Thanks to all for the helpful suggestions, I was able to get good start from
there.
Cheers,
Dylan
On Thursday 26 January 2006 12:03 pm, Gabor Grothendieck wrote:
Would this do?
boxplot(Sepal.Length ~ Species, iris, horizontal = TRUE)
library(Hmisc)
summary(Sepal.Length ~ Species, iris,
Greetings,
I have a set of bivariate data: one variable (vegetation type) which is
categorical, and one (computed annual insolation) which is continuous.
Plotting veg_type ~ insolation produces a nice overview of the patterns that
I can see in the source data. However, due to the large number
. Box
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Dylan Beaudette
Sent: Thursday, January 26, 2006 11:11 AM
To: r-help@stat.math.ethz.ch
Subject: [R] understanding patterns in categorical vs. continuous data
Greetings,
I have a set
You might prefer boxplot(insolation~veg_type) as a graphic. That will
give you quantiles. To get the actual numeric values you could
for (i in levels(veg_type)) {
print(i)
quantile(insolation[veg_type==i])
}
see ?quantile for more help.
Dylan Beaudette wrote:
Greetings,
I have a
Would this do?
boxplot(Sepal.Length ~ Species, iris, horizontal = TRUE)
library(Hmisc)
summary(Sepal.Length ~ Species, iris, fun = summary)
On 1/26/06, Dylan Beaudette [EMAIL PROTECTED] wrote:
Greetings,
I have a set of bivariate data: one variable (vegetation type) which is
categorical,
From: Dave Roberts
You might prefer boxplot(insolation~veg_type) as a graphic.
That will
give you quantiles. To get the actual numeric values you could
for (i in levels(veg_type)) {
print(i)
quantile(insolation[veg_type==i])
}
see ?quantile for more help.
If you want the
