使用 `rle` 函数和 `dplyr` `group_by` 命令映射分组变量

Question

我有一个包含三列的数据框，其信息类似于下面给出的数据框。现在我希望根据a 列中的信息提取信息搜索模式。

基于少数开发人员（@thelatemail 和@David T）的支持，我能够使用rle 函数识别模式，请参阅此处-using rle function to identify pattern。现在，我希望继续并将分组信息添加到提取的模式中。我尝试使用 dplyr do 函数 - 请参阅下面的代码。但是，这不起作用。

还提供了示例数据和所需的输出以供您参考。

##mycode that produces error - needs to be fixed
test <- data%>%
  group_by(b, c)%>%
  do(.,  data.frame(from = rle(.$a)$values), to = lead(rle(.$a)$values))

##code to create the data frame
a <- c( "a", "b", "b", "b", "a", "c", "a", "b", "d", "d", "d", "e", "f", "f", "e", "e")
b <- c(rep("experiment", times = 8), rep("control", times = 8))
c <- c(rep("A01", times = 4), rep("A02", times = 4), rep("A03", times = 4), rep("A04", times = 4))
data <- data.frame(c,b,a)

## desired output

    c      b         from  to    fromCount toCount
                    <chr> <chr>     <int>   <int>
 1 A01 experimental  a     b             1       3
 2 A02 experimental  a     c             1       1
 3 A02 experimental  c     a             1       1
 4 A02 experimental  a     b             1       1
 5 A03 control       d     e             3       1
 6 A04 control       f     e             2       2

与之前的帖子here 相比，由于我们将分组应用于a 列，因此信息被压缩了。

Answer 1

我们可以使用来自data.table的rleid

library(data.table)
library(dplyr)
data %>% 
  group_by(b, c, grp = rleid(a)) %>%
  summarise(from = first(a), fromCount = n()) %>% 
  mutate(to = lead(from), toCount = lead(fromCount)) %>%
  ungroup %>%
  select(-grp) %>% 
  filter(!is.na(to)) %>%
  arrange(c)
# A tibble: 6 x 6
#  b          c     from  fromCount to    toCount
#  <chr>      <chr> <chr>     <int> <chr>   <int>
#1 experiment A01   a             1 b           3
#2 experiment A02   a             1 c           1
#3 experiment A02   c             1 a           1
#4 experiment A02   a             1 b           1
#5 control    A03   d             3 e           1
#6 control    A04   f             2 e           2

---

或使用rle，在按'b'、'c'、summarise 和rle 分组后创建list 列，然后从@987654328 中的列中提取'值'和'长度' @，在'from'的lead上创建'to'，'toCount'，'fromCount'列filter出NA元素和arrange基于'c'列的行

data %>% 
    group_by(b, c) %>%
    summarise(rl = list(rle(a)), 
              from = rl[[1]]$values, 
              fromCount = rl[[1]]$lengths) %>% 
    mutate(to = lead(from), 
           toCount = lead(fromCount)) %>%
    ungroup %>% 
    select(-rl) %>% 
    filter(!is.na(to)) %>% 
    arrange(c)
# A tibble: 6 x 6
#  b          c     from  fromCount to    toCount
#  <chr>      <chr> <chr>     <int> <chr>   <int>
#1 experiment A01   a             1 b           3
#2 experiment A02   a             1 c           1
#3 experiment A02   c             1 a           1
#4 experiment A02   a             1 b           1
#5 control    A03   d             3 e           1
#6 control    A04   f             2 e           2

我们还可以使用map 循环遍历rle list 列（'rl'），提取组件，然后在lengths 中获取lead，values 中的tibble ，使用unnest_wider 创建列和unnest list 结构，filter 出NA 元素和arrange

library(tidyr)
library(purrr)
data %>% 
     group_by(b, c) %>%
     summarise(rl = list(rle(a))) %>%
     ungroup %>%
     mutate(out = map(rl, 
          ~ tibble(from = .x$values,
                   fromCount = .x$lengths,
                   to = lead(from), 
                   toCount = lead(fromCount)))) %>%
     unnest_wider(c(out)) %>% 
     unnest(from:toCount) %>%
     filter(!is.na(to)) %>% 
     arrange(c) %>% 
     select(-rl)

Answer 2

或者在tidyverse 中，创建一个函数来执行rle 以跟踪单个主题

rleSlice <- function(Tracking) {

  rlTrack <- rle(as.character(Tracking))  # Strip the levels from the factor, they interfere
  tibble(from = rlTrack$values, to = lead(rlTrack$values),
                 fromCount = rlTrack$lengths, toCount = lead(rlTrack$lengths)) %>% 
    filter(!is.na(to)) %>% 
    list()
}

确保它正常运行

[[1]]
rleSlice(c("a", "b", "b", "b", "c"))

A tibble: 2 x 4
  from  to    fromCount toCount
  <chr> <chr>     <int>   <int>
1 a     b             1       3
2 b     c             3       1

现在我们将分组并为每个参与者获取 rle

data %>% 
  as_tibble() %>% 
    # This is easier to track than all these a,b,c's
  rename(Subject = c, Test = b, Tracking = a) %>% 
  group_by(Subject, Test) %>% 
  summarise(Slice = rleSlice(Tracking)) %>% 
  unnest(col = "Slice") %>% 
  ungroup()

# A tibble: 6 x 6
  Subject Test       from  to    fromCount toCount
  <fct>   <fct>      <chr> <chr>     <int>   <int>
1 A01     experiment a     b             1       3
2 A02     experiment a     c             1       1
3 A02     experiment c     a             1       1
4 A02     experiment a     b             1       1
5 A03     control    d     e             3       1
6 A04     control    f     e             2       2