Aggregation by time period in lubridate

Question

This question asks about aggregation by time period in R, what pandas calls resampling. The most useful answer uses the XTS package to group by a given time period, applying

Answer 1

Here is an option using data.table after importing the csv:

library(data.table)

# convert the data.frame to data.table
bikecounts <- data.table(bikecounts)

# Calculate
bikecounts[, list(NB=sum(Northbound), SB=sum(Southbound)), by=as.Date(Date, format="%m/%d/%Y")]

        as.Date   NB   SB
  1: 2012-10-02 1165  773
  2: 2012-10-03 1761 1760
  3: 2012-10-04 1767 1708
  4: 2012-10-05 1590 1558
  5: 2012-10-06  926 1080
 ---                     
299: 2013-07-27 1212 1289
300: 2013-07-28  902 1078
301: 2013-07-29 2040 2048
302: 2013-07-30 2314 2226
303: 2013-07-31 2008 2076

Note, you can also use fread() ("fast read") from the data.table package to read in the CSV into a data.table in one step. The only draw back is you to manually convert the date/time from string.

eg: 
bikecounts <- fread("http://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD", header=TRUE, stringsAsFactors=FALSE)
setnames(bikecounts, c("Date", "Northbound", "Southbound"))
bikecounts[, Date := as.POSIXct(D, format="%m/%d/%Y %I:%M:%S %p")]