Grouping with the data.table package is done using the syntax `dt[i, j, by]`

Which can be read out loud as: "*Take dt, subset rows using i, then calculate j, grouped by by.*" Within the dt statement, multiple calculations or groups should be put in a list. Since an alias for `list()`

is `.()`

, both can be used interchangeably. In the examples below we use `.()`

.

**CODE:**

```
# Aggregating with data.table
library(data.table)
dt = data.table(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
print(dt)
# sum, grouping by one column
dt[,.(value=sum(value)),group]
# mean, grouping by one column
dt[,.(value=mean(value)),group]
# sum, grouping by multiple columns
dt[,.(value=sum(value)),.(group,subgroup)]
# custom function, grouping by one column
# in this example we want the sum of all values larger than 2 per group.
dt[,.(value=sum(value[value>2])),group]
```

**OUTPUT:**

```
> # Aggregating with data.table
> library(data.table)
>
> dt = data.table(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
> print(dt)
group subgroup value
1: Group 1 A 2.0
2: Group 1 A 2.5
3: Group 2 A 1.0
4: Group 2 A 2.0
5: Group 2 B 1.5
>
> # sum, grouping by one column
> dt[,.(value=sum(value)),group]
group value
1: Group 1 4.5
2: Group 2 4.5
>
> # mean, grouping by one column
> dt[,.(value=mean(value)),group]
group value
1: Group 1 2.25
2: Group 2 1.50
>
> # sum, grouping by multiple columns
> dt[,.(value=sum(value)),.(group,subgroup)]
group subgroup value
1: Group 1 A 4.5
2: Group 2 A 3.0
3: Group 2 B 1.5
>
> # custom function, grouping by one column
> # in this example we want the sum of all values larger than 2 per group.
> dt[,.(value=sum(value[value>2])),group]
group value
1: Group 1 2.5
2: Group 2 0.0
```