Re: [R] Finding combination of states
... and just for fun, here is a non-string version (more appropriate for
complex state labels??):
gvec <- function(ntimes, states, init, final, repeats = TRUE)
## ntimes: integer, number of unique times
## states: vector of unique states
## init: initial state
## final: final state
{
out <- cbind(init,
as.matrix(expand.grid(rep(list(states),ntimes -2 ))),final)
if(!repeats)
out[ apply(out,1,\(x)all(x[-1] != x[-ntimes])), ]
else out
}
yielding:
> gvec(4, letters[1:5], "b", "e", repeats = TRUE)
init Var1 Var2 final
[1,] "b" "a" "a" "e"
[2,] "b" "b" "a" "e"
[3,] "b" "c" "a" "e"
[4,] "b" "d" "a" "e"
[5,] "b" "e" "a" "e"
[6,] "b" "a" "b" "e"
[7,] "b" "b" "b" "e"
[8,] "b" "c" "b" "e"
[9,] "b" "d" "b" "e"
[10,] "b" "e" "b" "e"
[11,] "b" "a" "c" "e"
[12,] "b" "b" "c" "e"
[13,] "b" "c" "c" "e"
[14,] "b" "d" "c" "e"
[15,] "b" "e" "c" "e"
[16,] "b" "a" "d" "e"
[17,] "b" "b" "d" "e"
[18,] "b" "c" "d" "e"
[19,] "b" "d" "d" "e"
[20,] "b" "e" "d" "e"
[21,] "b" "a" "e" "e"
[22,] "b" "b" "e" "e"
[23,] "b" "c" "e" "e"
[24,] "b" "d" "e" "e"
[25,] "b" "e" "e" "e"
>
> gvec(4, letters[1:5], "b", "e", repeats = FALSE)
init Var1 Var2 final
[1,] "b" "c" "a" "e"
[2,] "b" "d" "a" "e"
[3,] "b" "e" "a" "e"
[4,] "b" "a" "b" "e"
[5,] "b" "c" "b" "e"
[6,] "b" "d" "b" "e"
[7,] "b" "e" "b" "e"
[8,] "b" "a" "c" "e"
[9,] "b" "d" "c" "e"
[10,] "b" "e" "c" "e"
[11,] "b" "a" "d" "e"
[12,] "b" "c" "d" "e"
[13,] "b" "e" "d" "e"
:-)
-- Bert
On Mon, Sep 4, 2023 at 2:04 PM Bert Gunter wrote:
> Well, if strings with repeats (as you defined them) are to be excluded, I
> think it's simple just to use regular expressions to remove them.
>
> e.g.
> g <- function(ntimes, states, init, final, repeats = TRUE)
>## ntimes: integer, number of unique times
>## states: vector of unique states
>## init: initial state
>## final: final state
> {
> out <- do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)),
> final))
> if(!repeats)
>out[-grep(paste(paste0(states,states), collapse = "|"),out)]
> else out
> }
> So:
>
> > g(4, LETTERS[1:5], "B", "E", repeats = FALSE)
> [1] "BCAE" "BDAE" "BEAE" "BABE" "BCBE" "BDBE" "BEBE" "BACE"
> [9] "BDCE" "BECE" "BADE" "BCDE" "BEDE"
>
> Perhaps not the most efficient way to do this, of course.
>
> Cheers,
> Bert
>
>
> On Mon, Sep 4, 2023 at 12:57 PM Eric Berger wrote:
>
>> My initial response was buggy and also used a deprecated function.
>> Also, it seems possible that one may want to rule out any strings where
>> the same state appears consecutively.
>> I say that such a string has a repeat.
>>
>> myExpand <- function(v, n) {
>> do.call(tidyr::expand_grid, replicate(n, v, simplify = FALSE))
>> }
>>
>> no_repeat <- function(s) {
>> v <- unlist(strsplit(s, NULL))
>> sum(v[-1]==v[-length(v)]) == 0
>> }
>>
>> f <- function(states, nsteps, first, last, rm_repeat=TRUE) {
>> if (nsteps < 3) stop("nsteps must be at least 3")
>> out <- paste(first,
>> myExpand(states, nsteps-2) |>
>> apply(MAR=1, \(x) paste(x, collapse="")),
>> last, sep="")
>> if (rm_repeat) {
>> ok <- sapply(out, no_repeat)
>> out <- out[ok]
>> }
>> out
>> }
>>
>> f(LETTERS[1:5],4,"B","E")
>>
>> # [1] "BABE" "BACE" "BADE" "BCAE" "BCBE" "BCDE" "BDAE" "BDBE" "BDCE"
>> "BEAE" "BEBE" "BECE" "BEDE"
>>
>> On Mon, Sep 4, 2023 at 10:33 PM Bert Gunter
>> wrote:
>>
>>> Sorry, my last line should have read:
>>>
>>> If neither this nor any of the other suggestions is what is desired, I
>>> think the OP will have to clarify his query.
>>>
>>> Bert
>>>
>>> On Mon, Sep 4, 2023 at 12:31 PM Bert Gunter
>>> wrote:
>>>
I think there may be some uncertainty here about what the OP requested.
My interpretation is:
n different times
k different states
Any state can appear at any time in the vector of times and can be
repeated
Initial and final states are given
So modifying Tim's expand.grid() solution a bit yields:
g <- function(ntimes, states, init, final){
## ntimes: integer, number of unique times
## states: vector of unique states
## init: initial state
## final: final state
do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)), final))
}
e.g.
> g(4, LETTERS[1:5], "B", "D")
[1] "BAAD" "BBAD" "BCAD" "BDAD" "BEAD" "BABD" "BBBD" "BCBD"
[9] "BDBD" "BEBD" "BACD" "BBCD" "BCCD" "BDCD" "BECD" "BADD"
[17] "BBDD" "BCDD" "BDDD" "BEDD" "BAED" "BBED" "BCED" "BDED"
[25] "BEED"
If neither this nor any of the other suggestions is not what is
desired, I think the OP will have to clarify his query.
Cheers,
Bert
On Mon, Sep 4, 2023 at 9:25 AM Ebert,Timothy Aaron
wrote:
> Does this work for you?
>
>
Re: [R] aggregate formula - differing results
On Mon, 4 Sep 2023, Ivan Calandra wrote:
Thanks Rui for your help; that would be one possibility indeed.
But am I the only one who finds that behavior of aggregate() completely
unexpected and confusing? Especially considering that dplyr::summarise() and
doBy::summaryBy() deal with NAs differently, even though they all use
mean(na.rm = TRUE) to calculate the group stats.
I agree with Rui that this behaves as documented but I also agree with
Ivan that the behavior is potentially confusing. Not so much because other
packages behave differently but mostly because the handling of missing
values differs between the different aggregate() methods.
Based on my teaching experience, I feel that a default of
na.action=na.pass would be less confusing, especially in the case with
multivariate "response".
In the univeriate case the discrepancy can be surprising - in the default
method you need na.rm=TRUE but in the formula method you get the same
result without additional arguments (due to na.action=na.omit). But in the
multivariate case the discrepancy is not obvious, especially for
beginners, because the results in other variables without NAs are affected
as well.
A minimal toy example is the following data with two groups (x = A vs. B)
and two "responses" (y without NAs and z with NA):
d <- data.frame(x = c("A", "A", "B", "B"), y = 1:4, z = c(1:3, NA))
d
## x y z
## 1 A 1 1
## 2 A 2 2
## 3 B 3 3
## 4 B 4 NA
Except for naming of the columns, both of the following summaries for y by
x (without NAs) yield the same result:
aggregate(d$y, list(d$x), FUN = mean)
aggregate(y ~ x, data = d, FUN = mean)
## x y
## 1 A 1.5
## 2 B 3.5
For a single variable _with_ NAs, the default method needs the na.rm =
TRUE argument, the fomula method does not. Again, except for naming of the
columns:
aggregate(d$z, list(d$x), FUN = mean, na.rm = TRUE)
aggregate(z ~ x, data = d, FUN = mean)
## x z
## 1 A 1.5
## 2 B 3.0
Conversely, if you do want the NAs in the groups, the following two are
the same (except for naming):
aggregate(d$z, list(d$x), FUN = mean)
aggregate(z ~ x, data = d, FUN = mean, na.action = na.pass)
## x z
## 1 A 1.5
## 2 B NA
But in the multivariate case, it is not so obvious why the following two
commands differ in their results for y (!), the variable without NAs, in
group B:
aggregate(d[, c("y", "z")], list(d$x), FUN = mean, na.rm = TRUE)
## Group.1 y z
## 1 A 1.5 1.5
## 2 B 3.5 3.0
^^^
aggregate(cbind(y, z) ~ x, data = d, FUN = mean)
## x y z
## 1 A 1.5 1.5
## 2 B 3.0 3.0
^^^
Hence, in my programming courses I tell students to use na.action=na.pass
in the formula method and to handle NAs in the FUN argument themselves.
I guess that this is not important enough to change the default in
aggregate.formula. Or are there R core members who also find that this
inconsistency between the different methods is worth addressing?
If not, maybe an explicit example could be added on the help page? Showing
something like this might help:
## default: omit enitre row 4 where z=NA
aggregate(cbind(y, z) ~ x, data = d, FUN = mean)
## x y z
## 1 A 1.5 1.5
## 2 B 3.0 3.0
## alternatively: omit row 4 only for z result but not for y result
aggregate(cbind(y, z) ~ x, data = d, FUN = mean, na.action = na.pass, na.rm =
TRUE)
## x y z
## 1 A 1.5 1.5
## 2 B 3.5 3.0
Best wishes,
Achim
On 04/09/2023 13:46, Rui Barradas wrote:
Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41, 121.37,
70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83, 1656.46,
1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2), Length =
c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854, 32.075, 21.337,
35.459), Width = c(45.982, 67.303, 52.679, 26.42, 25.149, 33.427, 20.683,
62.783, 26.417, 35.297), PLATWIDTH = c(38.84, NA, 15.33, 30.37, 11.44,
14.88, 13.86, NA, NA, 26.71), PLATTHICK = c(8.67, NA, 7.99, 11.69, 3.3,
16.52, 4.58, NA, NA, 9.35), EPA = c(78, NA, 78, 54, 72, 49, 56, NA, NA,
56), THICKNESS = c(10.97, NA, 9.36, 6.4, 5.89, 11.05, 4.9, NA, NA, 10.08),
WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9, 29.5, 4.5, NA, NA, 23), RAWMAT =
c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS",
"HORNFELS", "HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L,
111L, 112L, 113L, 114L, 115L), class = "data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean, na.rm
= TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using
Re: [R] Finding combination of states
Well, if strings with repeats (as you defined them) are to be excluded, I
think it's simple just to use regular expressions to remove them.
e.g.
g <- function(ntimes, states, init, final, repeats = TRUE)
## ntimes: integer, number of unique times
## states: vector of unique states
## init: initial state
## final: final state
{
out <- do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)),
final))
if(!repeats)
out[-grep(paste(paste0(states,states), collapse = "|"),out)]
else out
}
So:
> g(4, LETTERS[1:5], "B", "E", repeats = FALSE)
[1] "BCAE" "BDAE" "BEAE" "BABE" "BCBE" "BDBE" "BEBE" "BACE"
[9] "BDCE" "BECE" "BADE" "BCDE" "BEDE"
Perhaps not the most efficient way to do this, of course.
