Question

我有以下数据：

Name    Event

John    EventA
Anna    EventA
Dave    EventA
Stew    EventB
John    EventB
Anna    EventB
John    EventC
Stew    EventC
Dave    EventC

我想知道谁最做同样的事情。因此，例如在上面的示例中，我希望它返回前3个最相似的对：约翰＆amp;安娜，约翰＆amp;戴夫，约翰＆amp;炖。

我认为我需要制作一个如下所示的频率矩阵

Name    John    Anna    Dave     Stew
John     0       2       2        2
Anna     2       0       1        1
Dave     2       1       0        1
Stew     2       1       1        0

然后将其转换为类似的内容：

Pair          Frequency

John Anna         2
John Dave         2
John Stew         2
Anna Dave         1
Anna Stew         1
Dave Stew         1

但我不知道该怎么做。

我正在与R合作，所以如果有人知道这样做的方法，那将是一个巨大的帮助！

Answer 1

您可以使用table melt reshape2个#DATA df = structure(list(Name = c("John", "Anna", "Dave", "Stew", "John", "Anna", "John", "Stew", "Dave"), Event = c("EventA", "EventA", "EventA", "EventB", "EventB", "EventB", "EventC", "EventC", "EventC" )), .Names = c("Name", "Event"), row.names = c(NA, -9L), class = "data.frame") #Get Pairwise Frequency a = table(df) %*% t(table(df)) a # Name #Name Anna Dave John Stew # Anna 2 1 2 1 # Dave 1 2 2 1 # John 2 2 3 2 # Stew 1 1 2 2 #If you want, set diagonal elements to zero (From Karthik's comment) #diag(a) <- 0 library(reshape2) output = data.frame(melt(a)) colnames(output) = c("Name1", "Name2", "Value") #Remove the pair with oneself output = output[-(which(output$Name1 == output$Name2)),] output # Name1 Name2 Value #2 Dave Anna 1 #3 John Anna 2 #4 Stew Anna 1 #5 Anna Dave 1 #7 John Dave 2 #8 Stew Dave 1 #9 Anna John 2 #10 Dave John 2 #12 Stew John 2 #13 Anna Stew 1 #14 Dave Stew 1 #15 John Stew 2 #YOU CAN PASTE 'NAME1' and 'NAME2' to a 'PAIR' if necessary #output$PAIR = apply(output, 1, function(x) paste(sort(x[1:2]), collapse = " "))包。

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Answer 2

这似乎与您要求的更接近，并且仅使用基础R中的函数。使用来自@ d.b的答案中的“df”：

x <- as.table(tcrossprod(table(df)))
x[lower.tri(x, diag = TRUE)] <- NA
na.omit(data.frame(x))
#    Name Name.1 Freq
# 5  Anna   Dave    1
# 9  Anna   John    2
# 10 Dave   John    2
# 13 Anna   Stew    1
# 14 Dave   Stew    1
# 15 John   Stew    2

使用NA diag和lower.tri可以让我们轻松删除我们不感兴趣的值。

查找数据集中对的频率 - R.

2 个答案: