Whenever similar objects are to be handled with similar code, having the data
frames stored in lists or even as one big data frame is preferred. If you can
load them as such, half the complexity is addressed right there.
The for loop processing is usually wrapped up using base apply functions or
"plyr" package functions. Those idioms are not necessarily faster than for
loops, but they can wrap up some common split and assemble steps cleanly.
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Sent from my phone. Please excuse my brevity.
Kaiyin Zhong <kindlych...@gmail.com> wrote:
>> for (d in paste('df', 1:3, sep='')) {
>+ assign(d, as.data.frame(replicate(3, rnorm(4))))
>+ }
>> dats = list(df1,df2,df3)
>> for (i in 1:length(dats)) {
>+ names(dats[[i]]) = c('w', 'l', 'h')
>+ }
>> dats
>[[1]]
> w l h
>1 1.24319239 -0.05543649 0.05409178
>2 0.05124331 -1.89346950 0.33896273
>3 -1.69686777 -0.35963008 -0.91720034
>4 1.30786112 -0.23953238 0.94139356
>
>[[2]]
> w l h
>1 -1.238519 -0.12352187 -1.2577607
>2 1.180469 2.38836107 2.9139199
>3 1.494369 -0.07088712 0.2372746
>4 1.942535 1.47911615 1.1431675
>
>[[3]]
> w l h
>1 1.0198692 -1.4222194 1.9486072
>2 0.3057461 1.7630326 -0.6501801
>3 -0.5576854 -1.1637263 -0.1782680
>4 0.6625268 0.6913202 0.9588915
>> i = 1
>> for (n in paste('df', 1:3, sep='')) {
>+ assign(n, dats[[i]])
>+ i = i+1
>+ }
>> df1
> w l h
>1 1.24319239 -0.05543649 0.05409178
>2 0.05124331 -1.89346950 0.33896273
>3 -1.69686777 -0.35963008 -0.91720034
>4 1.30786112 -0.23953238 0.94139356
>
> [[alternative HTML version deleted]]
>
>______________________________________________
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>and provide commented, minimal, self-contained, reproducible code.
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