R 中的双循环：使用 .name_repair 指定修复？

在试图理解你在做什么的过程中，我偏离了你的原始代码。其中大部分并不一定有什么问题！

您的代码

至于您的分组代码...

你从

for(this in that) 
   for(this in that)
   {
       {

括号嵌套了 for 语句中的内容。你需要

for(this in that) {
   for(this in that) {

# or this works
for(this in that) 
   {
   for(this in that)
      {

当您指定条件时，您选择了整数。但是，您可以直接使用字符串，如for

for(i in method) { # i is a string

# versus

for(i in 1:length(method)) { # i is an integer

当你写嵌套语句时，你错过了一个右括号。for

for(j in 1:length(unique(example$group)) # end parentheses missing!

# should have been

for(j in 1:length(unique(example$group)))

# easier to see like this:

for(j in 1:length(
                  unique(
                         example$group
                        )
                  )
    )

您知道吗？可以将 RStudio 设置为使用“彩虹括号”，这非常适合确保不会错过右括号或括号。转到“工具”->“全局选项”->代码（弹出窗口中的左侧菜单）->显示（菜单弹出窗口中的顶部菜单）&“彩虹括号”是列表中的最后一项。这是我当前外观设置的样子：

提取组时，您选择的是数据集行，而不是唯一值。

# this selects jth row, then looks for unique values
groups_j = unique(example$group[j]) 

# you need to get the unique values, then iterate
group_j = unique(example$group)[j] 

# the j goes outside the call for unique()

在此代码中，您编写了 和 .我假设这等价于 和 ，因为我没有 中的任何内容。filefile$fsaexampleexample$groupfile

所有这些代码行都做同样的事情。请记住，它位于数据框中，但它也是环境中的一个对象。group

my_data_i = example[which(example$group == group_j), ] # this would work
my_data_i <- filter(example, groups == group_j)        # this would work
my_data_i <- example[group == group_j, ]               # this would work
my_data_i <- example[example$group == group_j, ]       # this would work

如果迭代了组而不是索引，则可以跳过创建，这是您唯一使用的 。group_jj

for(j in unique(example$group)) {
    my_data_i <- example[example$group == j, ]
}

当您单独使用 和 发送结果时，每次迭代都会覆盖数据。iij

第一个组迭代可以进入，但下一个组迭代可以绑定到该数据，也可以放在列表中的列表中。results[[i]]group

例如：

results[[1]] <- group 1, method 1
results[[1]] <- rbind(results[[1]], [group 2, method 1])

# or 

results[[1]][[1]] <- group 1, method 1
results[[1]][[2]] <- group 2, method 1

考虑到列表（上面）的这两个选项，第一个选项将允许您的剩余代码（创建最终代码、平均值等）在不进行任何更改的情况下工作。但是，如果使用第二个选项（如上所述），则需要修改该代码。

如果你离开这个会起作用：for(j1:length

if(j < 2) {
  results[[i]] <- final_i
} else {
  results[[i]] <- rbind(results[[i]], final_i) 
}

如果您使用 ，则可以使用以下命令：for(j in unique(example$group))

if(isTRUE(j == unique(example$group)[1])) { # isTRUE() to avoid null errors 
  results[[i]] <- final_i
} else {
  results[[i]] <- rbind(results[[i]], final_i) 
}

嵌套语句全部位于一个块中。for

results = list()
for (i in 1:length(method)) {  # bracket missing here; it was in the wrong place
  for (j in 1:length(unique(example$group))) { # missing a parentheses here
    # { # this needs to be after each for statement
    
    # groups_j = unique(example$group[j]) # you have selected the jth row, not the jth unique
    
    group_j = unique(example$group)[j] # the selection goes outside the call for unique()
    # use things like print or message to check what your function does
    # print(group_j)
    # message('this is a message ', group_j) # notice the different color in the console?
    
