R 在data.table中使用多个键以获得条件搜索
首先,我有一个R 在data.table中使用多个键以获得条件搜索,r,data.table,key,R,Data.table,Key,首先,我有一个数据表,我想根据某些条件获得一个子集,例如,我有 library(data.table) dt <- data.table(rn=1:10, B=rep(1:2, 5)) dt # rn B # 1: 1 1 # 2: 2 2 # 3: 3 1 # 4: 4 2 # 5: 5 1 # 6: 6 2 # 7: 7 1 # 8: 8 2 # 9: 9 1 #10: 10 2` 我想我可以做到: setkeyv(dt, c("rn", nameAsVect
数据表
,我想根据某些条件获得一个子集,例如,我有
library(data.table)
dt <- data.table(rn=1:10, B=rep(1:2, 5))
dt
# rn B
# 1: 1 1
# 2: 2 2
# 3: 3 1
# 4: 4 2
# 5: 5 1
# 6: 6 2
# 7: 7 1
# 8: 8 2
# 9: 9 1
#10: 10 2`
我想我可以做到:
setkeyv(dt, c("rn", nameAsVect))
max.count <- max(dt[, nameAsVect, with=FALSE])
dt[J(5:max(rn), max.count), ]
# rn B
#1: 5 2
#2: 6 2
#3: 7 2
#4: 8 2
#5: 9 2
#6: 10 2
setkeyv(dt,c(“rn”,nameAsVect))
max.count子句nomatch=0
在[]
中缺失
换行
dt[J(5:max(rn), max.count), nomatch=0]
结果将是:
rn B
1: 6 2
2: 8 2
3: 10 2
除了OP的方法外,还有其他方法不需要事先设置关键点
向量扫描&get()
向量扫描&eval(parse())
Matt Dowle在以下文章中提出的另一种方法:
非等联接
在v1.9.8版(2016年11月25日)中,data.table
已获得进行非等联接的能力
或者(我喜欢的方式)
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)
)
dt[rn >= 5 & get(nameAsVect) == max(get(nameAsVect))]
rn B
1: 6 2
2: 8 2
3: 10 2
eval(parse(text = sprintf("dt[rn >= 5 & %s == max(%s)]", nameAsVect, nameAsVect)))
rn B
1: 6 2
2: 8 2
3: 10 2
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
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
n_row <- 1e6L
set.seed(123L)
DT <- data.table(
rn = sample(1:10, n_row, TRUE),
B = sample(1:2, n_row, TRUE)
)
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)
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