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r - 根据起点和终点识别模式

问题描述

我想确定从 t1 开始到 t7 结束的活动的持续时间。起点是 t1，它记录了在 t1_1、t1_2、t1_3 等处发生的活动。例如，在 t1_2 到 t3_1 发生 id 12 活动的情况下（我想保存所有事件）。我想确定所有活动的开始和结束，因此活动发生超过 4 次（例如 4 次发生第 1 次）、持续时间和最频繁的活动。零定义了序列的边界（例如，序列以 1 结束和开始，以 0 开头）

输入：

id t1_1 t1_2 t1_3 t2_1 t2_2 t2_3 t3_1 t3_2 t3_3 t4_1 t4_2 t4_3 t5_1 t5_2 t5_3 t6_1 t6_2 t6_3 t7_1 t7_2 t7_3
12  0    1     1    1     1   1    1    0    0    0    1    0    0    1    0     1   1     1   1      0  1
123 0    0     0    1     1   1    0    0    0    1    1    1    1    1    1     0   0     0    1     1  1
 10  1   1     1    1     1    1    1   1    1    1    1    1    1    1    1     1   1     1    1     1  1

id 12 的输出

Id    Start/End                            Duration  Frequency
12   t1_2, t1_3, t2_1, t2_2, t2_3, t3_1           6        1
12   t6_1, t6_2, t6_3, t7_1                       4         1

样本数据

 df1 <- structure(list(serial = c(12L, 123L, 10L), t1_1 = c(0L, 0L, 1L), 
                t1_2 = c(1L, 0L, 1L), t1_3 = c(1L, 0L, 1L), t2_1 = c(0L, 
                1L, 1L), t2_2 = c(1L, 1L, 1L), t2_3 = c(0L, 1L, 1L), t3_1 = c(1L, 
                0L, 1L), t3_2 = c(0L, 0L, 1L), t3_3 = c(1L, 0L, 1L), t4_1 = c(0L, 
                1L, 1L), t4_2 = c(1L, 1L, 1L), t4_3 = c(0L, 1L, 1L), t5_1 = c(0L, 
                1L, 1L), t5_2 = c(1L, 1L, 1L), t5_3 = c(0L, 1L, 1L), t6_1 = c(1L, 
                0L, 1L), t6_2 = c(1L, 0L, 1L), t6_3 = c(1L, 0L, 1L), t7_1 = c(0L, 
                1L, 1L), t7_2 = c(0L, 1L, 1L), t7_3 = c(1L, 1L, 1L)), 
                class = "data.frame", row.names = c(NA, 
            -3L))

到目前为止的代码

df1 <- melt(setDT(df1), id.var = 'serial')
df1[, c('time', 'subtime') := tstrsplit(as.character(variable), "_", fixed = TRUE)]
df2 <- df1[, rle(value), by = .(serial, time)][lengths > 1 & values == 1, ]
df3 <- df1[df2, on = c('serial', 'time')]
df3 <- df3[, .(`Start/End` = paste0(time, '_', c(min(subtime), max(subtime)), collapse = " - "), 
               Duration = unique(lengths)), 
           by = .(serial, time)]
df3[, Frequency := .N, by = .(serial, `Start/End`)]
df3[, time := NULL]
df3[order(serial), ]

标签： rdataframedata.table

解决方案

我建议使用tidyverse函数的下一种方法。您想识别序列，以便下一个代码有用。主要思想是重新格式化数据并拆分时间变量 ( t)，以便您为序列创建 ID，然后聚合：

library(tidyverse)

df1 %>% arrange(serial) %>% pivot_longer(cols = -serial) %>%
  #Duplicate the variable with time
  mutate(name2=name) %>%
  #Split time so that you have categories by t1, t2,...
  separate(name2,into = c('var1','var2'),sep = '_') %>%
  #Group by main id, the categories and value
  group_by(serial,var1,value) %>%
  #Create an unique id for sequences
  mutate(id=cur_group_id()) %>%
  #Omit values in zero which are not patterns
  ungroup() %>% filter(value!=0) %>%
  #Aggregate with the new id
  group_by(serial,id) %>%
  #Compute outputs
  summarise(chain=paste0(name,collapse = ','),Duration=n()) %>%
  select(-id) -> dfprime

输出（我只包括serial12 个）：

# A tibble: 7 x 3
# Groups:   serial [1]
  serial chain          Duration
   <int> <chr>             <int>
1     12 t1_2,t1_3             2
2     12 t2_2                  1
3     12 t3_1,t3_3             2
4     12 t4_2                  1
5     12 t5_2                  1
6     12 t6_1,t6_2,t6_3        3
7     12 t7_3                  1

如果您想进行其他聚合，您可以处理最终数据帧。

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