such as `c("email", "IM")`
+#'   use:
+#'   - a combination of signals, such as `c("email", "IM")` (default)
+#'   - `"email"` for emails only
+#'   - `"IM"` for Teams messages only
+#'   - `"unscheduled_calls"` for Unscheduled Calls only
+#'   - `"meetings"` for Meetings only
 #'
 #' @param active_threshold A numeric value specifying the minimum number of
 #'   signals to be greater than in order to qualify as _active_. Defaults to 0.

man/flex_index.Rd

@@ -61,7 +61,7 @@ Flexibility Index and the component scores.
 }
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 
 Pass an Hourly Collaboration query and compute a Flexibility Index for the
 entire population. The Flexibility Index is a quantitative measure of the

man/network_describe.Rd

@@ -15,7 +15,7 @@ network_describe(
 \item{hrvar}{Character vector of length 3 containing the HR attributes to be used.}
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Returns a data frame that gives a percentage of the group combinations that best represent
 the population provided. Uses a person to person query.
 }

man/network_leiden.Rd

@@ -62,7 +62,7 @@ Set as \code{0} to co-erce to a fast plotting method every time, and \code{Inf}
 method.}
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Take a P2P network query and implement the Leiden community detection method. To run
 this function, you will require all the pre-requisites of the \strong{leiden} package installed,
 which includes Python and \strong{reticulate}.

man/network_louvain.Rd

@@ -55,7 +55,7 @@ Set as \code{0} to co-erce to a fast plotting method every time, and \code{Inf}
 method.}
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Take a P2P network query and implement the Louvain community detection method. The
 \strong{igraph} implementation of the Louvain method is used.
 }

man/network_p2p.Rd

@@ -81,7 +81,7 @@ Set as \code{0} to co-erce to a fast plotting method every time, and \code{Inf}
 method.}
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 
 Pass a data frame containing a person-to-person query and return a network visualization.
 Options are available for community detection using either the Louvain or the Leiden algorithms.

man/personas_hclust.Rd

@@ -38,7 +38,8 @@ of the clusters as per \code{keymetrics_scan()}.
 }
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#questioning}{\figure{lifecycle-questioning.svg}{options: alt='[Questioning]'}}}{\strong{[Questioning]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#questioning'><img src='figures/lifecycle-questioning.svg' alt='Questioning lifecycle'></a>}}{\strong{Questioning}}
+
 Apply hierarchical clustering to selected metrics. Person averages are computed prior to clustering.
 The hierarchical clustering uses cosine distance and the ward.D method
 of agglomeration.

man/remove_outliers.Rd

@@ -18,7 +18,7 @@ having removed the person-weeks that are below 2 standard
 deviations of each individual's collaboration activity.
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 This function takes in a selected metric and uses z-score (number of standard
 deviations) to identify and remove outlier weeks for individuals across time. There are applications
 in this for removing weeks with abnormally low collaboration

man/workpatterns_area.Rd

@@ -23,13 +23,14 @@ defaults to "Organization" but accepts any character vector, e.g. "LevelDesignat
 
 \item{mingroup}{Numeric value setting the privacy threshold / minimum group size, defaults to 5.}
 
-\item{signals}{Character vector to specify which collaboration metrics to use:
+\item{signals}{Character vector to specify which collaboration metrics to
+use:
 \itemize{
-\item "email" (default) for emails only
-\item "IM" for Teams messages only
-\item "unscheduled_calls" for Unscheduled Calls only
-\item "meetings" for Meetings only
-\item or a combination of signals, such as \code{c("email", "IM")}
+\item a combination of signals, such as \code{c("email", "IM")} (default)
+\item \code{"email"} for emails only
+\item \code{"IM"} for Teams messages only
+\item \code{"unscheduled_calls"} for Unscheduled Calls only
+\item \code{"meetings"} for Meetings only
 }}
 
 \item{return}{Character vector to specify what to return. Valid options include:

man/workpatterns_classify_bw.Rd

@@ -22,9 +22,14 @@ workpatterns_classify_bw(
 Defaults to NULL. This only affects the function when "table" is returned.}
 
 \item{signals}{Character vector to specify which collaboration metrics to
-use: - "email" (default) for emails only - "IM" for Teams messages only, -
-"unscheduled_calls" for Unscheduled Calls only - "meetings" for Meetings
-only - or a combination of signals, such as \code{c("email", "IM")}}
+use:
+\itemize{
+\item a combination of signals, such as \code{c("email", "IM")} (default)
+\item \code{"email"} for emails only
+\item \code{"IM"} for Teams messages only
+\item \code{"unscheduled_calls"} for Unscheduled Calls only
+\item \code{"meetings"} for Meetings only
+}}
 
 \item{start_hour}{A character vector specifying start hours,
 e.g. "0900"}
@@ -43,7 +48,7 @@ archetypes - "table": returns a summary table of the archetypes -
 over time}
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}} Apply a rule based
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}} Apply a rule based
 algorithm to emails sent by hour of day, using the binary week-based (bw)
 method.
 }

man/workpatterns_classify_pav.Rd

@@ -48,7 +48,7 @@ e.g. "1700"}
 }}
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Apply a rule based algorithm to emails or instant messages sent by hour of day.
 This uses a person-average volume-based (pav) method.
 }

man/workpatterns_hclust.Rd

@@ -68,7 +68,8 @@ are shown, e.g. x\% of signals are sent by y hour of the day.
 }
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
+
 Apply hierarchical clustering to emails sent by hour of day.
 The hierarchical clustering uses cosine distance and the ward.D method
 of agglomeration.

man/workpatterns_report.Rd

@@ -38,7 +38,7 @@ generated in the working directory. No outputs are directly returned by the
 function.
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 
 This function takes a Hourly Collaboration query and generates a HTML report
 on working patterns archetypes. Archetypes are created using the binary-week