如何格式化绘图旭日图的数据

问题描述 投票：0回答：3

我正在尝试使用 Plotly via R 制作旭日图。我正在努力解决层次结构所需的数据模型，无论是概念化它的工作原理，还是看看是否有任何简单的方法来转换常规数据框，其中的列代表不同的层次级别，转换为所需的格式。

我查看了 R 中的绘图旭日图示例，例如，here，并查看了参考页，但没有完全获得数据格式化的模型。 

# Create some fake data - say ownership and land use data with acreage
df <- data.frame(ownership=c(rep("private", 3), rep("public",3),rep("mixed", 3)), 
                 landuse=c(rep(c("residential", "recreation", "commercial"),3)),
                 acres=c(108,143,102, 300,320,500, 37,58,90))

# Just try some quick pie charts of acreage by landuse and ownership
plot_ly(data=df, labels= ~landuse, values= ~acres, type='pie')
plot_ly(data=df, labels= ~ownership, values= ~acres, type='pie')

# This doesn't render anything... not that I'd expect it to given the data format doesn't seem to match what's needed, 
# but this is what I'd intuitively expect to work
plot_ly(data=df, labels= ~landuse, parents = ~ownership, values= ~acres, type='sunburst')

鉴于上面的示例代码或类似代码，了解如何从数据 (

df
) 转换为绘图旭日图所需的格式会很有帮助。

r plotly r-plotly sunburst-diagram
3个回答
13
投票

你是绝对正确的，与plotly的R API的其余直观用法相比，为旭日（或树状图）图表准备数据是相当烦人的。

我遇到了同样的问题，并根据 

library(data.table)
编写了一个函数来准备数据，接受两种不同的 
data.frame
输入格式。

使用与您的结构类似的数据生成旭日图所需的格式可以在此处在具有重复标签的旭日图部分下看到。

对于您的示例，它应该如下所示：

         labels values         parents                           ids
 1:       total   1658            <NA>                         total
 2:     private    353           total               total - private
 3:      public   1120           total                total - public
 4:       mixed    185           total                 total - mixed
 5: residential    108 total - private total - private - residential
 6:  recreation    143 total - private  total - private - recreation
 7:  commercial    102 total - private  total - private - commercial
 8: residential    300  total - public  total - public - residential
 9:  recreation    320  total - public   total - public - recreation
10:  commercial    500  total - public   total - public - commercial
11: residential     37   total - mixed   total - mixed - residential
12:  recreation     58   total - mixed    total - mixed - recreation
13:  commercial     90   total - mixed    total - mixed - commercial

这是到达那里的代码：

library(data.table)
library(plotly)

DF <- data.table(ownership=c(rep("private", 3), rep("public",3),rep("mixed", 3)),
                 landuse=c(rep(c("residential", "recreation", "commercial"),3)),
                 acres=c(108, 143, 102, 300, 320, 500, 37, 58, 90))

as.sunburstDF <- function(DF, value_column = NULL, add_root = FALSE){
  require(data.table)
  
  colNamesDF <- names(DF)
  
  if(is.data.table(DF)){
    DT <- copy(DF)
  } else {
    DT <- data.table(DF, stringsAsFactors = FALSE)
  }
  
  if(add_root){
    DT[, root := "Total"]  
  }
  
  colNamesDT <- names(DT)
  hierarchy_columns <- setdiff(colNamesDT, value_column)
  DT[, (hierarchy_columns) := lapply(.SD, as.factor), .SDcols = hierarchy_columns]
  
  if(is.null(value_column) && add_root){
    setcolorder(DT, c("root", colNamesDF))
  } else if(!is.null(value_column) && !add_root) {
    setnames(DT, value_column, "values", skip_absent=TRUE)
    setcolorder(DT, c(setdiff(colNamesDF, value_column), "values"))
  } else if(!is.null(value_column) && add_root) {
    setnames(DT, value_column, "values", skip_absent=TRUE)
    setcolorder(DT, c("root", setdiff(colNamesDF, value_column), "values"))
  }
  
  hierarchyList <- list()
  
  for(i in seq_along(hierarchy_columns)){
    current_columns <- colNamesDT[1:i]
    if(is.null(value_column)){
      currentDT <- unique(DT[, ..current_columns][, values := .N, by = current_columns], by = current_columns)
    } else {
      currentDT <- DT[, lapply(.SD, sum, na.rm = TRUE), by=current_columns, .SDcols = "values"]
    }
    setnames(currentDT, length(current_columns), "labels")
    hierarchyList[[i]] <- currentDT
  }
  
