觀察一下數據格式
data_sleep<-read.csv("F:/data.csv",header=T) sleepfre<-as.character(data_sleep$X3.你的熬夜頻率是.) sleepfref<-factor(sleepfre, levels=c("每晚","經常","偶爾","幾乎不"),labels=c("5次及以上","3-4次","1-2次","幾乎不"),ordered=T) efficiency<-as.character(data_sleep$X6.熬夜時的學習或者工作效率如何.) efficiency<-factor(efficiency, levels=c("精神亢奮，效率很高","邊際收益遞減（即熬得越晚效率越低）"),labels=c('整晚精神亢奮','邊際收益遞減')) feeling_tomorrow<-as.character(data_sleep$X7.熬夜對你第二天的學習工作有多大的影響.) feeling_tomorrow<-factor(feeling_tomorrow,levels=c("熬夜使我快樂","正常，基本沒有影響","生理上很疲乏，但是還能撐過去","非常難受，大大降低工作效率"),labels=c("快樂","正常","勉強能堅持工作","工作效率極低")) datasleepn<-data.frame(sleepfref,efficiency,feeling_tomorrow) library(ggplot2) qplot(sleepfre,data =factor(sleepfre, levels=c("每晚","經常","偶爾","幾乎不")), geom = "bar")####這句命令運行不了 ###“Error: ggplot2 doesn't know how to deal with data of class factor” ###使用ggplot則沒有這個問題！！ ###在整理數據之前就轉化成因子數據的好處在於可以直接使呈現的bar有一個順序 ##盡管使用scale_x_discrete(limits=c())也能實現 fre <- ggplot(datasleepn, aes(x=sleepfref))+geom_bar(fill=c("#F46D43","#F46D43","#74ADD1","#74ADD1"),alpha=0.8) motherb<-theme_bw()+theme(panel.grid.major = element_line(colour=NA))+theme(panel.grid.minor = element_blank())+theme(panel.border = element_blank())+theme(axis.ticks.x = element_blank())+theme(axis.line.y = element_line())+theme(axis.title.x = element_blank()) ###scale_y_continuous(expand=c(0,0))讓y軸從0開始 fre+scale_y_continuous(expand=c(0,0))+labs(y="人數")+motherb
#############熬夜原因###### causes<-as.character(data_sleep$X5.你通常因為什么原因熬夜.) causes1<-strsplit(causes,split = "┋") causes2<-unlist(causes1) ##看一下結果table(causes2) ###causep<-factor(causes2,levels=c( "玩手機（包括但不限於夜聊、刷微博、看視頻等）" ,"學習（包括但不限於趕論文、寫作業、准備pre材料等）", "追劇或看電影、娛樂節目等" ,"刷課外書（比如小說）","轟趴刷夜","各種比賽、競賽等課外活動","班級、組織或社團事務"," 他人干擾 ","其他 "),labels=c("玩手機","學習"," 追劇"," 看小說","開party "," 課外競賽 ","社團事務 "," 他人干擾 ","其他 ")) ##p<-ggplot(causep,aes(x=causep))+geom_bar() ##上面兩行命令無法做圖 ##直接使用笨辦法 df <- data.frame(table(causes2)) fix(df) ##修改標簽名字 p<-ggplot(df,aes(x=熬夜原因,y=頻數))+geom_bar(stat = "identity") df
px1<-seq(from=0,to=10,length=10000) py1<-sqrt(5^2-(px1-5)^2) Project1x<-c(px1,rev(px1)) Project1y<-c(py1,-py1) Project1<-data.frame(lon=Project1x,lat=Project1y) Project1$group<-"玩手機" Project1$order<-1:nrow(Project1) Project2<-data.frame(lon=Project1x+13,lat=Project1y) Project2$group<-"學習" Project2$order<-1:nrow(Project2) Project3<-data.frame(lon=Project1x+26,lat=Project1y) Project3$group<-"小說追劇開party" Project3$order<-1:nrow(Project3) Project4<-data.frame(lon=Project1x+39,lat=Project1y) Project4$group<-"課外競賽、班級事務" Project4$order<-1:nrow(Project4) Project5<-data.frame(lon=Project1x+52,lat=Project1y) Project5$group<-"其他 " Project5$order<-1:nrow(Project5) Project<-rbind(Project1,Project2,Project3,Project4,Project5) ggplot(Project)+geom_path(aes(lon,lat,group=group)) ########################## Proj1<-Project1[,1:2]%>%filter(lat<=1.315789) Proj1[nrow(Proj1)+1,]<-c(8,1.315789) Proj1$group<-"玩手機" Proj1$order<-1:nrow(Proj1) Proj2<-Project2[,1:2]%>%filter(lat<=1) Proj2[nrow(Proj2)+1,]<-c(18,1) Proj2$group<-"學習" Proj2$order<-1:nrow(Proj2) Proj3<-Project3[,1:2]%>%filter(lat<=0.8684211) Proj3[nrow(Proj3)+1,]<-c(40,0.8684211) Proj3$group<-"小說追劇開party" Proj3$order<-1:nrow(Proj3) Proj4<-Project4[,1:2]%>%filter(lat<=0.5789474) Proj4$group<-"課外競賽、班級事務" Proj4$order<-1:nrow(Proj4) Proj5<-Project5[,1:2]%>%filter(lat<=-2.815789) Proj5$group<-"其他 " Proj5$order<-1:nrow(Proj5) Projdata<-rbind(Proj1,Proj2,Proj3,Proj4,Proj5) labeldata<-data.frame(x=seq(from=5,to=57,length=5),y=c(1.4,1.1,0.95,0.6,-2),label=sprintf("%2d%%",c(63,60,59,56,22))) ############### p1<-ggplot()+ geom_polygon(data=Projdata,aes(x=lon,y=lat,group=group),fill=c("#74ADD1"),col=NA,alpha=0.8)+ geom_path(data=Project,aes(x=lon,y=lat,group=group),col="black",size=1.2)+ geom_text(data=labeldata,aes(x=x,y=y+1,label=label),hjust=.5,size=5)+ scale_x_continuous(breaks=labeldata$x,labels=c("玩手機","學習","小說追劇\n開party","課外競賽\n班級事務","其他"))+ ylim(-5.5,6)+ theme_minimal()+ theme( panel.grid=element_blank(), axis.title=element_blank(), axis.text.y=element_blank(), axis.text.x = element_text(size = 13,face="bold"), plot.margin = unit(c(.2,.2,1,.2), "cm") ) p1
p <- ggplot(datasleepn, aes(x=efficiency,fill=feeling_tomorrow)) p+geom_bar() peffi<-p+geom_bar(position='stack')+scale_fill_manual(values = rev(c("#F46D43","#FDAE61","#ABD9E9","#74ADD1"))) motherb<-theme_bw()+theme(panel.grid.major = element_line(colour=NA))+theme(panel.grid.minor = element_blank())+theme(panel.border = element_blank())+theme(axis.ticks.x = element_blank())+theme(axis.line.y = element_line()) peffi+scale_y_continuous(expand=c(0,0))+labs(x="熬夜當晚效率",y="人數",fill="熬夜次日狀態")+motherb
