r - 将数据框转换为 treeNetwork 兼容列表

Question

考虑以下数据框:

   Country     Provinces          City Zone
1   Canada   Newfondland      St Johns    A
2   Canada           PEI Charlottetown    B
3   Canada   Nova Scotia       Halifax    C
4   Canada New Brunswick   Fredericton    D
5   Canada        Quebec            NA   NA
6   Canada        Quebec   Quebec City   NA
7   Canada       Ontario       Toronto    A
8   Canada       Ontario        Ottawa    B
9   Canada      Manitoba      Winnipeg    C
10  Canada  Saskatchewan        Regina    D

是否有一种巧妙的方法将其转换为 treeNetwork兼容列表(来自 networkD3 包)，格式为:

CanadaPC <- list(name = "Canada",
                 children = list(
                   list(name = "Newfoundland",
                        children = list(list(name = "St. John's",
                                             children = list(list(name = "A"))))),
                   list(name = "PEI",
                        children = list(list(name = "Charlottetown",
                                             children = list(list(name = "B"))))),
                   list(name = "Nova Scotia",
                        children = list(list(name = "Halifax",
                                             children = list(list(name = "C"))))),
                   list(name = "New Brunswick",
                        children = list(list(name = "Fredericton",
                                             children = list(list(name = "D"))))),
                   list(name = "Quebec",
                        children = list(list(name = "Quebec City"))),
                   list(name = "Ontario",
                        children = list(list(name = "Toronto",
                                             children = list(list(name = "A"))),
                                        list(name = "Ottawa",
                                             children = list(list(name = "B"))))),
                   list(name = "Manitoba",
                        children = list(list(name = "Winnipeg",
                                             children = list(list(name = "C"))))),
                   list(name = "Saskatchewan",
                        children = list(list(name = "Regina",
                                             children = list(list(name = "D")))))))

为了绘制具有任意级别集的 Reingold-Tilford 树:



我尝试了几个次优例程，包括 for 的困惑组合。循环，但我无法以所需的格式获得它。

理想情况下，该函数会缩放以将第一列视为 root (起点)和其他列将是不同级别的 child 。

编辑

一个 similar question被问到同一主题，@MrFlick 提供了一个有趣的递归函数。原始数据框有一组固定的级别。我介绍 NA s 以添加@MrFlick 初始解决方案中 Unresolved 另一个复杂级别(任意级别集)。

数据

structure(list(Country = c("Canada", "Canada", "Canada", "Canada", 
"Canada", "Canada", "Canada", "Canada", "Canada", "Canada"), 
    Provinces = c("Newfondland", "PEI", "Nova Scotia", "New Brunswick", 
    "Quebec", "Quebec", "Ontario", "Ontario", "Manitoba", "Saskatchewan"
    ), City = c("St Johns", "Charlottetown", "Halifax", "Fredericton", 
    NA, "Quebec City", "Toronto", "Ottawa", "Winnipeg", "Regina"
    ), Zone = c("A", "B", "C", "D", NA, NA, "A", "B", "C", 
    "D")), class = "data.frame", row.names = c(NA, -10L), .Names = c("Country", 
"Provinces", "City", "Zone"))

Answer 1

对于这种情况，更好的策略可能是递归 split()这是一个这样的实现。首先，这是示例数据

dd<-structure(list(Country = c("Canada", "Canada", "Canada", "Canada", 
"Canada", "Canada", "Canada", "Canada", "Canada", "Canada"), 
    Provinces = c("Newfondland", "PEI", "Nova Scotia", "New Brunswick", 
    "Quebec", "Quebec", "Ontario", "Ontario", "Manitoba", "Saskatchewan"
    ), City = c("St Johns", "Charlottetown", "Halifax", "Fredericton", 
    NA, "Quebec City", "Toronto", "Ottawa", "Winnipeg", "Regina"
    ), Zone = c("A", "B", "C", "D", NA, NA, "A", "B", "C", 
    "D")), class = "data.frame", row.names = c(NA, -10L), .Names = c("Country", 
"Provinces", "City", "Zone"))

请注意，'我已经替换了 "NA"字符串为真 NA值。现在，函数

rsplit <- function(x) {
    x <- x[!is.na(x[,1]),,drop=FALSE]
    if(nrow(x)==0) return(NULL)
    if(ncol(x)==1) return(lapply(x[,1], function(v) list(name=v)))
    s <- split(x[,-1, drop=FALSE], x[,1])
    unname(mapply(function(v,n) {if(!is.null(v)) list(name=n, children=v) else list(name=n)}, lapply(s, rsplit), names(s), SIMPLIFY=FALSE))
}

然后我们可以运行

rsplit(dd)

它似乎适用于测试数据。唯一的区别是子节点的排列顺序。