仅当其他值不存在时，如何在第一次实例后删除后续值

Question

当所有未来值均为 0 时，我试图在第一个零实例之后删除零。最终，我很想做这个 group_by 物种，但只是小步。这是一个例子；

# Sample
library(tidyverse)
id<-c("a","b","c","d","e","f","g","h","i","j")
time<-c(1,2,3,4,5,6,7,8,9,10)
value<-c(90, 50, 40, 0, 30, 30, 0, 10, 0, 0)
df<-data.frame(id, time, value)
df

   id time value
1   a    1    90
2   b    2    50
3   c    3    40
4   d    4     0
5   e    5    30
6   f    6    30
7   g    7     0
8   h    8    10
9   i    9     0
10  j   10     0

我想查看观察 ID“j”，并且只删除观察 ID“j”。我什至不知道从哪里开始。任何建议都非常感谢！

Answer 1

仅在基数 R 中。它使用 rle 来获取尾随零的数量（如果有）。然后使用head 对数据框进行子集化。

r <- rle(df$value == 0)
if(r$values[length(r$values)]) head(df, -(r$lengths[length(r$values)] - 1))
#  id time value
#1  a    1    90
#2  b    2    50
#3  c    3    40
#4  d    4     0
#5  e    5    30
#6  f    6    30
#7  g    7     0
#8  h    8    10
#9  i    9     0

你可以用上面的代码写一个函数，也许*apply它到组。

trailingZeros <- function(DF, col = "value"){
    r <- rle(DF[[col]] == 0)
    if(r$values[length(r$values)] && r$lengths[length(r$values)] > 1)
        head(DF, -(r$lengths[length(r$values)] - 1))
    else
        DF
}

trailingZeros(df)

请注意，这也适用于大量尾随零。

id2 <- c("a","b","c","d","e","f","g","h","i","j","k")
time2 <- c(1,2,3,4,5,6,7,8,9,10,11)
value2 <- c(90, 50, 40, 0, 30, 30, 0, 10, 0, 0, 0)    # One more zero at end
df2 <- data.frame(id = id2, time = time2, value = value2)

trailingZeros(df2)

Answer 2

这是 tidyverse 中的一个解决方案，它也适用于大量尾随零：

df <- tibble(id = letters[1:11], time = 1:11, 
             value = c(90,50,40,0,30,30,0,10,0,0,0))
df %>% 
  slice(n():1) %>% 
  slice(c(which(cumsum(value > 0) > 0)[1] - 1, which(cumsum(value > 0) > 0))) %>% 
  slice(n():1)

Answer 3

也适用于群组的 Tidyverse 解决方案

基于样本数据（未分组） 代码可以缩短，但这看起来非常可读;-)

df %>% 
  #arrange by id
  arrange( id ) %>%
  #no grouping valiable in sample data.. so don't use group_by here
  #group_by( group) %>%
  #create dummy's: position in group, last value of group, position of last non-zero in group, previous value (within group)
  mutate( pos_in_group = 1:n() ) %>%
  mutate( last_value = last( value ) ) %>%
  mutate( pos_last_not_zero = max( which( value != 0) ) ) %>%
  mutate( prev_value = lag( value ) ) %>%
  #filter all rows where: 
  #   the last value of the group != 0 AND 
  #   the previous row (within the group) != 0 AND 
  #  the position of the row is 'below' the last non-zero measurement (in the group)
  filter( !(last_value == 0 & prev_value == 0 & pos_in_group >= pos_last_not_zero + 1 ) ) %>%
  #throw away the dummy's
  select( -c( pos_in_group, last_value, pos_last_not_zero, prev_value ) )

#   id time value
# 1  a    1    90
# 2  b    2    50
# 3  c    3    40
# 4  d    4     0
# 5  e    5    30
# 6  f    6    30
# 7  g    7     0
# 8  h    8    10
# 9  i    9     0

涉及一些分组的示例

# Sample
library(tidyverse)
id<-c("a","b","c","d","e","f","g","h","i","j","k")
group<-c(1,1,1,1,1,1,2,2,2,2,2)
time<-c(1,2,3,4,5,6,7,8,9,10,11)
value = c(90,0,0,40,0,0,30,30,0,0,0)
df<-data.frame(id, group, time, value)

df
#    id group time value
# 1   a     1    1    90
# 2   b     1    2     0
# 3   c     1    3     0
# 4   d     1    4    40
# 5   e     1    5     0
# 6   f     1    6     0
# 7   g     2    7    30
# 8   h     2    8    30
# 9   i     2    9     0
# 10  j     2   10     0
# 11  k     2   11     0

df %>% 
  #arrange by id
  arrange( id ) %>%
  #group
  group_by( group) %>%
  #create dummy's: position in group, last value of group, position of last non-zero in group, previous value (within group)
  mutate( pos_in_group = 1:n() ) %>%
  mutate( last_value = last( value ) ) %>%
  mutate( pos_last_not_zero = max( which( value != 0) ) ) %>%
  mutate( prev_value = lag( value ) ) %>%
  #filter all rows where: 
  #   the last value of the group != 0 AND 
  #   the previous row (within the group) != 0 AND 
  #  the position of the row is 'below' the last non-zero measurement (in the group)
  filter( !(last_value == 0 & prev_value == 0 & pos_in_group >= pos_last_not_zero + 1 ) ) %>%
  #throuw away the dummy's
  select( -c( pos_in_group, last_value, pos_last_not_zero, prev_value ) )

# # A tibble: 8 x 4
# # Groups:   group [2]
#   id    group  time value
#   <fct> <dbl> <dbl> <dbl>
# 1 a         1     1    90
# 2 b         1     2     0
# 3 c         1     3     0
# 4 d         1     4    40
# 5 e         1     5     0
# 6 g         2     7    30
# 7 h         2     8    30
# 8 i         2     9     0