I can't speak to the intricacies of the formula but when I run the
ByDataFrame() function provided on a subsample of my data (n=50) it returned
only the very first id value in the output; the rest came out as NA
This is not to say it has not properly selected the rows with min(x$diff),
but I
On Jun 14, 2008, at 2:59 AM, T.D.Rudolph wrote:
I can't speak to the intricacies of the formula but when I run the
ByDataFrame() function provided on a subsample of my data (n=50) it
returned
only the very first id value in the output; the rest came out as
NA
This is not to say it has
I have a dataframe, x, with over 60,000 rows that contains one Factor, id,
with 27 levels.
The dataframe contains numerous continuous values (along column diff) per
day (column date) for every level of id. I would like to select only one
row per animal per day, i.e. that containing the minimum
on 06/13/2008 11:10 PM T.D.Rudolph wrote:
I have a dataframe, x, with over 60,000 rows that contains one Factor, id,
with 27 levels.
The dataframe contains numerous continuous values (along column diff) per
day (column date) for every level of id. I would like to select only one
row per
aggregate() is indeed a useful function in this case, but it only returns the
columns by which it was grouped. Is there a way I can use this while
simultaneously retaining all the other column values in the dataframe?
e.g. add superfluous (yet pertinent for later) column containing any
On Jun 14, 2008, at 1:25 AM, T.D.Rudolph wrote:
aggregate() is indeed a useful function in this case, but it only
returns the
columns by which it was grouped. Is there a way I can use this while
simultaneously retaining all the other column values in the dataframe?
e.g. add superfluous
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