有没有办法找到向量中最后一个元素的索引，该向量在R中有一个实际值（即不是N/a）
这是我的向量有没有办法找到向量中最后一个元素的索引，该向量在R中有一个实际值（即不是N/a）,r,na,R,Na,这是我的向量 x <- c("1", "1", "PNP004", "10", "10", NA, NA) xInd使用dplyr： dplyr::last(which(!is.na(yourvec))) 您可以从查找的结果中使用tail！是向量的.na tail(which(!is.na(x)), 1) #[1] 5 或者一个for循环 idxLNNA <- function
x <- c("1", "1", "PNP004", "10", "10", NA, NA)
xInd使用dplyr
：
dplyr::last(which(!is.na(yourvec)))
您可以从查找的结果中使用tail
！是向量的.na
tail(which(!is.na(x)), 1)
#[1] 5
或者一个for循环
idxLNNA <- function(x) {
if(length(x) > 0) {
for(i in length(x):1) if(!is.na(x[i])) break
if(i == 1 & is.na(x[i])) {0} else {i}
} else {0}
}
idxLNNA(x)
#[1] 5
或者从x
的length
中减去rev
命中
length(x) - which.min(rev(is.na(x))) + 1 #Will fail in case on only NA
#length(x) - match(FALSE, is.na(rev(x))) + 1 #Alternative
#[1] 5
基准：
library(microbenchmark)
fun <- alist(Dason = max(which(!is.na(x)))
, juljo = dplyr::last(which(!is.na(x)))
, GKiTail = tail(which(!is.na(x)), 1)
, GKiCum = which.max(cumsum(!is.na(x)))
, GKiRev = length(x) - which.min(rev(is.na(x))) + 1
, GKiMatch = length(x) - match(FALSE, is.na(rev(x))) + 1
, GKiFor = idxLNNA(x)
)
x <- numeric(1e6)
microbenchmark(list = fun, control=list(order="block"))
#Unit: microseconds
# expr min lq mean median uq max neval cld
# Dason 4855.744 5740.1355 7941.21809 7082.5535 7671.371 107165.201 100 bc
# juljo 4145.322 4616.7815 5756.38147 6134.9200 6625.008 7378.724 100 b
# GKiTail 4082.716 4434.3880 5576.70509 6051.1465 6489.966 7433.579 100 b
# GKiCum 6552.213 7445.1525 8632.12253 8988.8700 9420.481 16791.845 100 c
# GKiRev 4005.929 4138.4735 5845.70457 4212.7470 5851.034 101665.685 100 b
# GKiMatch 5180.600 5483.8545 7507.82723 5998.2760 7373.458 108327.967 100 bc
# GKiFor 1.541 1.5775 2.16462 1.6145 1.724 20.436 100 a
x <- rep(NA, 1e6) #Dason, GKiCum and GKiRev Fail
microbenchmark(list = fun[-c(1,4,5)], control=list(order="block"))
#Unit: milliseconds
# expr min lq mean median uq max neval cld
# juljo 3.011272 3.076340 3.525396 3.111676 3.494768 6.367839 100 a
# GKiTail 2.942336 3.014327 3.529691 3.063891 3.809653 6.136984 100 a
# GKiMatch 4.928626 4.975369 7.490588 5.039941 6.823780 98.194653 100 b
# GKiFor 155.078444 159.314918 163.706542 160.168266 163.464146 258.136977 100 c
x <- numeric(0) #Dason Fails
microbenchmark(list = fun[-1], control=list(order="block"))
Unit: nanoseconds
# expr min lq mean median uq max neval cld
# juljo 26794 27324.0 28694.75 27640.0 27933.0 120143 100 d
# GKiTail 6746 7027.5 7396.45 7206.5 7432.5 21898 100 c
# GKiCum 869 880.0 947.72 890.0 948.0 3403 100 a
# GKiRev 2466 2527.0 2657.99 2565.5 2652.0 8071 100 b
# GKiMatch 2739 2807.5 2919.78 2862.5 2935.5 5651 100 b
# GKiFor 492 512.5 671.74 537.5 604.5 9088 100 a
库（微基准）
有趣的可能是tail（哪一个（！is.na（x）），1）
@GKi为什么不发布一个答案？这可能对将来的其他人有用。谢谢！作品great@Dason谢谢我补充说，如果只有NA，则cumsum
将失败（返回1）。
length(x) - which.min(rev(is.na(x))) + 1 #Will fail in case on only NA
#length(x) - match(FALSE, is.na(rev(x))) + 1 #Alternative
#[1] 5
library(microbenchmark)
fun <- alist(Dason = max(which(!is.na(x)))
, juljo = dplyr::last(which(!is.na(x)))
, GKiTail = tail(which(!is.na(x)), 1)
, GKiCum = which.max(cumsum(!is.na(x)))
, GKiRev = length(x) - which.min(rev(is.na(x))) + 1
, GKiMatch = length(x) - match(FALSE, is.na(rev(x))) + 1
, GKiFor = idxLNNA(x)
)
x <- numeric(1e6)
microbenchmark(list = fun, control=list(order="block"))
#Unit: microseconds
# expr min lq mean median uq max neval cld
# Dason 4855.744 5740.1355 7941.21809 7082.5535 7671.371 107165.201 100 bc
# juljo 4145.322 4616.7815 5756.38147 6134.9200 6625.008 7378.724 100 b
# GKiTail 4082.716 4434.3880 5576.70509 6051.1465 6489.966 7433.579 100 b
# GKiCum 6552.213 7445.1525 8632.12253 8988.8700 9420.481 16791.845 100 c
# GKiRev 4005.929 4138.4735 5845.70457 4212.7470 5851.034 101665.685 100 b
# GKiMatch 5180.600 5483.8545 7507.82723 5998.2760 7373.458 108327.967 100 bc
# GKiFor 1.541 1.5775 2.16462 1.6145 1.724 20.436 100 a
x <- rep(NA, 1e6) #Dason, GKiCum and GKiRev Fail
microbenchmark(list = fun[-c(1,4,5)], control=list(order="block"))
#Unit: milliseconds
# expr min lq mean median uq max neval cld
# juljo 3.011272 3.076340 3.525396 3.111676 3.494768 6.367839 100 a
# GKiTail 2.942336 3.014327 3.529691 3.063891 3.809653 6.136984 100 a
# GKiMatch 4.928626 4.975369 7.490588 5.039941 6.823780 98.194653 100 b
# GKiFor 155.078444 159.314918 163.706542 160.168266 163.464146 258.136977 100 c
x <- numeric(0) #Dason Fails
microbenchmark(list = fun[-1], control=list(order="block"))
Unit: nanoseconds
# expr min lq mean median uq max neval cld
# juljo 26794 27324.0 28694.75 27640.0 27933.0 120143 100 d
# GKiTail 6746 7027.5 7396.45 7206.5 7432.5 21898 100 c
# GKiCum 869 880.0 947.72 890.0 948.0 3403 100 a
# GKiRev 2466 2527.0 2657.99 2565.5 2652.0 8071 100 b
# GKiMatch 2739 2807.5 2919.78 2862.5 2935.5 5651 100 b
# GKiFor 492 512.5 671.74 537.5 604.5 9088 100 a