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, ...)

Arguments

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.

Value

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

See Also

evalq, attach, assign, transform.

Examples

require(stats); require(graphics)
#examples from glm:

library(MASS)
with(anorexia, {
    anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
                    family = gaussian)
    summary(anorex.1)
})



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)

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))))

# 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"))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(base)
> png(filename="/home/ddbj/snapshot/RGM3/R_rel/result/base/with.Rd_%03d_medium.png", width=480, height=480)
> ### Name: with
> ### Title: Evaluate an Expression in a Data Environment
> ### Aliases: with with.default within within.list within.data.frame
> ### Keywords: data programming
> 
> ### ** Examples
> 
> require(stats); require(graphics)
> #examples from glm:
> ## No test: 
> library(MASS)
> with(anorexia, {
+     anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
+                     family = gaussian)
+     summary(anorex.1)
+ })

Call:
glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-14.1083   -4.2773   -0.5484    5.4838   15.2922  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  49.7711    13.3910   3.717 0.000410 ***
Prewt        -0.5655     0.1612  -3.509 0.000803 ***
TreatCont    -4.0971     1.8935  -2.164 0.033999 *  
TreatFT       4.5631     2.1333   2.139 0.036035 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 48.69504)

    Null deviance: 4525.4  on 71  degrees of freedom
Residual deviance: 3311.3  on 68  degrees of freedom
AIC: 489.97

Number of Fisher Scoring iterations: 2

> ## End(No test)
> 
> 
> 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)
  Ozone Solar.R Wind Month      S.cT cTemp   lOzone
1    41     190  7.4   May  9.793814  19.4 3.713572
2    36     118  8.0   May  5.315315  22.2 3.583519
3    12     149 12.6   May  6.394850  23.3 2.484907
4    18     313 11.5   May 18.742515  16.7 2.890372
5    NA      NA 14.3   May        NA  13.3       NA
6    28      NA 14.9   May        NA  18.9 3.332205
> 
> 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))))
[[1]]

Call:
glm(formula = lot1 ~ log(u), family = Gamma)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.04008  -0.03756  -0.02637   0.02905   0.08641  

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.0165544  0.0009275  -17.85 4.28e-07 ***
log(u)       0.0153431  0.0004150   36.98 2.75e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Gamma family taken to be 0.002446059)

    Null deviance: 3.51283  on 8  degrees of freedom
Residual deviance: 0.01673  on 7  degrees of freedom
AIC: 37.99

Number of Fisher Scoring iterations: 3


[[2]]

Call:
glm(formula = lot2 ~ log(u), family = Gamma)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.05574  -0.02925   0.01030   0.01714   0.06371  

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.0239085  0.0013265  -18.02 4.00e-07 ***
log(u)       0.0235992  0.0005768   40.91 1.36e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Gamma family taken to be 0.001813354)

    Null deviance: 3.118557  on 8  degrees of freedom
Residual deviance: 0.012672  on 7  degrees of freedom
AIC: 27.032

Number of Fisher Scoring iterations: 3


> 
> # 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"))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>