Jef, your terse reply was so constructive that you converted me! LOL!
That is an interesting point though that I remain a bit unclear on.
Both data.frame and as.data.frame can be used in some ways similarly as in:
> data.frame(matrix(1:12, nrow=3))
X1 X2 X3 X4
1 1 4 7 10
2 2 5 8 11
3
On 28/10/2023 4:45 p.m., Bert Gunter wrote:
Jeff, et. al. : but ...
Note that as.data.frame() *already* changes the matrix object by adding
column names of *its own choosing* when the matrix has none. So the issue
here is not *whether* col names should be added, but *what*/*how* they
should be.
Jeff, et. al. : but ...
Note that as.data.frame() *already* changes the matrix object by adding
column names of *its own choosing* when the matrix has none. So the issue
here is not *whether* col names should be added, but *what*/*how* they
should be. Unless you wish to extend your criticism to
Dear Rui,
I really thank you a lot for your precious R help. It is exactly what I was
trying to do! Once more, many thanks!
Best,
Sacha
Le vendredi 27 octobre 2023 à 09:36:18 UTC+2, Rui Barradas
a écrit :
Às 19:23 de 26/10/2023, varin sacha via R-help escreveu:
> Dear R-Experts,
>
Ah - that's an excellent point. Thanks.
> On Oct 28, 2023, at 14:54, Jeff Newmiller wrote:
>
> as.data.frame is a _converter_, while data.frame is a _constructor_.
> Changing the object contents is not what a conversion is for.
>
> On October 28, 2023 11:39:22 AM PDT, Boris Steipe
>
as.data.frame is a _converter_, while data.frame is a _constructor_. Changing
the object contents is not what a conversion is for.
On October 28, 2023 11:39:22 AM PDT, Boris Steipe
wrote:
>Thanks Duncan and Avi!
>
>That you could use NULL in a matrix() dimnames = list(...) argument wasn't
Thanks Duncan and Avi!
That you could use NULL in a matrix() dimnames = list(...) argument wasn't
clear to me. I thought that would be equivalent to a one-element list - and
thereby define rownames. So that's good to know.
The documentation could be more explicit - but it is probably more work
Борис,
Try this where you tell matrix the column names you want:
nouns <- as.data.frame(
matrix(c(
"gaggle",
"geese",
"dule",
"doves",
"wake",
"vultures"
),
ncol = 2,
byrow = TRUE,
dimnames=list(NULL, c("collective", "category"
Result:
> nouns
Using an offset of log(Effort) as in your second model is the more
standard way to approach this problem; it corresponds to assuming that
catch is strictly proportional to effort. Adding log(Effort) as a
covariate (as illustrated below) tests whether a power-law model (catch
propto
Sent a slightly shorter version of this to your email, this one is to
the list:
On 28/10/2023 1:54 p.m., Boris Steipe wrote:
> > I have been trying to create a data frame from some structured text
in a single expression. Reprex:
> >
> > nouns <- as.data.frame(
> >matrix(c(
> >
I have been trying to create a data frame from some structured text in a single
expression. Reprex:
nouns <- as.data.frame(
matrix(c(
"gaggle",
"geese",
"dule",
"doves",
"wake",
"vultures"
), ncol = 2, byrow = TRUE),
col.names = c("collective", "category")
You can also use the pivot_longer to do it:
library(tidyverse)
input <- structure(list(...1 = c(92.9925354, 76.0024254, 44.99547465,
28.00536465, 120.0068103, 31.9980405, 85.0071837, 40.1532933,
19.3120917, 113.12581575, 28.45843425, 114.400074, 143.925,
46.439634, 20.7845679, 50.82874575,
Paul,
I have snipped away your long message and want to suggest another approach
or way of thinking to consider.
You have received other good suggestions and I likely would have used
something like that, probably within the dplyr/tidyverse but consider
something simpler.
You seem to be viewing
Às 04:13 de 28/10/2023, Paul Bernal escreveu:
Dear friends,
I have the following dataframe:
dim(alajuela_df)
[1] 126 12
dput(alajuela_df)
structure(list(...1 = c(92.9925354, 76.0024254, 44.99547465,
28.00536465, 120.0068103, 31.9980405, 85.0071837, 40.1532933,
19.3120917, 113.12581575,
The tidyverse idiom looks very different but does what you want and I have come
to like it.
What idiom of R one likes, for the mostly small datasets I handle, is largely a
matter
of preferenceds for "readability", itself very personal. Here's my tidyverse
way of doing
what you wanted:
###
Colleagues,
I have a dataset that includes five variables.
- Catch: the catch number counted in some species (ind.)
- Effort: fishing effort (the number of fishing vessels)
- xx1, xx2, xx3: some environmental factors
As an overdispersion test on the “Catch” variable, I modeled with negative
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