Identify whether a habitual behaviour exists over a given interval of time

Usage

identify_habit(
  data,
  metric,
  threshold = 1,
  width,
  max_window,
  hrvar = NULL,
  return = "plot",
  plot_mode = "time"
)

Arguments

data

Data frame containing Person Query to be analysed. The data frame must have a PersonId, MetricDate and a column containing a metric for classifying behaviour.

metric

Character string specifying the metric to be analysed.

threshold

Numeric value specifying the minimum number of times the metric sum up to in order to be a valid count. A 'greater than or equal to' logic is used.

width

Integer specifying the number of qualifying counts to consider for a habit. The function assumes a weekly interval is used.

max_window

Integer specifying the maximum unit of dates to consider a qualifying window for a habit. If your data is grouped at a weekly level, then max_window = 12 would consider 12 weeks.

hrvar

Character string specifying the HR attribute or organisational variable to group by. Default is NULL.

return

Character string specifying the type of output to be returned. Valid options include:

• "data": Returns the data frame with the habit classification.

• "plot": Returns a ggplot object of a boxplot, showing the percentage of periods with where habitual behaviour occurred.

• "summary": Returns a summary table of the habit analysis.

plot_mode

Character string specifying the type of plot to be returned. Only applicable when return = "plot". Valid options include:

• "time": Returns a time series plot with the breakdown of users with habitual behaviour.

• "boxplot": Returns a boxplot of the percentage of periods with habitual behaviour.

Details

Each week is considered as a binary variable on whether sufficient action has been taken for that given week (a qualifying count). Sufficiency is determined by the threshold parameter. For instance, if the threshold is set to 2, this means that there must be 2 qualifying actions (e.g. summarise meeting in Copilot) in a week for there to be a qualifying count for the week. One way of determining the parameters would be to consider, how many counts of width should occur within a max_window period for it to be considered a habit?

Examples

# Return a plot
identify_habit(
  pq_data,
  metric = "Multitasking_hours",
  threshold = 1,
  width = 9,
  max_window = 12,
  return = "plot"
)


# Return a summary
identify_habit(
  pq_data,
  metric = "Multitasking_hours",
  threshold = 1,
  width = 9,
  max_window = 12,
  return = "summary"
)
#> # A tibble: 21 × 2
#>    Statistics                                    Value
#>    <chr>                                         <dbl>
#>  1 Most recent week - Total persons with habit 218    
#>  2 Most recent week - % of pop with habit        0.727
#>  3 Total Persons who have lost habit           128    
#>  4 % of Persons who have lost habit              0.427
#>  5 Total Persons who have gained habit         287    
#>  6 % of Persons who have gained habit            0.957
#>  7 Mean - % of Person-weeks with habit           0.405
#>  8 Min - % of Person-weeks with habit            0    
#>  9 p10                                           0.126
#> 10 p25                                           0.293
#> # ℹ 11 more rows