Package {ti}

ABC classification function

Description

• For your group variable, abc() will categorize which groups make up what proportion of the totals according to the category_values that you have entered

• The function returns a segment object which prints out the execution steps and actions that it will take to categorize your data

• Use calculate to return the results

Usage

abc(.data, category_values, .value)

Arguments

Details

• This function is helpful to understand which groups of make up what proportion of the cumulative contribution

• If you do not provide a .value then it will count the transactions per group, if you provide .value then it will sum() the .value per group

• The function creates a segment object, which pre-processes the data into its components

Value

abc object

Examples

contoso::sales |>
  dplyr::group_by(product_key) |>
  abc(c(.1,.5,.7,.96,1), .value = gross_margin)

Classify a group by proportion of a variable (A,B,C,...)

Description

This returns a table that will segment your data into A,B or C segments based on custom thresholds

Usage

abc_fn(x)

Arguments

Value

a dbi objection

Action class

Description

action carries the human-readable narrative shown by the print() methods. value is lower-cased and passed through make_action_cli() on assignment, which stores a named logical vector saying which of the five standard steps (aggregate, shift, compare, proportion of total, count distinct) the calculation performs. print_actions_steps() draws the checklist from it.

Usage

action(value = NULL, method = NA_character_)

Arguments

Value

An action S7 object.

All period-to-date

Description

This calculates the all-time 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

atd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

atd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard")

All-to-date execution function

Description

atd_fn() is the function that is called by atd() when passed through to calculate

Usage

atd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

atd() for the function's intent

Add Comprehensive Date-Based Attributes to a DBI lazy frame or tibble object

Description

This function takes a data frame and a date column and generates a wide set of derived date attributes. These include start/end dates for year, quarter, month, and week; day-of-week indicators; completed and remaining days in each time unit; and additional convenience variables such as weekend indicators.

It is designed for time-based feature engineering and is useful in reporting, forecasting, time-series modeling, and data enrichment workflows.

Usage

augment_standard_calendar(.data, .date)

Arguments

Details

The function creates the following groups of attributes:

1. Start and end dates

• year_start_date, year_end_date

• quarter_start_date, quarter_end_date

• month_start_date, month_end_date

• week_start_date, week_end_date

2. Day-of-week fields

• day_of_week – numeric day of the week (1–7)

• day_of_week_label – ordered factor label (e.g., Mon, Tue, …)

3. Duration fields

• days_in_year – total days in the year interval

• days_in_quarter – total days in the quarter interval

• days_in_month – total days in the month

4. Completed/remaining days

• days_complete_in_week, days_remaining_in_week

• days_complete_in_month, days_remaining_in_month

• days_complete_in_quarter, days_remaining_in_quarter

• days_complete_in_year, days_remaining_in_year

5. Miscellaneous

• weekend_indicator – equals 1 if Saturday or Sunday; otherwise 0

All date-derived fields ending in ⁠_date⁠ are coerced to class Date.

Value

A dbi or tibble containing the original data along with all generated date-based attributes.

Add Comprehensive Date-Based Attributes to a DBI lazy frame

Description

This function takes a data frame and a date column and generates a wide set of derived date attributes. These include start/end dates for year, quarter, month, and week; day-of-week indicators; completed and remaining days in each time unit; and additional convenience variables such as weekend indicators.

It is designed for time-based feature engineering and is useful in reporting, forecasting, time-series modeling, and data enrichment workflows.

Usage

augment_standard_calendar_dbi(.data, .date)

Arguments

Details

The function creates the following groups of attributes:

1. Start and end dates

• year_start_date, year_end_date

• quarter_start_date, quarter_end_date

• month_start_date, month_end_date

• week_start_date, week_end_date

2. Day-of-week fields

• day_of_week – numeric day of the week (1–7)

• day_of_week_label – ordered factor label (e.g., Mon, Tue, …)

3. Duration fields

• days_in_year – total days in the year interval

• days_in_quarter – total days in the quarter interval

• days_in_month – total days in the month

4. Completed/remaining days

• days_complete_in_week, days_remaining_in_week

• days_complete_in_month, days_remaining_in_month

• days_complete_in_quarter, days_remaining_in_quarter

• days_complete_in_year, days_remaining_in_year

5. Miscellaneous

• weekend_indicator – equals 1 if Saturday or Sunday; otherwise 0

All date-derived fields ending in ⁠_date⁠ are coerced to class Date.

Value

A dbi containing the original data along with all generated date-based attributes.

Add Comprehensive Date-Based Attributes to a Data Frame

Description

This function takes a data frame and a date column and generates a wide set of derived date attributes. These include start/end dates for year, quarter, month, and week; day-of-week indicators; completed and remaining days in each time unit; and additional convenience variables such as weekend indicators.

It is designed for time-based feature engineering and is useful in reporting, forecasting, time-series modeling, and data enrichment workflows.

Usage

augment_standard_calendar_tbl(.data, .date)

Arguments

Details

The function creates the following groups of attributes:

1. Start and end dates

• year_start_date, year_end_date

• quarter_start_date, quarter_end_date

• month_start_date, month_end_date

• week_start_date, week_end_date

2. Day-of-week fields

• day_of_week – numeric day of the week (1–7)

• day_of_week_label – ordered factor label (e.g., Mon, Tue, …)

3. Duration fields

• days_in_year – total days in the year interval

• days_in_quarter – total days in the quarter interval

• days_in_month – total days in the month

4. Completed/remaining days

• days_complete_in_week, days_remaining_in_week

• days_complete_in_month, days_remaining_in_month

• days_complete_in_quarter, days_remaining_in_quarter

• days_complete_in_year, days_remaining_in_year

5. Miscellaneous

• weekend_indicator – equals 1 if Saturday or Sunday; otherwise 0

All date-derived fields ending in ⁠_date⁠ are coerced to class Date.

Value

A tibble containing the original data along with all generated date-based attributes.

Execute time-intelligence or segments class objects to return the underlying transformed table

Description

The calculate() function takes an object created by a time function (like ytd(), mtd(), or qtd()) or a segment function (like cohort() or abc()) and executes the underlying transformation logic. It translates the function blueprint into an actionable query, returning the final data table.

Usage

calculate(x, ...)

## S7 method for class <ti::ti>
calculate(x, ...)

## S7 method for class <ti::segment_cohort>
calculate(x, ...)

## S7 method for class <ti::segment_abc>
calculate(x, ...)

Arguments

Details

The TI and segment functions in ti—such as ytd() or cohort() and others—are designed to be lazy and database-friendly. They do not perform the heavy data transformation immediately. Instead, they return a blueprint object (of class ti,segment_abc or segment_cohort) that contains all the parameters and logic needed for the calculation.

calculate() serves as the execution engine.

When called, it interprets the blueprint and generates optimized R code or SQL code (using the dbplyr package) that is then executed efficiently on the data source, whether it's an in-memory tibble or a 'DuckDB' table. This approach minimizes data transfer and improves performance for large datasets.

The resulting table will be sorted by the relevant date column to ensure the correct temporal ordering of the calculated metrics.

Value

dbi object

Examples

x <- ytd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard")
calculate(x)

Build a daily calendar with each day's period start date

Description

Returns one row per day with the calendar labels (year, quarter, month, week, day) and period_date, the first day of the period of time_unit that the day belongs to. Periods are defined by the labels, never by flooring dates, so a period is exactly the set of days that share its labels:

• standard calendar: weeks start on week_start and week 1 is the week that contains 1 January, so only the weeks around 1 January are partial (the same convention as Excel's WEEKNUM()). Weeks do not split at month ends; a week's month and quarter are those of its first day.

• retail calendars: fiscal year, quarter, month and week from generate_retail_calendar().

