R/IV_by_Period.R
IV_by_period.Rd
This function uses the Information Value algorithm to predict which Workplace Analytics metrics are most explained by the change in dates.
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
Workplace Analytics query output.
Start date of "before" time period in YYYY-MM-DD
.
Defaults to earliest date in dataset.
End date of "before" time period in YYYY-MM-DD
Start date of "after" time period in YYYY-MM-DD
.
Defaults to day after before_end.
End date of "after" time period in YYYY-MM-DD
. Defaults to
latest date in dataset.
Number of bins to cut the data into for Information Value analysis. Defaults to 10.
String specifying what to return. The current only valid
option is "table"
.
data frame containing all the variables and the corresponding Information Value.
Other Variable Association:
IV_report()
,
create_IV()
,
plot_WOE()
Other Information Value:
IV_report()
,
create_IV()
,
plot_WOE()
Other Time-series:
create_line_asis()
,
create_line()
,
create_period_scatter()
,
create_trend()
,
period_change()
# Returns a data frame
sq_data %>%
IV_by_period(
before_start = "2019-11-03",
before_end = "2019-11-09",
after_start = "2019-11-10",
after_end = "2019-11-16"
)
#> Variable IV
#> 1 After_hours_meeting_hours 4.063198e-02
#> 2 After_hours_collaboration_hours 3.214857e-02
#> 3 After_hours_email_hours 2.827746e-02
#> 4 After_hours_instant_messages 2.646030e-02
#> 5 Instant_messages_sent 2.390204e-02
#> 6 Generated_workload_email_recipients 2.351152e-02
#> 7 Total_emails_sent_during_meeting 2.253198e-02
#> 8 Meetings_with_manager 2.037033e-02
#> 9 Open_1_hour_block 2.031148e-02
#> 10 Workweek_span 1.875483e-02
#> 11 Email_hours 1.847501e-02
#> 12 Low_quality_meeting_hours 1.840299e-02
#> 13 Instant_Message_hours 1.832036e-02
#> 14 Generated_workload_instant_messages_hours 1.807253e-02
#> 15 Conflicting_meeting_hours 1.673358e-02
#> 16 Meetings 1.621851e-02
#> 17 Meeting_hours_during_working_hours 1.607176e-02
#> 18 Meeting_hours_with_manager 1.580855e-02
#> 19 Meeting_hours 1.547852e-02
#> 20 Multitasking_meeting_hours 1.512573e-02
#> 21 Collaboration_hours 1.431442e-02
#> 22 Time_in_self_organized_meetings 1.366691e-02
#> 23 Generated_workload_email_hours 1.362824e-02
#> 24 Call_hours 1.345850e-02
#> 25 Working_hours_in_calls 1.338040e-02
#> 26 Working_hours_email_hours 1.317128e-02
#> 27 Generated_workload_instant_messages_recipients 1.222653e-02
#> 28 Working_hours_instant_messages 1.194240e-02
#> 29 After_hours_in_calls 1.160868e-02
#> 30 Collaboration_hours_external 1.105485e-02
#> 31 Redundant_meeting_hours__organizational_ 1.065584e-02
#> 32 Total_focus_hours 1.064046e-02
#> 33 Emails_sent 1.023741e-02
#> 34 Generated_workload_meeting_hours 1.021543e-02
#> 35 External_network_size 9.104338e-03
#> 36 Open_2_hour_blocks 8.839182e-03
#> 37 Internal_network_size 8.784474e-03
#> 38 Working_hours_collaboration_hours 8.124579e-03
#> 39 Generated_workload_call_participants 7.956155e-03
#> 40 Generated_workload_call_hours 6.901190e-03
#> 41 Generated_workload_calls_organized 6.404576e-03
#> 42 Total_calls 4.848446e-03
#> 43 Meetings_with_manager_1_on_1 4.710288e-03
#> 44 Generated_workload_meetings_organized 4.059213e-03
#> 45 Generated_workload_meeting_attendees 3.425110e-03
#> 46 Meeting_hours_with_manager_1_on_1 3.214828e-03
#> 47 Networking_outside_company 1.235318e-03
#> 48 Manager_coaching_hours_1_on_1 2.339200e-04
#> 49 Networking_outside_organization 4.851778e-06
#> 50 Meetings_with_skip_level 0.000000e+00
#> 51 Meeting_hours_with_skip_level 0.000000e+00
#> 52 Redundant_meeting_hours__lower_level_ 0.000000e+00
#> 53 Layer 0.000000e+00
#> 54 HourlyRate 0.000000e+00