I have a data set along these lines:

```
df<-data.frame(sp=c(100, 100, 100, 101, 101, 101, 102, 102, 102),
type=c("C","C","C","H","H","H","C","C","C"),
country=c("A", "A", "A", "B", "B", "B", "C", "C", "C"),
vals=c(1,2,3,4,5,6,7,8,9)
)
```

I want to aggregate df$vals and bring the other variables through as well

At the moment I'm doing it like this:

```
multi.func<- function(x){
c(
n = length(x),
min = min(x, na.rm=TRUE),
max = max(x, na.rm=TRUE),
mean = mean(x, na.rm=TRUE)
)}
aggVals<-as.data.frame(do.call(rbind, by(df$vals, df$sp, FUN=multi.func, simplify=TRUE)))
aggVals$sp<-row.names(aggVals)
aggDescrip<-aggregate(cbind(as.character(type), as.character(country)) ~ sp, data=df, FUN=unique)
result<-merge(aggDescrip,aggVals)
```

This works well enough but I wondered if there's an easier way.

Thanks

Perhaps you should look into the `data.table`

package.

```
library(data.table)
DT <- data.table(df, key="sp")
DT[, list(type = unique(as.character(type)),
country = unique(as.character(country)),
n = .N, min = min(vals), max = max(vals),
mean = mean(vals)), by=key(DT)]
# sp type country n min max mean
# 1: 100 C A 3 1 3 2
# 2: 101 H B 3 4 6 5
# 3: 102 C C 3 7 9 8
```

If you want to stick with base R, here is another approach that might be of use (though `aggregate`

is probably more common):

```
unique(within(df, {
mean <- ave(vals, sp, FUN=mean)
max <- ave(vals, sp, FUN=max)
min <- ave(vals, sp, FUN=min)
n <- ave(vals, sp, FUN=length)
rm(vals)
}))
# sp type country n min max mean
# 1 100 C A 3 1 3 2
# 4 101 H B 3 4 6 5
# 7 102 C C 3 7 9 8
```

### Update: A variation on your initial attempt

I would suggest sticking with `data.table`

if possible, because the resulting code is easy to follow and the process of aggregation is quick.

However, with a little bit of modification, you can have (yet another) base R approach that is somewhat more direct.

First, modify your function so that instead of using `c()`

, use `data.frame`

. Also, add an argument that specifies which column needs to be aggregated.

```
multi.func <- function(x, value_column) {
data.frame(
n = length(x[[value_column]]),
min = min(x[[value_column]], na.rm=TRUE),
max = max(x[[value_column]], na.rm=TRUE),
mean = mean(x[[value_column]], na.rm=TRUE))
}
```

Second, use `lapply`

on your dataset, `split`

up by your grouping variable, `merge`

the output with your original dataset, and return the `unique`

values.

```
unique(merge(df[-4],
do.call(rbind, lapply(split(df, df$sp),
multi.func, value_column = "vals")),
by.x = "sp", by.y = "row.names"))
```