workpatterns_hclust {wpa}

Create a hierarchical clustering of email or IMs by hour of day

Description

Apply hierarchical clustering to emails sent by hour of day. The hierarchical clustering uses cosine distance and the ward.D method of agglomeration.

Usage

workpatterns_hclust(
  data,
  k = 4,
  return = "plot",
  values = "percent",
  signals = "email",
  start_hour = "0900",
  end_hour = "1700"
)

Arguments

data

A data frame containing data from the Hourly Collaboration query.

k

Numeric vector to specify the k number of clusters to cut by.

return

String specifying what to return. This must be one of the following strings:

• "plot"

• "data"

• "table"

• "plot-area"

• "hclust"

• "dist"

See Value for more information.

values

Character vector to specify whether to return percentages or absolute values in "data" and "plot". Valid values are:

• "percent": percentage of signals divided by total signals (default)

• "abs": absolute count of signals

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")

start_hour

A character vector specifying starting hours, e.g. "0900"

end_hour

A character vector specifying starting hours, e.g. "1700"

Details

The hierarchical clustering is applied on the person-average volume-based (pav) level. In other words, the clustering is applied on a dataset where the collaboration hours are averaged by person and calculated as % of total daily collaboration.

Value

A different output is returned depending on the value passed to the return argument:

• "plot": ggplot object of a bar plot (default)

• "data": data frame containing raw data with the clusters

• "table": data frame containing a summary table. Percentages of signals are shown, e.g. x% of signals are sent by y hour of the day.

• "plot-area": ggplot object. An overlapping area plot

• "hclust": hclust object for the hierarchical model

• "dist": distance matrix used to build the clustering model

See Also

Other Clustering: personas_hclust(), workpatterns_classify()

Other Working Patterns: flex_index(), identify_shifts(), identify_shifts_wp(), plot_flex_index(), workpatterns_area(), workpatterns_classify(), workpatterns_classify_bw(), workpatterns_classify_pav(), workpatterns_rank(), workpatterns_report()

Examples

# 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"))