  hierarchyDT <- rbindlist(hierarchyList, use.names = TRUE, fill = TRUE)
  
  parent_columns <- setdiff(names(hierarchyDT), c("labels", "values", value_column))
  hierarchyDT[, parents := apply(.SD, 1, function(x){fifelse(all(is.na(x)), yes = NA_character_, no = paste(x[!is.na(x)], sep = ":", collapse = " - "))}), .SDcols = parent_columns]
  hierarchyDT[, ids := apply(.SD, 1, function(x){paste(x[!is.na(x)], collapse = " - ")}), .SDcols = c("parents", "labels")]
  hierarchyDT[, c(parent_columns) := NULL]
  return(hierarchyDT)
}


sunburstDF <- as.sunburstDF(DF, value_column = "acres", add_root = TRUE)

plot_ly(data = sunburstDF, ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')

这里是函数接受的第二种

data.frame
格式的示例（
value_column = NULL
，因为它是根据数据计算出来的）：

DF2 <- data.frame(sample(LETTERS[1:3], 100, replace = TRUE),
                  sample(LETTERS[4:6], 100, replace = TRUE),
                  sample(LETTERS[7:9], 100, replace = TRUE),
                  sample(LETTERS[10:12], 100, replace = TRUE),
                  sample(LETTERS[13:15], 100, replace = TRUE),
                  stringsAsFactors = FALSE)

plot_ly(data = as.sunburstDF(DF2, add_root = TRUE), ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')

另请参阅库（sunburstR）作为替代方案。

编辑： 添加了关于来自 

count_to_sunburst()
的基于 dplyr 的 
library(plotme)
函数的基准（见下文），在我的系统上，该基准比 
data.table
版本慢大约 5 倍。

Unit: milliseconds
          expr     min       lq     mean   median       uq      max neval
        plotme 50.4618 53.09425 60.92404 55.37815 63.62315 122.3842   100
 ismirsehregal  8.6553 10.28870 12.63881 11.53760 12.26620 108.2025   100

重现基准测试的代码：

# devtools::install_github("yogevherz/plotme")

library(microbenchmark)
library(plotme)
library(dplyr)
library(data.table)
library(plotly)

DF <- data.frame(ownership=c(rep("private", 3), rep("public",3),rep("mixed", 3)),
                 landuse=c(rep(c("residential", "recreation", "commercial"),3)),
                 acres=c(108, 143, 102, 300, 320, 500, 37, 58, 90))

as.sunburstDF <- function(DF, value_column = NULL, add_root = FALSE){
  require(data.table)
  
  colNamesDF <- names(DF)
  
  if(is.data.table(DF)){
    DT <- copy(DF)
  } else {
    DT <- data.table(DF, stringsAsFactors = FALSE)
  }
  
  if(add_root){
    DT[, root := "Total"]  
  }
  
  colNamesDT <- names(DT)
  hierarchy_columns <- setdiff(colNamesDT, value_column)
  DT[, (hierarchy_columns) := lapply(.SD, as.factor), .SDcols = hierarchy_columns]
  
  if(is.null(value_column) && add_root){
    setcolorder(DT, c("root", colNamesDF))
  } else if(!is.null(value_column) && !add_root) {
    setnames(DT, value_column, "values", skip_absent=TRUE)
    setcolorder(DT, c(setdiff(colNamesDF, value_column), "values"))
  } else if(!is.null(value_column) && add_root) {
    setnames(DT, value_column, "values", skip_absent=TRUE)
    setcolorder(DT, c("root", setdiff(colNamesDF, value_column), "values"))
  }
  
  hierarchyList <- list()
  
  for(i in seq_along(hierarchy_columns)){
    current_columns <- colNamesDT[1:i]
    if(is.null(value_column)){
      currentDT <- unique(DT[, ..current_columns][, values := .N, by = current_columns], by = current_columns)
    } else {
      currentDT <- DT[, lapply(.SD, sum, na.rm = TRUE), by=current_columns, .SDcols = "values"]
    }
    setnames(currentDT, length(current_columns), "labels")
    hierarchyList[[i]] <- currentDT
  }
  
