绘制中国热力地图

「爱情、让人受尽委屈。」 2023-07-18 15:28 15阅读 0赞

## 热力地图 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70]

#### 数据展示情况 ####

![在这里插入图片描述][20200329143901447.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 1]

## 程序代码 ##

## 库包调用
    
    library(mapdata)
    library(ggplot2) 
    library(maps) 
    library(mapdata)
    library(readxl)
    library(ggplot2)
    
    ##  数据读取
    
    setwd("C:\\Users\\Administrator\\Desktop\\map")
    data=read_excel("PMDiff2017.xlsx")
    data=read.csv("PMDiff2017.csv")
    data=as.data.frame(data)
    str(data)
    data1=data[,c(5,6,8,10,12,14)]
    str(data1)
    data1 <- na.omit(data1)
    ## 数据筛选
    
    dat1=subset(data1,c(上午相对差异2.5>=0.5,上午相对差异2.5<=-0.5)) 
    str(dat1)  ##筛选出有42109条满足
    
    dat2=subset(data1,c(下午相对差异2.5>=0.5,下午相对差异2.5<=-0.5)) 
    str(dat2)  ##筛选出有47924条满足
    
    dat3=subset(data1,c(上午相对差异10>=0.5,上午相对差异10<=-0.5))
    str(dat3) ##筛选出有39787条满足
    
    dat4=subset(data1,c(下午相对差异10>=0.5,下午相对差异10<=-0.5)) 
    str(dat4)##筛选出有41450条满足
    
    ## 数据统计
    
    summary(dat1$上午相对差异2.5)
    dat1$相对差异2.5[dat1$上午相对差异2.5<=0.57] <-0.57 #"小于0.57"
    dat1$相对差异2.5[dat1$上午相对差异2.5<=0.69&dat1$上午相对差异2.5>0.57] <-0.69 #"0.57到0.69之间"
    dat1$相对差异2.5[dat1$上午相对差异2.5<=0.89&dat1$上午相对差异2.5>0.69] <-0.89 #"0.69到0.89之间"
    dat1$相对差异2.5[dat1$上午相对差异2.5<=5.03&dat1$上午相对差异2.5>0.89] <-0.53 #"0.89到0.53之间"
    
    
    
    ## 不同位置计数
    options(digits = 5)
    aax=dat1
    aax$freq <- paste(aax$slon,aax$slat,sep=',')
    str(dat1$slon)
    dax=as.data.frame(table(aax$freq),stringsAsFactors = F)
    dax <- dax[-1404,]
    tail(dax)
    re <- strsplit(dax$Var1,",")
    for(i in 1:dim(dax)[1])
     {
    dax$slon[i] <- as.numeric(re[[i]][1])
    dax$slat[i] <- as.numeric(re[[i]][2])
    }
    
    
    write.csv(dax,"test1.csv")
    
    
    head(dax)
    str(dax)
    tail(AA)
    options(digits = 5)
    AA$slon=as.numeric(as.character(AA$slon))
    AA$slat=as.numeric(as.character(AA$slat))
    str(AA)
    
    
    gsub('"','', dax1$slon) 
    gsub('"', '', dax1$slat)
    
    
    options(stringsAsFactors = TRUE)
    
    
    datexpr2=as.data.frame(lapply(dax1$slon,as.numeric))
    
    
    dax1$slon=as.numeric(as.factor(dax1$slon))
    dax1$slat=as.numeric(as.factor(dax1$slat))
     
    str(dax1)
    ## 地图绘制
    
    library(mapdata)
    str(dax1)
    dax1=dax
    summary(dax1$Freq)
    dax1$group[dax1$Freq<=7] <-"n<=7"
    dax1$group[dax1$Freq>7&dax1$Freq<=13] <-"7<n<=13"
    dax1$group[dax1$Freq>13&dax1$Freq<=25] <-"13<n<=25"
    dax1$group[dax1$Freq>25&dax1$Freq<=188] <-"25<n<=188"
    
    head(dax1$group)
    order(table(dax1$group))
    qplot(slon, slat,data=dax,main="上午相对差异2.5")+
    borders("china")+scale_colour_gradient(low="green",high="red")+ xlab ( "" ) + ylab ( "" )
    
    
    qplot(slon,slat,data=dax1,colour=Freq)+
    borders("china")+scale_colour_gradient(low="green",high="red")+
    coord_map(projection="mollweide")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 2]

参考联系网站：[https://blog.csdn.net/tandelin/article/details/87719623][https_blog.csdn.net_tandelin_article_details_87719623]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70]: /images/20230528/c6b165df15ef40989ee4f11e50369d9c.png
[20200329143901447.png]: /images/20230528/47c3f894442f4d1db7037187abed61f7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 1]: /images/20230528/61e527720b8d4230aa0b9eb4f0586e96.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 2]: /images/20230528/3fc63116d0e6486a9246527319e6a465.png
[https_blog.csdn.net_tandelin_article_details_87719623]: https://blog.csdn.net/tandelin/article/details/87719623