The behaviour of this function is very similar to attach
,
however it is environment specific, and
therefore only remains defined in a given function rather than in the Global Environment.
Hence, this function is much safer to use than the attach
, which
incidentally should never be used in your code. This
is useful primarily as a convenience function when you prefer to call the variable names
in condition
directly rather than indexing with condition$sample_size
or
with(condition, sample_size)
, for example.
Arguments
- ...
a comma separated list of
data.frame
,tibble
,list
, ormatrix
objects containing (column) elements that should be placed in the current working environment- omit
an optional character vector containing the names of objects that should not be attached to the current environment. For instance, if the objects named 'a' and 'b' should not be attached then use
omit = c('a', 'b')
. When NULL (default) all objects are attached- check
logical; check to see if the function will accidentally replace previously defined variables with the same names as in
condition
? Default isTRUE
, which will avoid this error- attach_listone
logical; if the element to be assign is a list of length one then assign the first element of this list with the associated name. This generally avoids adding an often unnecessary list 1 index, such as
name <- list[[1L]]
- RStudio_flags
logical; print R script output comments that disable flagged missing variables in RStudio? Requires the form
Attach(Design, RStudio_flags=TRUE)
or in an interactive debugging sessionAttach(condition, RStudio_flags=TRUE)
Details
Note that if you are using RStudio with the "Warn if variable used has no definition in scope"
diagnostic flag then using Attach()
will raise suspensions. To suppress such issues,
you can either disable such flags (the atomic solution) or evaluate the following output
in the R console and place the output in your working simulation file.
Attach(Design, RStudio_flags = TRUE)
References
Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations
with the SimDesign Package. The Quantitative Methods for Psychology, 16
(4), 248-280.
doi:10.20982/tqmp.16.4.p248
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
Carlo simulation. Journal of Statistics Education, 24
(3), 136-156.
doi:10.1080/10691898.2016.1246953
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
Design <- createDesign(N1=c(10,20),
N2=c(10,20),
sd=c(1,2))
Design
#> # A tibble: 8 × 3
#> N1 N2 sd
#> <dbl> <dbl> <dbl>
#> 1 10 10 1
#> 2 20 10 1
#> 3 10 20 1
#> 4 20 20 1
#> 5 10 10 2
#> 6 20 10 2
#> 7 10 20 2
#> 8 20 20 2
# does not use Attach()
Generate <- function(condition, fixed_objects ) {
# condition = single row of Design input (e.g., condition <- Design[1,])
N1 <- condition$N1
N2 <- condition$N2
sd <- condition$sd
group1 <- rnorm(N1)
group2 <- rnorm(N2, sd=sd)
dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
DV = c(group1, group2))
dat
}
# similar to above, but using the Attach() function instead of indexing
Generate <- function(condition, fixed_objects ) {
Attach(condition) # N1, N2, and sd are now 'attached' and visible
group1 <- rnorm(N1)
group2 <- rnorm(N2, sd=sd)
dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
DV = c(group1, group2))
dat
}
#####################
# NOTE: if you're using RStudio with code diagnostics on then evaluate + add the
# following output to your source file to manually support the flagged variables
Attach(Design, RStudio_flags=TRUE)
#> # !diagnostics suppress=N1,N2,sd
# Below is the same example, however with false positive missing variables suppressed
# when # !diagnostics ... is added added to the source file(s)
# !diagnostics suppress=N1,N2,sd
Generate <- function(condition, fixed_objects ) {
Attach(condition) # N1, N2, and sd are now 'attached' and visible
group1 <- rnorm(N1)
group2 <- rnorm(N2, sd=sd)
dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
DV = c(group1, group2))
dat
}