So, I got some great solutions to my dplyr mutation problem to share. Just wait until you see these things!

Remember, I was having trouble reconciling two date columns into a minimum value in the presence of NA values.

Here’s the fake data again:

library(wakefield)
library(tidyr)
library(dplyr)
library(data.table)

x <- r_data_frame(n=10,id,date_stamp(name='foo',random=TRUE))
y <- r_data_frame(n=10,id,date_stamp(name='bar',random=TRUE))

x$foo[base::sample(10,5)] <- NA
y$bar[base::sample(10,5)] <- NA

Eddie Niedermeyer Solves It Perfectly with pmin

And a shout out to Mark as well for suggesting pmin and his partial solution with data.table.

full_join(x,y,by='ID') %>% mutate(start = pmin(foo, bar, na.rm = TRUE))

## Source: local data frame [10 x 4]
## 
##    ID        foo        bar      start
## 1  01       <NA>       <NA>       <NA>
## 2  02 2015-01-28 2015-02-28 2015-01-28
## 3  03       <NA> 2015-03-28 2015-03-28
## 4  04 2014-10-28 2014-10-28 2014-10-28
## 5  05       <NA> 2014-08-28 2014-08-28
## 6  06 2015-05-28 2014-10-28 2014-10-28
## 7  07       <NA>       <NA>       <NA>
## 8  08 2014-07-28       <NA> 2014-07-28
## 9  09       <NA>       <NA>       <NA>
## 10 10 2014-09-28       <NA> 2014-09-28

But Kirill Kills It With dplyr AND data.table

Now this is a thing of beauty! A dplyr join, magrittr pipe action, and what do we see??!? data.table syntax with old school boolean T value?

Oh man, I’m lovin’ it!

Nice one, Kirill, nice one.

full_join(x,y,by='ID') %>% data.table %>% .[, start := pmin(foo, bar, na.rm = T)] %>% print

##     ID        foo        bar      start
##  1: 01       <NA>       <NA>       <NA>
##  2: 02 2015-01-28 2015-02-28 2015-01-28
##  3: 03       <NA> 2015-03-28 2015-03-28
##  4: 04 2014-10-28 2014-10-28 2014-10-28
##  5: 05       <NA> 2014-08-28 2014-08-28
##  6: 06 2015-05-28 2014-10-28 2014-10-28
##  7: 07       <NA>       <NA>       <NA>
##  8: 08 2014-07-28       <NA> 2014-07-28
##  9: 09       <NA>       <NA>       <NA>
## 10: 10 2014-09-28       <NA> 2014-09-28