Do Things Repeatedly

do() provides a natural syntax for repetition tuned to assist with replication and resampling methods.

Usage

do(object, ...)

# S3 method for class 'numeric'
do(object, ...)

# Default S3 method
do(object, ...)

Do(n = 1L, cull = NULL, mode = "default", algorithm = 1, parallel = TRUE)

# S3 method for class 'repeater'
print(x, ...)

# S4 method for class 'repeater,ANY'
e1 * e2

Arguments

object: an object
...: additional arguments
n: number of times to repeat
cull: function for culling output of objects being repeated. If NULL, a default culling function is used. The default culling function is currently aware of objects of types lme, lm, htest, table, cointoss, and matrix.
mode: target mode for value returned
algorithm: a number used to select the algorithm used. Currently numbers below 1 use an older algorithm and numbers >=1 use a newer algorithm which is faster in some situations.
parallel: a logical indicating whether parallel computation should be attempted using the parallel package (if it is installed and loaded).
x: an object created by do.
e1: an object (in cases documented here, the result of running do)
e2: an object (in cases documented here, an expression to be repeated)

Value

do returns an object of class repeater which is only useful in the context of the operator *. See the examples.

Note

do is a thin wrapper around Do to avoid collision with dplyr::do() from the dplyr package.

Naming

The names used in the object returned from do() are inferred from the objects created in each replication. Roughly, this the strategy employed.

If the objects have names, those names are inherited, if possible.
If the objects do not have names, but do() is used with a simple function call, the name of that function is used. Example: do(3) * mean(~height, data = Galton) produces a data frame with a variable named mean.
In cases where names are not easily inferred and a single result is produced, it is named result.

To get different names, one can rename the objects as they are created, or rename the result returned from do(). Example of the former: do(3) * c(mean_height = mean(~height, data = resample(Galton))). If you like the name result, try wrapping the expression in { }: do(3) * { mean(~ height, data = resample(Galton)) }.

Author

Daniel Kaplan ([email protected]) and Randall Pruim ([email protected])

Examples

do(3) * rnorm(1)
#>        rnorm
#> 1  0.9537124
#> 2 -0.3084539
#> 3 -0.1936198
do(3) * "hello"
#>   hello
#> 1 hello
#> 2 hello
#> 3 hello
do(3) * 1:4
#>   V1 V2 V3 V4
#> 1  1  2  3  4
#> 2  1  2  3  4
#> 3  1  2  3  4
do(3) * mean(rnorm(25))
#>          mean
#> 1 -0.32228041
#> 2  0.13824416
#> 3  0.04960635
do(3) * lm(shuffle(height) ~ sex + mother, Galton)
#>   Intercept       sexM      mother    sigma    r.squared        F numdf dendf
#> 1  72.74839 -0.1303453 -0.09238138 3.580112 0.0037920883 1.703419     2   895
#> 2  64.77094 -0.1727158  0.03244444 3.585030 0.0010531400 0.471777     2   895
#> 3  65.71198 -0.1644362  0.01769318 3.585709 0.0006749052 0.302224     2   895
#>   .row .index
#> 1    1      1
#> 2    1      2
#> 3    1      3
do(3) * anova(lm(shuffle(height) ~ sex + mother, Galton))
#>                  source  df           SS         MS          F      pval .row
#> sex...1             sex   1 1.200378e+01 12.0037764 0.93463244 0.3339232    1
#> mother...2       mother   1 8.294034e+00  8.2940336 0.64578618 0.4218366    2
#> Residuals...3 Residuals 895 1.149476e+04 12.8433124         NA        NA    3
#> sex...4             sex   1 1.623471e+00  1.6234711 0.12621332 0.7224747    1
#> mother...5       mother   1 1.130675e+00  1.1306750 0.08790193 0.7669303    2
#> Residuals...6 Residuals 895 1.151231e+04 12.8629142         NA        NA    3
#> sex...7             sex   1 1.758369e-01  0.1758369 0.01366807 0.9069572    1
#> mother...8       mother   1 8.987272e-01  0.8987272 0.06985945 0.7916024    2
#> Residuals...9 Residuals 895 1.151399e+04 12.8647908         NA        NA    3
#>               .index
#> sex...1            1
#> mother...2         1
#> Residuals...3      1
#> sex...4            2
#> mother...5         2
#> Residuals...6      2
#> sex...7            3
#> mother...8         3
#> Residuals...9      3
do(3) * c(sample.mean = mean(rnorm(25)))
#>   sample.mean
#> 1 -0.07759286
#> 2  0.22312550
#> 3  0.04760878
# change the names on the fly
do(3) * mean(~height, data = resample(Galton))
#>       mean
#> 1 66.61247
#> 2 66.69477
#> 3 66.88731
do(3) * c(mean_height = mean(~height, data = resample(Galton)))
#>   mean_height
#> 1    66.80846
#> 2    66.41080
#> 3    66.75022
# this results in the name being `result`
do(3) * { mean(~height, data = resample(Galton)) }
#>     result
#> 1 66.65891
#> 2 66.68875
#> 3 66.68641
set.rseed(1234)
do(3) * tally( ~sex|treat, data=resample(HELPrct))
#>   female.no male.no female.yes male.yes
#> 1        45     184         59      165
#> 2        52     179         58      164
#> 3        52     163         43      195
set.rseed(1234)  # re-using seed gives same results again
do(3) * tally( ~sex|treat, data=resample(HELPrct))
#>   female.no male.no female.yes male.yes
#> 1        45     184         59      165
#> 2        52     179         58      164
#> 3        52     163         43      195