Question

我试图在R中产生sankey diagram，这也被称为河流图。我已经看到了这个问题Sankey Diagrams in R?，其中列出了生成sankey图的各种各样的包。由于我有输入数据并且知道不同的工具/包，我可以生成这样的图表但是我的用法是：如何为这样的数据准备输入数据？

我们假设我们想要介绍用户如何在10天内在各个州之间迁移，并且启动数据集如下所示：

data.frame(userID = 1:100,
                     day1_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day2_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day3_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day4_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day5_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day6_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day7_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day8_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day9_state = sample(letters[1:8], replace = TRUE, size = 100),
                     day10_state = sample(letters[1:8], replace = TRUE, size = 100)
                     ) -> dt

现在，如果想要使用networkD3 package创建一个sankey图表，应该如何将此dt data.frame转换为所需的输入

这样我们就可以像这个例子那样输入

library(networkD3)
URL <- paste0(
        "https://cdn.rawgit.com/christophergandrud/networkD3/",
        "master/JSONdata/energy.json")
Energy <- jsonlite::fromJSON(URL)
# Plot
sankeyNetwork(Links = Energy$links, Nodes = Energy$nodes, Source = "source",
             Target = "target", Value = "value", NodeID = "name",
             units = "TWh", fontSize = 12, nodeWidth = 30)

修改

我找到了这样的脚本，在其他情况下准备数据并将其复制，所以我认为它现在可能已关闭：

https://github.com/mi2-warsaw/JakOniGlosowali/blob/master/sankey/sankey.R

Answer 1

我找到了这样的脚本，在其他情况下准备数据并将其复制，所以我认为它现在可能已关闭：

https://github.com/mi2-warsaw/JakOniGlosowali/blob/master/sankey/sankey.R

然后，此代码生成了有问题的data.frame

中提到的sankey图

fixtable <- function(...) {
    tab <- table(...)
    if (substr(colnames(tab)[1],1,1) == "_" &
                substr(rownames(tab)[1],1,1) == "_") {
        tab2 <- tab
        colnames(tab2) <- sapply(strsplit(colnames(tab2), split=" "), `[`, 1)
        rownames(tab2) <- sapply(strsplit(rownames(tab2), split=" "), `[`, 1)
        tab2[1,1] <- 0
        # mandat w klubie
        for (par in names(which(tab2[1,] > 0))) {
            delta = min(tab2[par, 1], tab2[1, par])
            tab2[par, par] = tab2[par, par] + delta
            tab2[1, par] = tab2[1, par] - delta
            tab2[par, 1] = tab2[par, 1] - delta
        }
        # przechodzi przez niezalezy
        for (par in names(which(tab2[1,] > 0))) {
            tab2["niez.", par] = tab2["niez.", par] + tab2[1, par]
            tab2[1, par] = 0
        }
        for (par in names(which(tab2[,1] > 0))) {
            tab2[par, "niez."] = tab2[par, "niez."] + tab2[par, 1]
            tab2[par, 1] = 0
        }

        tab[] <- tab2[] 
    }
    tab
}


flow2 <- rbind(
    data.frame(fixtable(z = paste0(dat$day1_state, " day1"), do = paste0(dat$day2_state, " day2"))),
    data.frame(fixtable(z = paste0(dat$day2_state, " day2"), do = paste0(dat$day3_state, " day3"))),
    data.frame(fixtable(z = paste0(dat$day3_state, " day3"), do = paste0(dat$day4_state, " day4"))),
    data.frame(fixtable(z = paste0(dat$day4_state, " day4"), do = paste0(dat$day5_state, " day5"))),
    data.frame(fixtable(z = paste0(dat$day5_state, " day5"), do = paste0(dat$day6_state, " day6"))),
    data.frame(fixtable(z = paste0(dat$day6_state, " day6"), do = paste0(dat$day7_state, " day7"))),
    data.frame(fixtable(z = paste0(dat$day7_state, " day7"), do = paste0(dat$day8_state, " day8"))),
    data.frame(fixtable(z = paste0(dat$day8_state, " day8"), do = paste0(dat$day9_state, " day9"))),
    data.frame(fixtable(z = paste0(dat$day9_state, " day9"), do = paste0(dat$day10_state, " day10"))))

flow2 <- flow2[flow2[,3] > 0,]

nodes2 <- data.frame(name=unique(c(levels(factor(flow2[,1])), levels(factor(flow2[,2])))))
nam2 <- seq_along(nodes2[,1])-1
names(nam2) <- nodes2[,1]

links2 <- data.frame(source = nam2[as.character(flow2[,1])],
                                        target = nam2[as.character(flow2[,2])],
                                        value = flow2[,3])

sankeyNetwork(Links = links, Nodes = nodes,
                            Source = "source", Target = "target",
                            Value = "value", NodeID = "name",
                            fontFamily = "Arial", fontSize = 12, nodeWidth = 40,
                            colourScale = "d3.scale.category20()")

Answer 2

I asked a similar question while ago.而且我想我最好在这里张贴如何使用tidyverse魔术来完成。

library(ggplot2)
library(ggalluvial)
library(tidyr)
library(dplyr)
library(stringr)

# The actual data preperation happens here
dt_new  <- dt  %>% 
gather(day, state, -userID)  %>% # Long format
mutate(day = str_match(day, "[0-9]+")[,1])  %>% # Get the numbers 
  mutate(day = as.integer(day), # Convert to proper data types
         state = as.factor(state))

这是数据dt_new的样子

   userID day state
1       1   1     d
2       2   1     d
3       3   1     g
4       4   1     a
5       5   1     a
6       6   1     d
7       7   1     d
8       8   1     b
9       9   1     d
10     10   1     e
...

现在绘制Sankey图：

  ggplot(dt_new,
       aes(x = day, stratum = state, alluvium = userID, fill = state, label = state)) +
  geom_stratum() +
  geom_text(stat = "stratum") +
  geom_flow()

这是输出

如何为R中的sankey图准备输入数据？

2 个答案: