We can use the formula method of aggregate. The variables on the 'rhs' of ~ are the grouping variables while the . represents all other variables in the 'df1' (from the example, we assume that we need the mean for all the columns except the grouping), specify the dataset and the function (mean).
aggregate(.~id1+id2, df1, mean)
Or we can use summarise_each from dplyr after grouping (group_by)
library(dplyr)
df1 %>%
group_by(id1, id2) %>%
summarise_each(funs(mean))
Or using summarise with across (dplyr devel version - ‘0.8.99.9000’)
df1 %>%
group_by(id1, id2) %>%
summarise(across(starts_with('val'), mean))
Or another option is data.table. We convert the 'data.frame' to 'data.table' (setDT(df1), grouped by 'id1' and 'id2', we loop through the subset of data.table (.SD) and get the mean.
library(data.table)
setDT(df1)[, lapply(.SD, mean), by = .(id1, id2)]
df1 <- structure(list(id1 = c("a", "a", "a", "a", "b", "b",
"b", "b"
), id2 = c("x", "x", "y", "y", "x", "y", "x", "y"),
val1 = c(1L,
2L, 3L, 4L, 1L, 4L, 3L, 2L), val2 = c(9L, 4L, 5L, 9L, 7L, 4L,
9L, 8L)), .Names = c("id1", "id2", "val1", "val2"),
class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8"))