实战：R环境下Echart的8种可视化

install.packages('devtools')

library(devtools)

install_github('yihui/recharts')   ##全局设定

Sys.setlocale("LC_CTYPE","Chs")

source("F://ORE//2.ORESERVER//oreSCRIPT//ECHART//echartR.R")   ##全局设定

install.packages('knitr')

library(knitr)

knitr::opts_chunk$set(message=FALSE,warning=FALSE,results='asis')

#数据集说明：汽车贷款违约数据

#使用的变量N:数值变量/T:因变量

##[N]fico_score信用评分

##[N]purch_price 汽车价格

##[N]loan_amt  贷款金额

##[T]bad_ind    是否违约

#读取数据与数据预处理

setwd("F://数据脚本课件//数据")

data=read.csv('accepts.csv')

data$bad_ind=as.factor(data$bad_ind)

data$bankruptcy_ind=as.factor(data$bankruptcy_ind)

data$used_ind=as.factor(data$used_ind)

#缺失值填补(均值/众数填补)

#定义缺失值填补函数

tianbu=function(data){

for (i in 1:ncol(data)){

if (sum(is.na(data[,i]))!=0){

if (class(data[,i])=='factor'){

a=(as.data.frame(table(data[,i])))

b=as.numeric(max(mode))

mode=as.character(a[which(a$Freq==b),1])

data[which(is.na(data[,i])=='TRUE'),i]=mode[1]

} #因子使用众数填补

else {

data[which(is.na(data[,i])=='TRUE'),i]=mean(data[,i],na.rm=T)

} #非因子使用平均数填补

}

else 

warning(paste('变量',colnames(data)[i],'无缺失无需填补',sep=''))}

}

#进行填补

tianbu(data=data)

data

warnings()

#echart语法见https://github.com/madlogos/recharts

#标注线(标注线性归回拟合线)

Line=t(c(1,'lm',"lm",F))

#标注点(标注两个异常值点)

Points=rbind(c(1,NA,1,111696,111166,F),c(1,NA,2,63700,28700,F))

#横纵轴设定

x=list(lab='汽车金额',color='blue',rotate=45)

y=list(lab='贷款额',color='blue',rotate=45)

#制图

echartR(data = data, x = ~purch_price, y = ~loan_amt,

type = 'scatter', palette='aetnaorange',

title = '散点图-汽车金额vs贷款额', 

subtitle = "(source: 汽车贷款数据)",

xAxis = x, yAxis = y,

markLine=Line,

markPoint=Points)

#标注点(标注两个异常值点)

Points=rbind(c(2,NA,1,760,96692,F),c(1,NA,2,632,111554,F))

#横纵轴设定

x=list(lab='信用评分',color='blue')

y=list(lab='贷款额',color='blue')

#制图

echartR(data = data, x = ~fico_score, y = ~loan_amt, series = ~bad_ind,

type = 'scatter', palette='aetnaorange', symbolList='circle',

scale=F, xAxis = x,yAxis = y,

title = '多系列散点图-信用评分vs贷款额', 

subtitle = "(source: 汽车贷款数据)",

markPoint=Points)

#数据集说明：中国各省人口GDP和人均寿命数据

#使用的变量N:数值变量

#Prov:省份

#GDP:GDP

#LIFE:平均寿命

#POPULATION:人口

#读取数据

China=read.csv('gdp.csv')

#Bubble 气泡图

echartR(data = China, x = ~Life, y = ~GDP, 

weight =Population, series = ~Prov, 

symbolList=c('circle'), 

type = 'bubble', palette='aetnaorange',

title = '部分省人均寿命-GDP-人口', 

subtitle = '(source: GDP)', xlab = '平均寿命', ylab = 'GDP',

pos=list(title=6,toolbox=3))

library(reshape2)

#数据描述：某化妆品公司数据集

#dis:大区(series)

#type:产品名(x)

#amount销量(y)

#读取数据与数据预处理

setwd("F://数据脚本课件//数据")

data=read.csv('col.csv')

#Tiled Column柱状图

echartR(data = data, x = ~type, y = ~amount,  

series = ~dis,stack=F,

type = 'bar', palette='aetnaorange',

title = "大区-产品-销量柱状图", 

subtitle = '(source: col)',

xlab = 'Parameter', ylab = '销售额')

#数据准备

setwd("F://数据脚本课件//数据")

data=read.csv('col.csv')

#1.ECHART PIE 饼图

echartR(data, x = ~type,  y = ~amount, type='pie',

palette='aetnaorange', 

title='化妆品产品饼图',

subtitle = '(source: col)')

echartR(data, x = ~type,  y = ~amount, type='ring',

palette='aetnaorange', 

title='化妆品产品饼图',

subtitle = '(source: col)')

#数据准备

setwd('F://数据脚本课件//数据')

dtgdp1=read.csv('CHINA-GDP-12-14.csv')[2:4]

#区域标注

echartR(dtgdp1, x = ~Prov, y = ~GDP, series= ~Year, 

type=c('map','china','area'),

title="title",

subtitle='(source: title)',

dataRangePalette='heat(5)',

dataRange=c('High',"Low"),splitNumber=5,

pos=list(toolbox=3))

#区域标注

worldgdp=data.frame(

country=c('United States of America','China','Japan','Germany',

'United Kingdom','France','Brazil', 'Italy','India','Russia',

'Canada','Australia','South Korea','Spain','Mexico','Indonesia',

'Netherlands','Turkey','Saudi Arabia','Switzerland'),

GDP=c(17418925,10380380,4616335,3859547,2945146,2846889,2353025,2147952,

2049501,1857461,1788717,1444189,1416949,1406855,1282725,888648,866354,

806108,752459,712050))

echartR(worldgdp, x = ~country, y = ~GDP, type=c('map','world','area'),

title="Nations with top 20 GDPs, 2014 (Million USD)",

subtitle = '(source: Wikipedia)',

dataRangePalette='heat(5)', dataRange=c("High","Low"), 

splitNumber=10, pos=list(toolbox=3))

#Point 点标注

#数据读取

chinapm=read.csv('chinapm25.csv',stringsAsFactors=F,header=T,sep=',')[,2:5]

#点标注

top5=head(chinapm[order(chinapm$PM,decreasing=T),],5)

top5$Name="Top 5"

top5$effect=T

top5=top5[,c(5,1,2,4,3,6)]

#制图

echartR(chinapm, x=~City, y=~PM25, xcoord=~xcoord, ycoord=~ycoord,

type=c('map','china','point'),

title='中国各市PM2.5情况',

subtitle="(source: chinapm)",

dataRange=c("High","Low"), pos=list(toolbox=3), 

dataRangePalette='heat(7)',

splitNumber=7,

markPoint=top5)

#Line 线标注

flight=read.csv('flight.csv',stringsAsFactors=F,header=T,sep=',')

flight$y=''

names(flight)

echartR(flight, x=~From, x1=~To, y=~y, series=~From, xcoord=~Xcoord.x, ycoord=~Ycoord.x,

xcoord1=~Xcoord.y, ycoord1=~Ycoord.y, type=c('map','china','line'),

pos=list(toolbox=3), title="南方航空公司主要航班线路")

#获取数据

setwd("F://数据脚本课件//数据")

santi=read.csv('santi.csv')

santi

#构建词云

echartR(santi[1:30,], x=~word, y=~TFIDF, type="wordcloud", 

title="《三体》特征词", palette='aetnaorange', 

subtitle="来源:三体")

setwd('F://数据脚本课件//数据')

wujiandao=read.csv('netLink.csv',stringsAsFactors=F)

echartR(wujiandao,

x=~Link,

y=~weight,

x1=~NodeVal,

series=~Series,type='force',

title='无间道',

pos=list(title=5,legend=10),

palette=c('orange','lightblue','orange','lightblue','orange','lightblue'))