javascript - R 中的 NetworkD3 桑基图 : How to calculate value for each link?

Question

我正在尝试按照 d3Network 的 R 端口示例来创建详细说明的桑基图(如下所述: https://christophergandrud.github.io/networkD3/ )。我加载以下示例“Energy”数据集:

    # Load energy projection data

    URL <- paste0("https://cdn.rawgit.com/christophergandrud/networkD3/",
    "master/JSONdata/energy.json")

    Energy <- jsonlite::fromJSON(URL)

导入“Energy”数据集会生成两个新的data.frame；节点和链接。查看链接数据可以发现以下格式:

    head(Energy$links)
      source target   value
  1        0      1 124.729
  2        1      2   0.597
  3        1      3  26.862
  4        1      4 280.322
  5        1      5  81.144
  6        6      2  35.000

“源”列指示源节点，“目标”列指示目标节点，而“值”列指示每个单独链接的值。

尽管这在概念上相当简单，但我在获取与 Energy$links data.frame 格式相同的数据集时遇到了巨大困难。我已经能够以以下格式获取数据，但对于如何进一步转换它完全一片空白:

   head(sampleSankeyData, n = 10L)
    clientID                node1
      <int>                <chr>
 1     23969 1 Community Services
 2     39199      1 Youth Justice
 3     23595      1 Mental Health
 4     15867 1 Community Services
 5     18295            3 Housing
 6     18295            2 Housing
 7     18295 1 Community Services
 8     18295            4 Housing
 9     15253            1 Housing
 10    27839 1 Community Services 

我想要做的是聚合每个链接的唯一客户端数量。例如，在上述数据子集中，由于客户端 18295，“1 Community Services”到“2 Housing”的链接应具有值 1(“2 Housing”到“3 Housing”的链接也应具有值 1)。 ”以及“3 住房”至“4 住房”)。因此，我希望能够获取与桑基图示例中的 Energy$links 格式相同的数据。

Answer 1

试试这个:

library(tidyverse)
library(stringr)
df <- tribble(
~number, ~clientID,         ~node1,
1 ,    23969, '1 Community Services',
2 ,    39199,      '1 Youth Justice',
3 ,    23595,      '1 Mental Health',
4 ,    15867, '1 Community Services',
5 ,    18295,            '3 Housing',
6 ,    18295,            '2 Housing',
7 ,    18295, '1 Community Services',
8 ,    18295,            '4 Housing',
9 ,    15253,            '1 Housing',
10,    27839, '1 Community Services')

df2 <- mutate(df, step=as.numeric(str_sub(node1, end=1))) %>%
  spread(step, node1, sep='_') %>%
  group_by(clientID) %>%
  summarise(step1 = sort(unique(step_1))[1],
            step2 = sort(unique(step_2))[1],
            step3 = sort(unique(step_3))[1],
            step4 = sort(unique(step_4))[1])

df3 <- bind_rows(select(df2,1,source=2,target=3),
            select(df2,1,source=3,target=4),
            select(df2,1,source=4,target=5)) %>%
  group_by(source, target) %>%
  summarise(clients=n())

并将其与networkD3一起使用...

links <- df3 %>% 
  dplyr::ungroup() %>% # ungroup just to be safe
  dplyr::filter(!is.na(source) & !is.na(target)) # remove lines without a link

# build the nodes data frame based on nodes in your links data frame
nodeFactors <- factor(sort(unique(c(links$source, links$target))))
nodes <- data.frame(name = nodeFactors)

# convert the source and target values to the index of the matching node in the 
# nodes data frame
links$source <- match(links$source, levels(nodeFactors)) - 1
links$target <- match(links$target, levels(nodeFactors)) - 1

# plot
library(networkD3)
sankeyNetwork(Links = links, Nodes = nodes, Source = 'source', 
              Target = 'target', Value = 'clients', NodeID = 'name')