    my_data_i <- example[group == group_j, ] # this would work
    
    method_i = method[i]
    name_1_i = paste0("col1_col_2", method_i)
    name_2_i = paste0("col3_col_4", method_i)
    
    p1_i = stringdistmatrix(my_data_i$col1, my_data_i$col2, method =  method_i, useNames = "string") %>%
      as_tibble(rownames = "a") %>%
      pivot_longer(-1, names_to = "b", values_to = name_1_i)
    
    p2_i = stringdistmatrix(my_data_i$col3, my_data_i$col4, method =  method_i, useNames = "string") %>%
      as_tibble(rownames = "a") %>%
      pivot_longer(-1, names_to = "b", values_to = name_2_i)
    
    p1_i = p1_i[,3]
    p2_i = p2_i[,3]
    
    final_i = cbind(p1_i, p2_i)
    
    # results[[i]] = final_i # you replace this content everytime you change groups
    # you need to append the values between groups (assuming you want one column per test type)
    # first append, then combine
    if(j < 2) { # use < instead of == to avoid null error
      results[[i]] <- final_i
    } else {
      results[[i]] <- rbind(results[[i]], final_i) 
    }
  } 
}

我的代码来完成相同的任务

我添加了一些检查和平衡，使其更具动态性。您可以将任意数量的列、方法或组发送到 。grpComp

这使用 、 和 。tidyversegluestringdist

第一个函数由另一个函数调用。

library(tidyverse)
library(stringdist)
library(glue)

strD <- function(c1, c2, mm) { # input column 1; column 2; measurement method
  res <- stringdistmatrix(c1, c2, method = mm, useNames = 'string')
  f_res <- matrix(res) # extract values and flatten
}

这是按组函数划分的距离。

grpComp <- function(fr, methods, grp) { # data frame of columns to compare,
  # methods to use, groups (vector same length as df rows)
  cnames <- names(fr)
  if(length(cnames) %% 2 != 0) {
    message('there are an uneven number of columns to compare')
    break # something's wrong
  }
  if(length(grp) != nrow(fr)) {
    message('there groups vector length must match number of rows in the data')
    break # something's wrong
  }
  # extract distances
  dists <- map(
    method, 
    function(j) {
      str_ds <- map_dfc(    # by column sets  
        seq(from = 1, to = length(cnames), by = 2),
        function(i) {
          str_gr <- map_dfr( # by group
            unique(grp),
            function(k) {
              as.data.frame(list(  # has to be list for `col.names` to work
                strD(fr[grp == k, cnames[i]],
                     fr[grp == k, cnames[i + 1]], j)), 
                optional = F, row.names = NULL, 
                col.names = paste0("c", i, i+1, '_', j))
            })        # combine groups by rows
          str_gr
        })            # combine methods by columns
      str_ds
    }) %>% do.call(cbind, .)
  ncnames <- names(dists) %>% substr(1, 3) %>% unique() # determine unique col groups
  for(m in ncnames) { # get averages for each comparison set
    dists <- mutate(dists, 
                    "ave_{m}" := rowMeans(across(contains(m))) %>% scale())
  }
  dists <- select(dists, contains('ave'))
}

这就是使用此代码的方式。

test5 <- grpComp(example[, 1:4], methods, example$group)

即使您的非分组数据函数正在工作，我想我也会包含该代码。

strComp <- function(fr, methods) { # data frame of columns to compare, methods to use
  cnames <- names(fr)
  if(length(cnames) %% 2 != 0) {
    message('there are an uneven number of columns to compare')
    break # something's wrong
  }
  # extract distances
  dists <- map(
    method, 
    function(j) {
      str_ds <- map_dfc(
        seq(from = 1, to = length(cnames), by = 2),
        function(i) {
          as.data.frame(list(  # has to be list for `col.names` to work
            strD(fr[, cnames[i]], fr[, cnames[i + 1]], j)), optional = F, 
            col.names = paste0("c", i, i+1, '_', j))
        })
      str_ds
    }) %>% do.call(cbind, .)
  ncnames <- names(dists) %>% substr(1, 3) %>% unique() # determine unique col groups
  for(k in ncnames) { # get averages for each comparison set
    dists <- mutate(dists, 
                    "ave_{k}" := rowMeans(across(contains(k))) %>% scale())
  }
  dists <- select(dists, contains('ave'))
}

要使用此功能，请执行以下操作：

test4 <- strComp(example[, 1:4], methods)