Thank you very much, Dan.
These work great. Two more great answers to my question.
Matthew
On 5/24/2016 4:15 PM, Nordlund, Dan (DSHS/RDA) wrote:
You have several options.
1. You could use the aggregate function. If your data frame is called DF, you
could do something like
with(DF, aggreg
Thanks, Tom. I was making a mistake looking at your example and that's
what my problem was.
Cool answer, works great. Thank you very much.
Matthew
On 5/24/2016 4:23 PM, Tom Wright wrote:
> Don't see that as being a big problem. If your data grows then dplyr
> supports connections to external
Don't see that as being a big problem. If your data grows then dplyr
supports connections to external databases. Alternately if you just want a
mean, most databases can do that directly in SQL.
On Tue, May 24, 2016 at 4:17 PM, Matthew
wrote:
> Thank you very much, Tom.
> This gets me thinking in
Thank you very much, Tom.
This gets me thinking in the right direction.
One thing I should have mentioned that I did not is that the number of
rows in the data frame will be a little over 40,000 rows.
On 5/24/2016 4:08 PM, Tom Wright wrote:
> Using dplyr
>
> $ library(dplyr)
> $ x<-data.frame(Len
You have several options.
1. You could use the aggregate function. If your data frame is called DF, you
could do something like
with(DF, aggregate(Length, list(Identifier), mean))
2. You could use the dplyr package like this
library(dplyr)
summarize(group_by(DF, Identifier), mean(Length)
Using dplyr
$ library(dplyr)
$ x<-data.frame(Length=c(321,350,340,180,198),
ID=c(rep('A234',3),'B123','B225') )
$ x %>% group_by(ID) %>% summarise(m=mean(Length))
On Tue, May 24, 2016 at 3:46 PM, Matthew
wrote:
> I have a data frame with 10 columns.
> In the last colum
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