数据文件:https://pan.baidu.com/s/1d3UKP4
代码:# ####回归分析 ----------------------------------------------------------------
####回归分析
options(digits=3)
###2018年2月6日更新
data1<-read.csv("C:\\Users\\shitong\\Desktop\\2014.csv",header=T,sep=",")
###描述性统计分析
names(data1)
summary(data1)
###增加日期
# data1$day1<-paste(data1$year,"年",data1$month,"月",data1$day,"日",sep="")
data1$date<-paste(data1$year,data1$month,data1$day,sep="-")
##转化成日期格式
library(lubridate)
data1$date<-as.Date(data1$date)
# names(mytable)[c(1,3,4,11)]<-c("date","Range","Level","Order")
data1$Year<-year(data1$date)
breaks<-c(0,50,100,150,200,300,500)
label<-c("excellent","good","Mild pollution","moderate pollution","heavy pollution ","serious pollution")
# 制作天津市 2014~2016三年度历史空气质量数据年度日历热图
library(dplyr)
library(openair)
filter(data1,Year==2014)%>%calendarPlot(pollutant="aqit",breaks=breaks,labels=label)
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