英文:
How to create groups based on cumulative values
问题
I would like to set groups based on sum of columns
ID Value Days
A 0 5
A 1200 20
A 300 40
A 1000 67
A 1000 90
A 1000 110
A 500 115
B 0 5
B 500 10
B 600 15
B 1000 25
B 2000 50
B 900 70
B 1000 90
Output
ID Group Days
A Doses A 20
A Doses B 90
A Doses C 115
B Doses A 15
B Doses B 50
B Doses C 70
I would like to sum values to create groups and retain the corresponding timestamps (days) when they occur.
Output (creating 3 groups)
Values
(Doses A) => 1000
(Doses B) => 3000
(Doses C) => 5000
英文:
I would like to set groups based on sum of columns
ID Value Days
A 0 5
A 1200 20
A 300 40
A 1000 67
A 1000 90
A 1000 110
A 500 115
B 0 5
B 500 10
B 600 15
B 1000 25
B 2000 50
B 900 70
B 1000 90
Output
ID Group Days
A Doses A 20
A Doses B 90
A Doses C 115
B Doses A 15
B Doses B 50
B Doses C 70
I would like to sum values to create groups and retain the corresponding timestamps (days) when they occur.
Output (creating 3 groups)
Values
(Doses A) => 1000
(Doses B) => 3000
(Doses C) => 5000
答案1
得分: 2
library(tidyverse)
df %>%
mutate(Value = cumsum(Value),
Doses = (Value >= 1000) + (Value >= 3000) + (Value >= 5000), .by = ID) %>%
filter(Value >= 1000) %>%
slice_head(by = c(ID, Doses)) %>%
mutate(Doses = paste('Doses', c('A', 'B', 'C')[Doses])) %>%
select(ID, Doses, Days)
英文:
library(tidyverse)
df %>%
mutate(Value = cumsum(Value),
Doses = (Value>=1000) + (Value>=3000) + (Value>=5000), .by=ID)%>%
filter(Value>=1000) %>%
slice_head(by = c(ID, Doses))%>%
mutate(Doses = paste('Doses', c('A', 'B', 'C')[Doses])) %>%
select(ID, Doses, Days)
#> # A tibble: 6 × 3
#> ID Doses Days
#> <chr> <chr> <dbl>
#> 1 A Doses A 20
#> 2 A Doses B 90
#> 3 A Doses C 115
#> 4 B Doses A 15
#> 5 B Doses B 50
#> 6 B Doses C 70
<sup>Created on 2023-06-28 with reprex v2.0.2</sup>
答案2
得分: 1
请尝试以下代码
```r
library(dplyr)
df %>% group_by(ID) %>% mutate(new=cumsum(Value),
new2=case_when(
new>=5000 ~ 'C',
new>=3000 ~ 'B',
new>=1000 ~ 'A'
)) %>%
filter(!is.na(new2)) %>%
group_by(ID,new2) %>%
slice_head(n=1) %>%
ungroup() %>%
select(-c(new,new2))
# 输出
# 一个 tibble: 6 × 3
ID Value Days
<chr> <dbl> <dbl>
1 A 1200 20
2 A 1000 90
3 A 500 115
4 B 600 15
5 B 2000 50
6 B 900 70
<details>
<summary>英文:</summary>
Please try the below code
```r
library(dplyr)
df %>% group_by(ID) %>% mutate(new=cumsum(Value),
new2=case_when(
new>=5000 ~ 'C',
new>=3000 ~ 'B',
new>=1000 ~ 'A'
)) %>%
filter(!is.na(new2)) %>%
group_by(ID,new2) %>%
slice_head(n=1) %>% ungroup() %>%
select(-c(new,new2))
# output
# A tibble: 6 × 3
ID Value Days
<chr> <dbl> <dbl>
1 A 1200 20
2 A 1000 90
3 A 500 115
4 B 600 15
5 B 2000 50
6 B 900 70
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