![[Stable]](figures/lifecycle-stable.svg)
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.
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
Examples
pedestrian %>% index_by()
#> # A tsibble: 66,037 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> # Groups: @ Date_Time [17,542]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630
#> 2 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826
#> 3 Birrarung Marr 2015-01-01 02:00:00 2015-01-01 2 567
#> 4 Birrarung Marr 2015-01-01 03:00:00 2015-01-01 3 264
#> 5 Birrarung Marr 2015-01-01 04:00:00 2015-01-01 4 139
#> 6 Birrarung Marr 2015-01-01 05:00:00 2015-01-01 5 77
#> 7 Birrarung Marr 2015-01-01 06:00:00 2015-01-01 6 44
#> 8 Birrarung Marr 2015-01-01 07:00:00 2015-01-01 7 56
#> 9 Birrarung Marr 2015-01-01 08:00:00 2015-01-01 8 113
#> 10 Birrarung Marr 2015-01-01 09:00:00 2015-01-01 9 166
#> # ℹ 66,027 more rows
# 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
#> # A tsibble: 95 x 4 [1M]
#> # Key: Sensor [4]
#> Sensor Year_Month Max_Count Min_Count
#> <chr> <mth> <int> <int>
#> 1 Birrarung Marr 2015 Jan 5524 1
#> 2 Birrarung Marr 2015 Feb 10121 1
#> 3 Birrarung Marr 2015 Mar 9858 1
#> 4 Birrarung Marr 2015 Apr 7293 1
#> 5 Birrarung Marr 2015 May 5129 1
#> 6 Birrarung Marr 2015 Jun 7556 0
#> 7 Birrarung Marr 2015 Jul 11224 1
#> 8 Birrarung Marr 2015 Aug 5684 0
#> 9 Birrarung Marr 2015 Sep 7757 0
#> 10 Birrarung Marr 2015 Oct 7085 1
#> # ℹ 85 more rows
index(monthly_ped)
#> Year_Month
# Using existing variable
pedestrian %>%
group_by_key() %>%
index_by(Date) %>%
summarise(
Max_Count = max(Count),
Min_Count = min(Count)
)
#> # A tsibble: 2,752 x 4 [1D]
#> # Key: Sensor [4]
#> Sensor Date Max_Count Min_Count
#> <chr> <date> <int> <int>
#> 1 Birrarung Marr 2015-01-01 1630 44
#> 2 Birrarung Marr 2015-01-02 352 1
#> 3 Birrarung Marr 2015-01-03 226 3
#> 4 Birrarung Marr 2015-01-04 852 4
#> 5 Birrarung Marr 2015-01-05 1427 3
#> 6 Birrarung Marr 2015-01-06 937 5
#> 7 Birrarung Marr 2015-01-07 708 4
#> 8 Birrarung Marr 2015-01-08 568 9
#> 9 Birrarung Marr 2015-01-09 1629 5
#> 10 Birrarung Marr 2015-01-10 2439 10
#> # ℹ 2,742 more rows
# 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))
#> # A tsibble: 16,512 x 3 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time4 Total_Count
#> <chr> <dttm> <int>
#> 1 Birrarung Marr 2015-01-01 00:00:00 3287
#> 2 Birrarung Marr 2015-01-01 04:00:00 316
#> 3 Birrarung Marr 2015-01-01 08:00:00 995
#> 4 Birrarung Marr 2015-01-01 12:00:00 2117
#> 5 Birrarung Marr 2015-01-01 16:00:00 1829
#> 6 Birrarung Marr 2015-01-01 20:00:00 406
#> 7 Birrarung Marr 2015-01-02 00:00:00 44
#> 8 Birrarung Marr 2015-01-02 04:00:00 555
#> 9 Birrarung Marr 2015-01-02 08:00:00 1193
#> 10 Birrarung Marr 2015-01-02 12:00:00 822
#> # ℹ 16,502 more rows
library(lubridate, warn.conflicts = FALSE)
# Annual trips by Region and State
tourism %>%
index_by(Year = ~ year(.)) %>%
group_by(Region, State) %>%
summarise(Total = sum(Trips))
#> # A tsibble: 1,520 x 4 [1Y]
#> # Key: Region, State [76]
#> # Groups: Region [76]
#> Region State Year Total
#> <chr> <chr> <dbl> <dbl>
#> 1 Adelaide South Australia 1998 2226.
#> 2 Adelaide South Australia 1999 2218.
#> 3 Adelaide South Australia 2000 2418.
#> 4 Adelaide South Australia 2001 2264.
#> 5 Adelaide South Australia 2002 2275.
#> 6 Adelaide South Australia 2003 2203.
#> 7 Adelaide South Australia 2004 2437.
#> 8 Adelaide South Australia 2005 2034.
#> 9 Adelaide South Australia 2006 2225.
#> 10 Adelaide South Australia 2007 2317.
#> # ℹ 1,510 more rows
# Rounding 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))
#> Error in mutate(ungrp, `:=`(!!idx2, f(!!sym(idx)))): ℹ In argument: `Year = f(Quarter)`.
#> Caused by error in `financial_year()`:
#> ! could not find function "financial_year"