Or try data.table 1.4 on r-forge, its grouping is faster than aggregate :
agg datatable
X100.012 0.008
X100 0.020 0.008
X1000 0.172 0.020
X1 1.164 0.144
X1e.05 9.397 1.180
install.packages("data.table", repos="http://R-Forge.R-project.org";)
require(data.t
This is very cool...thanks Hadley. When are you planning to release that
version?
On Thu, Apr 15, 2010 at 9:09 AM, hadley wickham wrote:
> On Thu, Apr 15, 2010 at 1:16 AM, Chuck wrote:
> > Depending on the size of the dataframe and the operations you are
> > trying to perform, aggregate or ddpl
The problem is that the new version of plyr is incompatible with
ggplot2, so I need to make some changes there before I can release it.
Hopefully this summer.
Hadley
On Thu, Apr 15, 2010 at 1:33 PM, Vijay Nori wrote:
> This is very cool...thanks Hadley. When are you planning to release that
> v
I think the development version also fixes that problem, but it's hard
to know without a reproducible example
Hadley
On Thu, Apr 15, 2010 at 2:33 PM, Jeff Newmiler wrote:
> This is good news, although I have recently encountered what I consider
> excessive memory usage in the addition of k
This is good news, although I have recently encountered what I consider
excessive memory usage in the addition of key columns that don't affect the
number of groups. For example, grouping by Year and Month, if I add
MonthBegin, a POSIXct column from which the Year and Month columns were
derive
On Thu, Apr 15, 2010 at 1:16 AM, Chuck wrote:
> Depending on the size of the dataframe and the operations you are
> trying to perform, aggregate or ddply may be better. In the function
> below, df has the same structure as your dataframe.
Current version of plyr:
agg ddply
X100.00
Depending on the size of the dataframe and the operations you are
trying to perform, aggregate or ddply may be better. In the function
below, df has the same structure as your dataframe.
Check out this code which runs aggregate and ddply for different
dataframe sizes.
Thank you for your help. The best I have found is to use the ddply function.
> pose
DESCRIPTION QUANITY CLOSING.PRICE
1 WHEAT May/101467.75
2 WHEAT May/101467.75
3 WHEAT May/101467.75
4 WHEAT May/1014
Hi Arnaud,
Try aggregate function
regards
M
arnaud Gaboury a écrit :
I have a data frame called "pose":
DESCRIPTION QUANITY CLOSING.PRICE
1 WHEAT May/101467.75
2 WHEAT May/102467.75
3 WHEAT May/101467.75
4 WHE
I have a data frame called "pose":
DESCRIPTION QUANITY CLOSING.PRICE
1 WHEAT May/101467.75
2 WHEAT May/102467.75
3 WHEAT May/101467.75
4 WHEAT May/101467.75
5 COTTON NO.2 May/101 78.1
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