如何解决如何根据另一个变量定义组？
我需要根据变量 (bmode) 的值为每个个体 (id) 定义补丁。
bmode> 1.75 被归类为“ARS”，bmode ≤ 1.75 被归类为“TRANS”，中间值被归类为“IND”。
现在，对于已预测 ARS 行为并在估计 bmode ≤ 1.75（TRANS 或 IND）的 3 个或更多连续位置后结束的 3 个或更多连续位置，我想定义一个补丁。
示例
lon lat id bmode States pacth
-39.0436 -18.02402 7617.05 1.535 IND
-39.0296 -18.02142 7617.05 1.844 ARS 1
-39.0588 -18.01219 7617.05 1.849 ARS 1
-38.9866 -18.06814 7617.05 1.871 ARS 1
-38.9284 -18.07950 7617.05 1.894 IND 1
-38.8484 -18.12347 7617.05 1.892 ARS 1
-38.7905 -18.17230 7617.05 1.887 ARS 1
-38.7301 -18.20727 7617.05 1.895 ARS 1
-38.6774 -18.23565 7617.05 1.610 TRANS
-38.6135 -18.27840 7617.05 1.567 TRANS
-38.5408 -18.32210 7617.05 1.604 ARS 1
-38.4892 -18.35557 7617.05 1.690 ARS 1
-38.3138 -18.48745 7617.05 1.883 TRANS
-38.2723 -18.52985 7617.05 1.900 IND
-38.1407 -18.59948 7617.05 1.914 TRANS
-38.2632 -18.56872 7617.05 1.889 TRANS
-38.2459 -18.62644 7617.05 1.923 IND
-38.2459 -18.62644 7617.05 1.923 ARS 2
-38.2459 -18.62644 7617.05 1.923 ARS 2
-38.2459 -18.62644 7617.05 1.923 ARS 2
-38.2459 -18.62644 7617.05 1.923 ARS 2
我的数据示例：
structure(list(lon = 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= 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= c(7617.05,7617.05,7618.05,10946.05,20162.03,20687.03,21791.03,21792.03,21800.03,21809.03,21809.03),bmode = c(1.535,1.844,1.849,1.871,1.894,1.892,1.887,1.895,1.91,1.867,1.904,1.89,1.88,1.883,1.9,1.914,1.889,1.923,1.901,1.908,1.918,1.921,1.899,1.869,1.906,1.903,1.902,1.907,1.913,1.898,1.919,1.909,1.932,1.917,1.911,1.925,1.915,1.897,1.92,1.93,1.924,1.884,1.916,1.912,NA,1.546,1.873,1.926,1.941,1.929,1.938,1.928,1.934,1.936,1.905,1.946,1.945,1.956,1.94,1.949,1.942,1.957,1.943,1.954,1.947,1.965,1.958,1.952,1.953,1.95,1.939,1.944,1.955,1.933,1.976,1.931,1.525,1.337,1.341,1.339,1.369,1.364,1.402,1.368,1.346,1.261,1.257,1.316,1.326,1.332,1.323,1.529,1.603,1.634,1.637,1.67,1.665,1.663,1.659,1.673,1.646,1.688,1.507,1.62,1.686,1.621,1.597,1.616,1.593,1.594,1.579,1.586,1.599,1.619,1.512,1.253,1.199,1.185,1.157,1.153,1.149,1.163,1.129,1.102,1.097,1.057,1.05,1.056,1.064,1.067,1.04,1.035,1.021,1.022,1.055,1.024,1.033,1.046,1.066,1.068,1.039,1.053,1.018,1.023,1.017,1.031,1.059,1.08,1.07,1.087,1.034,1.072,1.062,1.032,1.043,1.036,1.049,1.015,1.083,1.082,1.004,1.008,1.007,1.077,1.016,1.045,1.038,1.029,1.026,1.16,1.069,1.06,1.051,1.071,1.503,1.312,1.237,1.158,1.115,1.117,1.123,1.127,1.113,1.065,1.085,1.027,1.028,1.089,1.132,1.124,1.122,1.063,1.109,1.096,1.074,1.03,1.048,1.054,1.025,1.041,1.081,1.02,1.178,1.318,1.494,1.694,1.681,1.338,1.26,1.19,1.188,1.25,1.207,1.526,1.618,1.647,1.65,1.617,1.63,1.652,1.64,1.607,1.602,1.596,1.636,1.708,1.716,1.684,1.569,1.583,1.508,1.645,1.728,1.785,1.828,1.866,1.766,1.813,1.629,1.633,1.66,1.774,1.769,1.748,1.732,1.812,1.605,1.747,1.765,1.745,1.628,1.491,1.393,1.578,1.826,1.713,1.836,1.523,1.988,1.676,1.861,1.754,1.712,1.524,1.414,1.568,1.734,1.751,1.726,1.386,1.313,1.701,1.698,1.649,1.315,1.951,1.685,1.772,1.882,1.69,1.706,1.851,1.859,1.72,1.789,1.757,1.761,1.824,1.848,1.464,1.682,1.707,1.868,1.413,1.99,1.497,1.689,1.372,1.61,1.288,1.808,1.874,1.623,1.627,1.635,1.781,1.548,1.816,1.778,1.787,1.721,1.736,1.731,1.719,1.457,1.742,1.733,1.792,1.672,1.542,1.834,1.528,1.78,1.782,1.819,1.795,1.87,1.838,1.809,1.654,1.722,1.846,1.814,1.776,1.75,1.807,1.806,1.79,1.773,1.753,1.803,1.655,1.841,1.817,1.856,1.832,1.791,1.737,1.714,1.735,1.709,1.744,1.881
