Consider the following benchmark (R 3.4.1 on Windows machine):
library(rbenchmark)
mtx <- matrix(runif(1e8), ncol = 100)
df <- as.data.frame(mtx)
colnames(mtx) <- colnames(df) <- paste0("V", 1:100)
benchmark(
mtx[5000:7000, 80],
mtx[5000:7000, "V80"],
mtx[, "V80"][5000:7000],
mtx[, "V80", drop = FALSE][5000:7000, ],
mtx[5000:7000, , drop = FALSE][, "V80"],
#mtx$V80[5000:7000], # does not apply
replications = 5000
)
## test replications elapsed relative user.self sys.self user.child sys.child
## 4 mtx[, "V80", drop = FALSE][5000:7000, ] 5000 64.71 588.273 47.44 16.61 NA NA
## 3 mtx[, "V80"][5000:7000] 5000 72.15 655.909 52.90 18.18 NA NA
## 2 mtx[5000:7000, "V80"] 5000 0.11 1.000 0.11 0.00 NA NA
## 5 mtx[5000:7000, , drop = FALSE][, "V80"] 5000 7.47 67.909 5.89 1.47 NA NA
## 1 mtx[5000:7000, 80] 5000 0.13 1.182 0.12 0.00 NA NA
benchmark(
df[5000:7000, 80],
df[5000:7000, "V80"],
df[, "V80"][5000:7000],
df[, "V80", drop = FALSE][5000:7000, ],
df[5000:7000, , drop = FALSE][, "V80"],
df$V80[5000:7000],
replications = 5000
)
## test replications elapsed relative user.self sys.self user.child sys.child
## 6 df$V80[5000:7000] 5000 0.13 1.000 0.12 0.00 NA NA
## 4 df[, "V80", drop = FALSE][5000:7000, ] 5000 0.33 2.538 0.33 0.00 NA NA
## 3 df[, "V80"][5000:7000] 5000 0.17 1.308 0.17 0.00 NA NA
## 2 df[5000:7000, "V80"] 5000 0.15 1.154 0.16 0.00 NA NA
## 5 df[5000:7000, , drop = FALSE][, "V80"] 5000 13.63 104.846 12.91 0.39 NA NA
## 1 df[5000:7000, 80] 5000 0.19 1.462 0.17 0.00 NA NA
The time difference is pretty dramatic. Why is that? What is the recommended way of subsetting and why? Given the benchmarks, the mtx[i, colname] way for matrix and df$colname[i] (but it doesn't seem to make much difference) for data.frame seem to be most time-efficient, but are there any general reasons why we should prefer any of the approaches?