momtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard", lag_n=1)
#>
#> ── Month-to-date over previous month-to-date ───────────────────────────────────
#> Function: `momtd` was executed
#>
#> ── Description: ──
#>
#> This creates a daily `cumsum()` of the previous month quantity and compares it
#> with the daily `cumsum()` current month quantity from the start of the standard
#> calendar month to the end of the month
#>
#> ── Calendar: ──
#>
#> • The calendar aggregated order_date to the day time unit
#> • A standard calendar is created with 0 groups
#> • Calendar ranges from 2021-05-18 to 2024-04-20
#> • 222 days were missing and replaced with 0
#> • New date column date, year, quarter, month was created from order_date
#> ── Actions: ──
#> ✔Aggregate
#> ✔Shift 1 month
#> ✔Compare Previous month-to-date
#> ✖Proportion of Total
#> ✖Count Distinct
#>
#> ── Next Steps: ──
#>
#> • Use `calculate()` to return the results
#> ────────────────────────────────────────────────────────────────────────────────
#> Current period month to date compared to previous period month-to-date
Source: R/time_intelligence_classes.R
This calculates the monthly cumulative sum of targeted value using a standard or 5-5-4 calendar respecting any groups that are passed through with dplyr::group_by()
Use calculate to return the results
Usage
momtd(
.data,
.date,
.value,
calendar_type = "standard",
lag_n = 1,
fiscal_year_start = 1
)Arguments
-
.data -
tibble or dbi object (either grouped or ungrouped)
-
.date -
the date column to group by. Must be a Date or POSIXt type.
-
.value -
the value column to summarize. Must be numeric.
-
calendar_type -
select either ‘standard’, ‘445’, ‘454’, or ‘544’ calendar, see ‘Details’ for additional information
-
lag_n -
the number of periods to lag
-
fiscal_year_start -
integer 1-12, the month the fiscal year starts nearest to (default 1 = January). Only used with retail calendars (‘445’, ‘454’, ‘544’).
Value
ti object
Details
-
This function creates a complete calendar object that fills in any missing days, weeks, months, quarters, or years
-
If you provide a grouped object with
dplyr::group_by(), it will generate a complete calendar for each group -
The function creates a
tiobject, which pre-processes the data and arguments for further downstream functions
NA Handling
-
NA values in the
.valuecolumn propagate through cumulative sums (cumsum returns NA once an NA is encountered) -
NA values in the
.datecolumn are excluded from the calendar join and will not appear in results -
Missing dates (gaps in your data) are filled with 0 values, not NA
standard calendar
-
The standard calendar splits the year into 12 months (with 28–31 days each) and uses a 7-day week
-
It automatically accounts for leap years every four years to match the Gregorian calendar
5-5-4 calendar
-
The 5-5-4 calendar divides the fiscal year into 52 weeks (occasionally 53), organizing each quarter into two 5-week periods and one 4-week period.
-
This system is commonly used in retail and financial reporting
See Also
Other time_intelligence: atd(), dod(), mom(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()