Cheers,
Bert
On Mon, Sep 4, 2023 at 12:57 PM Eric Berger wrote:
> My initial response was buggy and also used a deprecated function.
> Also, it seems possible that one may want to rule out any strings where
> the same state appears consecutively.
> I say that such a string has a repeat.
>
> myExpand <- function(v, n) {
> do.call(tidyr::expand_grid, replicate(n, v, simplify = FALSE))
> }
>
> no_repeat <- function(s) {
> v <- unlist(strsplit(s, NULL))
> sum(v[-1]==v[-length(v)]) == 0
> }
>
> f <- function(states, nsteps, first, last, rm_repeat=TRUE) {
> if (nsteps < 3) stop("nsteps must be at least 3")
> out <- paste(first,
> myExpand(states, nsteps-2) |>
> apply(MAR=1, \(x) paste(x, collapse="")),
> last, sep="")
> if (rm_repeat) {
> ok <- sapply(out, no_repeat)
> out <- out[ok]
> }
> out
> }
>
> f(LETTERS[1:5],4,"B","E")
>
> # [1] "BABE" "BACE" "BADE" "BCAE" "BCBE" "BCDE" "BDAE" "BDBE" "BDCE"
> "BEAE" "BEBE" "BECE" "BEDE"
>
> On Mon, Sep 4, 2023 at 10:33 PM Bert Gunter
> wrote:
>
>> Sorry, my last line should have read:
>>
>> If neither this nor any of the other suggestions is what is desired, I
>> think the OP will have to clarify his query.
>>
>> Bert
>>
>> On Mon, Sep 4, 2023 at 12:31 PM Bert Gunter
>> wrote:
>>
>>> I think there may be some uncertainty here about what the OP requested.
>>> My interpretation is:
>>>
>>> n different times
>>> k different states
>>> Any state can appear at any time in the vector of times and can be
>>> repeated
>>> Initial and final states are given
>>>
>>> So modifying Tim's expand.grid() solution a bit yields:
>>>
>>> g <- function(ntimes, states, init, final){
>>>## ntimes: integer, number of unique times
>>>## states: vector of unique states
>>>## init: initial state
>>>## final: final state
>>> do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)), final))
>>> }
>>>
>>> e.g.
>>>
>>> > g(4, LETTERS[1:5], "B", "D")
>>> [1] "BAAD" "BBAD" "BCAD" "BDAD" "BEAD" "BABD" "BBBD" "BCBD"
>>> [9] "BDBD" "BEBD" "BACD" "BBCD" "BCCD" "BDCD" "BECD" "BADD"
>>> [17] "BBDD" "BCDD" "BDDD" "BEDD" "BAED" "BBED" "BCED" "BDED"
>>> [25] "BEED"
>>>
>>> If neither this nor any of the other suggestions is not what is desired,
>>> I think the OP will have to clarify his query.
>>>
>>> Cheers,
>>> Bert
>>>
>>> On Mon, Sep 4, 2023 at 9:25 AM Ebert,Timothy Aaron
>>> wrote:
>>>
Does this work for you?
t0<-t1<-t2<-LETTERS[1:5]
al2<-expand.grid(t0, t1, t2)
al3<-paste(al2$Var1, al2$Var2, al2$Var3)
al4 <- gsub(" ", "", al3)
head(al3)
Tim
-Original Message-
From: R-help On Behalf Of Eric Berger
Sent: Monday, September 4, 2023 10:17 AM
To: Christofer Bogaso
Cc: r-help
Subject: Re: [R] Finding combination of states
[External Email]
The function purrr::cross() can help you with this. For example:
f <- function(states, nsteps, first, last) {
paste(first, unlist(lapply(purrr::cross(rep(list(v),nsteps-2)),
\(x) paste(unlist(x), collapse=""))), last, sep="") } f(LETTERS[1:5],
3, "B", "E") [1] "BAE" "BBE" "BCE" "BDE" "BEE"
HTH,
Eric
On Mon, Sep 4, 2023 at 3:42 PM Christofer Bogaso <
bogaso.christo...@gmail.com> wrote:
>
> Let say I have 3 time points.as T0, T1, and T2.(number of such time
> points can be arbitrary) In each time point, an object can be any of 5
> states, A, B, C, D, E (number of such states can be arbitrary)
>
> I need to find all possible ways, how that object starting with state
> B (say) at time T0, can be on state E (example) in time T2
>
> For example one possibility is BAE etc.
>
> Is there any function available with R, that can give me a vector of
> such possibilities for arbitrary number of states, time, and for a
> given initial and final (desired) states?
>
> ANy pointer will be very appreciated.
>
> Thanks for your time.
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat/
>
Re: [R-es] problemas al instalar "dplyr" - problems installing "dplyr"
Muchas gracias por la solución. Me ha funcionado. Tenía instalada una versión un poco "vieja" de Rtools y eso puede haber influido. Saludos, José María --- Dr. José María Santiago Sáez (PhD) Madrid - España/Spain +34 646 165 291 jms...@picos.com https://sites.google.com/site/santiagojosemaria/ El 04.09.2023 21:42, Carlos Ortega escribió: Hola José, Veo que tienes Windows, por el path que aparece en el error... En este caso tienes que hacer esto: * No sé si en versiones anteriores instalaste las RTools. * Si no es así, conviene que lo hagas "dplyr" busca un compilador para instalarse y no lo encuentra. Esto es lo que justamente te ofrece las RTools. * Como estás usando la versión 4.3, tienes que instalarla desde aquí: https://cran.r-project.org/bin/windows/Rtools/rtools43/rtools.html Gracias! Carlos. El lun, 4 sept 2023 a las 21:30, J.M. Santiago () escribió: Hola a todos: He instalado las últimas versiones de R y R-studio y no consigo instalar el paquete "dplyr". Al intentarlo me da el siguiente mensaje. Por favor. si alguien tiene idea de cómo resolver este problema, por favor, necesito ayuda. Muchas gracias, José M. Hi, I have installed the last versions of R and R-studio but I can´t install the "dplyr" package. When I try, I get the following message. Please, if somebody can help me I will be very gratefull. Thank you very much. José M. Installing package into 'C:/Users/jmsan/AppData/Local/R/win-library/4.3' (as 'lib' is unspecified) There is a binary version available but the source version is later: binary source needs_compilation dplyr 1.1.2 1.1.3 TRUE installing the source package 'dplyr' trying URL 'https://cran.rstudio.com/src/contrib/dplyr_1.1.3.tar.gz' Content type 'application/x-gzip' length 1083635 bytes (1.0 MB) downloaded 1.0 MB * installing *source* package 'dplyr' ... ** package 'dplyr' successfully unpacked and MD5 sums checked ** using staged installation ** libs g++ -std=gnu++17 -I"C:/PROGRA~1/R/R-43~1.1/include" -DNDEBUG -I"c:/rtools43/x86_64-w64-mingw32.static.posix/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c chop.cpp -o chop.o sh: line 1: g++: command not found make: *** [C:/PROGRA~1/R/R-43~1.1/etc/x64/Makeconf:272: chop.o] Error 127 ERROR: compilation failed for package 'dplyr' * removing 'C:/Users/jmsan/AppData/Local/R/win-library/4.3/dplyr' Warning in install.packages : installation of package 'dplyr' had non-zero exit status --- Dr. José María Santiago Sáez (PhD) Madrid - España/Spain +34 646 165 291 jms...@picos.com https://sites.google.com/site/santiagojosemaria/ ___ R-help-es mailing list R-help-es@r-project.org https://stat.ethz.ch/mailman/listinfo/r-help-es -- Saludos, Carlos Ortega www.qualityexcellence.es [1] Links: -- [1] http://www.qualityexcellence.es___ R-help-es mailing list R-help-es@r-project.org https://stat.ethz.ch/mailman/listinfo/r-help-es
Re: [R-es] problemas al instalar "dplyr" - problems installing "dplyr"
Muchas gracias por la solución. Me ha funcionado. Tenía instalada una versión un poco "vieja" de Rtools y eso puede haber influido. Saludos, José María --- Dr. José María Santiago Sáez (PhD) Madrid - España/Spain +34 646 165 291 jms...@picos.com https://sites.google.com/site/santiagojosemaria/ El 04.09.2023 21:46, Emilio L. Cano escribió: Hola, Supongo que te estará preguntando si quieres instalar desde las fuentes porque hay una versión más nueva que la que hay en binario (supongo que usas Windows). Entonces, o respondes que NO a esa pregunta, o te instalas las Rtools . Espero que te sirva, un saludo, Emilio El 4 sept 2023, a las 21:23, J.M. Santiago escribió: Hola a todos: He instalado las últimas versiones de R y R-studio y no consigo instalar el paquete "dplyr". Al intentarlo me da el siguiente mensaje. Por favor. si alguien tiene idea de cómo resolver este problema, por favor, necesito ayuda. Muchas gracias, José M. Hi, I have installed the last versions of R and R-studio but I can´t install the "dplyr" package. When I try, I get the following message. Please, if somebody can help me I will be very gratefull. Thank you very much. José M. Installing package into 'C:/Users/jmsan/AppData/Local/R/win-library/4.3' (as 'lib' is unspecified) There is a binary version available but the source version is later: binary source needs_compilation dplyr 1.1.2 1.1.3 TRUE installing the source package 'dplyr' trying URL 'https://cran.rstudio.com/src/contrib/dplyr_1.1.3.tar.gz' Content type 'application/x-gzip' length 1083635 bytes (1.0 MB) downloaded 1.0 MB * installing *source* package 'dplyr' ... ** package 'dplyr' successfully unpacked and MD5 sums checked ** using staged installation ** libs g++ -std=gnu++17 -I"C:/PROGRA~1/R/R-43~1.1/include" -DNDEBUG -I"c:/rtools43/x86_64-w64-mingw32.static.posix/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c chop.cpp -o chop.o sh: line 1: g++: command not found make: *** [C:/PROGRA~1/R/R-43~1.1/etc/x64/Makeconf:272: chop.o] Error 127 ERROR: compilation failed for package 'dplyr' * removing 'C:/Users/jmsan/AppData/Local/R/win-library/4.3/dplyr' Warning in install.packages : installation of package 'dplyr' had non-zero exit status --- Dr. José María Santiago Sáez (PhD) Madrid - España/Spain +34 646 165 291 jms...@picos.com https://sites.google.com/site/santiagojosemaria/ <0a969aa8.gif> ___ R-help-es mailing list R-help-es@r-project.org https://stat.ethz.ch/mailman/listinfo/r-help-es___ R-help-es mailing list R-help-es@r-project.org https://stat.ethz.ch/mailman/listinfo/r-help-es
Re: [R] Finding combination of states
My initial response was buggy and also used a deprecated function.