The calendar starts 371 days before start_date so that the period containing start_date is dated by its true first day, even for 53-week fiscal years.

Usage

calendar_day_sql(
  .con,
  start_date,
  end_date,
  calendar_type = "standard",
  time_unit = "day",
  week_start = 7,
  fiscal_year_start = 1
)

Arguments

Value

A tbl_lazy with columns date, year, quarter, month, week, day and period_date.

One row per period that overlaps the requested range

Description

One row per period that overlaps the requested range

Usage

calendar_periods(day_dbi, start_date, time_unit)

Arguments

Value

A tbl_lazy with date (the period's first day) and the label columns for time_unit, taken from that first day.

Category class

Description

category holds the cumulative cut-points used by abc() to bucket group members. Assigning category_names derives one letter per cut-point from category_values, so the default three cut-points become a, b and c.

Usage

category(category_values = c(0.7, 0.96, 1), category_names = NULL)

Arguments

Value

A category S7 object.

Cohort Analysis

Description

Database-friendly cohort analysis function. A remake of https://github.com/PeerChristensen/cohorts, combining cohort_table_month, cohort_table_year, and cohort_table_day into a single package. Rewritten for database compatibility; runs entirely in SQL without writing tables.

Usage

cohort(.data, .date, .value, time_unit = "month", period_label = FALSE)

Arguments

Details

• Groups your .value column by shared time attributes from the .date column.

• Assigns each member to a cohort based on their first entry in .date.

• Aggregates the cohort by the time_unit argument (day, week, month, quarter, or year).

• Computes the distinct count of each cohort member over time.

Value

segment object

Internal function for the cohort segment object

Description

Internal function for the cohort segment object

Usage

cohort_fn(x)

Arguments

Value

function

Title

Description

Title

Usage

complete_standard_calendar(.data, x)

Arguments

Value

DBI object

Create Calendar Table

Description

create_calendar() summarizes a tibble to target time unit and completes the calendar to ensure no missing days, month, quarter or years. If a grouped tibble is passed through it will complete the calendar for each combination of the group

Usage

create_calendar(x, ...)

## S7 method for class <ti::ti>
create_calendar(x, ...)

Arguments

Details

This is in internal function to make it easier to ensure data has no missing dates to simplify the use of time intelligence functions downstream of the application. If you want to summarize to a particular group, simply pass the tibble through to the dplyr::group_by() argument prior to function and the function will make summarize and make a complete calendar for each group item.

Value

dbi object

Title

Description

Title

Usage

create_full_dbi(x)

Arguments

Value

DBI object

Datum class

Description

datum wraps the user's table together with the metadata every time-intelligence function needs: which column holds the date, what calendar the dates should be interpreted against, and how the table is grouped. Most of its properties are read-only and computed on access from data and date_vec, so they always reflect the current table.

Usage

datum(
  data = NULL,
  calendar_type = "standard",
  fiscal_year_start = 1,
  week_start = 7,
  date_vec = NULL
)

Arguments

Value

A datum S7 object.

Additional properties

@class_name

"dbi" if data is a database table, otherwise "tbl".

@date_quo

date_vec parsed to a symbol for tidy evaluation.

@min_date

The earliest value of the date column.

@max_date

The latest value of the date column.

@date_range

Number of days between min_date and max_date.

@date_count

Number of distinct dates present in the date column.

@date_missing

date_range minus date_count, i.e. gaps in the calendar.

@group_indicator

TRUE if data carries grouping variables.

@group_quo

The grouping variables as a list of symbols.

@group_vec

The grouping variables as a character vector.

@group_count

Number of grouping variables.

