Alexander Alexander - 22 days ago 6
R Question

Summing rows based on conditional in groups

Previously I asked related to this question but I need more elegant and general way to solve this.
I have data separated in groups and I want to sum some rows in range based on conditional. I prefer to use 'dplyr' to do this because it's more straight forward for me to understand.

The conditionals which I need as follows;

1: for group 1 ;
find the first occurrence of '10' and sum the rows after this occurrence to the end of the group and count how many rows.

2: for group 2;'find the last occurrence of '10' and and sum the rows before this occurrence to the beginning of the group and count how many rows!

3: for group 3; find the first occurrence of '10' and and sum the rows before this occurrence to the starting row of the group and count how many rows.

df <- data.frame(gr=rep(c(1,2,3),c(7,9,11)),
y_value=c(c(0,0,10,8,8,6,0),c(10,10,10,8,7,6,2,0,0), c(8,5,8,7,6,2,10,10,8,7,0)))


> df
gr y_value
1 1 0
2 1 0
3 1 10
4 1 8
5 1 8
6 1 6
7 1 0
8 2 10
9 2 10
10 2 10
11 2 8
12 2 7
13 2 6
14 2 2
15 2 0
16 2 0
17 3 8
18 3 5
19 3 8
20 3 7
21 3 6
22 3 2
23 3 10
24 3 10
25 3 8
26 3 7
27 3 0


It guess something like this should work but cannot figured out how to implement this to
dplyr


count <- function(y,gr){
if (any(y==10)&(gr==1)) {
*
*
*
if (any(y==10)&(gr==2))
*
*
*
*


}
}

df%>%
library(dplyr)

df %>%
group_by(gr) %>%
do(data.frame(.,count_rows=count(y_value,gr)))


expected output

> df
gr y_value sum nrow
1 1 0 22 4
2 1 0 22 4
3 1 10 22 4
4 1 8 22 4
5 1 8 22 4
6 1 6 22 4
7 1 0 22 4
8 2 10 23 6
9 2 10 23 6
10 2 10 23 6
11 2 8 23 6
12 2 7 23 6
13 2 6 23 6
14 2 2 23 6
15 2 0 23 6
16 2 0 23 6
17 3 8 28 6
18 3 5 28 6
19 3 7 28 6
20 3 6 28 6
21 3 2 28 6
22 3 10 28 6
23 3 10 28 6
24 3 8 28 6
25 3 7 28 6
26 3 0 28 6

Answer Source

Hope this helps!

#sample data
df <- data.frame(gr=rep(c(1,2,3),c(7,9,11)), 
                 y_value=c(c(0,0,10,8,8,6,0),c(10,10,10,8,7,6,2,0,0), c(8,5,8,7,6,2,10,10,8,7,0)))

library(dplyr)
df_temp <- df %>% 
  group_by(gr) %>% 
  mutate(rows_to_aggregate=cumsum(y_value==10)) %>% 
  filter(ifelse(gr==1 | gr==2, rows_to_aggregate !=0 & y_value!=10, rows_to_aggregate ==0)) %>% 
  mutate(nrow=n(), sum=sum(y_value)) %>%
  select(gr,sum,nrow) %>%
  distinct()

#final output
df<- left_join(df,df_temp, by='gr')