Also, it seems possible that one may want to rule out any strings where the
same state appears consecutively.
I say that such a string has a repeat.
myExpand <- function(v, n) {
do.call(tidyr::expand_grid, replicate(n, v, simplify = FALSE))
}
no_repeat <- function(s) {
v <- unlist(strsplit(s, NULL))
sum(v[-1]==v[-length(v)]) == 0
}
f <- function(states, nsteps, first, last, rm_repeat=TRUE) {
if (nsteps < 3) stop("nsteps must be at least 3")
out <- paste(first,
myExpand(states, nsteps-2) |>
apply(MAR=1, \(x) paste(x, collapse="")),
last, sep="")
if (rm_repeat) {
ok <- sapply(out, no_repeat)
out <- out[ok]
}
out
}
f(LETTERS[1:5],4,"B","E")
# [1] "BABE" "BACE" "BADE" "BCAE" "BCBE" "BCDE" "BDAE" "BDBE" "BDCE"
"BEAE" "BEBE" "BECE" "BEDE"
On Mon, Sep 4, 2023 at 10:33 PM Bert Gunter wrote:
> Sorry, my last line should have read:
>
> If neither this nor any of the other suggestions is what is desired, I
> think the OP will have to clarify his query.
>
> Bert
>
> On Mon, Sep 4, 2023 at 12:31 PM Bert Gunter
> wrote:
>
>> I think there may be some uncertainty here about what the OP requested.
>> My interpretation is:
>>
>> n different times
>> k different states
>> Any state can appear at any time in the vector of times and can be
>> repeated
>> Initial and final states are given
>>
>> So modifying Tim's expand.grid() solution a bit yields:
>>
>> g <- function(ntimes, states, init, final){
>>## ntimes: integer, number of unique times
>>## states: vector of unique states
>>## init: initial state
>>## final: final state
>> do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)), final))
>> }
>>
>> e.g.
>>
>> > g(4, LETTERS[1:5], "B", "D")
>> [1] "BAAD" "BBAD" "BCAD" "BDAD" "BEAD" "BABD" "BBBD" "BCBD"
>> [9] "BDBD" "BEBD" "BACD" "BBCD" "BCCD" "BDCD" "BECD" "BADD"
>> [17] "BBDD" "BCDD" "BDDD" "BEDD" "BAED" "BBED" "BCED" "BDED"
>> [25] "BEED"
>>
>> If neither this nor any of the other suggestions is not what is desired,
>> I think the OP will have to clarify his query.
>>
>> Cheers,
>> Bert
>>
>> On Mon, Sep 4, 2023 at 9:25 AM Ebert,Timothy Aaron
>> wrote:
>>
>>> Does this work for you?
>>>
>>> t0<-t1<-t2<-LETTERS[1:5]
>>> al2<-expand.grid(t0, t1, t2)
>>> al3<-paste(al2$Var1, al2$Var2, al2$Var3)
>>> al4 <- gsub(" ", "", al3)
>>> head(al3)
>>>
>>> Tim
>>>
>>> -Original Message-
>>> From: R-help On Behalf Of Eric Berger
>>> Sent: Monday, September 4, 2023 10:17 AM
>>> To: Christofer Bogaso
>>> Cc: r-help
>>> Subject: Re: [R] Finding combination of states
>>>
>>> [External Email]
>>>
>>> The function purrr::cross() can help you with this. For example:
>>>
>>> f <- function(states, nsteps, first, last) {
>>>paste(first, unlist(lapply(purrr::cross(rep(list(v),nsteps-2)),
>>> \(x) paste(unlist(x), collapse=""))), last, sep="") } f(LETTERS[1:5], 3,
>>> "B", "E") [1] "BAE" "BBE" "BCE" "BDE" "BEE"
>>>
>>> HTH,
>>> Eric
>>>
>>>
>>> On Mon, Sep 4, 2023 at 3:42 PM Christofer Bogaso <
>>> bogaso.christo...@gmail.com> wrote:
>>> >
>>> > Let say I have 3 time points.as T0, T1, and T2.(number of such time
>>> > points can be arbitrary) In each time point, an object can be any of 5
>>> > states, A, B, C, D, E (number of such states can be arbitrary)
>>> >
>>> > I need to find all possible ways, how that object starting with state
>>> > B (say) at time T0, can be on state E (example) in time T2
>>> >
>>> > For example one possibility is BAE etc.
>>> >
>>> > Is there any function available with R, that can give me a vector of
>>> > such possibilities for arbitrary number of states, time, and for a
>>> > given initial and final (desired) states?
>>> >
>>> > ANy pointer will be very appreciated.
>>> >
>>> > Thanks for your time.
>>> >
>>> > __
>>> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> > https://stat/
>>> > .ethz.ch%2Fmailman%2Flistinfo%2Fr-help=05%7C01%7Ctebert%40ufl.edu
>>> > %7C25cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84
>>> > %7C0%7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAw
>>> > MDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C
>>> > ta=TM4jGF39Gy3PH0T3nnQpT%2BLogkVxifv%2Fudv9hWPwbss%3D=0
>>> > PLEASE do read the posting guide
>>> > http://www.r/
>>> > -project.org%2Fposting-guide.html=05%7C01%7Ctebert%40ufl.edu%7C25
>>> > cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84%7C0%
>>> > 7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiL
>>> > CJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C=5n
>>> > PTLmsz0lOz47t41u578t9oI0i7BOgIX53yx8CesLs%3D=0
>>> > and provide commented, minimal, self-contained, reproducible code.
>>>
>>> __
>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>
Re: [R-es] problemas al instalar "dplyr" - problems installing "dplyr"
Hola,Supongo que te estará preguntando si quieres instalar desde las fuentes porque hay una versión más nueva que la que hay en binario (supongo que usas Windows).Entonces, o respondes que NO a esa pregunta, o te instalas las Rtools .Espero que te sirva, un saludo,EmilioEl 4 sept 2023, a las 21:23, J.M. Santiago escribió: Hola a todos: He instalado las últimas versiones de R y R-studio y no consigo instalar el paquete "dplyr". Al intentarlo me da el siguiente mensaje. Por favor. si alguien tiene idea de cómo resolver este problema, por favor, necesito ayuda. Muchas gracias, José M. Hi, I have installed the last versions of R and R-studio but I can´t install the "dplyr" package. When I try, I get the following message. Please, if somebody can help me I will be very gratefull. Thank you very much. José M. Installing package into ‘C:/Users/jmsan/AppData/Local/R/win-library/4.3’ (as ‘lib’ is unspecified) There is a binary version available but the source version is later: binary source needs_compilation dplyr 1.1.2 1.1.3 TRUE installing the source package ‘dplyr’ trying URL 'https://cran.rstudio.com/src/contrib/dplyr_1.1.3.tar.gz' Content type 'application/x-gzip' length 1083635 bytes (1.0 MB) downloaded 1.0 MB * installing *source* package 'dplyr' ... ** package 'dplyr' successfully unpacked and MD5 sums checked ** using staged installation ** libs g++ -std=gnu++17 -I"C:/PROGRA~1/R/R-43~1.1/include" -DNDEBUG -I"c:/rtools43/x86_64-w64-mingw32.static.posix/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c chop.cpp -o chop.o sh: line 1: g++: command not found make: *** [C:/PROGRA~1/R/R-43~1.1/etc/x64/Makeconf:272: chop.o] Error 127 ERROR: compilation failed for package 'dplyr' * removing 'C:/Users/jmsan/AppData/Local/R/win-library/4.3/dplyr' Warning in install.packages : installation of package ‘dplyr’ had non-zero exit status ---Dr. José María Santiago Sáez (PhD)Madrid - España/Spain+34 646 165 291jms...@picos.comhttps://sites.google.com/site/santiagojosemaria/ <0a969aa8.gif> ___R-help-es mailing listR-help-es@r-project.orghttps://stat.ethz.ch/mailman/listinfo/r-help-es___ R-help-es mailing list R-help-es@r-project.org https://stat.ethz.ch/mailman/listinfo/r-help-es
Re: [R] Finding combination of states
Sorry, my last line should have read:
If neither this nor any of the other suggestions is what is desired, I
think the OP will have to clarify his query.
Bert
On Mon, Sep 4, 2023 at 12:31 PM Bert Gunter wrote:
> I think there may be some uncertainty here about what the OP requested. My
> interpretation is:
>
> n different times
> k different states
> Any state can appear at any time in the vector of times and can be repeated
> Initial and final states are given
>
> So modifying Tim's expand.grid() solution a bit yields:
>
> g <- function(ntimes, states, init, final){
>## ntimes: integer, number of unique times
>## states: vector of unique states
>## init: initial state
>## final: final state
> do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)), final))
> }
>
> e.g.
>
> > g(4, LETTERS[1:5], "B", "D")
> [1] "BAAD" "BBAD" "BCAD" "BDAD" "BEAD" "BABD" "BBBD" "BCBD"
> [9] "BDBD" "BEBD" "BACD" "BBCD" "BCCD" "BDCD" "BECD" "BADD"
> [17] "BBDD" "BCDD" "BDDD" "BEDD" "BAED" "BBED" "BCED" "BDED"
> [25] "BEED"
>
> If neither this nor any of the other suggestions is not what is desired, I
> think the OP will have to clarify his query.
>
> Cheers,
> Bert
>
> On Mon, Sep 4, 2023 at 9:25 AM Ebert,Timothy Aaron wrote:
>
>> Does this work for you?
>>
>> t0<-t1<-t2<-LETTERS[1:5]
>> al2<-expand.grid(t0, t1, t2)
>> al3<-paste(al2$Var1, al2$Var2, al2$Var3)
>> al4 <- gsub(" ", "", al3)
>> head(al3)
>>
>> Tim
>>
>> -Original Message-
>> From: R-help On Behalf Of Eric Berger
>> Sent: Monday, September 4, 2023 10:17 AM
>> To: Christofer Bogaso
>> Cc: r-help
>> Subject: Re: [R] Finding combination of states
>>
>> [External Email]
>>
>> The function purrr::cross() can help you with this. For example:
>>
>> f <- function(states, nsteps, first, last) {
>>paste(first, unlist(lapply(purrr::cross(rep(list(v),nsteps-2)),
>> \(x) paste(unlist(x), collapse=""))), last, sep="") } f(LETTERS[1:5], 3,
>> "B", "E") [1] "BAE" "BBE" "BCE" "BDE" "BEE"
>>
>> HTH,
>> Eric
>>
>>
>> On Mon, Sep 4, 2023 at 3:42 PM Christofer Bogaso <
>> bogaso.christo...@gmail.com> wrote:
>> >
>> > Let say I have 3 time points.as T0, T1, and T2.(number of such time
>> > points can be arbitrary) In each time point, an object can be any of 5
>> > states, A, B, C, D, E (number of such states can be arbitrary)
>> >
>> > I need to find all possible ways, how that object starting with state
>> > B (say) at time T0, can be on state E (example) in time T2
>> >
>> > For example one possibility is BAE etc.
>> >
>> > Is there any function available with R, that can give me a vector of
>> > such possibilities for arbitrary number of states, time, and for a
>> > given initial and final (desired) states?
>> >
>> > ANy pointer will be very appreciated.
>> >
>> > Thanks for your time.
>> >
>> > __
>> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> > https://stat/
>> > .ethz.ch%2Fmailman%2Flistinfo%2Fr-help=05%7C01%7Ctebert%40ufl.edu
>> > %7C25cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84
>> > %7C0%7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAw
>> > MDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C
>> > ta=TM4jGF39Gy3PH0T3nnQpT%2BLogkVxifv%2Fudv9hWPwbss%3D=0
>> > PLEASE do read the posting guide
>> > http://www.r/
>> > -project.org%2Fposting-guide.html=05%7C01%7Ctebert%40ufl.edu%7C25
>> > cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84%7C0%
>> > 7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiL
>> > CJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C=5n
>> > PTLmsz0lOz47t41u578t9oI0i7BOgIX53yx8CesLs%3D=0
>> > and provide commented, minimal, self-contained, reproducible code.
>>
>> __
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.r-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>> __
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Finding combination of states
I think there may be some uncertainty here about what the OP requested. My
interpretation is:
n different times
k different states
Any state can appear at any time in the vector of times and can be repeated
Initial and final states are given
So modifying Tim's expand.grid() solution a bit yields:
g <- function(ntimes, states, init, final){
## ntimes: integer, number of unique times
## states: vector of unique states
## init: initial state
## final: final state
do.call(paste0,c(init,expand.grid(rep(list(states), ntimes-2)), final))
}
e.g.
> g(4, LETTERS[1:5], "B", "D")
[1] "BAAD" "BBAD" "BCAD" "BDAD" "BEAD" "BABD" "BBBD" "BCBD"
[9] "BDBD" "BEBD" "BACD" "BBCD" "BCCD" "BDCD" "BECD" "BADD"
[17] "BBDD" "BCDD" "BDDD" "BEDD" "BAED" "BBED" "BCED" "BDED"
[25] "BEED"
If neither this nor any of the other suggestions is not what is desired, I
think the OP will have to clarify his query.
Cheers,
Bert
On Mon, Sep 4, 2023 at 9:25 AM Ebert,Timothy Aaron wrote:
> Does this work for you?
>
> t0<-t1<-t2<-LETTERS[1:5]
> al2<-expand.grid(t0, t1, t2)
> al3<-paste(al2$Var1, al2$Var2, al2$Var3)
> al4 <- gsub(" ", "", al3)
> head(al3)
>
> Tim
>
> -Original Message-
> From: R-help On Behalf Of Eric Berger
> Sent: Monday, September 4, 2023 10:17 AM
> To: Christofer Bogaso
> Cc: r-help
> Subject: Re: [R] Finding combination of states
>
> [External Email]
>
> The function purrr::cross() can help you with this. For example:
>
> f <- function(states, nsteps, first, last) {
>paste(first, unlist(lapply(purrr::cross(rep(list(v),nsteps-2)),
> \(x) paste(unlist(x), collapse=""))), last, sep="") } f(LETTERS[1:5], 3,
> "B", "E") [1] "BAE" "BBE" "BCE" "BDE" "BEE"
>
> HTH,
> Eric
>
>
> On Mon, Sep 4, 2023 at 3:42 PM Christofer Bogaso <
> bogaso.christo...@gmail.com> wrote:
> >
> > Let say I have 3 time points.as T0, T1, and T2.(number of such time
> > points can be arbitrary) In each time point, an object can be any of 5
> > states, A, B, C, D, E (number of such states can be arbitrary)
> >
> > I need to find all possible ways, how that object starting with state
> > B (say) at time T0, can be on state E (example) in time T2
> >
> > For example one possibility is BAE etc.
> >
> > Is there any function available with R, that can give me a vector of
> > such possibilities for arbitrary number of states, time, and for a
> > given initial and final (desired) states?
> >
> > ANy pointer will be very appreciated.
> >
> > Thanks for your time.
> >
> > __
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat/
> > .ethz.ch%2Fmailman%2Flistinfo%2Fr-help=05%7C01%7Ctebert%40ufl.edu
> > %7C25cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84
> > %7C0%7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAw
> > MDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C
> > ta=TM4jGF39Gy3PH0T3nnQpT%2BLogkVxifv%2Fudv9hWPwbss%3D=0
> > PLEASE do read the posting guide
> > http://www.r/
> > -project.org%2Fposting-guide.html=05%7C01%7Ctebert%40ufl.edu%7C25
> > cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84%7C0%
> > 7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiL
> > CJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C=5n
> > PTLmsz0lOz47t41u578t9oI0i7BOgIX53yx8CesLs%3D=0
> > and provide commented, minimal, self-contained, reproducible code.
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.r-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R-es] problemas al instalar "dplyr" - problems installing "dplyr"
Hola a todos: He instalado las últimas versiones de R y R-studio y no consigo instalar el paquete "dplyr". Al intentarlo me da el siguiente mensaje. Por favor. si alguien tiene idea de cómo resolver este problema, por favor, necesito ayuda. Muchas gracias, José M. Hi, I have installed the last versions of R and R-studio but I can´t install the "dplyr" package. When I try, I get the following message. Please, if somebody can help me I will be very gratefull. Thank you very much. José M. Installing package into 'C:/Users/jmsan/AppData/Local/R/win-library/4.3' (as 'lib' is unspecified) There is a binary version available but the source version is later: binary source needs_compilation dplyr 1.1.2 1.1.3 TRUE installing the source package 'dplyr' trying URL 'https://cran.rstudio.com/src/contrib/dplyr_1.1.3.tar.gz' Content type 'application/x-gzip' length 1083635 bytes (1.0 MB) downloaded 1.0 MB * installing *source* package 'dplyr' ... ** package 'dplyr' successfully unpacked and MD5 sums checked ** using staged installation ** libs g++ -std=gnu++17 -I"C:/PROGRA~1/R/R-43~1.1/include" -DNDEBUG -I"c:/rtools43/x86_64-w64-mingw32.static.posix/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c chop.cpp -o chop.o sh: line 1: g++: command not found make: *** [C:/PROGRA~1/R/R-43~1.1/etc/x64/Makeconf:272: chop.o] Error 127 ERROR: compilation failed for package 'dplyr' * removing 'C:/Users/jmsan/AppData/Local/R/win-library/4.3/dplyr' Warning in install.packages : installation of package 'dplyr' had non-zero exit status --- Dr. José María Santiago Sáez (PhD) Madrid - España/Spain +34 646 165 291 jms...@picos.com https://sites.google.com/site/santiagojosemaria/___ R-help-es mailing list R-help-es@r-project.org https://stat.ethz.ch/mailman/listinfo/r-help-es
Re: [R] Finding combination of states
Does this work for you?
t0<-t1<-t2<-LETTERS[1:5]
al2<-expand.grid(t0, t1, t2)
al3<-paste(al2$Var1, al2$Var2, al2$Var3)
al4 <- gsub(" ", "", al3)
head(al3)
Tim
-Original Message-
From: R-help On Behalf Of Eric Berger
Sent: Monday, September 4, 2023 10:17 AM
To: Christofer Bogaso
Cc: r-help
Subject: Re: [R] Finding combination of states
[External Email]
The function purrr::cross() can help you with this. For example:
f <- function(states, nsteps, first, last) {
paste(first, unlist(lapply(purrr::cross(rep(list(v),nsteps-2)),
\(x) paste(unlist(x), collapse=""))), last, sep="") } f(LETTERS[1:5], 3, "B",
"E") [1] "BAE" "BBE" "BCE" "BDE" "BEE"
HTH,
Eric
On Mon, Sep 4, 2023 at 3:42 PM Christofer Bogaso
wrote:
>
> Let say I have 3 time points.as T0, T1, and T2.(number of such time
> points can be arbitrary) In each time point, an object can be any of 5
> states, A, B, C, D, E (number of such states can be arbitrary)
>
> I need to find all possible ways, how that object starting with state
> B (say) at time T0, can be on state E (example) in time T2
>
> For example one possibility is BAE etc.
>
> Is there any function available with R, that can give me a vector of
> such possibilities for arbitrary number of states, time, and for a
> given initial and final (desired) states?
>
> ANy pointer will be very appreciated.
>
> Thanks for your time.
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat/
> .ethz.ch%2Fmailman%2Flistinfo%2Fr-help=05%7C01%7Ctebert%40ufl.edu
> %7C25cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84
> %7C0%7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAw
> MDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C
> ta=TM4jGF39Gy3PH0T3nnQpT%2BLogkVxifv%2Fudv9hWPwbss%3D=0
> PLEASE do read the posting guide
> http://www.r/
> -project.org%2Fposting-guide.html=05%7C01%7Ctebert%40ufl.edu%7C25
> cee5ce26a8423daaa508dbad51c402%7C0d4da0f84a314d76ace60a62331e1b84%7C0%
> 7C0%7C638294338934034595%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiL
> CJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C=5n
> PTLmsz0lOz47t41u578t9oI0i7BOgIX53yx8CesLs%3D=0
> and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.r-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Problems with installing R packages from source and running C++ in R, even on fresh R installation
В Mon, 04 Sep 2023 12:05:38 + Christophe Bousquet пишет: > I will try compiling R from source when I am back from holidays, and > ask you if I need assistance. Make sure to compile with DEBUG=1 so that the compiler flags needed to emit debugging information will be enabled. Good luck! -- Best regards, Ivan __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Book Recommendation
This is a great find for those of us lurking on this thread. Thanks for sharing Greg (and of course Paul). On 8/30/2023 3:52 PM, Greg Snow wrote: Stephen, I see lots of answers with packages and resources, but not book recommendations. I have used Introduction to Data Technologies by Paul Murrell (https://www.stat.auckland.ac.nz/~paul/ItDT/) to teach SQL and database design and would recommend looking at it as a possibility. On Mon, Aug 28, 2023 at 9:47 AM Stephen H. Dawson, DSL via R-help wrote: Good Morning, I am doing some research to develop a new course where I teach. I am looking for a book to use in the course content to teach accomplishing SQL in R. Does anyone know of a book on this topic to recommend for consideration? Thank You, -- *Stephen Dawson, DSL* /Executive Strategy Consultant/ Business & Technology +1 (865) 804-3454 http://www.shdawson.com __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] aggregate formula - differing results
Ivan:
Just one perhaps extraneous comment.
You said that you were surprised that aggregate() and group_by() did not
have the same behavior. That is a misconception on your part. As you know,
the tidyverse recapitulates the functionality of many base R functions; but
it makes no claims to do so in exactly the same way and, indeed, often
makes deliberate changes to "improve" behavior. So if you wish to use both,
you should *expect* such differences, which, of course, are documented in
the man pages (and often elsewhere).
Cheers,
Bert
On Mon, Sep 4, 2023 at 5:21 AM Ivan Calandra wrote:
> Haha, got it now, there is an na.action argument (which defaults to
> na.omit) to aggregate() which is applied before calling mean(na.rm =
> TRUE). Thank you Rui for pointing this out.
>
> So running it with na.pass instead of na.omit gives the same results as
> dplyr::group_by()+summarise():
> aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE,
> na.action = na.pass)
>
> Cheers,
> Ivan
>
> On 04/09/2023 13:56, Rui Barradas wrote:
> > Às 12:51 de 04/09/2023, Ivan Calandra escreveu:
> >> Thanks Rui for your help; that would be one possibility indeed.
> >>
> >> But am I the only one who finds that behavior of aggregate()
> >> completely unexpected and confusing? Especially considering that
> >> dplyr::summarise() and doBy::summaryBy() deal with NAs differently,
> >> even though they all use mean(na.rm = TRUE) to calculate the group
> >> stats.
> >>
> >> Best wishes,
> >> Ivan
> >>
> >> On 04/09/2023 13:46, Rui Barradas wrote:
> >>> Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
> Dear useRs,
>
> I have just stumbled across a behavior in aggregate() that I cannot
> explain. Any help would be appreciated!
>
> Sample data:
> my_data <- structure(list(ID = c("FLINT-1", "FLINT-10",
> "FLINT-100", "FLINT-101", "FLINT-102", "HORN-10", "HORN-100",
> "HORN-102", "HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77,
> 142.79, 130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5),
> SurfaceArea = c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47,
> 1169.26, 444.61, 1791.48, 461.15, 1127.2), Length = c(44.384,
> 29.831, 43.869, 48.011, 54.109, 41.742, 23.854, 32.075, 21.337,
> 35.459), Width = c(45.982, 67.303, 52.679, 26.42, 25.149, 33.427,
> 20.683, 62.783, 26.417, 35.297), PLATWIDTH = c(38.84, NA, 15.33,
> 30.37, 11.44, 14.88, 13.86, NA, NA, 26.71), PLATTHICK = c(8.67, NA,
> 7.99, 11.69, 3.3, 16.52, 4.58, NA, NA, 9.35), EPA = c(78, NA, 78,
> 54, 72, 49, 56, NA, NA, 56), THICKNESS = c(10.97, NA, 9.36, 6.4,
> 5.89, 11.05, 4.9, NA, NA, 10.08), WEIGHT = c(34.3, NA, 25.5, 18.6,
> 14.9, 29.5, 4.5, NA, NA, 23), RAWMAT = c("FLINT", "FLINT", "FLINT",
> "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS",
> "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L,
> 114L, 115L), class = "data.frame")
>
> 1) Simple aggregation with 2 variables:
> aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN =
> mean, na.rm = TRUE)
>
> 2) Using the dot notation - different results:
> aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
>
> 3) Using dplyr, I get the same results as #1:
> group_by(my_data, RAWMAT) %>%
> summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
>
> 4) It gets weirder: using all columns in #1 give the same results
> as in #2 but different from #1 and #3
> aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
> PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN =
> mean, na.rm = TRUE)
>
> So it seems it is not only due to the notation (cbind() vs. dot).
> Is it a bug? A peculiar thing in my dataset? I tend to think this
> could be due to some variables (or their names) as all notations
> seem to agree when I remove some variables (although I haven't
> found out which variable(s) is (are) at fault), e.g.:
>
> my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10",
> "FLINT-100", "FLINT-101", "FLINT-102", "HORN-10", "HORN-100",
> "HORN-102", "HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77,
> 142.79, 130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5),
> SurfaceArea = c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47,
> 1169.26, 444.61, 1791.48, 461.15, 1127.2), Length = c(44.384,
> 29.831, 43.869, 48.011, 54.109, 41.742, 23.854, 32.075, 21.337,
> 35.459), Width = c(45.982, 67.303, 52.679, 26.42, 25.149, 33.427,
> 20.683, 62.783, 26.417, 35.297), RAWMAT = c("FLINT", "FLINT",
> "FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS",
> "HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L,
> 112L, 113L, 114L, 115L), class = "data.frame")
>
> aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT,
>
Re: [R] Finding combination of states
The function purrr::cross() can help you with this. For example:
f <- function(states, nsteps, first, last) {
paste(first, unlist(lapply(purrr::cross(rep(list(v),nsteps-2)),
\(x) paste(unlist(x), collapse=""))), last, sep="")
}
f(LETTERS[1:5], 3, "B", "E")
[1] "BAE" "BBE" "BCE" "BDE" "BEE"
HTH,
Eric
On Mon, Sep 4, 2023 at 3:42 PM Christofer Bogaso
wrote:
>
> Let say I have 3 time points.as T0, T1, and T2.(number of such time
> points can be arbitrary) In each time point, an object can be any of 5
> states, A, B, C, D, E (number of such states can be arbitrary)
>
> I need to find all possible ways, how that object starting with state
> B (say) at time T0, can be on state E (example) in time T2
>
> For example one possibility is BAE etc.
>
> Is there any function available with R, that can give me a vector of
> such possibilities for arbitrary number of states, time, and for a
> given initial and final (desired) states?
>
> ANy pointer will be very appreciated.
>
> Thanks for your time.
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] [Pkg-Collaboratos] BioShapes Almost-Package
Thank you very much for all the responses; especially Duncan's guidance. I will add some further ideas on workflows below. There were quite a few views on GitHub; but there is not much to see, as there is absolutely no documentation. I have added in the meantime a basic example: https://github.com/discoleo/BioShapes/blob/main/Examples.Bioshapes.png The actual code can do a lot more. Some ideas on workflows: 1. Most code is written as C/C++; the R code is a thin wrapper around the C/C++ functions It is practical to embed the documentation with the R code - as there is no complex code anyway. The same may apply for small packages. 2. Complex R code The comments may clutter the code. It is also difficult to maintain this documentation, as the comments are less easily readable. Separating the documentation from the code is a good idea. Unfortunately, this is not so obvious when you start working on your first package. Many thanks, Leonard On 9/4/2023 5:47 AM, Jeff Newmiller wrote: Leonard... the reason roxygen exists is to allow markup in source files to be used to automatically generate the numerous files required by standard R packages as documented in Writing R Extensions. If your goal is to not use source files this way then the solution is to not use roxygen at all. Just create those files yourself by directly editing them from scratch. On September 3, 2023 7:06:09 PM PDT, Leonard Mada via R-help wrote: Thank you Bert. Clarification: Indeed, I am using an add-on package: it is customary for that package - that is what I have seen - to have the entire documentation included as comments in the R src files. (But maybe I am wrong.) I will try to find some time over the next few days to explore in more detail the R documentation. Although, I do not know how this will interact with the add-on package. Sincerely, Leonard On 9/4/2023 4:58 AM, Bert Gunter wrote: 1. R-package-devel is where queries about package protocols should go. 2. But... "Is there a succinct, but sufficiently informative description of documentation tools?" "Writing R Extensions" (shipped with R) is *the* reference for R documentation. Whether it's sufficiently "succinct" for you, I cannot say. "I find that including the documentation in the source files is very distracting." ?? R documentation (.Rd) files are separate from source (.R) files. Inline documentation in source files is an "add-on" capability provided by optional packages if one prefers to do this. Such packages parse the source files to extract the documentation into the .Rd files/ So not sure what you mean here. Apologies if I have misunderstood. " I would prefer to have only basic comments in the source files and an expanded documentation in a separate location." If I understand you correctly, this is exactly what the R package process specifies. Again, see the "Writing R Extensions" manual for details. Also, if you wish to have your package on CRAN, it requires that the package documents all functions in the package as specified by the "Writing ..." manual. Again, further questions and elaboration should go to the R-package-devel list, although I think the manual is really the authoritative resource to follow. Cheers, Bert On Sun, Sep 3, 2023 at 5:06 PM Leonard Mada via R-help wrote: Dear R-List Members, I am looking for collaborators to further develop the BioShapes almost-package. I added a brief description below. A.) BioShapes (Almost-) Package The aim of the BioShapes quasi-package is to facilitate the generation of graphical objects resembling biological and chemical entities, enabling the construction of diagrams based on these objects. It currently includes functions to generate diagrams depicting viral particles, liposomes, double helix / DNA strands, various cell types (like neurons, brush-border cells and duct cells), Ig-domains, as well as more basic shapes. It should offer researchers in the field of biological and chemical sciences a tool to easily generate diagrams depicting the studied biological processes. The package lacks a proper documentation and is not yet released on CRAN. However, it is available on GitHub: https://github.com/discoleo/BioShapes Although there are 27 unique cloners on GitHub, I am still looking for contributors and collaborators. I would appreciate any collaborations to develop it further. I can be contacted both by email and on GitHub. B.) Documentation Tools Is there a succinct, but sufficiently informative description of documentation tools? I find that including the documentation in the source files is very distracting. I would prefer to have only basic comments in the source files and an expanded documentation in a separate location. This question may be more appropriate for the R-package-devel list. I
Re: [R] Time out error while connecting to Github repository
> siddharth sahasrabudhe via R-help
> on Sun, 3 Sep 2023 09:54:28 +0530 writes:
> I want to access the .csv file from my github
> repository. While connecting to the Github repository I am
> getting the following error:
> Error in curl::curl_fetch_memory(file) : Timeout was
> reached: [raw.githubusercontent.com] Failed to connect to
> raw.githubusercontent.com port 443 after 5250 ms: Timed
> out
> The R-code is as below:
library(tidyverse) ## << UNNEEDED !
library(rio)
> data <- import("
>
https://raw.githubusercontent.com/siddharth-sahasrabudhe/Youtube-video-files/main/deck.csv
> ")
> Can you please suggest how I can able to resolve this
> issue?
Yes: You used the wrong "file name", because you added a
/
on both ends by breaking the line.
This works nicely:
> require(rio)
> dd <-
> import("https://raw.githubusercontent.com/siddharth-sahasrabudhe/Youtube-video-files/main/deck.csv;)
> str(dd)
'data.frame': 52 obs. of 3 variables:
$ face : chr "king" "queen" "jack" "ten" ...
$ suit : chr "spades" "spades" "spades" "spades" ...
$ value: int 13 12 11 10 9 8 7 6 5 4 ...
> --
> Regards Siddharth Sahasrabudhe
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Finding combination of states
Let say I have 3 time points.as T0, T1, and T2.(number of such time points can be arbitrary) In each time point, an object can be any of 5 states, A, B, C, D, E (number of such states can be arbitrary) I need to find all possible ways, how that object starting with state B (say) at time T0, can be on state E (example) in time T2 For example one possibility is BAE etc. Is there any function available with R, that can give me a vector of such possibilities for arbitrary number of states, time, and for a given initial and final (desired) states? ANy pointer will be very appreciated. Thanks for your time. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] aggregate formula - differing results
Haha, got it now, there is an na.action argument (which defaults to
na.omit) to aggregate() which is applied before calling mean(na.rm =
TRUE). Thank you Rui for pointing this out.
So running it with na.pass instead of na.omit gives the same results as
dplyr::group_by()+summarise():
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE,
na.action = na.pass)
Cheers,
Ivan
On 04/09/2023 13:56, Rui Barradas wrote:
Às 12:51 de 04/09/2023, Ivan Calandra escreveu:
Thanks Rui for your help; that would be one possibility indeed.
But am I the only one who finds that behavior of aggregate()
completely unexpected and confusing? Especially considering that
dplyr::summarise() and doBy::summaryBy() deal with NAs differently,
even though they all use mean(na.rm = TRUE) to calculate the group
stats.
Best wishes,
Ivan
On 04/09/2023 13:46, Rui Barradas wrote:
Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10",
"FLINT-100", "FLINT-101", "FLINT-102", "HORN-10", "HORN-100",
"HORN-102", "HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77,
142.79, 130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5),
SurfaceArea = c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47,
1169.26, 444.61, 1791.48, 461.15, 1127.2), Length = c(44.384,
29.831, 43.869, 48.011, 54.109, 41.742, 23.854, 32.075, 21.337,
35.459), Width = c(45.982, 67.303, 52.679, 26.42, 25.149, 33.427,
20.683, 62.783, 26.417, 35.297), PLATWIDTH = c(38.84, NA, 15.33,
30.37, 11.44, 14.88, 13.86, NA, NA, 26.71), PLATTHICK = c(8.67, NA,
7.99, 11.69, 3.3, 16.52, 4.58, NA, NA, 9.35), EPA = c(78, NA, 78,
54, 72, 49, 56, NA, NA, 56), THICKNESS = c(10.97, NA, 9.36, 6.4,
5.89, 11.05, 4.9, NA, NA, 10.08), WEIGHT = c(34.3, NA, 25.5, 18.6,
14.9, 29.5, 4.5, NA, NA, 23), RAWMAT = c("FLINT", "FLINT", "FLINT",
"FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS",
"HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L,
114L, 115L), class = "data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN =
mean, na.rm = TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using dplyr, I get the same results as #1:
group_by(my_data, RAWMAT) %>%
summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
4) It gets weirder: using all columns in #1 give the same results
as in #2 but different from #1 and #3
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN =
mean, na.rm = TRUE)
So it seems it is not only due to the notation (cbind() vs. dot).
Is it a bug? A peculiar thing in my dataset? I tend to think this
could be due to some variables (or their names) as all notations
seem to agree when I remove some variables (although I haven't
found out which variable(s) is (are) at fault), e.g.:
my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10",
"FLINT-100", "FLINT-101", "FLINT-102", "HORN-10", "HORN-100",
"HORN-102", "HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77,
142.79, 130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5),
SurfaceArea = c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47,
1169.26, 444.61, 1791.48, 461.15, 1127.2), Length = c(44.384,
29.831, 43.869, 48.011, 54.109, 41.742, 23.854, 32.075, 21.337,
35.459), Width = c(45.982, 67.303, 52.679, 26.42, 25.149, 33.427,
20.683, 62.783, 26.417, 35.297), RAWMAT = c("FLINT", "FLINT",
"FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS",
"HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L,
112L, 113L, 114L, 115L), class = "data.frame")
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT,
data = my_data2, FUN = mean, na.rm = TRUE)
aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
group_by(my_data2, RAWMAT) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
Thank you in advance for any hint.
Best wishes,
Ivan
*LEIBNIZ-ZENTRUM*
*FÜR ARCHÄOLOGIE*
*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)
*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany
T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calan...@leiza.de
leiza.de
ResearchGate
LEIZA is a foundation under public law of the State of
Rhineland-Palatinate and the City of Mainz. Its headquarters are in
Mainz. Supervision is carried out by the Ministry of Science and
Health of the State of Rhineland-Palatinate. LEIZA is a research
museum of the Leibniz Association.
__
Re: [R] Problems with installing R packages from source and running C++ in R, even on fresh R installation
> If you're up to compiling R from source [] and using a symbolic > debugger [**] to step through Rcmd.exe, we could try to do that. > Murphy's law says that the copy of Rcmd.exe you'll build from source > will work well and refuse to reproduce the problem for you to > investigate. (Beyond that, there is binary-level debugging, which I'm > not well versed in.) Dear Ivan, Yes, I would be up for trying, but this week I need to finish some stuff before going on holidays. I will try compiling R from source when I am back from holidays, and ask you if I need assistance. Many thanks again for trying to solve this issue. Best regards, Christophe __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] aggregate formula - differing results
Às 12:51 de 04/09/2023, Ivan Calandra escreveu:
Thanks Rui for your help; that would be one possibility indeed.
But am I the only one who finds that behavior of aggregate() completely
unexpected and confusing? Especially considering that dplyr::summarise()
and doBy::summaryBy() deal with NAs differently, even though they all
use mean(na.rm = TRUE) to calculate the group stats.
Best wishes,
Ivan
On 04/09/2023 13:46, Rui Barradas wrote:
Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102",
"HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79,
130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea =
c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47, 1169.26, 444.61,
1791.48, 461.15, 1127.2), Length = c(44.384, 29.831, 43.869, 48.011,
54.109, 41.742, 23.854, 32.075, 21.337, 35.459), Width = c(45.982,
67.303, 52.679, 26.42, 25.149, 33.427, 20.683, 62.783, 26.417,
35.297), PLATWIDTH = c(38.84, NA, 15.33, 30.37, 11.44, 14.88, 13.86,
NA, NA, 26.71), PLATTHICK = c(8.67, NA, 7.99, 11.69, 3.3, 16.52,
4.58, NA, NA, 9.35), EPA = c(78, NA, 78, 54, 72, 49, 56, NA, NA, 56),
THICKNESS = c(10.97, NA, 9.36, 6.4, 5.89, 11.05, 4.9, NA, NA, 10.08),
WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9, 29.5, 4.5, NA, NA, 23), RAWMAT
= c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS",
"HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")), row.names = c(1L,
2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class = "data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using dplyr, I get the same results as #1:
group_by(my_data, RAWMAT) %>%
summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
4) It gets weirder: using all columns in #1 give the same results as
in #2 but different from #1 and #3
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN =
mean, na.rm = TRUE)
So it seems it is not only due to the notation (cbind() vs. dot). Is
it a bug? A peculiar thing in my dataset? I tend to think this could
be due to some variables (or their names) as all notations seem to
agree when I remove some variables (although I haven't found out
which variable(s) is (are) at fault), e.g.:
my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102",
"HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79,
130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea =
c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47, 1169.26, 444.61,
1791.48, 461.15, 1127.2), Length = c(44.384, 29.831, 43.869, 48.011,
54.109, 41.742, 23.854, 32.075, 21.337, 35.459), Width = c(45.982,
67.303, 52.679, 26.42, 25.149, 33.427, 20.683, 62.783, 26.417,
35.297), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT",
"HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")),
row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L),
class = "data.frame")
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT,
data = my_data2, FUN = mean, na.rm = TRUE)
aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
group_by(my_data2, RAWMAT) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
Thank you in advance for any hint.
Best wishes,
Ivan
*LEIBNIZ-ZENTRUM*
*FÜR ARCHÄOLOGIE*
*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)
*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany
T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calan...@leiza.de
leiza.de
ResearchGate
LEIZA is a foundation under public law of the State of
Rhineland-Palatinate and the City of Mainz. Its headquarters are in
Mainz. Supervision is carried out by the Ministry of Science and
Health of the State of Rhineland-Palatinate. LEIZA is a research
museum of the Leibniz Association.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Hello,
You can define a vector of the columns of interest and subset the data
with it. Then the default na.action = na.omit will no longer remove
the rows with NA vals in at least one column and
Re: [R] aggregate formula - differing results
Thanks Rui for your help; that would be one possibility indeed.
But am I the only one who finds that behavior of aggregate() completely
unexpected and confusing? Especially considering that dplyr::summarise()
and doBy::summaryBy() deal with NAs differently, even though they all
use mean(na.rm = TRUE) to calculate the group stats.
