[R-es] pregunta

[Jesús Para Fernández]

Ok, gracias!!

> From: klaus.langohr en upc.edu
> To: r-help-es en r-project.org
> Date: Wed, 28 Oct 2015 10:00:02 +0100
> Subject: Re: [R-es] pregunta
> 
> Hola Jesús,
> 
> Usando la función by, prueba con
> 
> by(datos$Gain, datos$Diet, mean, na.rm=T)
> 
> para obviar los NA y con
> 
> by(datos$Gain, datos$Diet, function(x) sum(is.na(x)))
> 
> para contar los missings en cada categoría.
> 
> Puedes sustituir la función by también por la función tapply.
> 
> Klaus.
> 
> 
> On 28/10/2015 9:07, Jesús Para Fernández wrote:
> >
> >
> >
> > Me gusta la respuesta uqe has dado, pero si por ejemplo, alguno de los datos tiene datos faltantes, entonces devuelve NA.
> >
> > He probado con:
> > sapply(split(datos$uno, as.factor(datos$dos)), mean(na.rm=TRUE))
> >
> > pero da fallo.
> >
> > ¿Cómo se podría hacer para que devolviera además la media obviando los NA y que contara el numero de NA por categoria?
> >
> >> Date: Wed, 28 Oct 2015 00:13:45 +0100
> >> From: cof en qualityexcellence.es
> >> To: jbetancourt en iscmc.cmw.sld.cu
> >> CC: r-help-es en r-project.org
> >> Subject: Re: [R-es] pregunta
> >>
> >> Otras variantes con y sin paquetes adicionales...
> >>
> >>> sapply(split(datIn$Gain, as.factor(datIn$Diet)), mean)
> >>   d1  d2  d3
> >> 280 278 312
> >>> by(datIn$Gain, datIn$Diet, mean)
> >> datIn$Diet: d1
> >> [1] 280
> >> --------------------------------------------------------------
> >> datIn$Diet: d2
> >> [1] 278
> >> --------------------------------------------------------------
> >> datIn$Diet: d3
> >> [1] 312
> >>> library(dplyr)
> >>> summarise(group_by(datIn, Diet), mean(Gain))
> >> Source: local data frame [3 x 2]
> >>
> >>     Diet mean(Gain)
> >>    (chr)      (dbl)
> >> 1    d1        280
> >> 2    d2        278
> >> 3    d3        312
> >>> library(sqldf)
> >>> sqldf("select Diet,avg(Gain) from datIn group by Diet")
> >>    Diet avg(Gain)
> >> 1   d1       280
> >> 2   d2       278
> >> 3   d3       312
> >>
> >>
> >> Saludos,
> >> Carlos Ortega
> >> www.qualityexcellence.es
> >>
> >> 2015-10-27 22:45 GMT+01:00 eric <ericconchamunoz en gmail.com>:
> >>
> >>> tambien te sirve la funcion data.table ... si no tienes instalado el
> >>> paquete:
> >>>
> >>> install.packages("data.table")
> >>> library(data.table)
> >>> jbe <- as.data.table(read.table("tusdatos.txt"))
> >>> jbe.ave <- jbe[, .("ave"=mean(Gain)), by=.(Diet)]
> >>>
> >>>> jbe.ave
> >>>     Diet ave
> >>> 1:   d1 280
> >>> 2:   d2 278
> >>> 3:   d3 312
> >>>
> >>>
> >>> Saludos.
> >>>
> >>> Eric.
> >>>
> >>>
> >>> On 10/27/2015 05:16 PM, jbetancourt wrote:
> >>>
> >>>> Estimados
> >>>>
> >>>> Cuando existia epicalc,  hab�a una  manera muy f�cil de determinar la
> >>>> media de una variable (en esta caso Gain)  por grupos, en este caso (Diet).
> >>>> ?Como se puede hacer ahora?
> >>>>
> >>>>         Diet Gain
> >>>> 1    d1  270
> >>>> 2    d1  300
> >>>> 3    d1  280
> >>>> 4    d1  280
> >>>> 5    d1  270
> >>>> 6    d2  290
> >>>> 7    d2  250
> >>>> 8    d2  280
> >>>> 9    d2  290
> >>>> 10   d2  280
> >>>> 11   d3  290
> >>>> 12   d3  340
> >>>> 13   d3  330
> >>>> 14   d3  300
> >>>> 15   d3  300
> >>>>
> >>>> Saludos
> >>>> Jos�
> >>>>
> >>>>          [[alternative HTML version deleted]]
> >>>>
> >>>>
> >>>>
> >>>> _______________________________________________
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> >>>>
> >>>>
> >>> --
> >>> Forest Engineer
> >>> Master in Environmental and Natural Resource Economics
> >>> Ph.D. student in Sciences of Natural Resources at La Frontera University
> >>> Member in AguaDeTemu2030, citizen movement for Temuco with green city
> >>> standards for living
> >>>
> >>> Nota: Las tildes se han omitido para asegurar compatibilidad con algunos
> >>> lectores de correo.
> >>>
> >>>
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> >>>
> >>
> >>
> >> -- 
> >> Saludos,
> >> Carlos Ortega
> >> www.qualityexcellence.es
> >>
> >> 	[[alternative HTML version deleted]]
> >>
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