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今天这篇推文小编给大家介绍一个一直想绘制的可视化作品-「航线地图」，网上有很多类似的教程，但在小编看来，都或多或少有些不完美，小编这次就对其进行完善和美化，主要内容如下：

航线地图所需数据处理航线地图绘制完善美化航线地图所需数据处理

这一部分主要是对数据进行处理，我们直接给出代码步骤，文中用到的数据，文末都有免费获取方式。

导入所需包

library(tidyverse)library(data.table)library(geosphere)library(grid)library(jpeg)library(plyr)library(ggtext)数据导入和处理

#导入数据airlines-fread("airlines.dat",sep=",",skip=1)airports-fread("airports.dat",sep=",")routes-fread("routes.dat",sep=",")#使用谷歌夜间影像图片作为背景图earth-readJPEG("BlackMarble__01deg.jpg",native=TRUE)earth-rasterGrob(earth,interpolate=TRUE)#数据转换和整理#添加列名colnames(airlines)-c("airline_id","name","alias","iata","icao","callisign","country","active")colnames(airports)-c("airport_id","name","city","country","iata","icao","latitude","longitude","altitude","timezone","dst","tz_database_time_zone","type","source")colnames(routes)-c("airline","airline_id","source_airport","source_airport_id","destination_airport","destination_airport_id","codeshare","stops","equipment")#类型转换routesairline_id-as.numeric(routesairline_id)#airlines与routes数据融合flights-left_join(routes,airlines,by="airline_id")#flights与airports数据融合airports_orig-airports[,c(5,7,8)]colnames(airports_orig)-c("source_airport","source_airport_lat","source_airport_long")airports_dest-airports[,c(5,7,8)]colnames(airports_dest)-c("destination_airport","destination_airport_lat","destination_airport_long")flights-left_join(flights,airports_orig,by="source_airport")flights-left_join(flights,airports_dest,by="destination_airport")#剔除缺失值flights-na.omit(flights,cols=c("source_airport_long","source_airport_lat","destination_airport_long","destination_airport_lat"))#按航空公司拆分数据集flights_split-split(flights,flightsname)#计算两点之间的距离(geosphere包计算)flights_all-lapply(flights_split,function(x)gcIntermediate(x[,c("source_airport_long","source_airport_lat")],x[,c("destination_airport_long","destination_airport_lat")],n=,breakAtDateLine=FALSE,addStartEnd=TRUE,sp=TRUE))#转换为数据框flights_fortified-lapply(flights_all,function(x)ldply(x

lines,fortify))#Unsplitlistsflights_fortified-do.call("rbind",flights_fortified)flights_fortifiedname-rownames(flights_fortified)flights_fortifiedname-gsub("\\..*","",flights_fortifiedname)flights_points-flights_fortified%%group_by(group)%%filter(row_number()==1

row_number()==n())

由于绘制全部机场的数据较大(小编的本子太菜，耗时太长)，这里我们选择两个机场线路进行绘制，首先我们进行站点筛选和对应的数据选择：

flights_subset-c("DeltaAirLines","Lufthansa")flights_subset-flights_fortified[flights_fortifiedname%in%flights_subset,]flights_subset_points-flights_subset%%group_by(group)%%filter(row_number()==1

row_number()==n())

接下来，我们就将选择的数据进行可视化展示。

航线地图绘制完善美化

在已有的教程中，可视化结果都不够完美(或颜色单一、或没有明显图例)，这里小编进行了必要绘图元素的添加，代码如下：

library(extrafont)#添加字体windowsFonts(Cinzel=windowsFont("Cinzel"))fonts()#设置颜色color_set-c("#9b5de5","#f15bb5")#可视化绘制light_map2-ggplot()+annotation_custom(earth,xmin=-,xmax=,ymin=-90,ymax=90)+geom_path(data=flights_subset,aes(long,lat,group=id,colour=name),alpha=0.1,lineend="round",lwd=0.1)+geom_point(data=flights_subset_points,aes(long,lat),alpha=0.8,size=0.1,colour="white")+scale_color_manual(values=color_set,name="Airlines")+labs(title="Exampleofchartsaboutspanstyle=color:#D20F26FlightsatNight/span",subtitle="processedchartswithspanstyle=color:#1A73E8geospherelibrary/span",caption="Visualizationbyspanstyle=color:#DDDataCharm/span")+theme_minimal(base_family="Cinzel")+theme(plot.title=element_markdown(hjust=0.5,vjust=.5,color="grey60",face=bold,size=22,margin=margin(t=5,b=5)),plot.subtitle=element_markdown(color="grey60",hjust=0,vjust=.5,size=12,margin=margin(t=3,b=3)),plot.caption=element_markdown(color="grey60",face=bold,size=8),text=element_text(family="Cinzel"),panel.background=element_rect(fill=#F,colour=#F),plot.background=element_rect(fill=#F,colour=#F),panel.grid.major=element_blank(),panel.grid.minor=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),legend.position=c(0.1,0.15),legend.box.margin=margin(0.2,0,0,0,"cm"),legend.box.spacing=unit(0.1,"cm"),legend.key.size=unit(1.5,"lines"),legend.background=element_rect(colour=NA,fill=NA),legend.key=element_rect(colour=NA,fill=NA,size=10),legend.title=element_text(family="Cinzel",color="grey60",size=10,face="bold"),legend.text=element_text(family="Cinzel",color="grey60",size=9,face="bold"))+guides(color=guide_legend(title.position="top",title.hjust=0.5,nrow=2,label.position="right",override.aes=list(size=0.5,alpha=0.8)))

大家可以重点看theme()关于图例(legend)的参数设置和guides()中对color图例映射的定制化设置，注意，这里用到了override.aes(),小伙伴们需要掌握哦~~可视化结果如下：

Twoairlinechartsexample

后续：后来小编试着将结果保存成pdf格式，保存时间较短，但存在保存时字体不存在错误，可通过如下样例进行解决：

library(tidyverse)library(extrafont)fonts()df-data.frame(x=c(1:10),y=c(1:10))#DummydatawindowsFonts(Calibri=windowsFont("Calibri"))plot-ggplot(df,aes(x,y))+#Dummyplotgeom_point()+labs(title="CorrectfontinR,NOfontsatallinpdf:-(")+theme(text=element_text(size=15,family="TwCenMTCondensedExtraBold"))ggsave("df_plot.pdf",plot=plot,device=cairo_pdf,dpi=)

注意ggsave()部分，小伙伴们可尝试绘制多个机场的航线图，方法都是一样的哈~~

总结

今天的这篇推文算是满足小编一直想绘制的一幅可视化作品，当然，也有读者私信小编要求绘制类似图表，感兴趣的小伙伴可在获取数据后自行进行练习再现哦~~

数据获取

整理不易，感谢大家帮忙分享，