Question

我想转换这样的转码

library(tidyverse)
library(lubridate)
turndata_wide <- tibble(turnID = 1:4,
                        code = c("a", "b", "a", "g"), 
                        start = c(ymd_hms("2019_05_25 00:00:05"), 
                                  ymd_hms("2019_05_25 00:00:02"), 
                                  ymd_hms("2019_05_25 00:00:10"),
                                  ymd_hms("2019_05_25 00:00:01")),
                        end   = c(ymd_hms("2019_05_25 00:00:08"), 
                                  ymd_hms("2019_05_25 00:00:07"), 
                                  ymd_hms("2019_05_25 00:00:15"),
                                  ymd_hms("2019_05_25 00:00:25")))

这会导致

> turndata_wide
# A tibble: 4 x 4
turnID code  start               end                
<int> <chr> <dttm>              <dttm>             
1      1 a     2019-05-25 00:00:05 2019-05-25 00:00:08
2      2 b     2019-05-25 00:00:02 2019-05-25 00:00:07
3      3 a     2019-05-25 00:00:10 2019-05-25 00:00:15
4      4 g     2019-05-25 00:00:01 2019-05-25 00:00:25

我们称之为（社会科学家）的时间码。看起来应该像

# A tibble: 25 x 4
time                    a     b     g
<dttm>              <dbl> <dbl> <dbl>
1 2019-05-25 00:00:01    NA    NA     1
2 2019-05-25 00:00:02    NA     1     1
3 2019-05-25 00:00:03    NA     1     1
4 2019-05-25 00:00:04    NA     1     1
5 2019-05-25 00:00:05     1     1     1
6 2019-05-25 00:00:06     1     1     1
7 2019-05-25 00:00:07     1     1     1
8 2019-05-25 00:00:08     1    NA     1
9 2019-05-25 00:00:09    NA    NA     1
10 2019-05-25 00:00:10     1    NA     1
# … with 15 more rows

我已经构建了一个（行人和丑陋的）解决方案，但是可以肯定的是，还有更好的解决方案。我的（丑陋的）方法是：

每转创建long_df
以每转“全职行”的身份加入df
每转加入这些full_dfs
传播代码

## Loop over steps 1) + 2) ########################################

df_per_turn_list <- list()

for(i in 1:nrow(turndata_wide)){
  data_turn_temp <- turndata_wide[i,]%>%
    gather(startend, time, start, end)%>%
    full_join(.,
              tibble(time = seq.POSIXt(from = min(.$time), 
                                       to =   max(.$time),
                                       by = "sec"),
                     code = .$code[1],
                     turnID = .$turnID[1]))%>%
    select(-startend)%>%
    arrange(time)
  temp_name <- paste("data_turn_", i, sep = "")

  df_per_turn_list[[temp_name]] <- data_turn_temp
}

## Steps 3) + 4): Join dfs_per turn and spread codes ########
reduce(df_per_turn_list, full_join)%>%
  mutate(dummy_one = 1)%>%
  select(-turnID)%>%
  spread(code, dummy_one)%>%
  arrange(time)

Answer 1

使用tidyverse中的cSplit_e和splitstackshape的一种方法。我们每秒在start和end之间创建一个序列，每秒group_by创建一个序列，并将其转换为逗号分隔的值，然后使用cSplit_e将它们转换为二进制列。

library(tidyverse)

turndata_wide %>%
  mutate(time = map2(start, end, seq, by = "1 sec")) %>%
  unnest(cols = time) %>%
  select(-start, -end) %>%
  group_by(time) %>%
  summarise(code = toString(code)) %>%
  splitstackshape::cSplit_e("code", type = "character", drop = TRUE)

返回的输出为：

#                  time code_a code_b code_g
#1  2019-05-25 00:00:01     NA     NA      1
#2  2019-05-25 00:00:02     NA      1      1
#3  2019-05-25 00:00:03     NA      1      1
#4  2019-05-25 00:00:04     NA      1      1
#5  2019-05-25 00:00:05      1      1      1
#6  2019-05-25 00:00:06      1      1      1
#7  2019-05-25 00:00:07      1      1      1
#8  2019-05-25 00:00:08      1     NA      1
#9  2019-05-25 00:00:09     NA     NA      1
#10 2019-05-25 00:00:10      1     NA      1
#11 2019-05-25 00:00:11      1     NA      1
#12 2019-05-25 00:00:12      1     NA      1
#13 2019-05-25 00:00:13      1     NA      1
#14 2019-05-25 00:00:14      1     NA      1
#15 2019-05-25 00:00:15      1     NA      1
#16 2019-05-25 00:00:16     NA     NA      1
#17 2019-05-25 00:00:17     NA     NA      1
#18 2019-05-25 00:00:18     NA     NA      1
#19 2019-05-25 00:00:19     NA     NA      1
#20 2019-05-25 00:00:20     NA     NA      1
#21 2019-05-25 00:00:21     NA     NA      1
#22 2019-05-25 00:00:22     NA     NA      1
#23 2019-05-25 00:00:23     NA     NA      1
#24 2019-05-25 00:00:24     NA     NA      1
#25 2019-05-25 00:00:25     NA     NA      1

使用dplyr将“开始停止数据”（又称转码）转换为长格式（又称时间码）

1 个答案: