tableau-重绘沃罗诺伊树图

Xwyturbo / 2022-11-25

使用voronoiTreemap画图很快，但是有很多参数难以调整。

library(voronoiTreemap)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6      ✔ purrr   0.3.5 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(writexl)
library(readxl)
library(ggpubr)
df <- read_xlsx("D:/wenyu/Rrojects/vTreemap/df.xlsx")
df %>% 
  knitr::kable()

h1	h2	h3	color	weight	codes	province	prod
Total	East	Anhui	#CED7BA	4.35	AH	AH	95.20
Total	North	Beijing	#009593	5.05	BJ	BJ	110.30
Total	South	Chongqing	#E4D1B3	3.80	CQ	CQ	82.90
Total	South	Fujian	#E4D1B3	4.88	FJ	FJ	106.60
Total	South	Guangdong	#E4D1B3	4.66	GD	GD	101.70
Total	North	Gansu	#009593	1.72	GS	GS	37.60
Total	South	Guangxi	#E4D1B3	4.04	GX	GX	88.10
Total	South	Guizhou	#E4D1B3	3.11	GZ	GZ	67.90
Total	East	Henan	#CED7BA	3.55	HA	HA	77.60
Total	East	Hubei	#CED7BA	4.19	HB	HB	91.40
Total	North	Hebei	#009593	3.25	HE	HE	71.00
Total	South	Hainan	#E4D1B3	3.56	HI	HI	77.80
Total	Nodata	Hongkonng	#D35C79	0.01	HK	HK	0.01
Total	North	Heilongjiang	#009593	0.52	HL	HL	11.40
Total	South	Hunan	#E4D1B3	3.75	HN	HN	81.90
Total	North	Jilin	#009593	0.65	JL	JL	14.10
Total	East	Jiangsu	#CED7BA	4.47	JS	JS	97.50
Total	South	Jiangxi	#E4D1B3	4.03	JX	JX	88.00
Total	North	Liaoning	#009593	1.44	LN	LN	31.50
Total	Nodata	Macao	#D35C79	0.01	MO	MO	0.01
Total	North	InnerMongoriaIM	#009593	1.36	NM	NM	29.60
Total	North	Ningxia	#009593	2.49	NX	NX	54.30
Total	North	Qinghai	#009593	1.95	QH	QH	42.60
Total	South	Sichuan	#E4D1B3	3.81	SC	SC	83.10
Total	East	Shandong	#CED7BA	4.13	SD	SD	90.10
Total	East	Shanghai	#CED7BA	4.20	SH	SH	91.60
Total	North	Shanxi	#009593	2.60	SN	SX	29.30
Total	North	Shaanxi	#009593	1.34	SX	SX	56.80
Total	North	Tianjing	#009593	4.37	TJ	TJ	95.50
Total	Nodata	Taiwan	#D35C79	0.01	TW	TW	0.01
Total	North	Xingjiang	#009593	3.76	XJ	XJ	82.10
Total	South	Xizang	#E4D1B3	0.55	XZ	XZ	11.90
Total	South	Yunnan	#E4D1B3	3.87	YN	YN	84.40
Total	East	Zhejiang	#CED7BA	4.54	ZJ	ZJ	99.10

solar_json <- vt_export_json(vt_input_from_df(df, 
                                            scaleToPerc = FALSE,
                                            hierachyVar0 = "h1",
                                            hierachyVar1 = "h2", 
                                            hierachyVar2 = "h3", 
                                            colorVar = "color", 
                                            weightVar="prod",
                                            labelVar = "codes"))
p <- vt_d3(solar_json,label = T, 
      color_border = "#000000",width = 1000,
      legend = TRUE, legend_title = "Urban Economic Region",
      seed = 3,
      size_border = "1px")
p

使用tableau绘制沃罗诺伊树图需要先转换数据，登录下面这个网站，然后转换数据。

网址：https://observablehq.com/@ladataviz/wip-voronoi-data-generator

注意，上传的数据最好是csv格式,调整对应字段，然后下载数据！

split by相当于观测值的编码，对应示例数据的province字段. size by相当于具体的变量值，对应示例数据的prod字段. group by字段相当于分组字段，对应示例数据的h2字段.。

打开tableau，添加数据，转到工作表。

首先将path字段拖拽至维度窗口，接着在标记窗口，选择多边形，从维度窗口，依次将group、split、path字段拖拽到标记窗口下，前两个字段调整为颜色，path字段调整为路径。将度量窗口的x、y，分布拖拽至列功能区、在行功能区，将度量修改为平均值，这个时候图就出来了。下面需要增加标签,再将将度量窗口的x拖拽至列功能区，将度量修改为平均值，同时修改为双轴。修改标记窗口下，平均值(x)(2)相关参数，选择文本，窗口下只保留split字段、value字段(value字段是从度量窗口拖拽过来的，还需要调整为平均值)。至此，图的基本雏形就有了，下面调整字体啥的，不做过多赘述，试试就可以了。

参考链接

[1] https://www.jianshu.com/p/c094ac0cb19d.