  hierarchyDT <- rbindlist(hierarchyList, use.names = TRUE, fill = TRUE)
  
  parent_columns <- setdiff(names(hierarchyDT), c("labels", "values", value_column))
  hierarchyDT[, parents := apply(.SD, 1, function(x){fifelse(all(is.na(x)), yes = NA_character_, no = paste(x[!is.na(x)], sep = ":", collapse = " - "))}), .SDcols = parent_columns]
  hierarchyDT[, ids := apply(.SD, 1, function(x){paste(x[!is.na(x)], collapse = " - ")}), .SDcols = c("parents", "labels")]
  hierarchyDT[, c(parent_columns) := NULL]
  return(hierarchyDT)
}

microbenchmark(plotme = {
  DF %>% 
    rename(n = acres) %>% 
    count_to_sunburst()
}, ismirsehregal = {
  plot_ly(data = as.sunburstDF(DF, value_column = "acres", add_root = TRUE), ids = ~ids, labels= ~labels, parents = ~parents, values= ~values, type='sunburst', branchvalues = 'total')  
})
3
投票

有专门用于此任务的 

plotme
包：

library(plotme)
library(dplyr)

df %>% 
  rename(n = acres) %>% 
  count_to_sunburst()

要安装软件包，请运行：

devtools::install_github("yogevherz/plotme")

有关包装的更多信息这里。

0
投票

我已经尝试了 ismirsehregal 上面建议的方法，最终将数据帧转换为旭日图的适当格式真是太棒了。 然而，正如您在创建的图中所看到的，存在不应该存在的间隙。 这是输入数据：

                      labels values               parents                                              ids
 1:                    Total   2347                  <NA>                                            Total
 2:            External User   1264                 Total                            Total - External User
 3:                      UHB   1083                 Total                                      Total - UHB
 4:                      ARI      3 Total - External User                      Total - External User - ARI
 5:                      BCH    223 Total - External User                      Total - External User - BCH
 6:           Castle Vale RU      2 Total - External User           Total - External User - Castle Vale RU
 7: City Hospital Birmingham    150 Total - External User Total - External User - City Hospital Birmingham
 8:                      DRI      1 Total - External User                      Total - External User - DRI
 9:                       GP      7 Total - External User                       Total - External User - GP
10:                  HCH WVT     13 Total - External User                  Total - External User - HCH WVT
11:          Jersey Path lab      3 Total - External User          Total - External User - Jersey Path lab
12:                      LCH      1 Total - External User                      Total - External User - LCH
13:                      PCH      1 Total - External User                      Total - External User - PCH
14:                      PEH     33 Total - External User                      Total - External User - PEH
15:                      PHB     25 Total - External User                      Total - External User - PHB
16:          Priory Hospital      1 Total - External User          Total - External User - Priory Hospital
17:                      RHH     10 Total - External User                      Total - External User - RHH
18:                      RSH     20 Total - External User                      Total - External User - RSH
19:                     RWHT    183 Total - External User                     Total - External User - RWHT
20:                      SFH      1 Total - External User                      Total - External User - SFH
21:                      SHB      2 Total - External User                      Total - External User - SHB
22:              Sandwell GH      4 Total - External User              Total - External User - Sandwell GH
23:            Scunthorpe GH     45 Total - External User            Total - External User - Scunthorpe GH
24:       Spire Little Aston      6 Total - External User       Total - External User - Spire Little Aston
25:                     UHCW     12 Total - External User                     Total - External User - UHCW
26:                     UHNM    297 Total - External User                     Total - External User - UHNM
27:                      WAH    216 Total - External User                      Total - External User - WAH
28:                      WMH      1 Total - External User                      Total - External User - WMH
29:                   WoSSVC      4 Total - External User                   Total - External User - WoSSVC
30:                      BHH    544           Total - UHB                                Total - UHB - BHH
31:                      GHH    279           Total - UHB                                Total - UHB - GHH
32:                       QE     87           Total - UHB                                 Total - UHB - QE
33:                      SOL     90           Total - UHB                                Total - UHB - SOL
34:        UHB (no location)     83           Total - UHB                  Total - UHB - UHB (no location)
                      labels values               parents                                              ids

显然，这是旭日图......

有谁知道为什么我似乎无法覆盖整个圆圈并且只能覆盖 50%？ 我仔细检查了我的数据，没有发现总计和小计之间有任何差异。

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