在 base R 中，为什么 Select 列，然后过滤行比反之更快：过滤行，然后 Select 列

下面的代码根据$Weight列中的值更改$type列中的值.

n <- 1e3; m <- n*10
Treshold <- 50
wts      <-runif(m)
df  <- data.frame(id=seq_len(m), weight=wts * 100, type='L')

library(microbenchmark)
microbenchmark(
"df-col-row" = (df$type[df$weight < Treshold]   <- "M"),
"df-row-col" = (df[df$weight < Treshold, ]$type <- "M")
)
#
#Unit: microseconds
#       expr   min     lq    mean median     uq    max neval
# df-col-row  80.6  87.65 145.429  89.55 104.55 5109.1   100
# df-row-col 564.9 586.10 618.496 592.40 618.90 1601.0   100

为什么第一种替代方案比第二种方案快？

Update 1
正如预期的那样，添加的列越多，差异就越大.

d9  <- data.frame(type='L', weight=wts * 100, c3=3, c4=4, c5=5, c6=6, c7=7, c8=8, c9=9)
microbenchmark(
"df-row-9col" = (d9[d9$weight < Treshold, ]$type <- "M")
)
# nit: microseconds
#         expr   min      lq     mean median      uq  max neval
# df-row-9col 950.1 1091.55 1267.982 1111.1 1172.45 5806   100

Update 2
在第一个备选方案中，df被复制一次，在第二个备选方案中，被复制两次.

tracemem(df)
df$type[df$weight < Treshold]   <- "M"    # Alt 1.
#tracemem[0x000002c92d2b87c8 -> 0x000002c92d2b9498]: $<-.data.frame $<- 

df[df$weight < Treshold, ]$type <- "M"    # Alt 2.
#tracemem[0x000002c92d2b9498 -> 0x000002c92d2b9ad8]: 
#tracemem[0x000002c92d2b9ad8 -> 0x000002c92d2c47d8]: [<-.data.frame [<-
untracemem(df)