R: Consolidating duplicate observations?

I have a large data frame with approximately 500,000 observations (identified by "ID") and 150+ variables. Some observations only appear once; others appear multiple times (upwards of 10 or so). I would like to "collapse" these multiple observations so that there is only one row per unique ID, and that all information in columns 2:150 are concatenated. I do not need any calculations run on these observations, just a quick munging.

I've tried:

df.new <- group_by(df,"ID")

and also:

library(data.table)
dt = data.table(df)
dt.new <- dt[, lapply(.SD, na.omit), by = "ID"]

and unfortunately neither have worked. Any help is appreciated!

Solution

Using basic R:

df = data.frame(ID = c("a","a","b","b","b","c","d","d"),
                day = c("1","2","3","4","5","6","7","8"),
                year = c(2016,2017,2017,2016,2017,2016,2017,2016),
                stringsAsFactors = F)

> df
  ID day year
1  a   1 2016
2  a   2 2017
3  b   3 2017
4  b   4 2016
5  b   5 2017
6  c   6 2016
7  d   7 2017
8  d   8 2016

Do:

z = aggregate(df[,2:3], 
              by = list(id = df$ID), 
              function(x){ paste0(x, collapse = "/") }
              )

Result:

> z
  id   day           year
1  a   1/2      2016/2017
2  b 3/4/5 2017/2016/2017
3  c     6           2016
4  d   7/8      2017/2016

EDIT

If you want to avoid "collapsing" NA do:

z = aggregate(df[,2:3], 
              by = list(id = df$ID), 


        function(x){ paste0(x[!is.na(x)],collapse = "/") })

For a data frame like:

> df
  ID  day year
1  a    1 2016
2  a    2   NA
3  b    3 2017
4  b    4 2016
5  b <NA> 2017
6  c    6 2016
7  d    7 2017
8  d    8 2016

The result is:

> z
  id day           year
1  a 1/2           2016
2  b 3/4 2017/2016/2017
3  c   6           2016
4  d 7/8      2017/2016