In ahorawzy/Mreport: Monthly Report of traffic condition

环境准备

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)

load_base()
load_sample_base()

数据准备

jdg4_29 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06_G4_29.csv")
dim(jdg4_29)

jdg4_30 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06_G4_30.csv")
dim(jdg4_30)

jdg4_28 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06_G4_28.csv")
dim(jdg4_28)

29日数据分析

hy28 <- jdg4_28[jdg4_28$地市名称=="衡阳市" & jdg4_28$行驶方向=="断面",]
hy29 <- jdg4_29[jdg4_29$地市名称=="衡阳市" & jdg4_28$行驶方向=="断面",]
hy30 <- jdg4_30[jdg4_30$地市名称=="衡阳市" & jdg4_28$行驶方向=="断面",]

x28 <- ddply(hy28,"小时",summarise,mean(机动车当量))
x28$小时 <- paste("28-",x28$小时,sep = "")

x29 <- ddply(hy29,"小时",summarise,mean(机动车当量))
x29$小时 <- paste("29-",x29$小时,sep = "")

x30 <- ddply(hy30,"小时",summarise,mean(机动车当量))
x30$小时 <- paste("30-",x30$小时,sep = "")

x <- rbind(x28,x29,x30);x %>% kable()

x <- rbind(x28,x29,x30)
names(x) <- c("day","Wmean")
x$day <- 1:nrow(x)
ggplot(x,aes(x=day,y=Wmean))+geom_line(colour="steelblue")+geom_point(colour="steelblue")+
  scale_x_continuous(breaks = seq(0,72,4))+
  geom_point(aes(x=45,y=2390.333),colour="red",size=3)+
  geom_point(aes(x=58,y=1362.333),colour="green",size=3)+
  xlab("时刻")+ylab("平均机动车当量")

ggsave(filename = "D:\\交大云同步\\实习\\06_月度分析报告\\6月分析\\绘图\\G4车祸.png",width=9,height=5,dpi=600)

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