我有一个数据框，其中每一行都有一个 ID 和活动值。下面是一个示例数据框：
test=data.frame(
start=c(1,50,100,150,200,250,300,350,400,450,500,100,150,200),
end=c(50,100,150,200,250,300,350,400,450,500,550,150,200,250),
sub_id=c("subid_1_1","subid_1_2","subid_1_3","subid_1_4","subid_1_5","subid_1_6","subid_1_7","subid_1_8","subid_1_9","subid_1_10","subid_1_11","subid_2_1","subid_2_2","subid_2_3"),
id=c(rep("id_1",11),rep("id_2",3)),
activity=c(-0.2,-0.6,-1,-1.2,-1.6,-1.6,-0.5,0.2,-1.2,-0.8,0.1,0.1,1.2,0.5))
print(test)
start end sub_id id activity
1 1 50 subid_1_1 id_1 -0.2
2 50 100 subid_1_2 id_1 -0.6
3 100 150 subid_1_3 id_1 -1.0
4 150 200 subid_1_4 id_1 -1.2
5 200 250 subid_1_5 id_1 -1.6
6 250 300 subid_1_6 id_1 -1.6
7 300 350 subid_1_7 id_1 -0.5
8 350 400 subid_1_8 id_1 0.2
9 400 450 subid_1_9 id_1 -1.2
10 450 500 subid_1_10 id_1 -0.8
11 500 550 subid_1_11 id_1 0.1
12 100 150 subid_2_1 id_2 0.1
13 150 200 subid_2_2 id_2 1.2
14 200 250 subid_2_3 id_2 0.5
对于具有相同 ID 的每一行：
我合并了所有没有被值分隔的行 > -1，这些行接近最小活动值。并保留合并行的起始值和结束值的第一个值，并在所选行上计算平均值。
我这样做了：
library(dplyr)
threshold <- -1
test.group <- test %>%
mutate(grp = cumsum(activity > threshold))
print(test.group, row.names = F)
test.result <-
test.group %>%
subset(activity <= -1) %>%
group_by(id, grp) %>%
arrange(activity) %>%
summarise(
start.min = first(start),
end.min = first(end),
sub_id.min = first(sub_id),
activity.min = first(activity),
start = min(start),
end = max(end),
activity = mean(activity)
) %>%
ungroup() %>%
select(start, end, id, activity, start.min, end.min, activity.min)
我的问题是我不能有连续 min(activity) 的 min(start.min) 和 max(end.min)。我总是显示一个区域的最小活动。但是，如果我有 2 个等于最小值，我该怎么做？
预期输出：
start end id activity start.min end.min activity.min
<dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 100 300 id_1 -1.2 200 300 -1.6
2 400 450 id_1 -1.2 400 450 -1.2
我们可以data.table::rleid用来创建组，删除行activity <= -1并找到每个组中的相关数字。
library(dplyr)
test %>%
group_by(gr = data.table::rleid(activity <= -1)) %>%
filter(activity <= -1) %>%
summarise(start_1 = first(start),
end_1 = last(end),
id = first(id),
activity_1 = mean(activity),
activity.min = min(activity),
start.min = min(start[activity == activity.min]),
end.min = max(end[activity == activity.min])) %>%
select(-gr)
# start_1 end_1 id activity_1 activity.min start.min end.min
# <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
#1 100 300 id_1 -1.35 -1.6 200 300
#2 400 450 id_1 -1.2 -1.2 400 450
本文收集自互联网，转载请注明来源。
如有侵权，请联系 [email protected] 删除。
我来说两句