I don't know why you'd use lubridate for this. If you're just looking for something less awesome than xts you could try this
tapply(bikecounts$Northbound, as.Date(bikecounts$Date, format="%m/%d/%Y"), sum)
Basically, you just need to split
by Date, then apply a function.
lubridate could be used for creating a grouping factor for split-apply problems. So, for example, if you want the sum for each month (ignoring year)
tapply(bikecounts$Northbound, month(mdy_hms(bikecounts$Date)), sum)
But, it's just using wrappers for base R functions, and in the case of the OP, I think the base R function as.Date
is the easiest (as evidenced by the fact that the other Answers also ignored your request to use lubridate ;-) ).
Something that wasn't covered by the Answer to the other Question linked to in the OP is split.xts
. period.apply
splits an xts
at endpoints
and applies a function to each group. You can find endpoints that are useful for a given task with the endpoints
function. For example, if you have an xts object, x
, then endpoints(x, "months")
would give you the row numbers that are the last row of each month. split.xts
leverages that to split an xts object -- split(x, "months")
would return a list of xts objects where each component was for a different month.
Although, split.xts()
and endpoints()
are primarily intended for xts
objects, they also work on some other objects as well, including plain time based vectors. Even if you don't want to use xts objects, you still may find uses for endpoints()
because of its convenience or its speed (implemented in C)
> split.xts(as.Date("1970-01-01") + 1:10, "weeks")
[[1]]
[1] "1970-01-02" "1970-01-03" "1970-01-04"
[[2]]
[1] "1970-01-05" "1970-01-06" "1970-01-07" "1970-01-08" "1970-01-09"
[6] "1970-01-10" "1970-01-11"
> endpoints(as.Date("1970-01-01") + 1:10, "weeks")
[1] 0 3 10
I think lubridate's best use in this problem is for parsing the "Date" strings into POSIXct objects. i.e. the mdy_hms
function in this case.
Here's an xts
solution that uses lubridate
to parse the "Date" strings.
x <- xts(bikecounts[, -1], mdy_hms(bikecounts$Date))
period.apply(x, endpoints(x, "days"), sum)
apply.daily(x, sum) # identical to above
For this specific task, xts
also has an optimized period.sum
function (written in Fortran) that is very fast
period.sum(x, endpoints(x, "days"))