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Flag unusual outlook time settings for work day start and end time

Source: R/flag_outlooktime.R
flag_outlooktime.Rd

This function flags unusual outlook calendar settings for start and end time of work day.

Usage

flag_outlooktime(data, threshold = c(4, 15), return = "message")

Arguments

data

A data frame containing a Person Query.

threshold

A numeric vector of length two, specifying the hour threshold for flagging. Defaults to c(4, 15).

return

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

  • "text" (default)

  • "message"

  • "data"

Value

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

  • "text": string. A diagnostic message.

  • "message": message on console. A diagnostic message.

  • "data": data frame. Data where flag is present.

See Value for more information.

See also

Other Data Validation: check_query(), extract_hr(), flag_ch_ratio(), flag_em_ratio(), flag_extreme(), hr_trend(), hrvar_count(), hrvar_count_all(), hrvar_trend(), identify_churn(), identify_holidayweeks(), identify_inactiveweeks(), identify_nkw(), identify_outlier(), identify_privacythreshold(), identify_shifts(), identify_tenure(), track_HR_change(), validation_report()

Examples

# Demo with `pq_data` example where Outlook Start and End times are imputed
spq_df <- pq_data

spq_df$WorkingStartTimeSetInOutlook <- "6:30"

spq_df$WorkingEndTimeSetInOutlook <- "23:30"

# Return a message
flag_outlooktime(spq_df, threshold = c(5, 13))
#> [Warning]  100% (6900) of the person-date rows in the data have extreme Outlook settings.
#> 0% (0)  have an Outlook workday shorter than 5 hours, while 100% (6900) have a workday longer than 13 hours.

# Return data
flag_outlooktime(spq_df, threshold = c(5, 13), return = "data")
#> # A tibble: 6,900 × 5
#>    PersonId                   WorkdayRange WorkdayFlag WorkdayFlag1 WorkdayFlag2
#>    <chr>                             <dbl> <lgl>       <lgl>        <lgl>       
#>  1 7d99f98f-c0a6-4df9-b2c3-e…           17 TRUE        FALSE        TRUE        
#>  2 68d86466-5864-45f1-9c15-6…           17 TRUE        FALSE        TRUE        
#>  3 8c64bf0d-57fe-4a89-a538-d…           17 TRUE        FALSE        TRUE        
#>  4 3a12dcc6-c5d5-46e4-a1d2-0…           17 TRUE        FALSE        TRUE        
#>  5 a8f3ad16-01ea-4991-8d88-0…           17 TRUE        FALSE        TRUE        
#>  6 d83e45d2-a702-4ec8-a84a-b…           17 TRUE        FALSE        TRUE        
#>  7 137926f2-3ca2-494f-8d70-9…           17 TRUE        FALSE        TRUE        
#>  8 29f24721-5f90-4219-a628-7…           17 TRUE        FALSE        TRUE        
#>  9 f0dff723-68b7-4f89-98e3-c…           17 TRUE        FALSE        TRUE        
#> 10 f04fbe6e-bf02-42df-847d-a…           17 TRUE        FALSE        TRUE        
#> # ℹ 6,890 more rows