Detect SQL dialect from a DBI connection

Description

Detect SQL dialect from a DBI connection

Usage

detect_sql_dialect(.con)

Arguments

Value

A character string: "snowflake", "duckdb", or "postgres"

Current period day over previous period day

Description

This calculates the daily value compared to the previous day's 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

dod(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

dod(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

Day-over-day execution function

Description

dod_fn() is the function that is called by dod() when passed through to calculate

Usage

dod_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

dod() for the function's intent

Function class

Description

fn is the blueprint half of a lazy time-intelligence object. It holds the transformation to run (fn_exec) plus the metadata that describes it, which is what lets ytd() and friends return an object cheaply and defer the real work to calculate().

Usage

fn(
  fn_exec = function() NULL,
  fn_name = NA_character_,
  fn_long_name = NA_character_,
  shift = NA_character_,
  compare = NA_character_,
  label = FALSE,
  new_date_column_name = NA_character_,
  lag_n = 0
)

Arguments

Value

An fn S7 object.

Generate a retail (4-4-5, 4-5-4, or 5-4-4) calendar mapping table

Description

Generate a retail (4-4-5, 4-5-4, or 5-4-4) calendar mapping table

Usage

generate_retail_calendar(
  start_date,
  end_date,
  calendar_type,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

Value

A tibble with columns: date, year, quarter, month, week

Explain settings that have no effect

Description

week_start and fiscal_year_start exist on every time intelligence function because the retail calendars build every fiscal period from whole weeks. On the standard calendar they matter much less: fiscal_year_start does nothing, and week_start only affects the week functions and the week column. This returns one sentence per setting that was changed from its default but has no effect (or only relabels the week column), for build_ti() to show as a message and print_assumptions() to print.

Usage

ignored_settings(x)

Arguments

Value

A named character vector, possibly empty.

Validate an input is YYYY-MM-DD format

Description

Validate an input is YYYY-MM-DD format

Usage

is_yyyy_mm_dd(x)

Arguments

Value

logical

Record which of the standard steps a calculation performs

Description

Record which of the standard steps a calculation performs

Usage

make_action_cli(x)

Arguments

Value

A named logical vector with one element per standard step: aggregate, shift, compare, proportion of total and count distinct.

Coerce data into a DuckDB-backed lazy table

Description

Ensures the input is a database-backed object. If a data.frame is provided, it is registered into a temporary, in-memory DuckDB instance. If already a tbl_dbi, it is returned unchanged.

Usage

make_db_tbl(x)

Arguments

Details

When converting a data.frame, this function preserves existing dplyr groups. It uses DuckDB's duckdb_register, which is a virtual registration and does not perform a physical copy of the data, making it extremely fast.

Value

A tbl_dbi object backed by DuckDB.

Current full period month over previous full period month

Description

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

mom(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

mom(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

month-over-month execution function

Description

mom_fn() is the function that is called by mom() when passed through to calculate

Usage

mom_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

mom() for the function's intent

Current period month to date compared to previous period month-to-date

Description

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,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

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()

Examples

momtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard", lag_n=1)

Current year to date over previous year-to-date for tibble objects

Description

momtd_fn() is the function that is called by momtd() when passed through to calculate

Usage

momtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

momtd() for the function's intent

Current period month-to-date

Description

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

mtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

mtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard")

Month-to-date execution function

Description

mtd_fn() is the function that is called by mtd() when passed through to calculate

Usage

mtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

mtd() for the function's intent

Current month-to-date over full previous period month

Description

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

mtdopm(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

mtdopm(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Month-to-date over full previous month execution function

Description

mtdopm_fn() is the function that is called by mtdopm() when passed through to calculate

Usage

mtdopm_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

mtdopm() for the function's intent

Period that each date belongs to

Description

Labels dates with their period in R, using the same definitions as the calendar SQL: weeks start on week_start and week 1 contains 1 January on the standard calendar; fiscal labels from generate_retail_calendar() on the retail calendars.

Usage

period_keys(
  dates,
  time_unit,
  calendar_type = "standard",
  week_start = 7,
  fiscal_year_start = 1
)

Arguments

Value

A character vector of period keys, one per date.

