| with | R Documentation |
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 class 'list' within(data, expr, keepAttrs = TRUE, ...)
data |
data to use for constructing an environment. For the
default |
expr |
expression to evaluate; particularly for {
a <- somefun()
b <- otherfun()
.....
rm(unused1, temp)
}
|
keepAttrs |
for the |
... |
arguments to be passed to (future) methods. |
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.
For with, the value of the evaluated expr. For
within, the modified object.
For interactive use this is very effective and nice to read. For
programming however, i.e., in one's functions, more care is
needed, and typically one should refrain from using with(), as,
e.g., variables in data may accidentally override local
variables, see the reference.
Further, when using modeling or graphics functions with an explicit
data argument (and typically using formulas),
it is typically preferred to use the data argument of that
function rather than to use with(data, ...).
Thomas Lumley (2003) Standard nonstandard evaluation rules. https://developer.r-project.org/nonstandard-eval.pdf
evalq, attach, assign,
transform.
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"))
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