Krdytkyu

My data :

data <- c(1,5,11,15,24,31,32,65)

There are 2 neighbours: 31 and 32. I wish to remove them and keep only the mean value (e.g. 31.5), in such a way data would be :

data <- c(1,5,11,15,24,31.5,65)

It seems simple, but I wish to do it automatically, and sometimes with vectors containing more neighbours. For instance :

data_2 <- c(1,5,11,15,24,31,32,65,99,100,101,140)

Here is another idea that creates an id via cumsum(c(TRUE, diff(a) > 1)), where 1 shows the gap threshold, i.e.

#our group variable

grp <- cumsum(c(TRUE, diff(a) > 1))



#keep only groups with length 1 (i.e. with no neighbor)

i1 <- a[!!!ave(a, grp, FUN = function(i) length(i) > 1)] 



#Find the mean of the groups with more than 1 rows,

i2 <- unname(tapply(a, grp, function(i)mean(i[length(i) > 1])))



#Concatenate the above 2 (eliminating NAs from i2) to get final result

c(i1, i2[!is.na(i2)])

#[1]  1.0  5.0 11.0 15.0 24.0 65.0 31.5

You can also wrap it in a function. I left the gap as a parameter so you can adjust,

get_vec <- function(x, gap) {

    grp <- cumsum(c(TRUE, diff(x) > gap))

    i1 <- x[!!!ave(x, grp, FUN = function(i) length(i) > 1)]

    i2 <- unname(tapply(x, grp, function(i) mean(i[length(i) > 1])))

    return(c(i1, i2[!is.na(i2)]))

}



get_vec(a, 1)

#[1]  1.0  5.0 11.0 15.0 24.0 65.0 31.5



get_vec(a_2, 1)

#[1]   1.0   5.0  11.0  15.0  24.0  65.0 140.0  31.5 100.0

DATA:

a <- c(1,5,11,15,24,31,32,65)

a_2 <- c(1, 5, 11, 15, 24, 31, 32, 65, 99, 100, 101, 140)

Here is my solution, which uses run-length encoding to identify groups:

foo <- function(x) {

  y <- x - seq_along(x) #normalize to zero differences in groups

  ind <- rle(y) #run-length encoding

  ind$values <- ind$lengths != 1 #to find groups

  ind$values[ind$values] <- cumsum(ind$values[ind$values]) #group ids

  ind <- inverse.rle(ind)

  xnew <- x

  xnew[ind != 0] <- ave(x, ind, FUN = mean)[ind != 0] #calculate means

  xnew[!(duplicated(ind) & ind != 0)] #remove duplicates from groups

}



foo(data)

#[1]  1.0  5.0 11.0 15.0 24.0 31.5 65.0

foo(data_2)

#[1]   1.0   5.0  11.0  15.0  24.0  31.5  65.0 100.0 140.0

data_3 <- c(1, 2, 4, 1, 2)

foo(data_3)

#[1] 1.5 4.0 1.5

I assume that you don't need an extremely efficient solution. If you do, I'd recommend a simple C++ for loop in Rcpp.

I have a data.table based solution, same could be translated into dplyr I guess:

library(data.table)

df <- data.table(data2 = c(1,5,11,15,24,31,32,65,99,100,101,140))

df[,neighbours := ifelse(c(0,diff(data_2)) == 1,1,0)]

df[,neighbours := c(neighbours[1:(.N-1)],1),by = rleid(neighbours)]

df[,neigh_seq := rleid(neighbours)]



unique(df[,ifelse(neighbours == 1,mean(data2),data2),by = neigh_seq])



   neigh_seq    V1

1:         1   1.0

2:         1   5.0

3:         1  11.0

4:         1  15.0

5:         1  24.0

6:         2  31.5

7:         3  65.0

8:         4 100.0

9:         5 140.0

What it does :
first line set neigbours to 1 if the difference with following number is 1

 1:     1          0

 2:     5          0

 3:    11          0

 4:    15          0

 5:    24          0

 6:    31          0

 7:    32          1

 8:    65          0

 9:    99          0

10:   100          1

11:   101          1

12:   140          0

I wanr to group so that neighbour variable is 1 for all neigbours. I need to add 1 to each end of each groups:

df[,neighbours := c(neighbours[1:(.N-1)],1),by = rleid(neighbours)]

    data2 neighbours

 1:     1          0

 2:     5          0

 3:    11          0

 4:    15          0

 5:    24          0

 6:    31          1

 7:    32          1

 8:    65          0

 9:    99          1

10:   100          1

11:   101          1

12:   140          0

Then after I just do a grouping on changing neighbour value, and set the value to mean if they are neihbours

df[,ifelse(neighbours == 1,mean(data2),data2),by = rleid(neighbours)]

    rleid    V1

 1:     1   1.0

 2:     1   5.0

 3:     1  11.0

 4:     1  15.0

 5:     1  24.0

 6:     2  31.5

 7:     2  31.5

 8:     3  65.0

 9:     4 100.0

10:     4 100.0

11:     4 100.0

12:     5 140.0

and take the unique values. And voila.

This is a dplyr version, also using as a grouping variable cumsum(c(1,diff(x)!=1)):

library(dplyr)

data_2 %>% data.frame(x = .) %>% 

group_by(id = cumsum(c(1,diff(x)!=1))) %>% 

summarise(res = mean(x)) %>% 

select(res)

# A tibble: 9 x 1

    res

  <dbl>

1   1.0

2   5.0

3  11.0

4  15.0

5  24.0

6  31.5

7  65.0

8 100.0

9 140.0