tsibble (version 0.9.2)

index_by: Group by time index and collapse with summarise()

Description

stable

index_by() is the counterpart of group_by() in temporal context, but it only groups the time index. The following operation is applied to each partition of the index, similar to group_by() but dealing with index only. index_by() + summarise() will update the grouping index variable to be the new index. Use ungroup() to remove the index grouping vars.

Usage

index_by(.data, ...)

Arguments

.data

A tbl_ts.

...

If empty, grouping the current index. If not empty, a single expression is required for either an existing variable or a name-value pair. A lambda expression is supported, for example ~ as.Date(.) where . refers to the index variable. The index functions that can be used, but not limited:

• lubridate::year: yearly aggregation

• yearquarter: quarterly aggregation

• yearmonth: monthly aggregation

• yearweek: weekly aggregation

• as.Date or lubridate::as_date: daily aggregation

• lubridate::ceiling_date, lubridate::floor_date, or lubridate::round_date: fine-resolution aggregation

• Extract time components functions, such as lubridate::hour() & lubridate::day()

• other index functions from other packages or self-defined functions

Details

• A index_by()-ed tsibble is indicated by @ in the "Groups" when displaying on the screen.

Examples

# NOT RUN {
pedestrian %>% index_by()
# Monthly counts across sensors
library(dplyr, warn.conflicts = FALSE)
monthly_ped <- pedestrian %>%
  group_by_key() %>%
  index_by(Year_Month = ~ yearmonth(.)) %>%
  summarise(
    Max_Count = max(Count),
    Min_Count = min(Count)
  )
monthly_ped
index(monthly_ped)

# Using existing variable
pedestrian %>%
  group_by_key() %>%
  index_by(Date) %>%
  summarise(
    Max_Count = max(Count),
    Min_Count = min(Count)
  )

# Attempt to aggregate to 4-hour interval, with the effects of DST
pedestrian %>%
  group_by_key() %>%
  index_by(Date_Time4 = ~ lubridate::floor_date(., "4 hour")) %>%
  summarise(Total_Count = sum(Count))

library(lubridate, warn.conflicts = FALSE)
# Annual trips by Region and State
tourism %>%
  index_by(Year = ~ year(.)) %>%
  group_by(Region, State) %>%
  summarise(Total = sum(Trips))

# Rouding to financial year, using a custom function
financial_year <- function(date) {
  year <- year(date)
  ifelse(quarter(date) <= 2, year, year + 1)
}
tourism %>%
  index_by(Year = ~ financial_year(.)) %>%
  summarise(Total = sum(Trips))
# }

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