On Jun 29, 2010, at 3:05 PM, Yi wrote:
Hi, folks,
I am sorry that I did not state the problem correctly yesterday.
Please let me address the problem by the following codes:
first=c('u','b','e','k','j','c','u','f','c','e')
second
=
c
('usa
','Brazil
','England','Korea','Japan','China','usa','France','China','England')
third=1:10
data=data.frame(first,second,third)
## You may understand values in the first column are the unique
codes for
those in the second column.
####So 'u' is only for usa. Replicate values appear the same rows
for the
first and second columns.
### Now I want to delete replicate rows with the same values in first
(sceond) rows
####and sum up values in the third column for the same values.
mm=melt(data,id='first')
sum=cast(mm,first~variable,sum) ### This does not work.
###I tried another way to do this
mm= melt(data, id='first',measure='third')
sum=cast(mm,first~variable,sum)
## But then the problem is how to 'merge' the result with the second
column
in the dataset.
> data$summed <- ave(data$third, data$first, FUN=sum)
#computed sums within groups defined by "first"
> data[!duplicated(data$first), c("first", "second", "summed")]
#remove duplicates and leave out "third"
first second summed
1 u usa 8
2 b Brazil 2
3 e England 13
4 k Korea 4
5 j Japan 5
6 c China 15
8 f France 8
The expected dataframe is like this:
(I showed a wrong expected dataframe yesterday.)
first second third
1 u usa 8
2 b Brazil 2
3 e England 13
4 k Korea 4
5 j Japan 5
6 c China 15
8 f France 8
Thanks in advance.
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and provide commented, minimal, self-contained, reproducible code.
