Hi:
Here's another approach using the reshape package:
library(reshape)
# id defines the grouping variables; the others are stacked, creating
# new variables 'variable' (containing the variable names as factor levels)
and
# 'value' (which holds the corresponding values)
dfm <- melt(df, id = c('country', 'food'))
# Strip off the leading 'X' and redefine as a factor with new levels
dfm$year <- factor(gsub('X', '', dfm$variable), levels = 2000:2003)
# Using foods as a groups = argument in xyplot():
xyplot(value ~ year | country, groups = food, data = dfm, type = c('p',
'l'))
# Using country and food as conditioning variables
xyplot(value ~ variable | country * food, data = dfm)
HTH,
Dennis
On Thu, Feb 17, 2011 at 10:41 PM, Geoff Russell
<geoffrey.russ...@gmail.com>wrote:
> df=data.frame(country=c("A","A","A","B","B","B"),
> food=rep(c("Apples","Pears","Bananas"),2),
> X2000=c(4,5,6,7,6,8),
> X2001=c(4,5,6,7,6,8),
> X2002=c(4,5,6,7,6,8),
> X2003=c(4,5,6,7,6,8));
>
> I have data in the above form trying to get a plot of each fruit over time
> year conditioned on country and food.
>
> I tried,
>
> xyplot(X2000+X2001+X2002+X2003~2000:2003|country*food,df)
>
> But think I need to transform the data, just not sure
> how. Any help gratefully received.
>
> Cheers,
> Geoff.
>
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>
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