一个有点冗长但希望也更透明的解决方案,它产生与@jay.sf 相同的结果
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
d1<-data.frame(ID=c(1:6),
Locx1=c(100,121,146,194,162,182),
Locx2=c(148,170,184,236,196,190),
Locy1=c(119,173,104,164,188,142),
Locy2=c(168,180,120,210,190,213))
d2 <- d1 %>% mutate(seqx = purrr::map2(Locx1, Locx2, .f = ~seq(.x, .y, 1)),
seqy = purrr::map2(Locy1, Locy2, .f = ~seq(.x, .y, 1)),
intersection = purrr::map2(seqx, seqy, .f = ~intersect(.x, .y)),
overlap = purrr::map2_dbl(intersection, seqy, .f = ~length(.x)/length(.y)),
my_condition = overlap >= 0.5
)
d2 %>% select(-contains('seq'), -intersection)
#> ID Locx1 Locx2 Locy1 Locy2 overlap my_condition
#> 1 1 100 148 119 168 0.6000000 TRUE
#> 2 2 121 170 173 180 0.0000000 FALSE
#> 3 3 146 184 104 120 0.0000000 FALSE
#> 4 4 194 236 164 210 0.3617021 FALSE
#> 5 5 162 196 188 190 1.0000000 TRUE
#> 6 6 182 190 142 213 0.1250000 FALSE
由 reprex 包 (v0.3.0) 于 2020-09-03 创建