如何为 plotly sunburst 图格式化数据
How to format data for plotly sunburst diagram
我正在尝试通过 R 使用 Plotly 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将具有代表不同层次级别的列的常规数据框转换为所需的格式。
我查看了 R 中绘制的旭日形图表的示例，例如 here, and seen the reference page，但并未完全了解数据格式化的模型。
# 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')
根据上面的示例代码或类似代码，了解如何将数据 (df) 转换为绘图式旭日图所需的格式会很有帮助。
与 plotly R API 的其他直观用法相比，您完全正确，为森伯斯特（或树状图）图表准备数据相当烦人。
我遇到了同样的问题，写了一个基于library(data.table)的函数来准备数据，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here部分Sunburst with Repeated Labels.
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格式的例子（value_column = NULL，因为它是根据数据计算的）：
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： 添加了一个关于来自 library(plotme) 的基于 dplyr 的 count_to_sunburst() 函数的基准（见下文），在我的系统上它慢了大约 5 倍比 data.table 版本。
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')
})
有专门用于此任务的 plotme 软件包：
library(plotme)
library(dplyr)
df %>%
rename(n = acres) %>%
count_to_sunburst()
安装包运行：
devtools::install_github("yogevherz/plotme")
有关套餐的更多信息 here。
我正在尝试通过 R 使用 Plotly 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将具有代表不同层次级别的列的常规数据框转换为所需的格式。
我查看了 R 中绘制的旭日形图表的示例，例如 here, and seen the reference page，但并未完全了解数据格式化的模型。
# 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')
根据上面的示例代码或类似代码，了解如何将数据 (df) 转换为绘图式旭日图所需的格式会很有帮助。
与 plotly R API 的其他直观用法相比，您完全正确，为森伯斯特（或树状图）图表准备数据相当烦人。
我遇到了同样的问题，写了一个基于library(data.table)的函数来准备数据，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here部分Sunburst with Repeated Labels.
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格式的例子（value_column = NULL，因为它是根据数据计算的）：
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： 添加了一个关于来自 library(plotme) 的基于 dplyr 的 count_to_sunburst() 函数的基准（见下文），在我的系统上它慢了大约 5 倍比 data.table 版本。
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')
})
有专门用于此任务的 plotme 软件包：
library(plotme)
library(dplyr)
df %>%
rename(n = acres) %>%
count_to_sunburst()
安装包运行：
devtools::install_github("yogevherz/plotme")
有关套餐的更多信息 here。