r 从数据框中的列创建邻接矩阵

Question

我有兴趣测试一些网络可视化技术，但在尝试这些功能之前，我想使用数据框构建邻接矩阵（从，到），如下所示。

 Id   Gender   Col_Cold_1  Col_Cold_2  Col_Cold_3  Col_Hot_1  Col_Hot_2   Col_Hot_3  
 10   F         pain       sleep        NA         infection  medication  walking
 14   F         Bump       NA           muscle     NA         twitching   flutter
 17   M                    pain         hemoloma   Callus     infection   
 18   F         muscle                  pain                  twitching   medication

我的目标是创建一个如下的邻接矩阵

1) All values in columns with keyword Cold will contribute to the rows  
2) All values in columns with keyword Hot will contribute to the columns

例如，pain, sleep, Bump, muscle, hemaloma 是具有关键字 Cold 的列下的单元格值，它们将形成行和单元格值，例如 infection, medication, Callus, walking, twitching, flutter 位于包含关键字 Hot 的列，这将形成关联矩阵的列。

最终所需的输出应如下所示：

           infection  medication  walking  twitching  flutter  Callus
     pain  2          2           1        1                   1
    sleep  1          1           1
     Bump                                  1          1
   muscle             1                    1
 hemaloma  1                                                   1

[pain, infection] = 2 因为疼痛和感染之间的关联在原始数据框中出现了两次：一次在第 1 行，一次在第 3 行。
[pain, medication]=2 因为疼痛和药物之间的关联在第 1 行和第 4 行中出现两次。

非常感谢任何有关生成此类关联矩阵的建议或建议。

可重现数据集

df = structure(list(id = c(10, 14, 17, 18), Gender = structure(c(1L, 1L, 2L, 1L), .Label = c("F", "M"), class = "factor"), Col_Cold_1 = structure(c(4L, 2L, 1L, 3L), .Label = c("", "Bump", "muscle", "pain"), class = "factor"), Col_Cold_2 = structure(c(4L, 2L, 3L, 1L), .Label = c("", "NA", "pain", "sleep"), class = "factor"), Col_Cold_3 = structure(c(1L, 3L, 2L, 4L), .Label = c("NA", "hemaloma", "muscle", "pain" ), class = "factor"), Col_Hot_1 = structure(c(4L, 3L, 2L, 1L), .Label = c("", "Callus", "NA", "infection"), class = "factor"), Col_Hot_2 = structure(c(2L, 3L, 1L, 3L), .Label = c("infection", "medication", "twitching"), class = "factor"), Col_Hot_3 = structure(c(4L, 2L, 1L, 3L), .Label = c("", "flutter", "medication", "walking" ), class = "factor")), .Names = c("id", "Gender", "Col_Cold_1", "Col_Cold_2", "Col_Cold_3", "Col_Hot_1", "Col_Hot_2", "Col_Hot_3" ), row.names = c(NA, -4L), class = "data.frame")

Answer 1

一种方法是将数据集做成"tidy"形式，然后使用xtabs。首先，进行一些清理工作：

df[] <- lapply(df, as.character)  # Convert factors to characters
df[df == "NA" | df == "" | is.na(df)] <- NA  # Make all blanks NAs

现在，整理数据集：

library(tidyr)
library(dplyr)
out <- do.call(rbind, sapply(grep("^Col_Cold", names(df), value = T), function(x){
  vars <- c(x, grep("^Col_Hot", names(df), value = T))
  setNames(gather_(select(df, one_of(vars)), 
    key_col = x,
    value_col = "value",
    gather_cols = vars[-1])[, c(1, 3)], c("cold", "hot"))
}, simplify = FALSE))

想法是 "pair" 每个 "cold" 列与每个 "hot" 列组成一个长数据集。 out 看起来像这样：

out
#        cold        hot
# 1      pain  infection
# 2      Bump       <NA>
# 3      <NA>     Callus
# 4    muscle       <NA>
# 5      pain medication
# ...

最后，使用xtabs得到想要的输出：

xtabs(~ cold + hot, na.omit(out))
#           hot
# cold       Callus flutter infection medication twitching walking
#   Bump          0       1         0          0         1       0
#   hemaloma      1       0         1          0         0       0
#   muscle        0       1         0          1         2       0
#   pain          1       0         2          2         1       1
#   sleep         0       0         1          1         0       1

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