将函数应用于组的组合，固定1个组

debugcn 发表于 Dev

用户名

我有一些看起来像的数据：

   grp    date                id              Y
   <chr>  <dttm>              <chr>       <dbl>
 1 group1 2020-09-01 00:00:00 04003      17039.
 2 group1 2020-09-01 00:00:00 04006      13233.
 3 group1 2020-09-01 00:00:00 04011_AM    7918.
 4 group1 2020-09-01 00:00:00 0401301_AD 22586.
 5 group1 2020-09-01 00:00:00 0401303    20527.
 6 group1 2020-09-01 00:00:00 0401305    29422.
 7 group2 2020-09-01 00:00:00 22017_AM    7088.
 8 group2 2020-09-01 00:00:00 22021_AM    8134.
 9 group2 2020-09-01 00:00:00 22039_AM   15842.
10 group2 2020-09-01 00:00:00 22048      16142.

哪个有不同的组。我还有一个功能：

normaliseData <-function(m){
  (m - min(m)) / (max(m) - min(m))
}

我想通过成对值的最小值和最大值对组进行归一化，保持group1固定不变。也就是说，我想规范化数据修复，group1因此它将具有以下组合。

group1 和 group2
group1 和 group3
group1 和 group4

数据：

data <- structure(list(grp = c("group1", "group1", "group1", "group1", 
"group1", "group1", "group2", "group2", "group2", "group2", "group2", 
"group2", "group3", "group3", "group3", "group3", "group3", "group3", 
"group4", "group4", "group4", "group4", "group4", "group4"), 
    date = structure(c(1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400
    ), tzone = "UTC", class = c("POSIXct", "POSIXt")), id = c("04003", 
    "04006", "04011_AM", "0401301_AD", "0401303", "0401305", 
    "22017_AM", "22021_AM", "22039_AM", "22048", "22053_AM", 
    "22054_AM", "28002", "28004", "2800501", "2800502", "2800503", 
    "2800504", "31010_AM", "31015_AM", "31016", "31019_AM", "31023", 
    "31029_AM"), Y = c(17039.329, 13232.982, 7917.693, 22585.676, 
    20527.113, 29422.471, 7087.536, 8134.265, 15842.035, 16142.111, 
    11493.981, 6556.387, 22086.768, 11325.882, 53449.067, 83662.101, 
    78508.089, 66107.125, 5095.169, 5590.531, 17796.439, 6028.701, 
    39271.698, 3642.281)), row.names = c(NA, -24L), groups = structure(list(
    grp = c("group1", "group2", "group3", "group4"), .rows = structure(list(
        1:6, 7:12, 13:18, 19:24), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, 4L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

编辑：

我希望应用以下内容：

#Min / max from group1 and group2
    data %>% 
      filter(grp == "group1" | grp == "group2") %>% 
      mutate(
        normedOut = normaliseData(Y)
      )

#Min / max from group1 and group3
    data %>% 
      filter(grp == "group1" | grp == "group3") %>% 
      mutate(
        normedOut = normaliseData(Y)
      )

#Min / max from group1 and group4
    data %>% 
      filter(grp == "group1" | grp == "group4") %>% 
      mutate(
        normedOut = normaliseData(Y)
      )

Lathan Liou

purrr根据我对您的问题的理解，这是一种选择。我们创建一个向量，groups其中包含我们感兴趣的三组固定group1循环的感兴趣的组。我们使用您想要的过滤器和变异序列，然后在groups向量中创建包含标准化数据的每组命名的列。这将导致一个数据帧包含3个新列，每个列代表组1和另一组之间的归一化Y。NA将填充没有配对的位置（例如，在group2和group3之间）

groups <- c("group2", "group3", "group4")
groups %>%
  purrr::map_dfr(~ data %>%
        filter(grp == "group1" | grp == .x) %>%
        mutate(!!.x := normaliseData(Y)))

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