PavoDive - 2 months ago 6

R Question

I had a data.table like this:

`library(data.table)`

dt <- data.table(a = c(rep("A", 3), rep("B", 3)), b = c(1, 3, 5, 2, 4, 6))

I needed to perform an operation (forecast) on the values for each

`a`

`dt <- dt[, x := .(list(b)), by = a][, .SD[1,], by = a, .SDcols = "x"]`

Now I wanted to "melt" (that's the thing that comes to mind)

`dt`

I could do it for very few levels of

`a`

`dt2 <- rbind(expand.grid(dt[1, a], dt[1, x[[1]]]), expand.grid(dt[2, a], dt[2, x[[1]]]))`

but of course, the solution is impractical for more levels of

`a`

I've tried

`dt2 <- dt[, expand.grid(a, x[[1]]), by = a]`

which results in

`dt2`

## a Var1 Var2

## 1: A A 1

## 2: A A 3

## 3: A A 5

## 4: B A 2

## 5: B A 4

## 6: B A 6

it's interesting to notice that

`Var1`

`a`

Is there a better approach to achieve this?

Expected output will be the result of

`dt2[, .(a, Var2)]`

- Corrected "melt" for "dcast".

Answer

You are looking for a method to `nest`

(convert a column from a atomic vector type to list type) and `unnest`

(the opposite direction) in a data.table way. This is different from reshaping data which either `spread`

a column values to row header(`dcast`

) or `gather`

the row headers to a column values(`melt`

):

In data.table syntax, you can use `list`

and `unlist`

on the target column to summarize or broadcast it along with group variables:

Say if we are starting from:

```
dt
# a b
# 1: A 1
# 2: A 3
# 3: A 5
# 4: B 2
# 5: B 4
# 6: B 6
```

To repeat what you have achieved in your first step, i.e. `nest`

column `b`

, you can do:

```
dt_nest <- dt[, .(b = list(b)), a]
dt_nest
# a b
# 1: A 1,3,5
# 2: B 2,4,6
```

To go the opposite direction, use `unlist`

with the group variable:

```
dt_nest[, .(b = unlist(b)), a]
# a b
# 1: A 1
# 2: A 3
# 3: A 5
# 4: B 2
# 5: B 4
# 6: B 6
```

Source (Stackoverflow)

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