In ahorawzy/Mreport: Monthly Report of traffic condition

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

library(Mreport)
library(plyr)
library(ggplot2)
library(reshape2)
library(knitr)
library(lubridate)

load_base()
load_sample_base()

7.1 强降雨与台风等极端天气影响

7.1.1 7月成都强降雨影响

jdcd07 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd201807成都.csv",stringsAsFactors = F)
jdcd06 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd201806成都.csv",stringsAsFactors = F)

jdcd07s <- handle_gather_forday(jdcd07)
jdcd06s <- handle_gather_forday(jdcd06)

cd07 <- ddply(jdcd07s,"day",summarise,Wmean = weighted.mean(cars,w=mileage))
cd07$day <- as.numeric(cd07$day)
cd07$day <- factor(cd07$day,ordered = T,levels = 1:31)
cd07 <- cd07[order(cd07$day),]
rownames(cd07) <- 1:31

ggplot(data=cd07,aes(cd07$day,cd07$Wmean))+
  geom_point(colour="steelblue")+
  geom_line(aes(as.numeric(cd07$day),cd07$Wmean),colour="steelblue")+
  ylim(15000,23000)+
  xlab("7月日期")+
  ylab("单日交通量")
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\7月分析\\绘图\\成都7月.png",dpi=600,height=4.5,width=9)

kable(cd07)

(15471-19676)/19676

caculate_increaseratio(caculate_carsmean(jdcd07s,"city"),caculate_carsmean(jdcd06s,"city"))

caculate_increaseratio(caculate_carsmean(jdcd07s,"county"),caculate_carsmean(jdcd06s,"county"))

7.1.2 台风“玛莉亚”对温州的影响

jdwz <- read.csv("D:\\data\\sx_raw\\交调数据\\jd20180710-13温州.csv",stringsAsFactors = F)
dim(jdwz)

names(jdwz)

jdwzs <- jdwz[,c(1,2,3,4,5,22)]
names(jdwzs) <- c("day","hour","index","name","mileage","cars")

x <- caculate_carsmean(jdwzs,c("hour","day"))
x$hour <- factor(as.numeric(x$hour),levels = 1:24,ordered = T)
x <- x[order(x$hour),]

ggplot(x,aes(as.numeric(x$hour),x$Wmean,group=x$day,colour=x$day))+
  geom_point()+
  geom_line()+
  xlab("小时")+
  ylab("交通量")+
  scale_x_continuous(breaks = c(0,24,seq(0,24,2)))+
  labs(colour="日期")
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\7月分析\\绘图\\温州7月.png",dpi=600,height=4.5,width=9)

y <- dcast(x,x$hour~x$day)
colMeans(y[-1])

(361-735)/735

7.1.3 台风“安比”影响

jdab <- read.csv("D:\\data\\sx_raw\\交调数据\\jd20180719-25全国.csv")
dim(jdab)

jdabs <- handle_gather_forday(jdab)
names(jdabs)

x <- caculate_carsmean(jdabs,c("province","day"))
x <- x[x$province %in% c("上海市","江苏省","北京市",
                         "天津市","浙江省","山东省"),]
y <- dcast(x,x$province~x$day)
y

ggplot(x,aes(x$province,as.factor(x$day),fill=x$Wmean))+
  geom_tile()+
  coord_flip()+
  xlab("省级行政区")+
  ylab("日期")+
  labs(fill="单日交通量")+
  theme(axis.text.y = element_text(size=12))+
  theme(axis.text.x = element_text(size=12))
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\7月分析\\绘图\\台风安比.png",dpi=600,height=4.5,width=9)

7.2 暑期旅游景区周边公路

7.2.1 全国旅游景区情况

jdnew <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_07_2.csv")
jdlast <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06_new.csv")
jdprevious <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2017_07_2.csv")

jdnews <- handle_gather(jdnew)
jdlasts <- handle_gather(jdlast)
jdpreviouss <- handle_gather(jdprevious)
usefulstation <- intersect(jdnews$index,jdlasts$index)
jdnews <- jdnews[jdnews$index %in% usefulstation,]
jdlasts <- jdlasts[jdlasts$index %in% usefulstation,]
jdpreviouss <- jdpreviouss[jdpreviouss$index %in% usefulstation,]

scenery1807 <- caculate_carsmean(jdnews,"scenery")
scenery1806 <- caculate_carsmean(jdlasts,"scenery")
scenery1707 <- caculate_carsmean(jdpreviouss,"scenery")

tb <- caculate_increaseratio(scenery1807,scenery1707)
hb <- caculate_increaseratio(scenery1807,scenery1806)
x <- merge_outcome(scenery1807,tb,hb,bywhat = "scenery")

t <- table(jdnews$province,jdnews$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[,c("province","scenery","now","previous","last")]
kable(g[order(g$province),])

7.2.2 贵州旅游景区情况

jdgz <- read.csv("D:\\data\\sx_raw\\交调数据\\jd201807贵州.csv")
dim(jdgz)

jdgzs <- handle_gather_forday(jdgz)
names(jdgzs)

xp <- jdgzs[jdgzs$label=="小坡观测站",]
dim(xp)

xp <- xp[order(xp$day),]

ggplot(xp,aes(day,passenger_cars))+
  geom_point(colour="steelblue")+geom_line(colour="steelblue")+
  ylim(0,12000)+labs(x="日期",y="客车交通量")+
  scale_x_continuous(breaks = c(1,31,seq(1,31,2)))
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\7月分析\\绘图\\小坡.png",dpi=600,height=4.5,width=9)

dss <- jdgzs[jdgzs$label=="大山哨观测站",]
dim(dss)

dss <- dss[order(dss$day),]

ggplot(dss,aes(day,passenger_cars))+
  geom_point(colour="steelblue")+geom_line(colour="steelblue")+
  labs(x="日期",y="客车交通量")+ylim(0,40000)+
  scale_x_continuous(breaks = c(1,31,seq(1,31,2)))
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\7月分析\\绘图\\大山哨.png",dpi=600,height=4.5,width=9)

x <- caculate_level_passcarsmean(jdgzs,"day")
x <- x[order(x$day),c(1,2,3)]
x <- melt(x,id.vars = "day")

ggplot(x,aes(day,value,group=variable,colour=variable))+
  geom_point()+
  geom_line()+
  scale_x_continuous(breaks = c(1,31,seq(1,31,2)))+
  labs(x="日期",y="单日交通量",colour="道路等级")+
  ylim(0,20000)
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\7月分析\\绘图\\贵州高速.png",dpi=600,height=4.5,width=9)