dplyr how to count cycles in the records - r

Question

For example, if I have records like:
A B
1 2
2 3
3 1
1 2
2 1
Let's say one cycle is from 1 (to 2 to 3) back to 1,so I need my data frame to be like
No. A B
cycle1 1 2
cycle1 2 3
cycle1 3 1
cycle2 1 2
cycle2 2 1
Or a better way for me, I just need to record the time the same record appears, like
Time A B
Time1 1 2
Time1 2 3
Time1 3 1
Time2 1 2
Time1 2 1
I need to do this because I have to use summarize function in dplyr to do calculation but I cannot group data by A and B directly. The order of the data is also important.

Answer 1

Is this what you want ?
library(zoo)
T1=which(df$A==1)
T2=1:length(T1)
T2=paste('cycle',T2 )
df$No=NA
df$No[T1]=T2
df$No=na.locf(df$No)
df
A B No
1 1 2 cycle 1
2 2 3 cycle 1
3 3 1 cycle 1
4 1 2 cycle 2
5 2 1 cycle 2
#the reason: keep the row Id with the calculation
library(dplyr)
df%>%group_by(A,B)%>%mutate(Time=paste('Time',row_number()))
A B Time
<int> <int> <chr>
1 1 2 Time 1
2 2 3 Time 1
3 3 1 Time 1
4 1 2 Time 2
5 2 1 Time 1

Answer 2

Create an augmented 'diff' variable. c(NA , diff (your_var)). Within a sequence group this will be 1. Set your group to change at the logical falsity of that proposition. (My first iteration on the algorithm wasn't quite correct so modified it slightly.)
dat %>% as_tibble() %>% mutate(G = cumsum( c(-1, diff(A)) < 0 ) )
# A tibble: 5 x 3
A B G
<int> <int> <int>
1 1 2 1
2 2 3 1
3 3 1 1
4 1 2 2
5 2 1 2
dat %>% as_tibble() %>% mutate(G = paste0( "time", cumsum( c(-1, diff(A)) < 0 ) ))
# A tibble: 5 x 3
A B G
<int> <int> <chr>
1 1 2 time1
2 2 3 time1
3 3 1 time1
4 1 2 time2
5 2 1 time2
One could also test for A=1, but then sequences like 1,2,3,2,3,4 would not get properly split.