r - 在组之间查找最接近的匹配，然后是下一个最接近的匹配，直到进行了指定数量的匹配

Question

我想找到两个组之间变量的最接近的匹配(最小差异)，但如果已经进行了最接近的匹配，则继续进行下一个最接近的匹配，直到进行了 n 次匹配。

我使用了这个 answer 中的代码(下)找到最接近的匹配 value之间Samples对于所有组的每个成对分组(即 Location by VAR )。

但是，有很多重复，顶级匹配为Sample.x 1、2 和 3 可能都是 Sample.y 1.

我想要的是为 Sample.x 找到下一个最接近的匹配项2，然后是 3，依此类推，直到我指定了不同的 ( Sample.x - Sample.y ) 匹配次数。但是Sample.x的顺序不重要，我只是在寻找 Sample.x 之间的前 n 个匹配项和 Sample.y对于给定的分组。

我试图用 dplyr::distinct 做到这一点如下所示。但我不确定如何为 Sample.y 使用不同的条目过滤数据帧，然后再次过滤最小的 DIFF .但是，这不一定会导致唯一的 Sample配对。

有没有一种聪明的方法可以用 dplyr 在 R 中完成这个？这种类型的操作有名称吗？

 df01 <- data.frame(Location = rep(c("A", "C"), each =10), 
                   Sample = rep(c(1:10), times =2),
                   Var1 =  signif(runif(20, 55, 58), digits=4),
                   Var2 = rep(c(1:10), times =2)) 
df001 <- data.frame(Location = rep(c("B"), each =10), 
                    Sample = rep(c(1:10), times =1),
                    Var1 = c(1.2, 1.3, 1.4, 1.6, 56, 110.1, 111.6, 111.7, 111.8, 120.5),
                    Var2 = c(1.5, 10.1, 10.2, 11.7, 12.5, 13.6, 14.4, 18.1, 20.9, 21.3))
df <- rbind(df01, df001)
dfl <- df %>% gather(VAR, value, 3:4)

df.result <- df %>% 
  # get the unique elements of Location
  distinct(Location) %>% 
  # pull the column as a vector
  pull %>% 
  # it is factor, so convert it to character
  as.character %>% 
  # get the pairwise combinations in a list
  combn(m = 2, simplify = FALSE) %>%
  # loop through the list with map and do the full_join
  # with the long format data dfl
  map(~ full_join(dfl %>% 
                    filter(Location == first(.x)), 
                  dfl %>% 
                    filter(Location == last(.x)), by = "VAR") %>% 
        # create a column of absolute difference
        mutate(DIFF = abs(value.x - value.y)) %>%
        # grouped by VAR, Sample.x
        group_by(VAR, Sample.x) %>%
        # apply the top_n with wt as DIFF
        # here I choose 5, 
        # and then hope that this is enough to get a smaller n of final matches
        top_n(-5, DIFF) %>%
        mutate(GG = paste(Location.x, Location.y, sep="-")))

res1 <- rbindlist(df.result)
res2 <- res1 %>% group_by(GG, VAR) %>% distinct(Sample.y)    
res3 <- res2 %>% group_by(GG, VAR) %>% top_n(-2, DIFF)

Answer 1

我编辑上面产生 df.result 的代码通过删除行 top_n(-5, DIFF) %>% .现在 res1包含 Sample.x 的所有匹配项和 Sample.y .

然后我用了 res1在下面的代码中。这可能并不完美，但它所做的是找到最接近的 Sample.y匹配 Sample.x 的第一个条目.那么这两个 Samples从数据框中过滤。匹配重复，直到为 Sample.y 的每个唯一值找到匹配项.结果可能会有所不同，具体取决于首先进行的匹配。

  fun <- function(df) {
  HowMany <- length(unique(df$Sample.y))
  i <- 1
  MyList_FF <- list()
  df_f <- df
  while (i <= HowMany){
    res1 <- df_f %>%
      group_by(grp, VAR, Sample.x) %>%
      filter(DIFF == min(DIFF)) %>%
      ungroup() %>%
      mutate(Rank1 = dense_rank(DIFF))

    res2 <- res1 %>% group_by(grp, VAR) %>% filter(rank(Rank1, ties.method="first")==1)

    SY <- as.numeric(res2$Sample.y)
    SX <- as.numeric(res2$Sample.x)
    res3 <- df_f %>% filter(Sample.y != SY) # filter Sample.y
    res4 <- res3 %>% filter(Sample.x != SX) # filter Sample.x
    df_f <- res4

    MyList_FF[[i]] <- res2

    i <- i + 1
  }
  do.call("rbind", MyList_FF) # https://stackoverflow.com/a/55542822/1670053
}

df <- res1
MyResult <- df %>%
  dplyr::group_split(grp, VAR) %>%
  map_df(fun)