In ahorawzy/Mreport: Monthly Report of traffic condition

本实验任务是：

1. 尝试按公路等级分开计算
2. 设计同比计算模式

1. 首先回顾前两天实验

1.1 数据准备

options(stringsAsFactors = FALSE)

library(Mreport)
library(plyr)
library(ggplot2)
library(reshape2)

load_base()
load_sample_base()

ls()

jd201805 <- read.csv("D:\\data\\sx_raw\\交调数据\\jd2018_05.csv")
dim(jd201805)

当量计算

jd201805 <- caculate_equivalent(jd201805)

筛选属性

jd201805s <- select_atts(jd201805)

合并线路

jd201805all <- handle_mergeline(jd201805s,station_line)

合并样本库

jd <- handle_mergesample(jd201805all,sample_base)
names(jd)

1.2 筛选交调站

筛选出的交调站满足：

1. 连续式交调站；
2. 行政等级为：国高，普通国道，省高，普通省道

jd05new <- jd[jd$index %in% station_use,]
#jd05new <- jd

1.3 使用封装后的函数计算分组加权平均

机动车当量

x <- caculate_carsmean(jd05new,"citygroup");x

class(x)

1.4 使用封装后函数绘制柱状图

caculate_carsmean(jd05new,"citygroup") %>% 
  gg_boxplot(xangle = 15,xlab="城市群",ylab="月均日机动车当量加权平均")

caculate_passcarsmean(jd05new,"citygroup") %>% 
  gg_boxplot(xangle = 15,xlab="城市群",ylab="月均日客车当量加权平均")  

caculate_frecarsmean(jd05new,"citygroup") %>% 
  gg_boxplot(xangle = 15,xlab="城市群",ylab="月均日货车当量加权平均")  

至此前两次实验的内容回顾完毕，开始今天的实验。

2. 开始今天的实验

2.1 区分国省道的计算

将行政等级合并到分析数据中

names(station_useful)

roadlevel <- station_useful[,c(1,5)]
names(roadlevel) <- c("index","level")

unique(roadlevel$level)

dim(jd05new)

jd201805new <- merge(jd05new,roadlevel,by="index",all.x = T)
dim(jd201805new)

names(jd201805new)

合并完成

分组尝试

ddply(jd201805new,c("citygroup","level"),summarise,
      Wmean = weighted.mean(freight_cars,w=mileage)) %>% 
  na.omit() %>% dcast(citygroup~level)

新定义了citygroup2样本库，将“长三角，海峡西岸”和“珠三角，海峡西岸”合并到“海峡西岸”

ddply(jd201805new,c("citygroup2","level"),summarise,
      Wmean = weighted.mean(freight_cars,w=mileage)) %>% 
  na.omit() %>% dcast(citygroup2~level)

caculate_frecarsmean(jd201805new,"citygroup2")

ddply(jd201805new,c("threestrategy","level"),summarise,
      Wmean = weighted.mean(freight_cars,w=mileage)) %>% 
  na.omit() %>% dcast(threestrategy~level)

ddply(jd201805new,c("roadhub","level"),summarise,
      Wmean = weighted.mean(freight_cars,w=mileage)) %>% 
  na.omit() %>% dcast(roadhub~level)

封装到包

caculate_level_carsmean(jd201805new,"citygroup2")

caculate_level_passcarsmean(jd201805new,"citygroup2")

珠三角普通省道值被极大值所干扰

caculate_level_frecarsmean(jd201805new,"citygroup2")

封装完成

2.2 环比比较

环比是指2018年5月比2017年5月，由于暂时没有2017年5月数据，而且此处设计计算框架时也用不到，所以可以用非加权平均数模拟2017年5月数据，由此开发环比计算框架。

尝试

x201705 <- ddply(jd201805new,c("citygroup2","level"),summarise,
      Wmean = mean(cars)) %>% 
  na.omit() %>% dcast(citygroup2~level)
x201705

x201805 <- caculate_level_carsmean(jd201805new,"citygroup2")
x201805

yearratio <- function(nowdf,thendf){
  x <- nowdf
  for (i in 2:length(x)) {
    x[[i]] <- nowdf[[i]]/thendf[[i]]
  }
  return(x)
}

yearratio(x201805,x201705)

封装到包

caculate_yearratio(x201805,x201705)

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