OP 方法还有其他替代方法,不需要预先设置密钥
矢量扫描&get()
dt[rn >= 5 & get(nameAsVect) == max(get(nameAsVect))]
rn B
1: 6 2
2: 8 2
3: 10 2
矢量扫描&eval(parse())
马特·道尔 (Matt Dowle) 在他的回答选择/分配给 data.table 变量,其名称存储在字符向量中 https://stackoverflow.com/a/12392269/3817004:
eval(parse(text = sprintf("dt[rn >= 5 & %s == max(%s)]", nameAsVect, nameAsVect)))
rn B
1: 6 2
2: 8 2
3: 10 2
非等值连接
版本 v1.9.8(CRAN 2016 年 11 月 25 日发布),data.table
已经获得了做事的能力非等值连接.
max.count <- dt[, max(get(nameAsVect))]
dt[dt[.(5, max.count), on = c("rn>=V1", paste0(nameAsVect, "==V2")), which = TRUE]]
rn B
1: 6 2
2: 8 2
3: 10 2
或者(我喜欢的方式)
mdt <- dt[, c(.(rn = 5), lapply(.SD, max)), .SDcols = nameAsVect]
dt[dt[mdt, on = c("rn>=rn", nameAsVect), which = TRUE]]
rn B
1: 6 2
2: 8 2
3: 10 2
基准
创建基准数据:
n_row <- 1e6L
set.seed(123L)
DT <- data.table(
rn = sample(1:10, n_row, TRUE),
B = sample(1:2, n_row, TRUE)
)
运行基准测试:
library(microbenchmark)
bm <- microbenchmark(
vec_scan_hard_coded = {
dt <- copy(DT)
dt[rn >= 5L & B == 2L]
},
OP_keyed = {
dt <- copy(DT)
setkeyv(dt, c("rn", nameAsVect))
max.count <- max(dt[, nameAsVect, with=FALSE])
dt[J(5:max(rn), max.count), nomatch = 0L]
},
vec_scan_get = {
dt <- copy(DT)
dt[rn >= 5 & get(nameAsVect) == max(get(nameAsVect))]
},
vec_scan_eval_parse = {
dt <- copy(DT)
eval(parse(text = sprintf("dt[rn >= 5 & %s == max(%s)]", nameAsVect, nameAsVect)))
},
nej1 = {
dt <- copy(DT)
max.count <- dt[, max(get(nameAsVect))]
dt[dt[.(5, max.count), on = c("rn>=V1", paste0(nameAsVect, "==V2")), which = TRUE]]
},
nej1_keyed = {
dt <- copy(DT)
setkeyv(dt, c("rn", nameAsVect))
max.count <- dt[, max(get(nameAsVect))]
dt[dt[.(5, max.count), on = c("rn>=V1", paste0(nameAsVect, "==V2")), which = TRUE]]
},
nej2 = {
dt <- copy(DT)
mdt <- dt[, c(.(rn = 5), lapply(.SD, max)), .SDcols = nameAsVect]
dt[dt[mdt, on = c("rn>=rn", nameAsVect), which = TRUE]]
},
nej2_keyed = {
dt <- copy(DT)
setkeyv(dt, c("rn", nameAsVect))
mdt <- dt[, c(.(rn = 5), lapply(.SD, max)), .SDcols = nameAsVect]
dt[dt[mdt, on = c("rn>=rn", nameAsVect), which = TRUE]]
},
times = 100L
)
print(bm)
对于 1 M 行和大约 30 万行的结果集,矢量扫描方法是最快的:
Unit: milliseconds
expr min lq mean median uq max neval cld
vec_scan_hard_coded 19.03159 20.86890 42.70820 24.38040 27.57417 219.5682 100 a
OP_keyed 31.49025 34.50825 52.46168 37.74204 40.84953 194.7676 100 a
vec_scan_get 20.60384 25.75461 46.37579 27.29287 29.55892 185.5867 100 a
vec_scan_eval_parse 20.81188 23.92598 36.81940 26.69742 29.27687 183.5323 100 a
nej1 53.85361 59.32608 85.32623 62.12509 65.15083 227.1221 100 b
nej1_keyed 52.89946 58.37457 77.38969 61.03312 64.32072 221.3292 100 b
nej2 53.25590 59.69762 88.92513 61.98481 65.05738 285.2495 100 b
nej2_keyed 53.25061 58.61453 81.22925 61.14885 63.56159 274.0207 100 b