Correlation between groups in R data.table

Question

Is there a way of elegantly calculating the correlations between values if those values are stored by group in a single column of a data.table (other than converting the dat

Answer 1

I've since found an even simple alternative for doing this. You were actually pretty close with your dt[, cor(value, value), by="group"] approach. What you actually need is to first do a Cartesian join on the dates, and then group by. I.e.

dt[dt, allow.cartesian=T][, cor(value, value), by=list(group, group.1)]

This has the advantage that it will join the series together (rather than assume they are the same length). You can then cast this into matrix form, or leave it as it is to plot as a heatmap in ggplot etc.

Full Example

setkey(dt, id)
c <- dt[dt, allow.cartesian=T][, list(Cor = cor(value, value.1)), by = list(group, group.1)]
c

   group group.1       Cor
1:     a       a 1.0000000
2:     b       a 0.1556371
3:     a       b 0.1556371
4:     b       b 1.0000000

dcast(c, group~group.1, value.var = "Cor")

  group         a         b
1     a 1.0000000 0.1556371
2     b 0.1556371 1.0000000

Answer 2

There is no simple way to do this with data.table. The first way you've provided:

cor(dt["a"]$value, dt["b"]$value)

Is probably the simplest.

An alternative is to reshape your data.table from "long" format, to "wide" format:

> dtw <- reshape(dt, timevar="group", idvar="id", direction="wide")
> dtw
   id    value.a    value.b
1:  1 -0.6264538  0.3295078
2:  2  0.1836433 -0.8204684
3:  3 -0.8356286  0.4874291
4:  4  1.5952808  0.7383247
> cor(dtw[,list(value.a, value.b)])
          value.a   value.b
value.a 1.0000000 0.1556371
value.b 0.1556371 1.0000000

---

Update: If you're using data.table version >= 1.9.0, then you can use dcast.data.table instead which'll be much faster. Check this post for more info.

dcast.data.table(dt, id ~ group)

Answer 3

I don't know a way to get it in matrix form straight away, but I find this solution useful:

dt[, {x = value; dt[, cor(x, value), by = group]}, by=group]

   group group        V1
1:     a     a 1.0000000
2:     a     b 0.1556371
3:     b     a 0.1556371
4:     b     b 1.0000000

since you started with a molten dataset and you end up with a molten representation of the correlation.

Using this form you can also choose to just calculate certain pairs, in particular it is a waste of time calculating both off diagonals. For example:

 dt[, {x = value; g = group; dt[group <= g, list(cor(x, value)), by = group]}, by=group]
   group group        V1
1:     a     a 1.0000000
2:     b     a 0.1556371
3:     b     b 1.0000000

Alternatively, this form works just as well for the cross correlation between two sets (i.e. the block off diagonal)

library(data.table)
set.seed(1)             # reproducibility
dt1 <- data.table(id=1:4, group=rep(letters[1:2], c(4,4)), value=rnorm(8))
dt2 <- data.table(id=1:4, group=rep(letters[3:4], c(4,4)), value=rnorm(8))
setkey(dt1, group)
setkey(dt2, group)

dt1[, {x = value; g = group; dt2[, list(cor(x, value)), by = group]}, by=group]

   group group          V1
1:     a     c -0.39499814
2:     a     d  0.74234458
3:     b     c  0.96088312
4:     b     d  0.08016723

Obviously, if you ultimately want these in matrix form, then you can use dcast or dcast.data.table, however, notice that in the above examples you have two columns with the same name, to fix this it is worth renaming them in the j function. For the original problem:

dcast.data.table(dt[, {x = value; g1=group; dt[, list(g1, g2=group, c =cor(x, value)), by = group]}, by=group], g1~g2, value.var = "c")

   g1         a         b
1:  a 1.0000000 0.1556371
2:  b 0.1556371 1.0000000