Plot the distribution of percentage change between periods of a Viva Insights metric by the number of employees.

Source: R/period_change.R

This function also presents the p-value for the null hypothesis that the variable has not changed, using a Wilcox signed-rank test.

period_change(
  data,
  compvar,
  before_start = min(as.Date(data$Date, "%m/%d/%Y")),
  before_end,
  after_start = as.Date(before_end) + 1,
  after_end = max(as.Date(data$Date, "%m/%d/%Y")),
  return = "count"
)

Arguments

data

Person Query as a dataframe including date column named "Date" This function assumes the data format is MM/DD/YYYY as is standard in a Viva Insights query output.

compvar

comparison variable to compare person change before and after For example, "Collaboration_hours"

before_start

Start date of "before" time period in YYYY-MM-DD

before_end

End date of "before" time period in YYYY-MM-DD

after_start

Start date of "after" time period in YYYY-MM-DD

after_end

End date of "after" time period in YYYY-MM-DD

return

Character vector specifying whether to return plot as Count or Percentage of Employees. Valid inputs include:

  • "count" (default)

  • "percentage"

  • "table"

Value

ggplot object showing a bar plot (histogram) of change for two time intervals.

See also

Other Visualization: afterhours_dist(), afterhours_fizz(), afterhours_line(), afterhours_rank(), afterhours_summary(), afterhours_trend(), collaboration_area(), collaboration_dist(), collaboration_fizz(), collaboration_line(), collaboration_rank(), collaboration_sum(), collaboration_trend(), create_bar(), create_bar_asis(), create_boxplot(), create_bubble(), create_dist(), create_fizz(), create_inc(), create_line(), create_line_asis(), create_period_scatter(), create_rank(), create_sankey(), create_scatter(), create_stacked(), create_tracking(), create_trend(), email_dist(), email_fizz(), email_line(), email_rank(), email_summary(), email_trend(), external_dist(), external_fizz(), external_line(), external_network_plot(), external_rank(), external_sum(), hr_trend(), hrvar_count(), hrvar_trend(), internal_network_plot(), keymetrics_scan(), meeting_dist(), meeting_fizz(), meeting_line(), meeting_quality(), meeting_rank(), meeting_summary(), meeting_trend(), meetingtype_dist(), meetingtype_dist_ca(), meetingtype_dist_mt(), meetingtype_summary(), mgrcoatt_dist(), mgrrel_matrix(), one2one_dist(), one2one_fizz(), one2one_freq(), one2one_line(), one2one_rank(), one2one_sum(), one2one_trend(), workloads_dist(), workloads_fizz(), workloads_line(), workloads_rank(), workloads_summary(), workloads_trend(), workpatterns_area(), workpatterns_rank()

Other Time-series: IV_by_period(), create_line(), create_line_asis(), create_period_scatter(), create_trend()

Other Flexible: create_bar(), create_bar_asis(), create_boxplot(), create_bubble(), create_density(), create_dist(), create_fizz(), create_hist(), create_inc(), create_line(), create_line_asis(), create_period_scatter(), create_rank(), create_sankey(), create_scatter(), create_stacked(), create_tracking(), create_trend()

Other Flexible Input: create_ITSA()

Author

Mark Powers mark.powers@microsoft.com

Examples

# Run plot
period_change(sq_data, compvar = "Workweek_span", before_end = "2019-12-29")


# \donttest{
# Run plot with more specific arguments
period_change(sq_data,
              compvar = "Workweek_span",
              before_start = "2019-12-15",
              before_end = "2019-12-29",
              after_start = "2020-01-05",
              after_end = "2020-01-26",
              return = "percentage")

# }