),States = structure(c(2L,1L,2L,3L,1L),.Label = c("ARS","IND","TRANS"),class = "factor")),row.names = c(1L,4L,5L,6L,7L,8L,9L,10L,11L,12L,13L,14L,15L,16L,17L,18L,19L,20L,21L,22L,23L,24L,25L,26L,27L,28L,29L,30L,31L,32L,33L,34L,35L,36L,37L,38L,39L,40L,41L,42L,43L,44L,45L,46L,47L,48L,49L,50L,51L,52L,53L,54L,55L,56L,57L,58L,59L,60L,61L,62L,63L,64L,65L,66L,67L,68L,69L,70L,71L,72L,73L,74L,75L,76L,77L,78L,79L,80L,81L,82L,83L,84L,85L,86L,87L,88L,89L,90L,91L,92L,93L,94L,95L,96L,97L,98L,99L,100L,101L,102L,103L,104L,105L,106L,107L,108L,109L,110L,111L,112L,113L,114L,115L,116L,117L,118L,119L,120L,121L,122L,123L,124L,125L,126L,127L,128L,129L,130L,131L,132L,133L,134L,135L,136L,137L,138L,139L,431L,432L,433L,434L,435L,436L,437L,438L,439L,440L,441L,442L,444L,445L,446L,447L,450L,475L,476L,477L,478L,479L,480L,481L,482L,483L,484L,485L,486L,491L,492L,493L,494L,495L,496L,497L,498L,499L,500L,501L,502L,503L,504L,505L,506L,507L,508L,509L,510L,511L,512L,513L,514L,515L,516L,517L,518L,519L,520L,521L,522L,523L,527L,528L,529L,530L,531L,532L,533L,534L,535L,536L,537L,538L,539L,540L,541L,542L,543L,544L,545L,546L,548L,549L,550L,551L,552L,553L,554L,555L,556L,557L,558L,559L,560L,561L,562L,563L,564L,565L,566L,567L,568L,569L,570L,571L,572L,573L,574L,575L,576L,577L,578L,579L,580L,581L,582L,583L,584L,585L,586L,587L,588L,589L,590L,591L,592L,593L,594L,595L,596L,597L,598L,599L,601L,602L,603L,604L,605L,606L,607L,608L,609L,610L,611L,612L,613L,614L,615L,616L,617L,618L,619L,620L,621L,622L,623L,624L,625L,626L,627L,628L,629L,630L,631L,632L,633L,634L,635L,636L,637L,638L,639L,640L,641L,642L,643L,644L,645L,646L,647L,648L,649L,650L,651L,652L,653L,654L,655L,656L,657L,658L,659L,660L,661L,662L,663L,664L,665L,666L,667L,668L,669L,670L,671L,672L,673L,674L,675L,676L,677L,678L,679L,680L,681L,682L,683L,684L,685L,686L,687L,688L,689L,690L,691L,692L,693L,694L,695L,696L,697L,698L,699L,700L,701L,702L,703L,704L,705L,706L,707L,708L,709L,710L,711L,712L,713L,714L,715L,716L,717L,718L,719L,720L,721L,722L,723L,724L,725L,726L,727L,728L,729L,730L,731L,732L,733L,734L,735L,736L,737L,738L,739L,740L,741L,742L,743L,744L,745L,746L,747L,748L,749L,750L,751L,752L,753L,754L,755L,756L,757L,758L,759L,760L,761L,762L,763L,764L,765L,766L,767L,768L,769L,770L,771L,772L,773L,774L,775L,776L,777L,778L,779L,780L,781L,782L,783L,784L,785L,786L,787L,788L,789L,790L,791L,792L,793L,794L,795L,796L,797L,798L,799L,800L,801L,802L,803L,804L,805L,806L,807L,808L,809L,810L,811L,812L,813L,814L,815L,816L,817L,818L,819L,820L,821L,822L,823L,824L,825L,826L,827L),class = "data.frame")
解决方法
编辑：以下内容将为您提供一些帮助。
library(dplyr)
df %>%
group_by(id) %>%
mutate(group = cumsum(case_when(States != 'ARS' ~ TRUE,States == 'ARS' &
lag(States != 'ARS') ~ TRUE,TRUE ~ FALSE))) %>%
group_by(id,group) %>%
mutate(isPatch = if_else('ARS' %in% States & n() > 2,TRUE,FALSE),tempPatch = if_else(row_number() == min(row_number()) &
isPatch == TRUE,FALSE)) %>%
group_by(id) %>%
mutate(patch = if_else(isPatch == TRUE,cumsum(tempPatch),NA_integer_)) %>%
select(colnames(df),patch)
# # A tibble: 500 x 6
# # Groups: id [9]
# lon lat id bmode States patch
# <dbl> <dbl> <dbl> <dbl> <fct> <int>
# 1 -39.0 -18.0 7617. 1.54 IND NA
# 2 -39.0 -18.0 7617. 1.84 ARS 1
# 3 -39.1 -18.0 7617. 1.85 ARS 1
# 4 -39.0 -18.1 7617. 1.87 ARS 1
# 5 -38.9 -18.1 7617. 1.89 ARS 1
# 6 -38.8 -18.1 7617. 1.89 ARS 1
# 7 -38.8 -18.2 7617. 1.89 ARS 1
# 8 -38.7 -18.2 7617. 1.90 ARS 1
# 9 -38.7 -18.2 7617. 1.91 ARS 1
# 10 -38.6 -18.3 7617. 1.87 ARS 1
# # … with 490 more rows
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