In ahorawzy/Mreport: Monthly Report of traffic condition

options(stringsAsFactors = F)

library(Mreport)
library(ggplot2)

load_base()
load_sample_base()

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

jd201805 <- caculate_equivalent(jd201805)

jd201805s <- select_atts(jd201805)

jd201805all <- handle_mergeline(jd201805s,station_line)

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

筛选出连续式交调站的国省高速、普通公路为jdnew

jdnew <- jd[jd$index %in% station_use,]
dim(jdnew)

对比加权平均与普通平均

x_wmean <- ddply(jdnew,c("citygroup"),summarise,Wmean_cars=weighted.mean(cars,w=mileage))
x_wmean <- x_wmean[order(x_wmean$Wmean_cars),]
rownames(x_wmean) <- 1:nrow(x_wmean)
x_wmean <- na.omit(x_wmean);x_wmean

x_factor <- factor(as.integer(rownames(x_wmean)),labels = x_wmean$citygroup)

x_mean <- ddply(jdnew,c("citygroup"),summarise,mean_cars=weighted.mean(cars))
x_mean <- x_mean[order(x_mean$mean_cars),];x_mean

有一定的差异。总体来说加权的偏小。

柱状图

ggplot(data=x_wmean,aes(x=x_factor,y=Wmean_cars))+
  geom_bar(fill="steelblue",stat = "identity")+
  theme(axis.text.x = element_text(size=10,angle = 15))+
  xlab("城市群")+ylab("加权平均月均日交通量")

将分类求加权平均函数封装

caculate_carsmean(jdnew,"citygroup")

caculate_passcarsmean(jdnew,"citygroup")

caculate_frecarsmean(jdnew,"citygroup")

封装柱状图绘制函数

gg_boxplot(x,xangle = 15,xlab="城市群",ylab="月均日")

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