with: Evaluate an Expression in a Data Environment

Description

Evaluate an R expression in an environment constructed from data, possibly modifying (a copy of) the original data.

Usage

with(data, expr, …)
within(data, expr, …)
# S3 method for list
within(data, expr, keepAttrs = TRUE, …)

Arguments

data

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.

expr

expression to evaluate; particularly for within() often a “compound” expression, i.e., of the form

   {
     a <- somefun()
     b <- otherfun()
     .....
     rm(unused1, temp)
   }

keepAttrs

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.

Value

For with, the value of the evaluated expr. For within, the modified object.

Details

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.

References

Thomas Lumley (2003) Standard nonstandard evaluation rules. http://developer.r-project.org/nonstandard-eval.pdf

Examples

Run this code

# 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"))
# }

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning