R中的分箱生理时间序列数据:计算在每个分箱中度过的时间

Question

我有一个数据集，其中包含来自多个参与者的平均动脉血压(MAP)随时间的变化。下面是一个示例数据框架:

df=structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L), Time = structure(1:14, .Label = c("11:02:00", 
             "11:03:00", "11:04:00", "11:05:00", "11:06:00", "11:07:00", "11:08:00", 
             "13:30:00", "13:31:00", "13:32:00", "13:33:00", "13:34:00", "13:35:00", 
             "13:36:00"), class = "factor"), MAP = c(90.27999878, 84.25, 74.81999969, 
                                                     80.87000275, 99.38999939, 81.51000214, 71.51000214, 90.08999634, 
                                                     88.75, 84.72000122, 83.86000061, 94.18000031, 98.54000092, 51
             )), class = "data.frame", row.names = c(NA, -14L))

我已经将数据分组:例如MAP 40-60、60-80、80-100，并在额外的列map_bin中添加了一个唯一的标志(1、2或3)。这是我到目前为止的代码:

library(dplyr)
#Mean Arterial Pressure                                                                                                                     
#Bin 1=40-60; Bin 2=60-80; Bin 3=80-100
map_bin=c("1","2","3")
output <- as_tibble(df) %>% 
mutate(map_bin = case_when(
MAP >= 40 & MAP < 60 ~ map_bin[1],
MAP >= 60 & MAP < 80 ~ map_bin[2],
MAP >= 80 & MAP < 100 ~ map_bin[3]
))

对于我希望在另一列中计算的每个ID, MAP在每个bin中的总时间。我希望得到以下输出:

<表类>ID时间MAPmap_binmap_bin_durtbody><<tr>111:02:0090.2799987835111:03:0084.2535111:04:0074.8199996922111:05:0080.8700027535111:06:0099.3899993935111:07:0081.5100021435111:08:0071.5100021422213:30:0090.0899963436213:31:0088.7536213:32:0084.7200012236213:33:0083.8600006136213:34:0094.1800003136213:35:0098.5400009236213:36:005111

如果您的Time列始终为1分钟持续时间，则可以使用add_count-

library(dplyr)
output <- output %>% add_count(ID, map_bin, name = 'map_bin_dur')
output
#      ID Time       MAP map_bin map_bin_dur
#   <int> <fct>    <dbl> <chr>         <int>
# 1     1 11:02:00  90.3 3                 5
# 2     1 11:03:00  84.2 3                 5
# 3     1 11:04:00  74.8 2                 2
# 4     1 11:05:00  80.9 3                 5
# 5     1 11:06:00  99.4 3                 5
# 6     1 11:07:00  81.5 3                 5
# 7     1 11:08:00  71.5 2                 2
# 8     2 13:30:00  90.1 3                 6
# 9     2 13:31:00  88.8 3                 6
#10     2 13:32:00  84.7 3                 6
#11     2 13:33:00  83.9 3                 6
#12     2 13:34:00  94.2 3                 6
#13     2 13:35:00  98.5 3                 6
#14     2 13:36:00  51   1                 1