Previous period month-to-date

Description

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

pmtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

pmtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Previous month-to-date execution function

Description

pmtd_fn() is the function that is called by pmtd() when passed through to calculate

Usage

pmtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

pmtd() for the function's intent

Prior period quarter-to-date

Description

• This calculates the quarterly 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

pqtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

pqtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Previous quarter-to-date for tibble objects

Description

pqtd_fn() is the function that is called by pqtd() when passed through to calculate

Usage

pqtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

pqtd() for the function's intent

Print ti objects

Description

Print ti objects

Print segment objects

Print segment objects

Usage

## S7 method for class <ti::ti>
print(x, ...)

## S7 method for class <ti::segment_abc>
print(x, ...)

## S7 method for class <ti::segment_cohort>
print(x, ...)

Arguments

Value

ti object

segment object

segment object

Print action steps

Description

Print action steps

Usage

print_actions_steps(x)

Arguments

Value

cli messages

Print the assumptions behind a time intelligence calculation

Description

One line per convention that decides the numbers: what a row is, how periods are compared, how weeks and fiscal years are defined, and how groups are handled. Built only from the object's settings; it runs no query.

Usage

print_assumptions(x)

Arguments

Value

print

Print calendar information block

Description

Print calendar information block

Usage

print_calendar_info(x)

Arguments

Value

print

Prints function header info

Description

Prints function header info

Usage

print_fn_info(x)

Arguments

Value

print

Prints functions next steps

Description

Prints functions next steps

Usage

print_next_steps()

Value

print

Previous period week-to-date

Description

This calculates the weekly cumulative sum of targeted value for the previous week 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

pwtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

pwtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Previous week-to-date execution function

Description

pwtd_fn() is the function that is called by pwtd() when passed through to calculate

Usage

pwtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

pwtd() for the function's intent

Previous period year-to-date

Description

• For each group, pytd() will create the running annual sum of a value based on the calendar type for the previous year compared to the current year calendar date

• If no period exists, it will return NA

• The function returns a ti object which prints out the summary of steps and actions that will take to create the calendar table and calculations

• Use calculate to return the results

Usage

pytd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

pytd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Previous year-to-date execution function

Description

pytd_fn() is the function that is called by pytd() when passed through to calculate

Usage

pytd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

pytd() for the function's intent

Current full period quarter over previous full period quarter

Description

• This calculates the full quarter value compared to the previous quarter value respecting any groups that are passed through with dplyr::group_by()

• Use calculate to return the results

Usage

qoq(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

qoq(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

Quarter-over-quarter execution function

Description

qoq_fn() is the function that is called by qoq() when passed through to calculate

Usage

qoq_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

qoq() for the function's intent

Current period quarter-to-date compared to previous period quarter-to-date

Description

• This calculates the annual 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

qoqtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

qoqtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Current quarter to date over previous quarter-to-date for tibble objects

Description

qoqtd_fn() is the function that is called by qoqtd() when passed through to calculate

Usage

qoqtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

qoqtd() for the function's intent

Current period quarter-to-date

Description

This calculates the quarterly 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

qtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

qtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard")

Quarter-to-date execution function

Description

qtd_fn() is the function that is called by qtd() when passed through to calculate

Usage

qtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

qtd() for the function's intent

Current period quarter-to-date over previous period quarter

Description

• This calculates the quarterly cumulative sum of a targeted value and compares it with the previous full quarter's total value respecting any groups that are passed through with dplyr::group_by()

• Use calculate to return the results

Usage

qtdopq(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

qtdopq(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

quarter-to-date over previous quarter execution function

Description

qtdopq_fn() is the function that is called by qtdopq() when passed through to calculate

Usage

qtdopq_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

qtdopq() for the function's intent

ABC segmentation class

Description

segment_abc is the lazy object returned by abc(). It mirrors ti but swaps the time-shift metadata for a category object holding the bucket cut-points. Pass it to calculate() to run the segmentation.

Usage

segment_abc(
  datum = datum(),
  category = category(),
  time_unit = time_unit(),
  fn = fn(),
  action = action(),
  value = value()
)

Arguments

Value

A segment_abc S7 object.

Cohort segmentation class

Description

segment_cohort is the lazy object returned by cohort(). It groups records by the period in which each group member first appears, then measures each cohort forward through time. Pass it to calculate() to run the segmentation.

Usage

segment_cohort(
  datum = datum(),
  time_unit = time_unit(),
  fn = fn(),
  action = action(),
  value = value()
)

Arguments

Value

A segment_cohort S7 object.

Generate a Cross-Dialect SQL Date Series

Description

Creates a lazy dbplyr table containing a continuous sequence of dates. The function automatically detects the SQL dialect of the connection and dispatches the most efficient native series generator (e.g., GENERATE_SERIES for DuckDB/Postgres or GENERATOR for Snowflake).

Usage

seq_date_sql(
  .con,
  start_date,
  end_date,
  calendar_type = "standard",
  time_unit = "day",
  week_start = 7,
  fiscal_year_start = 1
)

Arguments

Details

This function is designed to be "nestable," meaning the resulting SQL can be used safely inside larger dplyr pipelines. It avoids WITH clauses in dialects like DuckDB to prevent parser errors when dbplyr wraps the query in a subquery (e.g., ⁠SELECT * FROM (...) AS q01⁠).

For unit testing, the function supports dbplyr simulation objects. If a TestConnection is detected, it returns a lazy_frame to avoid metadata field queries that would otherwise fail on a mock connection.

Value

A tbl_lazy (SQL) object with one row per period: date (the period's first day) and the calendar label columns for time_unit.

Generate dialect-appropriate date addition SQL

Description

Generate dialect-appropriate date addition SQL

Usage

sql_date_add(.con, unit, n_expr, date_col = "date")

Arguments

Value

A dplyr::sql object

Same point in time in an earlier period

Description

SQL for the cutoff date used by the previous-period-to-date functions: date moved back lag_n weeks, months, quarters or years. Moving back by months clamps to the end of a shorter month (31 May minus 3 months is 29 February), and when date is the last day of its month the cutoff is the last day of the earlier month, so 28 February 2025 compares with the whole of February 2024 and 30 April with the whole of January.

Usage

sql_period_cutoff(.con, unit, lag_n)

Arguments

Value

A SQL expression on the date column

Time-intelligence class

Description

ti is the object returned by every time-intelligence function (ytd(), yoy(), mtd() and so on). It composes the data (datum), the granularity (time_unit), the measure (value), the calculation blueprint (fn) and the print narrative (action) into a single lazy object. Nothing is computed until it is passed to calculate().

Usage

ti(
  datum = datum(),
  time_unit = time_unit(),
  value = value(),
  fn = fn(),
  action = action()
)

Arguments

Value

A ti S7 object.

Time unit class

Description

time_unit records the calendar granularity that a date column is floored to before a time-intelligence calculation is run. The value is lower-cased on assignment and must be one of "day", "week", "month", "quarter" or "year".

Usage

time_unit(value = "day")

Arguments

Value

A time_unit S7 object.

Value class

Description

value records which column is being measured and what the resulting column should be called. Assigning new_column_name_vec prefixes the supplied string onto value_vec, so setting it to "ytd" for a value column of sales gives ytd_sales.

Usage

value(value_vec = NA_character_, new_column_name_vec = character(0))

Arguments

Value

A value S7 object.

Additional properties

@value_quo

value_vec parsed to a symbol for tidy evaluation.

@new_column_name_quo

new_column_name_vec parsed to a symbol.

