高维数据 | R语言数据可视化之日历图

黑妹的小屋

发布于 2020-08-06 15:07:48

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发布于 2020-08-06 15:07:48

R语言数据可视化日历图

日历图，在环境与生态指标的动态监测中应用普遍，特别适用于显示不同时间段的指标情况。比如污染物中重金属含量、空气中PM2.5变化情况。在金融行业中检测股票收盘价、回测信号等指标中也很常见。生物医药领域的血糖或血压日记录值，新型冠状病毒的逐日确诊数量等等。通过时间分布的日历图动态监测数据，以弥补普通线图的不足。

随机生成数据

#构建数据框

>Data<data.table(date=seq(as.Date("1/01/2019","%d/%m/%Y"),as.Date("31/12/2019","%d/%m/%Y"),"days"),PM2.5=runif(365,50,350)
> Data
           date     PM2.5
  1: 2019-01-01  87.79283
  2: 2019-01-02 319.75490
  3: 2019-01-03 325.30976
  4: 2019-01-04 188.61730
  5: 2019-01-05 144.84692
 ---                     
361: 2019-12-27 137.78985
362: 2019-12-28 128.80200
363: 2019-12-29 250.66169
364: 2019-12-30 159.78224
365: 2019-12-31 207.37999

数据处理

> Data$Year<-as.integer(strftime(Data$date,'%Y'))#年份
> Data$month<-as.integer(strftime(Data$date,'%m'))#月份
> Data$week<-as.integer(strftime(Data$date,'%W')) #周数，通常是以星期一作为一周的第一天
> Data$weekday<-as.integer(strftime(Data2$date,'%u'))#周数，以小数形式表示的工作日(1 - 7，星期一是1)
> Data$day<-strftime(Data2$date,'%d')#天数，以十进制数(01-31)表示的月份的日期。
> Data$month1<-month.abb[Data$month]#月份英文缩写
> Data$weekday1<-factor(Data$weekday,levels=(1:7),labels=(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")),ordered=TRUE)
> Data<-Data%>%group_by(month1)%>%mutate(monthweek=1+week-min(week))
> Data
# A tibble: 365 x 10
# Groups:   month1 [12]
   date       PM2.5  Year month  week weekday day   month1 weekday1 monthweek
   <date>     <dbl> <int> <int> <int>   <int> <chr> <chr>  <ord>        <dbl>
 1 2019-01-01  87.8  2019     1     0       2 01    Jan    Tue              1
 2 2019-01-02 320.   2019     1     0       3 02    Jan    Wed              1
 3 2019-01-03 325.   2019     1     0       4 03    Jan    Thu              1
 4 2019-01-04 189.   2019     1     0       5 04    Jan    Fri              1
 5 2019-01-05 145.   2019     1     0       6 05    Jan    Sat              1
 6 2019-01-06 109.   2019     1     0       7 06    Jan    Sun              1
 7 2019-01-07 138.   2019     1     1       1 07    Jan    Mon              2
 8 2019-01-08 207.   2019     1     1       2 08    Jan    Tue              2
 9 2019-01-09 229.   2019     1     1       3 09    Jan    Wed              2
10 2019-01-10  63.5  2019     1     1       4 10    Jan    Thu              2
# ... with 355 more rows

绘图主题

#设置图片中的线条，字体，颜色等参数

>mytheme=theme(plot.title = element_text(face = "bold",hjust = 0.5,size=20,color = "black"),axis.title=element_text(face = "bold",hjust = 0.5,size=12,color="black"))

绘图

>ggplot(Data,aes(weekday1,monthweek,fill=PM2.5))
+geom_tile(colour="white")
+scale_fill_gradient(low="#56B1F7",high="#132B43",space="Lab",na.value = "grey50",guide="colourbar",aesthetics = "fill")
+geom_text(aes(label=day),size=3,colour="black",position="identity")
+facet_wrap(~month1,nrow=4)
+scale_y_reverse()+labs(title="Daily PM2.5 in 2019")
+xlab("Weekday")+ylab("Monthweek")+mytheme

>ggplot(Data,aes(weekday1,monthweek,fill=PM2.5))
+geom_tile(colour="white")
+scale_fill_gradient(low="Yellow",high="Red",space="Lab",na.value = "grey50",guide="colourbar",aesthetics = "fill")
+geom_text(aes(label=day),size=3,colour="black",position="identity")
+facet_wrap(~month1,nrow=4)+scale_y_reverse()
+labs(title="Daily PM2.5 in 2019")+xlab("Weekday")
+ylab("Monthweek")+mytheme