Best wishes,
Ivan
On 04/09/2023 13:46, Rui Barradas wrote:
Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102",
"HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79,
130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea =
c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47, 1169.26, 444.61,
1791.48, 461.15, 1127.2), Length = c(44.384, 29.831, 43.869, 48.011,
54.109, 41.742, 23.854, 32.075, 21.337, 35.459), Width = c(45.982,
67.303, 52.679, 26.42, 25.149, 33.427, 20.683, 62.783, 26.417,
35.297), PLATWIDTH = c(38.84, NA, 15.33, 30.37, 11.44, 14.88, 13.86,
NA, NA, 26.71), PLATTHICK = c(8.67, NA, 7.99, 11.69, 3.3, 16.52,
4.58, NA, NA, 9.35), EPA = c(78, NA, 78, 54, 72, 49, 56, NA, NA, 56),
THICKNESS = c(10.97, NA, 9.36, 6.4, 5.89, 11.05, 4.9, NA, NA, 10.08),
WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9, 29.5, 4.5, NA, NA, 23), RAWMAT
= c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS",
"HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")), row.names = c(1L,
2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class = "data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using dplyr, I get the same results as #1:
group_by(my_data, RAWMAT) %>%
summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
4) It gets weirder: using all columns in #1 give the same results as
in #2 but different from #1 and #3
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN =
mean, na.rm = TRUE)
So it seems it is not only due to the notation (cbind() vs. dot). Is
it a bug? A peculiar thing in my dataset? I tend to think this could
be due to some variables (or their names) as all notations seem to
agree when I remove some variables (although I haven't found out
which variable(s) is (are) at fault), e.g.:
my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102",
"HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79,
130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea =
c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47, 1169.26, 444.61,
1791.48, 461.15, 1127.2), Length = c(44.384, 29.831, 43.869, 48.011,
54.109, 41.742, 23.854, 32.075, 21.337, 35.459), Width = c(45.982,
67.303, 52.679, 26.42, 25.149, 33.427, 20.683, 62.783, 26.417,
35.297), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT",
"HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")),
row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L),
class = "data.frame")
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT,
data = my_data2, FUN = mean, na.rm = TRUE)
aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
group_by(my_data2, RAWMAT) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
Thank you in advance for any hint.
Best wishes,
Ivan
*LEIBNIZ-ZENTRUM*
*FÜR ARCHÄOLOGIE*
*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)
*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany
T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calan...@leiza.de
leiza.de
ResearchGate
LEIZA is a foundation under public law of the State of
Rhineland-Palatinate and the City of Mainz. Its headquarters are in
Mainz. Supervision is carried out by the Ministry of Science and
Health of the State of Rhineland-Palatinate. LEIZA is a research
museum of the Leibniz Association.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Hello,
You can define a vector of the columns of interest and subset the data
with it. Then the default na.action = na.omit will no longer remove
the rows with NA vals in at least one column and the results are the
same.
However, this will
Re: [R] aggregate formula - differing results
Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
25.149, 33.427, 20.683, 62.783, 26.417, 35.297), PLATWIDTH = c(38.84,
NA, 15.33, 30.37, 11.44, 14.88, 13.86, NA, NA, 26.71), PLATTHICK =
c(8.67, NA, 7.99, 11.69, 3.3, 16.52, 4.58, NA, NA, 9.35), EPA = c(78,
NA, 78, 54, 72, 49, 56, NA, NA, 56), THICKNESS = c(10.97, NA, 9.36, 6.4,
5.89, 11.05, 4.9, NA, NA, 10.08), WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9,
29.5, 4.5, NA, NA, 23), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT",
"FLINT", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")),
row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class =
"data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using dplyr, I get the same results as #1:
group_by(my_data, RAWMAT) %>%
summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
4) It gets weirder: using all columns in #1 give the same results as in
#2 but different from #1 and #3
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
So it seems it is not only due to the notation (cbind() vs. dot). Is it
a bug? A peculiar thing in my dataset? I tend to think this could be due
to some variables (or their names) as all notations seem to agree when I
remove some variables (although I haven't found out which variable(s) is
(are) at fault), e.g.:
my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
25.149, 33.427, 20.683, 62.783, 26.417, 35.297), RAWMAT = c("FLINT",
"FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS",
"HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L,
113L, 114L, 115L), class = "data.frame")
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT, data =
my_data2, FUN = mean, na.rm = TRUE)
aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
group_by(my_data2, RAWMAT) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
Thank you in advance for any hint.
Best wishes,
Ivan
*LEIBNIZ-ZENTRUM*
*FÜR ARCHÄOLOGIE*
*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)
*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany
T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calan...@leiza.de
leiza.de
ResearchGate
LEIZA is a foundation under public law of the State of
Rhineland-Palatinate and the City of Mainz. Its headquarters are in
Mainz. Supervision is carried out by the Ministry of Science and Health
of the State of Rhineland-Palatinate. LEIZA is a research museum of the
Leibniz Association.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Hello,
You can define a vector of the columns of interest and subset the data
with it. Then the default na.action = na.omit will no longer remove the
rows with NA vals in at least one column and the results are the same.
However, this will not give the mean values of the other numeric
columns, just of those two.
# define a vector of columns of interest
cols <- c("Length", "Width", "RAWMAT")
# 1) Simple aggregation with 2 variables, select cols:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data[cols], FUN =
mean, na.rm = TRUE)
# 2) Using the dot notation - if cols are selected, equal results:
aggregate(. ~ RAWMAT, data
Re: [R] [Pkg-Collaboratos] BioShapes Almost-Package
> Duncan Murdoch > on Mon, 4 Sep 2023 04:51:32 -0400 writes: > On 03/09/2023 10:47 p.m., Jeff Newmiller wrote: >> Leonard... the reason roxygen exists is to allow markup >> in source files to be used to automatically generate the >> numerous files required by standard R packages as >> documented in Writing R Extensions. >> >> If your goal is to not use source files this way then the >> solution is to not use roxygen at all. Just create those >> files yourself by directly editing them from scratch. > Just a bit of elaboration on Jeff's suggestion -- here's > the workflow I prefer to using Roxygen. > Once you have a function that works: > 1. install the package 2. set your working directory > to the package "man" directory 3. run > `prompt(functionname)` 4. edit `functionname.Rd` in the > "man" directory, which will already be filled in as a > skeleton help file, with comments describing what else > to add. > Don't run prompt() again after editing, or you'll lose > all your edits. But this is a good way to get started. > I think for the first few times the comments are really > helpful, but I wouldn't mind a way to suppress them. > Duncan Murdoch Me neither. A new option, not changing the default, would make sense. Martin __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] [Pkg-Collaboratos] BioShapes Almost-Package
> Jeff Newmiller > on Sun, 03 Sep 2023 19:47:32 -0700 writes: > Leonard... the reason roxygen exists is to allow markup in > source files to be used to automatically generate the > numerous files required by standard R packages as > documented in Writing R Extensions. If your goal is to > not use source files this way then the solution is to not > use roxygen at all. Just create those files yourself by > directly editing them from scratch. Yes. Many experienced R programmers do not use Roxygen (or use it only rarely; e.g., together with ESS (Emacs Speaks Statistics) to make the initial creation or sometime a thorough updating the help pages man/*.Rd more convenient). There are different tastes and different work flows for different people. Martin > On September 3, 2023 7:06:09 PM PDT, Leonard Mada via > R-help wrote: >> Thank you Bert. >> >> >> Clarification: >> >> Indeed, I am using an add-on package: it is customary for >> that package - that is what I have seen - to have the >> entire documentation included as comments in the R src >> files. (But maybe I am wrong.) >> >> >> I will try to find some time over the next few days to >> explore in more detail the R documentation. Although, I >> do not know how this will interact with the add-on >> package. >> >> >> Sincerely, >> >> >> Leonard >> >> >> On 9/4/2023 4:58 AM, Bert Gunter wrote: >>> 1. R-package-devel is where queries about package >>> protocols should go. >>> >>> 2. But... "Is there a succinct, but sufficiently >>> informative description of documentation tools?" >>> "Writing R Extensions" (shipped with R) is *the* >>> reference for R documentation. Whether it's sufficiently >>> "succinct" for you, I cannot say. >>> >>> "I find that including the documentation in the source >>> files is very distracting." ?? R documentation (.Rd) >>> files are separate from source (.R) files. Inline >>> documentation in source files is an "add-on" capability >>> provided by optional packages if one prefers to do >>> this. Such packages parse the source files to extract >>> the documentation into the .Rd files/ So not sure what >>> you mean here. Apologies if I have misunderstood. >>> >>> " I would prefer to have only basic comments in the >>> source files and an expanded documentation in a separate >>> location." If I understand you correctly, this is >>> exactly what the R package process specifies. Again, see >>> the "Writing R Extensions" manual for details. >>> >>> Also, if you wish to have your package on CRAN, it >>> requires that the package documents all functions in the >>> package as specified by the "Writing ..." manual. >>> >>> Again, further questions and elaboration should go to >>> the R-package-devel list, although I think the manual is >>> really the authoritative resource to follow. >>> >>> Cheers, Bert >>> >>> >>> >>> On Sun, Sep 3, 2023 at 5:06 PM Leonard Mada via R-help >>> wrote: >>> >>> Dear R-List Members, >>> >>> I am looking for collaborators to further develop the >>> BioShapes almost-package. I added a brief description >>> below. >>> >>> A.) BioShapes (Almost-) Package >>> >>> The aim of the BioShapes quasi-package is to facilitate >>> the generation of graphical objects resembling >>> biological and chemical entities, enabling the >>> construction of diagrams based on these objects. It >>> currently includes functions to generate diagrams >>> depicting viral particles, liposomes, double helix / DNA >>> strands, various cell types (like neurons, brush-border >>> cells and duct cells), Ig-domains, as well as more basic >>> shapes. >>> >>> It should offer researchers in the field of biological >>> and chemical sciences a tool to easily generate diagrams >>> depicting the studied biological processes. >>> >>> The package lacks a proper documentation and is not yet >>> released on CRAN. However, it is available on GitHub: >>> https://github.com/discoleo/BioShapes >>> >>> Although there are 27 unique cloners on GitHub, I am >>> still looking for contributors and collaborators. I >>> would appreciate any collaborations to develop it >>> further. I can be contacted both by email and on GitHub. >>> >>> >>> B.) Documentation Tools >>> >>> Is there a succinct, but sufficiently informative >>> description of documentation tools? I find that >>> including the documentation in the source files is very >>> distracting. I would prefer to have only basic comments >>> in the source files and an expanded documentation in a >>> separate location. >>>
[R] Time out error while connecting to Github repository
I want to access the .csv file from my github repository. While connecting
to the Github repository I am getting the following error:
Error in curl::curl_fetch_memory(file) :
Timeout was reached: [raw.githubusercontent.com] Failed to connect to
raw.githubusercontent.com port 443 after 5250 ms: Timed out
The R-code is as below:
library(tidyverse)
library(rio)
data <- import("
https://raw.githubusercontent.com/siddharth-sahasrabudhe/Youtube-video-files/main/deck.csv
")
Can you please suggest how I can able to resolve this issue?
