index_by | R Documentation |
summarise()
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.
index_by(.data, ...)
.data |
A |
... |
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
|
A index_by()
-ed tsibble is indicated by @
in the "Groups" when
displaying on the screen.
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)) # 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))
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