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

jdgd <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_06_GuangDong.csv")
dim(jdgd)

jdgd <- split_day(jdgd)
jdgd <- caculate_equivalent(jdgd)
jdgd <- select_atts_forday(jdgd)
jdgd <- handle_mergeline(jdgd,station_line)
jdgd <- handle_mergesample(jdgd,sample_base)
jdgd <- merge(jdgd,roadlevel,by="index",all.x = T)
jdgd <- subset(jdgd,index %in% station_use)

1. 分日分析

(gd <- ddply(jdgd,"day",summarise,Wmean = weighted.mean(cars,w=mileage))) %>% kable()

gg_boxplot(gd,xlabname = "日期",ylabname = "每日机动车当量")
ggsave(file="D:\\交大云同步\\实习\\06_月度分析报告\\6月分析\\绘图\\广东6月.png",dpi=600)

6月8日广东暴雨，交通量下降明显。

2. 分市分析

jdgd08 <- subset(jdgd,day=="08")
jdgd01 <- subset(jdgd,day=="01")

gd08 <- caculate_carsmean(jdgd08,"city")

gd01 <- caculate_carsmean(jdgd01,"city")
x1 <- caculate_increaseratio(gd08,gd01)

merge(gd08,x1,by="city") %>% kable()

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