How to use dplyr as alternative to aggregate

Question

I have a dataframe times that looks like this:

user     time
A        7/7/2010
B        7/12/2010
C        7/12/2010
A        7/12/2010 
C        7/         


        

Answer 1

Updated Answer: Based on your comment, how about this:

library(dplyr)

# Data (with a few additions)
times = read.table(text="user     time
A        7/7/2010
B        7/12/2010
B 7/13/2010
C        7/12/2010
A        7/12/2010 
A 7/13/2010
C        7/15/2010", header=TRUE, stringsAsFactors=FALSE)

times$time = as.Date(times$time, "%m/%d/%Y")

times

  user       time
1    A 2010-07-07
2    B 2010-07-12
3    B 2010-07-13
4    C 2010-07-12
5    A 2010-07-12
6    A 2010-07-13
7    C 2010-07-15

times %>% group_by(user) %>%
  summarise(First=min(time),
            Last=max(time),
            N = n(),
            minDiff=min(diff(time)),
            meanDiff=mean(diff(time)),
            NumDiffUniq = length(unique(diff(time))))

   user      First       Last     N        minDiff       meanDiff NumDiffUniq
1     A 2010-07-07 2010-07-13     3         1 days         3 days           2
2     B 2010-07-12 2010-07-13     2         1 days         1 days           1
3     C 2010-07-12 2010-07-15     2         3 days         3 days           1

Original Answer:

I'm not clear on what you're trying to accomplish. If you just want your data frame to be sorted, then with dplyr you would do:

library(dplyr)

times.sorted = times %>% arrange(user, time)

If you want time to become a string of dates for each user, then you could do:

times.summary = times %>% group_by(user) %>%
  summarise(time = paste(time, collapse=","))

But note that for each user this will result in a single string containing the dates.

times.summary

   user                time
1     A  7/7/2010,7/12/2010
2     B           7/12/2010
3     C 7/12/2010,7/15/2010

If you actually want each cell to be a vector of dates, you could make each cell a list (though there might be a better way). For example:

times.new = times %>% group_by(user) %>%
  summarise(time = list(as.vector(time)))

times.new$time

[[1]]
[1] "7/7/2010"  "7/12/2010"

[[2]]
[1] "7/12/2010"

[[3]]
[1] "7/12/2010" "7/15/2010"

But if your goal is to analyze your data by group, then you don't actually need to do any of the above. You can use base, dplyr, or data.table functions to perform any analysis by group without first sorting your data.

Answer 2

Based on the dplyr solution by eipi10 and the suggestion of nrussell, I've written the following solution using data.table.

First you need to format the variable times:

times$time = as.Date(times$time, "%m/%d/%Y")

Then you'll need to convert times to a data.table using:

library(data.table)
times <- as.data.table(times)

Overwriting times was useful for my purposes but you may want to instantiate a new variable. After formatting your dataframe as a data.table just do:

new.times <- times[, 
                    .(first = min(time),
                      last = max(time),
                      n = .N,
                      meandiff = mean(diff(time)),
                      mindiff = min(diff(time)),
                      numdiffuniq = length(unique(diff(time))),
                      by='user')]

Running on a linux virtual machine with 128G RAM and using a sample of 1000 entires, the elapsed runtime was 0.43s.

See this tutorial for more on data.table.