Dear list,
I have two things I am struggling...
# First
set.seed(123)
myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T),
Feed = sample(letters[1:5], 20, replace=T),
value=rnorm(20) )
# Mean for Light
myD$meanLight <- unlist( lapply( myD$Light,
function(x) mean( myD$value[myD$Light == x]) ) )
# Mean for Feed
myD$meanFeed <- unlist( lapply( myD$Feed,
function(x) mean( myD$value[myD$Feed == x]) ) )
myD
# I would like to get a new Var "meanLightFeed"
# holding the "Group-Mean" for each combination (eg. A:a = 0.821581)
# by(myD$value, list(myD$Light, myD$Feed), mean)[[1]]
# Second
set.seed(321)
myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T),
value=rnorm(20) )
w1 <- tapply(myD$value, myD$Light, mean)
w1
# > w1
# A B
# 0.4753412 -0.2108387
myfun <- function(x) (myD$value > w1[x] & myD$value < w1[x] * 1.5)
I would like to have a TRUE/FALSE-Variable depend on the constraint in
"myfun" for each level in "Light"...
As always - thanks for any help!!
Patrick
Have a look at cast() form the reshape package.
library(reshape)
set.seed(123)
myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T),
Feed = sample(letters[1:5], 20, replace=T),
value=rnorm(20) )
cast(myD, Light ~ ., fun = mean)
cast(myD, Feed ~ ., fun = mean)
cast(myD, Light + Feed ~ ., fun = mean)
cast(myD, Light ~ Feed, fun = mean)
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Patrick Hausmann
Verzonden: dinsdag 3 februari 2009 16:12
Aan: r-help at r-project.org
Onderwerp: [R] lapply and aggregate function
Dear list,
I have two things I am struggling...
# First
set.seed(123)
myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T),
Feed = sample(letters[1:5], 20, replace=T),
value=rnorm(20) )
# Mean for Light
myD$meanLight <- unlist( lapply( myD$Light,
function(x) mean( myD$value[myD$Light == x]) )
)
# Mean for Feed
myD$meanFeed <- unlist( lapply( myD$Feed,
function(x) mean( myD$value[myD$Feed == x]) ) )
myD
# I would like to get a new Var "meanLightFeed"
# holding the "Group-Mean" for each combination (eg. A:a = 0.821581)
# by(myD$value, list(myD$Light, myD$Feed), mean)[[1]]
# Second
set.seed(321)
myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T),
value=rnorm(20) )
w1 <- tapply(myD$value, myD$Light, mean)
w1
# > w1
# A B
# 0.4753412 -0.2108387
myfun <- function(x) (myD$value > w1[x] & myD$value < w1[x] * 1.5)
I would like to have a TRUE/FALSE-Variable depend on the constraint in
"myfun" for each level in "Light"...
As always - thanks for any help!!
Patrick
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You might want to look at the doBy package For (1), you could use summaryBy(value~Light+Feed,data=myD, FUN=mean) and for (2), the transformBy function would be helpful David Freedman Patrick Hausmann wrote:> > Dear list, > > I have two things I am struggling... > > # First > set.seed(123) > myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T), > Feed = sample(letters[1:5], 20, replace=T), > value=rnorm(20) ) > > # Mean for Light > myD$meanLight <- unlist( lapply( myD$Light, > function(x) mean( myD$value[myD$Light == x]) ) ) > # Mean for Feed > myD$meanFeed <- unlist( lapply( myD$Feed, > function(x) mean( myD$value[myD$Feed == x]) ) ) > myD > > # I would like to get a new Var "meanLightFeed" > # holding the "Group-Mean" for each combination (eg. A:a = 0.821581) > # by(myD$value, list(myD$Light, myD$Feed), mean)[[1]] > > > # Second > set.seed(321) > myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T), > value=rnorm(20) ) > > w1 <- tapply(myD$value, myD$Light, mean) > w1 > # > w1 > # A B > # 0.4753412 -0.2108387 > > myfun <- function(x) (myD$value > w1[x] & myD$value < w1[x] * 1.5) > > I would like to have a TRUE/FALSE-Variable depend on the constraint in > "myfun" for each level in "Light"... > > As always - thanks for any help!! > Patrick > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > >-- View this message in context: http://www.nabble.com/lapply-and-aggregate-function-tp21811834p21812057.html Sent from the R help mailing list archive at Nabble.com.
> # Second > set.seed(321) > myD <- data.frame( Light = sample(LETTERS[1:2], 10, replace=T), > value=rnorm(20) ) > > w1 <- tapply(myD$value, myD$Light, mean) > w1 > # > w1 > # A B > # 0.4753412 -0.2108387 > > myfun <- function(x) (myD$value > w1[x] & myD$value < w1[x] * 1.5) > > I would like to have a TRUE/FALSE-Variable depend on the constraint in > "myfun" for each level in "Light"...You could use ddply from the plyr package for this: install.packages("plyr") library(plyr) ddply(myD, .(Light), transform, constraint = value > mean(value) & value < mean(value) * 1.5) This applies the transform function to each subset defined by Light, and then joins all the pieces back together in a single data frame. You can use a similar approach for the other parts: myD <- ddply(myD, .(Light), transform, meanLight = mean(value)) myD <- ddply(myD, .(Feed), transform, meanFeed = mean(value)) myD <- ddply(myD, .(Feed, Light), transform, meanFeedLight = mean(value)) Hadley -- http://had.co.nz/