--
Regards
Siddharth Sahasrabudhe
[[alternative HTML version deleted]]
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Re: [R] aggregate formula - differing results
Thanks Iago for the pointer.
It then means that na.rm = TRUE is not applied in the same way within
aggregate() as opposed to dplyr::group_by() + summarise(), right? Within
aggregate, it behaves like na.omit(), that is, it excludes the
incomplete cases (whole rows), whereas with group_by() + summarise() it
is applied on each vector (variable), which is what I actually would expect.
I hadn't showed it, but doBy::summaryBy() produces the same results as
group_by() + summarise().
Ivan
On 04/09/2023 12:45, Iago Giné Vázquez wrote:
> It seems that the issue are the missings. If in #1 you use the
> dataset na.omit(my_data) instead of my_data, you get the same output
> that in #2 and in #4, where all observations with missing data are
> removed since you are including all the variables.
>
>
> The second dataset has no issue since it has no missing data.
>
> Iago
>
> *De:* R-help de part de Ivan Calandra
>
> *Enviat el:* dilluns, 4 de setembre de 2023 11:44
> *Per a:* R-help
> *Tema:* [R] aggregate formula - differing results
> Dear useRs,
>
> I have just stumbled across a behavior in aggregate() that I cannot
> explain. Any help would be appreciated!
>
> Sample data:
> my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
> "FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
> "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
> 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
> 1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
> Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
> 32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
> 25.149, 33.427, 20.683, 62.783, 26.417, 35.297), PLATWIDTH = c(38.84,
> NA, 15.33, 30.37, 11.44, 14.88, 13.86, NA, NA, 26.71), PLATTHICK =
> c(8.67, NA, 7.99, 11.69, 3.3, 16.52, 4.58, NA, NA, 9.35), EPA = c(78,
> NA, 78, 54, 72, 49, 56, NA, NA, 56), THICKNESS = c(10.97, NA, 9.36, 6.4,
> 5.89, 11.05, 4.9, NA, NA, 10.08), WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9,
> 29.5, 4.5, NA, NA, 23), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT",
> "FLINT", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")),
> row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class =
> "data.frame")
>
> 1) Simple aggregation with 2 variables:
> aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean,
> na.rm = TRUE)
>
> 2) Using the dot notation - different results:
> aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
>
> 3) Using dplyr, I get the same results as #1:
> group_by(my_data, RAWMAT) %>%
> summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
>
> 4) It gets weirder: using all columns in #1 give the same results as in
> #2 but different from #1 and #3
> aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
> PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN = mean,
> na.rm = TRUE)
>
> So it seems it is not only due to the notation (cbind() vs. dot). Is it
> a bug? A peculiar thing in my dataset? I tend to think this could be due
> to some variables (or their names) as all notations seem to agree when I
> remove some variables (although I haven't found out which variable(s) is
> (are) at fault), e.g.:
>
> my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
> "FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
> "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
> 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
> 1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
> Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
> 32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
> 25.149, 33.427, 20.683, 62.783, 26.417, 35.297), RAWMAT = c("FLINT",
> "FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS",
> "HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L,
> 113L, 114L, 115L), class = "data.frame")
>
> aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT, data =
> my_data2, FUN = mean, na.rm = TRUE)
>
> aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
>
> group_by(my_data2, RAWMAT) %>%
> summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
>
>
> Thank you in advance for any hint.
> Best wishes,
> Ivan
>
>
>
>
> *LEIBNIZ-ZENTRUM*
> *FÜR ARCHÄOLOGIE*
>
> *Dr. Ivan CALANDRA*
> **Head of IMPALA (IMaging Platform At LeizA)
>
> *MONREPOS* Archaeological Research Centre, Schloss Monrepos
> 56567 Neuwied, Germany
>
> T: +49 2631 9772 243
> T: +49 6131 8885 543
> ivan.calan...@leiza.de
>
> leiza.de
> ResearchGate
>
>
> LEIZA is a foundation under public law
Re: [R] aggregate formula - differing results
It seems that the issue are the missings. If in #1 you use the dataset
na.omit(my_data) instead of my_data, you get the same output that in #2 and in
#4, where all observations with missing data are removed since you are
including all the variables.
The second dataset has no issue since it has no missing data.
Iago
De: R-help de part de Ivan Calandra
Enviat el: dilluns, 4 de setembre de 2023 11:44
Per a: R-help
Tema: [R] aggregate formula - differing results
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
25.149, 33.427, 20.683, 62.783, 26.417, 35.297), PLATWIDTH = c(38.84,
NA, 15.33, 30.37, 11.44, 14.88, 13.86, NA, NA, 26.71), PLATTHICK =
c(8.67, NA, 7.99, 11.69, 3.3, 16.52, 4.58, NA, NA, 9.35), EPA = c(78,
NA, 78, 54, 72, 49, 56, NA, NA, 56), THICKNESS = c(10.97, NA, 9.36, 6.4,
5.89, 11.05, 4.9, NA, NA, 10.08), WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9,
29.5, 4.5, NA, NA, 23), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT",
"FLINT", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")),
row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class =
"data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using dplyr, I get the same results as #1:
group_by(my_data, RAWMAT) %>%
summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
4) It gets weirder: using all columns in #1 give the same results as in
#2 but different from #1 and #3
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
So it seems it is not only due to the notation (cbind() vs. dot). Is it
a bug? A peculiar thing in my dataset? I tend to think this could be due
to some variables (or their names) as all notations seem to agree when I
remove some variables (although I haven't found out which variable(s) is
(are) at fault), e.g.:
my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
25.149, 33.427, 20.683, 62.783, 26.417, 35.297), RAWMAT = c("FLINT",
"FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS",
"HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L,
113L, 114L, 115L), class = "data.frame")
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT, data =
my_data2, FUN = mean, na.rm = TRUE)
aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
group_by(my_data2, RAWMAT) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
Thank you in advance for any hint.
Best wishes,
Ivan
*LEIBNIZ-ZENTRUM*
*F�R ARCH�OLOGIE*
*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)
*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany
T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calan...@leiza.de
leiza.de
ResearchGate
LEIZA is a foundation under public law of the State of
Rhineland-Palatinate and the City of Mainz. Its headquarters are in
Mainz. Supervision is carried out by the Ministry of Science and Health
of the State of Rhineland-Palatinate. LEIZA is a research museum of the
Leibniz Association.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[[alternative HTML version deleted]]
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R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting
[R] aggregate formula - differing results
Dear useRs,
I have just stumbled across a behavior in aggregate() that I cannot
explain. Any help would be appreciated!
Sample data:
my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
25.149, 33.427, 20.683, 62.783, 26.417, 35.297), PLATWIDTH = c(38.84,
NA, 15.33, 30.37, 11.44, 14.88, 13.86, NA, NA, 26.71), PLATTHICK =
c(8.67, NA, 7.99, 11.69, 3.3, 16.52, 4.58, NA, NA, 9.35), EPA = c(78,
NA, 78, 54, 72, 49, 56, NA, NA, 56), THICKNESS = c(10.97, NA, 9.36, 6.4,
5.89, 11.05, 4.9, NA, NA, 10.08), WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9,
29.5, 4.5, NA, NA, 23), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT",
"FLINT", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")),
row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class =
"data.frame")
1) Simple aggregation with 2 variables:
aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
2) Using the dot notation - different results:
aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
3) Using dplyr, I get the same results as #1:
group_by(my_data, RAWMAT) %>%
summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
4) It gets weirder: using all columns in #1 give the same results as in
#2 but different from #1 and #3
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH,
PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN = mean,
na.rm = TRUE)
So it seems it is not only due to the notation (cbind() vs. dot). Is it
a bug? A peculiar thing in my dataset? I tend to think this could be due
to some variables (or their names) as all notations seem to agree when I
remove some variables (although I haven't found out which variable(s) is
(are) at fault), e.g.:
my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100",
"FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", "HORN-103",
"HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 130.1, 140.41,
121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = c(1736.87, 1571.83,
1656.46, 1247.18, 1177.47, 1169.26, 444.61, 1791.48, 461.15, 1127.2),
Length = c(44.384, 29.831, 43.869, 48.011, 54.109, 41.742, 23.854,
32.075, 21.337, 35.459), Width = c(45.982, 67.303, 52.679, 26.42,
25.149, 33.427, 20.683, 62.783, 26.417, 35.297), RAWMAT = c("FLINT",
"FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS", "HORNFELS", "HORNFELS",
"HORNFELS", "HORNFELS")), row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L,
113L, 114L, 115L), class = "data.frame")
aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT, data =
my_data2, FUN = mean, na.rm = TRUE)
aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
group_by(my_data2, RAWMAT) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
Thank you in advance for any hint.
Best wishes,
Ivan
*LEIBNIZ-ZENTRUM*
*FÜR ARCHÄOLOGIE*
*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)
*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany
T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calan...@leiza.de
leiza.de
ResearchGate
LEIZA is a foundation under public law of the State of
Rhineland-Palatinate and the City of Mainz. Its headquarters are in
Mainz. Supervision is carried out by the Ministry of Science and Health
of the State of Rhineland-Palatinate. LEIZA is a research museum of the
Leibniz Association.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] [Pkg-Collaboratos] BioShapes Almost-Package
On 03/09/2023 10:47 p.m., Jeff Newmiller wrote: Leonard... the reason roxygen exists is to allow markup in source files to be used to automatically generate the numerous files required by standard R packages as documented in Writing R Extensions. If your goal is to not use source files this way then the solution is to not use roxygen at all. Just create those files yourself by directly editing them from scratch. Just a bit of elaboration on Jeff's suggestion -- here's the workflow I prefer to using Roxygen. Once you have a function that works: 1. install the package 2. set your working directory to the package "man" directory 3. run `prompt(functionname)` 4. edit `functionname.Rd` in the "man" directory, which will already be filled in as a skeleton help file, with comments describing what else to add. Don't run prompt() again after editing, or you'll lose all your edits. But this is a good way to get started. I think for the first few times the comments are really helpful, but I wouldn't mind a way to suppress them. Duncan Murdoch __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
