I wonder if the following question has an elegant solution in dplyr.

To provide a simple reproducible example, consider the following data.frame:

```
df <- data.frame( a=1:5, b=2:6, c=3:7,
ref=c("a","a","b","b","c"),
stringsAsFactors = FALSE )
```

Here `a`

,`b`

,`c`

are regular numeric variables while `ref`

is meant to reference **which column is the "main" value for that observation**. For example:

```
a b c ref
1 1 2 3 a
2 2 3 4 a
3 3 4 5 b
4 4 5 6 b
5 5 6 7 c
```

For example, for observation 3, `ref==b`

and thus column `b`

contains the *main* value. While for observation 1, `ref==a`

and thus column `a`

contains the main value.

Having this data.frame the question is to create the new column with `main`

values for each observation using dplyr.

```
a b c ref main
1 1 2 3 a 1
2 2 3 4 a 2
3 3 4 5 b 4
4 4 5 6 b 5
5 5 6 7 c 7
```

I'll probably need to use dplyr for that since this one operation is a part of a longer dplyr `%>%`

data transformation chain.

Answer:

Here's a simple, fast way that allows you to stick with `dplyr`

chaining:

```
require(data.table)
df %>% setDT %>% .[,main:=get(ref),by=ref]
# a b c ref main
# 1: 1 2 3 a 1
# 2: 2 3 4 a 2
# 3: 3 4 5 b 4
# 4: 4 5 6 b 5
# 5: 5 6 7 c 7
```

Thanks to @akrun for the idea for the fastest way and benchmarking to show it (see his answer).

`setDT`

modifies the class of `df`

so you won't have to convert to `data.table`

again in future chains.

The conversion should work with any future code in the chain, but both `dplyr`

and `data.table`

are under active development, so to be on the safe side, one could instead use

```
df %>% data.table %>% .[,main:=get(ref),by=ref]
```

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