Grouping the dataframe based on one variable - r

Question

I have a dataframe with 10 variables all of them numeric, and one of the variable name is age, I want to group the observation based on age.example. age 17 to 18 one group, 19-22 another group and then each row should be attached to each group. And resulting should be a dataframe for further manipulations.
Model of the dataframe:
A B AGE
25 50 17
30 42 22
50 60 19
65 105 17
355 400 21
68 47 20
115 98 18
25 75 19
And I want result like
17-18
A B AGE
25 50 17
65 105 17
115 98 18
19-22
A B AGE
30 42 22
50 60 19
355 400 21
68 47 20
115 98 18
25 75 19
I did group the dataset according to Age var using the split function, now my concern is how I could manipulate the grouped data. Eg:the answer looked like
$1
A B AGE
25 50 17
65 105 17
115 98 18
$2
A B AGE
30 42 22
50 60 19
355 400 21
68 47 20
115 98 18
25 75 19
My question is how can I access each group for further manipulation?
for eg: if I want to do t-test for each group separately?

Answer 1

The split function will work with dataframes. Use either cut with 'breaks' or findInterval with an appropriate set of cutpoints (named 'vec' if you are using named parameters) as the criterion for grouping, the second argument to split. The default for cut is intervals closed on the right and default for findInterval is closed on the left.
> split(dat, findInterval(dat$AGE, c(17, 19.5, 22.5)))
$`1`
A B AGE
1 25 50 17
3 50 60 19
4 65 105 17
7 115 98 18
8 25 75 19
$`2`
A B AGE
2 30 42 22
5 355 400 21
6 68 47 20

Answer 2

Here is the approach with cut
lst <- split(df1, cut(df1$AGE, breaks=c(16, 18, 22), labels=FALSE))
lst
# $`1`
# A B AGE
#1 25 50 17
#4 65 105 17
#7 115 98 18
#$`2`
# A B AGE
#2 30 42 22
#3 50 60 19
#5 355 400 21
#6 68 47 20
#8 25 75 19
Update
If you need to find the sum, mean of columns for each "list" element
lapply(lst, function(x) rbind(colSums(x[-3]),colMeans(x[-3])))
But, if the objective is to find the summary statistics based on the group, it can be done using any of the aggregating functions
library(dplyr)
df1 %>%
group_by(grp=cut(AGE, breaks=c(16, 18, 22), labels=FALSE)) %>%
summarise_each(funs(sum=sum(., na.rm=TRUE),
mean=mean(., na.rm=TRUE)), A:B)
# grp A_sum B_sum A_mean B_mean
#1 1 205 253 68.33333 84.33333
#2 2 528 624 105.60000 124.80000
Or using aggregate from base R
do.call(data.frame,
aggregate(cbind(A,B)~cbind(grp=cut(AGE, breaks=c(16, 18, 22),
labels=FALSE)), df1, function(x) c(sum=sum(x), mean=mean(x))))
data
df1 <- structure(list(A = c(25L, 30L, 50L, 65L, 355L, 68L, 115L, 25L
), B = c(50L, 42L, 60L, 105L, 400L, 47L, 98L, 75L), AGE = c(17L,
22L, 19L, 17L, 21L, 20L, 18L, 19L)), .Names = c("A", "B", "AGE"
), class = "data.frame", row.names = c(NA, -8L))