In ahorawzy/Mreport: Monthly Report of traffic condition

options(stringsAsFactors = F)
options(digits = 3)
rm(list = ls())
source("D:\\R\\packages\\Mreport\\scripts\\caculate.R", encoding = "utf-8")
source("D:\\R\\packages\\Mreport\\scripts\\select.R", encoding = "utf-8")
source("D:\\R\\packages\\Mreport\\scripts\\split.R", encoding = "utf-8")

library(Mreport)
library(plyr)
library(dplyr)
library(ggplot2)
library(reshape2)
library(knitr)
library(leaflet)
library(leafletCN)
library(parallel)

load_base()
load_sample_base()

options(stringsAsFactors = F)

9月重点关注-国庆中秋专题

国庆节

gq2018 <- read.csv("D:\\data\\sx_raw\\交调数据\\9月重点\\jdnational2018-3.csv")
gq2017 <- read.csv("D:\\data\\sx_raw\\交调数据\\9月重点\\jdnational2017-3.csv")
dim(gq2018)
dim(gq2017)

gq2018s <- handle_gather_formd(gq2018)
gq2018s <- guoqing_transform(gq2018s)
gq2017s <- handle_gather_formd(gq2017)
gq2017s <- guoqing_transform(gq2017s)

按日分析

result_present2(gq2018s,gq2017s,"md","cars") %>% arrange(md) %>% kable()

按道路等级

caculate_carsmean(gq2018s,c("md","level")) %>% arrange(level) %>% dcast(md~level) %>% kable()

x <- caculate_carsmean(gq2018s,c("md","level"))
ggplot(x,aes(x=md,y=Wmean,group=level,color=level))+geom_point()+geom_line()+
  labs(x="日期",y="平均日交通量",color="公路等级")+
  scale_y_continuous(breaks = c(0,50000,seq(0,50000,5000)))
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\9月分析\\绘图\\国庆节分公路等级交通量-3.png",dpi=600,height=4.5,width=9)

分车型

(x <- caculate_passcarsmean(gq2018s,"md"))

(y <- caculate_frecarsmean(gq2018s,"md"))

z <- merge(x,y,by="md")
names(z)[2:3] <- c("客车交通量","货车交通量")
z <- melt(z)

ggplot(z,aes(x=md,y=value,group=variable,color=variable))+geom_point()+geom_line()+
  ylim(5000,12000)+labs(x="日期",y="平均交通量",color="车类")
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\9月分析\\绘图\\国庆节分车类型交通量-3.png",dpi=600,height=4.5,width=9)

分省分析

caculate_carsmean(gq2018s,"province") %>% 
  geojsonMap("China",palette = "Reds",legendTitle = "交通量图例")

x <- result_present2(gq2018s,gq2017s,"province","cars")
x$province <- factor(x$province,levels=province_level,ordered = T)
x <- arrange(x,province)
kable(x)

公路枢纽

result_present2(gq2018s,gq2017s,"roadhub","cars") %>% kable()

x <- caculate_carsmean(gq2018s,c("roadhub","md"))
ggplot(x,aes(x=md,y=Wmean,group=roadhub,colour=roadhub))+geom_point()+geom_line()+
  ylim(0,50000)+labs(x="日期",y="平均交通量",colour="区域")
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\9月分析\\绘图\\国庆节分区域交通量-3.png",dpi=600,height=4.5,width=9)

通道分析

result_present2(gq2018s,gq2017s,"horizon10","cars") %>% kable()

result_present2(gq2018s,gq2017s,"vertical10","cars") %>% kable()

城市群分析

result_present2(gq2018s,gq2017s,"citygroup2","cars") %>% kable()

机场

result_present2(gq2018s,gq2017s,"airport","cars") %>% kable()

省界

x <- result_present2(gq2018s,gq2017s,"provincedistinct","cars")
y <- merge(gq2018s,x[-1,],by="provincedistinct")

x <- gq2018s[!is.na(gq2018s$provincedistinct),]
y <- x[,c(1,2,4,5,12)]

reg <- leafletCN::leafletGeo("China")
pal <- colorNumeric("Reds",log(y$cars+1))
leaflet(y) %>% 
  addProviderTiles("OpenStreetMap.BlackAndWhite") %>% 
  addPolygons(data = reg, stroke = TRUE, smoothFactor = 1, fillOpacity = 0, weight = 1) %>% 
  addCircles(lng = ~lng,lat = ~lat,popup = ~index,label = ~label,radius = ~cars/5,color = ~pal(log(y$cars+1)))

大城市出入口

result_present2(gq2018s,gq2017s,"bigcityio","cars") %>% arrange(desc(now)) %>% kable()

旅游景区

x <- result_present2(gq2018s,gq2017s,"scenery","cars")
t <- table(gq2018s$province,gq2018s$scenery) %>% as.data.frame()
t <- t[t$Freq!=0,c(1,2)]
names(t) <- c("province","scenery")
g <- merge(x,t,by="scenery")
g$province <- factor(g$province,ordered = T,levels=province_level)
g <- g[order(g$province),c(4,1,2,3)]
kable(g)

20条重点通道

jd201820 <- read.csv("D:\\data\\sx_raw\\交调数据\\9月重点\\jdnational2018-20route-3.csv")
jd201720 <- read.csv("D:\\data\\sx_raw\\交调数据\\9月重点\\jdnational2017-20route-3.csv")
dim(jd201820)
dim(jd201720)

jd201820s <- handle_gather_formd_line(jd201820)
jd201820s <- guoqing_transform(jd201820s)
jd201720s <- handle_gather_formd_line(jd201720)
jd201720s <- guoqing_transform(jd201720s)
dim(jd201820s)
dim(jd201720s)

x <- caculate_carsmean(jd201820s,c("lineindex","linename"))
x <- arrange(x,desc(Wmean))
kable(x)

result_present2(jd201820s,jd201720s,c("lineindex"),"cars") %>% merge(x[1:2],by="lineindex") %>% arrange(desc(now)) %>% `[`(c(1,4,2,3))%>% kable()

ahorawzy/Mreport documentation built on May 3, 2019, 3:40 p.m.