Evaluate an R expression in an environment constructed from data, possibly modifying (a copy of) the original data.
with(data, expr, …)
within(data, expr, …)
# S3 method for list
within(data, expr, keepAttrs = TRUE, …)data to use for constructing an environment. For the
default with method this may be an environment, a list, a
data frame, or an integer as in sys.call. For within,
it can be a list or a data frame.
expression to evaluate; particularly for within()
often a “compound” expression, i.e., of the form
{
a <- somefun()
b <- otherfun()
.....
rm(unused1, temp)
}
for the list method of within(),
a logical specifying if the resulting list should keep
the attributes from data and have its
names in the same order. Often this is unneeded as
the result is a named list anyway, and then keepAttrs =
FALSE is more efficient.
arguments to be passed to (future) methods.
For with, the value of the evaluated expr. For
within, the modified object.
with is a generic function that evaluates expr in a
local environment constructed from data. The environment has
the caller's environment as its parent. This is useful for
simplifying calls to modeling functions. (Note: if data is
already an environment then this is used with its existing parent.)
Note that assignments within expr take place in the constructed
environment and not in the user's workspace.
within is similar, except that it examines the environment
after the evaluation of expr and makes the corresponding
modifications to a copy of data (this may fail in the data
frame case if objects are created which cannot be stored in a data
frame), and returns it. within can be used as an alternative
to transform.
Thomas Lumley (2003) Standard nonstandard evaluation rules. http://developer.r-project.org/nonstandard-eval.pdf
# NOT RUN {
with(mtcars, mpg[cyl == 8 & disp > 350])
# is the same as, but nicer than
mtcars$mpg[mtcars$cyl == 8 & mtcars$disp > 350]
require(stats); require(graphics)
# examples from glm:
with(data.frame(u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12)),
list(summary(glm(lot1 ~ log(u), family = Gamma)),
summary(glm(lot2 ~ log(u), family = Gamma))))
aq <- within(airquality, { # Notice that multiple vars can be changed
lOzone <- log(Ozone)
Month <- factor(month.abb[Month])
cTemp <- round((Temp - 32) * 5/9, 1) # From Fahrenheit to Celsius
S.cT <- Solar.R / cTemp # using the newly created variable
rm(Day, Temp)
})
head(aq)
# example from boxplot:
with(ToothGrowth, {
boxplot(len ~ dose, boxwex = 0.25, at = 1:3 - 0.2,
subset = (supp == "VC"), col = "yellow",
main = "Guinea Pigs' Tooth Growth",
xlab = "Vitamin C dose mg",
ylab = "tooth length", ylim = c(0, 35))
boxplot(len ~ dose, add = TRUE, boxwex = 0.25, at = 1:3 + 0.2,
subset = supp == "OJ", col = "orange")
legend(2, 9, c("Ascorbic acid", "Orange juice"),
fill = c("yellow", "orange"))
})
# alternate form that avoids subset argument:
with(subset(ToothGrowth, supp == "VC"),
boxplot(len ~ dose, boxwex = 0.25, at = 1:3 - 0.2,
col = "yellow", main = "Guinea Pigs' Tooth Growth",
xlab = "Vitamin C dose mg",
ylab = "tooth length", ylim = c(0, 35)))
with(subset(ToothGrowth, supp == "OJ"),
boxplot(len ~ dose, add = TRUE, boxwex = 0.25, at = 1:3 + 0.2,
col = "orange"))
legend(2, 9, c("Ascorbic acid", "Orange juice"),
fill = c("yellow", "orange"))
# }