当所有将来值均为0时，我试图在零的第一个实例之后删除零。最终，我希望执行此group_by种类，但要踩一些脚。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”，而只删除观察ID“ j”。我什至不知道从哪里开始。任何建议，不胜感激！
答案 0 :(得分：1)
仅在基数R中。它使用rle来获取尾随零的数量（如果有）。然后用head子集数据框。
r <- rle(df$value == 0)
if(r$values[length(r$values)]) head(df, -(r$lengths[length(r$values)] - 1))
# id time value
#1 a 1 90
#2 b 2 50
#3 c 3 40
#4 d 4 0
#5 e 5 30
#6 f 6 30
#7 g 7 0
#8 h 8 10
#9 i 9 0
您可以使用上面的代码编写一个函数，也可以*apply将其分组。
trailingZeros <- function(DF, col = "value"){
r <- rle(DF[[col]] == 0)
if(r$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)
答案 1 :(得分：0)
这是tidyverse中的一个解决方案，它也可以处理大量的尾随零：
tibbly %>%
select_if(is.numeric) %>%
select_if(colSums(.) > 12)
答案 2 :(得分：0)
Tidyverse解决方案也适用于组
基于示例数据（无分组） 代码可以缩短，但这看起来很可读；-）
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
涉及某些分组的示例
# 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