向量内的平均邻居

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10

我的数据:

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

有2个邻居: 31 and 32 . 我希望删除它们并仅保留平均值(例如 31.5 ),以这种方式数据将是:

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

这看起来很简单,但我希望自动完成,有时候会使用包含更多邻居的向量 . 例如 :

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

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4回答

  • 3

    这是通过 cumsum(c(TRUE, diff(a) > 1)) 创建id的另一个想法,其中 1 显示间隙阈值,即

    #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
    

    您也可以将其包装在一个函数中 . 我把间隙留作参数让你可以调整,

    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)
    

  • 7

    我有一个基于data.table的解决方案,同样可以翻译成dplyr我猜:

    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
    

    它的作用:如果与以下数字的差异为1,则第一行将neigbours设置为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
    

    我想分组,以便所有neigbours的 neighbour 变量为1 . 我需要在每个组的每一端添加1:

    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
    

    然后,我只是在更改 neighbour 值时进行分组,并将值设置为表示它们是否为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
    

    并采取独特的 Value 观 . 瞧 .


  • 1

    这是一个 dplyr 版本,也用作分组变量 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
    

  • 2

    这是我的解决方案,它使用行程编码来识别组:

    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
    

    我假设你不推荐在Rcpp中使用简单的C for 循环 .

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