Current full period week over full previous period week

Description

This calculates the full week value compared to the previous week 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

wow(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

wow(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

Week-over-week execution function

Description

wow_fn() is the function that is called by wow() when passed through to calculate

Usage

wow_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

wow() for the function's intent

Current period Week-to-date over previous period week-to-date

Description

This calculates the weekly cumulative sum of targeted value and compares it with the previous week's cumulative sum 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

wowtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

wowtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Week-to-date over previous week-to-date execution function

Description

wowtd_fn() is the function that is called by wowtd() when passed through to calculate

Usage

wowtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

wowtd() for the function's intent

Current period week-to-date

Description

This calculates the weekly 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

wtd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtdopw(), yoy(), yoytd(), ytd(), ytdopy()

Examples

wtd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard")

Week-to-date execution function

Description

wtd_fn() is the function that is called by wtd() when passed through to calculate

Usage

wtd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

wtd() for the function's intent

Current period week-to-date over full previous period week

Description

This calculates the weekly cumulative sum of targeted value and compares it with the full previous week's total 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

wtdopw(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), yoy(), yoytd(), ytd(), ytdopy()

Examples

wtdopw(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Week-to-date over full previous week execution function

Description

wtdopw_fn() is the function that is called by wtdopw() when passed through to calculate

Usage

wtdopw_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

wtdopw() for the function's intent

Current full period year over previous full period year

Description

• This calculates the full year value compared to the previous year value respecting any groups that are passed through with dplyr::group_by()

• Use calculate to return the results

Usage

yoy(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoytd(), ytd(), ytdopy()

Examples

yoy(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

Current year-to-date over previous period year-to-date execution function

Description

yoy_fn() is the function that is called by yoy() when passed through to calculate

Usage

yoy_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

yoy() for the function's intent

Current period year-to-date compared to previous period year-to-date

Description

• This calculates the annual cumulative sum of targeted value and compares it with the previous period's annual cumulative to date sum 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

yoytd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), ytd(), ytdopy()

Examples

yoytd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard",lag_n=1)

Current year to date over previous year-to-date execution function

Description

yoytd_fn() is the function that is called by yoytd() when passed through to calculate

Usage

yoytd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

yoytd() for the function's intent

Current period year-to-date

Description

• For each group, ytd() will create the running annual sum of a value based on the calendar type specified

• The function returns a ti object which prints out the summary of steps and actions that will take to create the calendar table and calculations

• Use calculate to return the results

Usage

ytd(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytdopy()

Examples

ytd(contoso::sales,.date=order_date,.value=quantity,calendar_type="standard")

Year-to-date execution function

Description

ytd_fn() is the function that is called by ytd() when passed through to calculate

Usage

ytd_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object with dplyr::collect()

Value

dbi object

See Also

ytd() for the function's class

Current period year-to-date compared to full previous period

Description

• This calculates the full year value compared to the previous year 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

ytdopy(
  .data,
  .date,
  .value,
  calendar_type = "standard",
  lag_n = 1,
  fiscal_year_start = 1,
  week_start = 7
)

Arguments

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 ti object, which pre-processes the data and arguments for further downstream functions

NA Handling

• NA values in the .value column propagate through cumulative sums (cumsum returns NA once an NA is encountered)

• NA values in the .date column 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

Value

ti object

See Also

Other time_intelligence: atd(), dod(), mom(), momtd(), mtd(), mtdopm(), pmtd(), pqtd(), pwtd(), pytd(), qoq(), qoqtd(), qtd(), qtdopq(), wow(), wowtd(), wtd(), wtdopw(), yoy(), yoytd(), ytd()

Examples

ytdopy(contoso::sales,.date=order_date,.value=quantity,calendar_type='standard',lag_n=1)

Year-to-date over full prior period year

Description

ytdopy_fn() is the function that is called by ytdopy() when passed through to calculate

Usage

ytdopy_fn(x)

Arguments

Details

This is internal non exported function that is nested in ti class and is called upon when the underlying function is called by calculate This will return a dbi object that can converted to a tibble object withdplyr::collect()

Value

dbi object

See Also

ytdopy() for the function's intent