Question

我想找到两个组之间变量的最接近匹配（最小的差异），但是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，直到完成n个匹配为止。

我使用此answer（如下）中的代码来查找value之间Samples与所有组的每个成对分组的最接近匹配（即Location VAR。

但是，重复次数很多，Sample.x 1、2和3的最高匹配项可能都是Sample.y1。

我要查找的是Sample.x 2，然后3等的下一个最接近的匹配项，直到我指定了不同的（Sample.x-Sample.y）个匹配项为止制作。但是Sample.x的顺序并不重要，我只是在寻找给定分组的Sample.x和Sample.y之间的前n个匹配项。

我尝试使用dplyr::distinct进行此操作，如下所示。但是我不确定如何对Sample.y使用不同的条目来过滤数据帧，然后再对最小的DIFF进行过滤。但是，这不一定会导致唯一的Sample配对。

使用dplyr在R中完成此操作是否明智？这种操作有名称吗？

 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

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

然后我在下面的代码中使用了res1。这可能并不完美，但是它的作用是找到Sample.y的第一项最近的Sample.x匹配项。然后，将这两个Samples从数据帧中过滤掉。重复进行匹配，直到找到Sample.y的每个唯一值都匹配为止。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)

找到组之间最接近的匹配项，然后查找下一个最接近的匹配项，直到完成指定数量的匹配项

1 个答案: