undant)
--
*__*
*Frank T. Burbrink, Ph.D.*
*Curator in Charge*
*Department of Herpetology*
*American Museum of Natural History*
*Central Park West at 79th Street*
*New York, NY 10024-5192*
*Website: https://sites.google.com/view/frank-burbrink-website/
<https://sites.google.com/v
names(b) <- c("state", "locus")
> > by.state1(a,b)
>
>state locus state locus
> 1 AR 5AR 2
> 2 AR 6AR 3
> 3 IL 1IL 1
> 4 IL 1IL 1
> 5 LA 2 NA
> 6 LA 2 NA
&
.
While this is clunky I can just write a function to do it all at once.
On Wed, May 27, 2015 at 6:20 AM, Frank Burbrink
wrote:
> Thanks Bill,
>
> However, unique(merge(x, y, by = 1, all=T)) is giving me:
>
>state locus.x locus.y
> 1 AR 5 2
> 2 AR
> unique(merge(x, y, by = 1, all=T))
>
> Gabor Grothendieck's sqldf package is very useful if you're more
> comfortable with SQL-type syntax, see:
>
> https://github.com/ggrothendieck/sqldf
>
> Best Regards,
>
> William (Bill) Michels, Ph.D.
>
&g
Hello All,
I am attempting to merge two data frames that naturally contain duplicate
entries, however when using either merge or dMerge I get even more
duplicates.
For example:
data.frame(state=c("IL", "IL", "LA","LA", "MS","MS", "AR", "AR"),
locus=c(1,1,2,2,3,4,5,6))->x
data.frame(state=c("IL"
Hi Guys,
Using the PGLMM function in Picante it is theoretically possible to
generate other models (than the 5 flagged ones indicated) by
differently structuring the independent variable (Y), dependent
variables (X), and covariance matrices (VV). I was wondering anyone
could give me some advice (
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