In ahorawzy/Mreport: Monthly Report of traffic condition

knitr::opts_chunk$set(echo = F,message = F,warning = F)

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(ggplot2)
library(reshape2)
library(knitr)
library(leaflet)
library(leafletCN)
library(dplyr)
library(stringr)
library(RColorBrewer)
library(stringr)

load_base()
load_sample_base()

1. 11月3日兰州南收费站事故

分小时

days <- c(1027,1028,1103,1104)
jdall <- list()
for(i in 1:length(days)){
  path <- paste(c("D:\\data\\sx_raw\\交调数据\\11月重点\\lzn",days[i],".csv"),collapse = "")
  jdall[[i]] <- read.csv(path)
}
names(jdall) <- days
sapply(jdall,dim)

jdalls <- lapply(jdall,handle_gather_formdh)
sapply(jdalls, dim)

jdallbind <- Reduce(f = rbind,jdalls)

ggplot(jdallbind,aes(jdallbind$hour,jdallbind$cars,group=jdallbind$md,color=jdallbind$md))+
  geom_point()+geom_line()+
  #scale_color_brewer(palette="Dark2",direction = -1)+
  scale_color_manual(values=c("blue","green","red","orange"))+
  scale_x_continuous(breaks = seq(2,24,2))+
  labs(x="小时",y="机动车当量",color="日期")+
  geom_point(aes(x=19,y=700),color="red",size=3)

ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\11月分析\\绘图\\兰州南分小时.png",dpi=600,height=4.5,width=9)

分日

lznfr <- read.csv("D:\\data\\sx_raw\\交调数据\\11月重点\\lzn11.csv",stringsAsFactors=F)
dim(lznfr)

lznfrs <- handle_gather_formd(lznfr)
names(lznfrs)

lznfrs$md <- 1:30

ggplot(lznfrs,aes(lznfrs$md,lznfrs$cars,group=1))+
  geom_point(color="steelblue")+geom_line(color="steelblue")+
  scale_x_continuous(breaks = seq(1,30,2))+
  geom_point(aes(x=3,y=11752),color="red",size=3)+
  labs(x="日期",y="机动车当量")

ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\11月分析\\绘图\\兰州南分日.png",dpi=600,height=4.5,width=9)

2. 雅安车祸

ya1102 <- read.csv("D:\\data\\sx_raw\\交调数据\\11月重点\\ya1102.csv",stringsAsFactors=F)
ya1103 <- read.csv("D:\\data\\sx_raw\\交调数据\\11月重点\\ya1103.csv",stringsAsFactors=F)
dim(ya1102)

ya1102 <- subset(ya1102,ya1102$行驶方向 != "断面")
ya1103 <- subset(ya1103,ya1103$行驶方向 != "断面")

ya1102$小时 <- factor(ya1102$小时,ordered = T,levels = unique(ya1102$小时))
ya1103$小时 <- factor(ya1103$小时,ordered = T,levels = unique(ya1103$小时))
yaall <- rbind(ya1102,ya1103)

yaall$dh <- paste(str_sub(yaall$观测日期,-2,-1),yaall$小时,sep = "-")
yaall$dh <- factor(yaall$dh,ordered = T,levels=unique(yaall$dh))

ggplot(yaall,aes(x=yaall$dh,y=yaall$机动车当量,group=yaall$行驶方向,color=yaall$行驶方向))+
  geom_point()+geom_line()+
  scale_x_discrete(breaks=unique(yaall$dh)[seq(2,48,4)])+
  scale_color_manual(values=c("blue","red"))+
  geom_point(aes(x="02-16",y=918),color="red",size=3)+
  labs(x="日期-时间",y="机动车当量",color="行驶方向")

ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\11月分析\\绘图\\雅安分小时.png",dpi=600,height=4.5,width=9)

3. 重庆万州

days <- c(20,21,27,28)
jdall <- list()
for(i in 1:length(days)){
  path <- paste(c("D:\\data\\sx_raw\\交调数据\\11月重点\\cqwz",days[i],".csv"),collapse="")
  jdall[[i]] <- read.csv(path)
}
names(jdall) <- days

sapply(jdall, dim)

jdalls <- lapply(jdall, handle_gather_formdh)
names(jdalls[[1]])

x <- lapply(jdalls, caculate_carsmean,"hour")
x <- lapply(x, arrange, hour)
x <- Reduce(merge_list(bywhat = "hour"),x)
names(x)[2:5] <- c("10-20","10-21","10-27","10-28")
x <- x[-4]
x <- melt(x,id.vars = "hour")

ggplot(x,aes(x=hour,y=value,group=variable,colour=variable))+
  geom_point()+
  geom_line()+
  scale_x_continuous(breaks=seq(0,24,2))+
  labs(x="小时",y="机动车交通量",color="日期")

ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\11月分析\\绘图\\重庆分小时.png",dpi=600,height=4.5,width=9)

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