聚合具有特定共享值的行
Aggregate rows with specific shared value
我想按如下方式汇总我的数据:
- 仅聚合状态 = 0 的连续行
- 保持年龄,总结积分
示例数据:
da <- data.frame(userid = c(1,1,1,1,2,2,2,2), status = c(0,0,0,1,1,1,0,0), age = c(10,10,10,11,15,16,16,16), points = c(2,2,2,6,3,5,5,5))
da
userid status age points
1 1 0 10 2
2 1 0 10 2
3 1 0 10 2
4 1 1 11 6
5 2 1 15 3
6 2 1 16 5
7 2 0 16 5
8 2 0 16 5
我想要:
da2
userid status age points
1 1 0 10 6
2 1 1 11 6
3 2 1 15 3
4 2 1 16 5
5 2 0 16 10
您可以使用 group_by
来自 dplyr
:
da %>% group_by(da$userid, cumsum(da$status), da$status)
%>% summarise(age=max(age), points=sum(points))
输出:
`da$userid` `cumsum(da$status)` `da$status` age points
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0 0 10 6
2 1 1 1 11 6
3 2 2 1 15 3
4 2 3 0 16 10
5 2 3 1 16 5
和上面的想法完全一样:
library(dplyr)
data1 <- data %>% group_by(userid, age, status) %>%
filter(status == 0) %>%
summarise(points = sum(points))
data2 <- data %>%
group_by(userid, age, status) %>%
filter(status != 0) %>%
summarise(points = sum(points))
data <- rbind(data1,
data2)
我们需要更加小心您指定的 status
等于 0。我认为 Quang Hoang 的代码仅适用于您的特定示例。
希望对你有所帮助。
da %>%
mutate(grp = with(rle(status),
rep(seq_along(values), lengths)) + cumsum(status != 0)) %>%
group_by_at(vars(-points)) %>%
summarise(points = sum(points)) %>%
ungroup() %>%
select(-grp)
## A tibble: 5 x 4
# userid status age points
# <dbl> <dbl> <dbl> <dbl>
#1 1 0 10 6
#2 1 1 11 6
#3 2 0 16 10
#4 2 1 15 3
#5 2 1 16 5
我想按如下方式汇总我的数据:
- 仅聚合状态 = 0 的连续行
- 保持年龄,总结积分
示例数据:
da <- data.frame(userid = c(1,1,1,1,2,2,2,2), status = c(0,0,0,1,1,1,0,0), age = c(10,10,10,11,15,16,16,16), points = c(2,2,2,6,3,5,5,5))
da
userid status age points
1 1 0 10 2
2 1 0 10 2
3 1 0 10 2
4 1 1 11 6
5 2 1 15 3
6 2 1 16 5
7 2 0 16 5
8 2 0 16 5
我想要:
da2
userid status age points
1 1 0 10 6
2 1 1 11 6
3 2 1 15 3
4 2 1 16 5
5 2 0 16 10
您可以使用 group_by
来自 dplyr
:
da %>% group_by(da$userid, cumsum(da$status), da$status)
%>% summarise(age=max(age), points=sum(points))
输出:
`da$userid` `cumsum(da$status)` `da$status` age points
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0 0 10 6
2 1 1 1 11 6
3 2 2 1 15 3
4 2 3 0 16 10
5 2 3 1 16 5
和上面的想法完全一样:
library(dplyr)
data1 <- data %>% group_by(userid, age, status) %>%
filter(status == 0) %>%
summarise(points = sum(points))
data2 <- data %>%
group_by(userid, age, status) %>%
filter(status != 0) %>%
summarise(points = sum(points))
data <- rbind(data1,
data2)
我们需要更加小心您指定的 status
等于 0。我认为 Quang Hoang 的代码仅适用于您的特定示例。
希望对你有所帮助。
da %>%
mutate(grp = with(rle(status),
rep(seq_along(values), lengths)) + cumsum(status != 0)) %>%
group_by_at(vars(-points)) %>%
summarise(points = sum(points)) %>%
ungroup() %>%
select(-grp)
## A tibble: 5 x 4
# userid status age points
# <dbl> <dbl> <dbl> <dbl>
#1 1 0 10 6
#2 1 1 11 6
#3 2 0 16 10
#4 2 1 15 3
#5 2 1 16 5