數據挖掘與數據分析 第一章課后習題及答案

第一章課后習題答案詳解請讀者分別創(chuàng)建數值型、字符型、復數型以及邏輯型變量，并給這些變量賦值。#1.創(chuàng)建數值型、字符型、復數型以及邏輯型變量，并給這些變量賦值。#數值型變量a<-c(1,2,3)aclass(a)#字符型變量b<-"ILikeR"bclass(b)#復數型c<-1+2icclass(c)#邏輯型變量d<-(1+1==3)dclass(d)2.分別用數據框、向量讀入下表的數據。IDColorLogicFloat1blueTRUE1.042yellowFALSE5.153whiteTRUE8.434pinkTRUE6.72#使用數據框讀入數據ID<-c(1,2,3,4)Color<-c("blue","yellow","white","pink")Logic<-c("TRUE","FALSE","TRUE","TRUE")Float<-c(1.04,5.15,8.43,6.72)data<-data.frame(ID,Color,Logic,Float)names(data)<-c("ID","Color","Logic","Float")Data輸出：IDColorLogicFloat11blueTRUE1.0422yellowFALSE5.1533whiteTRUE8.4344pinkTRUE6.72#使用向量讀入數據data1<-cbind(ID,Color,Logic,Float)names(data1)<-c("ID","Color","Logic","Float")data1輸出：IDColorLogicFloat[1,]"1""blue""TRUE""1.04"[2,]"2""yellow""FALSE""5.15"[3,]"3""white""TRUE""8.43"[4,]"4""pink""TRUE""6.72"attr(,"names")[1]"ID""Color""Logic""Float"NANANANANANANA[12]NANANANANA3.某班級的學生成績如下表所示：IDNameGrade1Peter782Tony563Lucy904Tina855Bob626Tom98求該班級學生成績的平均值#寫入數據ID<-c(1:6)Name<-c("Peter","Tony","Lucy","Tina","Bob","Tom")Grade<-c(78,56,90,85,62,98)class_grade<-data.frame(ID,Name,Grade)names(class_grade)<-c("ID","Name","Grade")#(1)該班級學生平均成績mean_grade<-mean(class_grade[,3])mean_grade輸出：78.16667求該班級學生成績的方差#標準差sd_grade<-sd(class_grade[,3])#方差DV<-sd_grade**2DV輸出：266.5667（3）該班級規(guī)定，學生成績Grade低于60分為不合格，成績在[60,70)為合格，成績在[70,80)為一般，成績在[80,90)良好，成績在[90,100]為優(yōu)秀。請讀者運用條件語句和循環(huán)語句判斷該班級學生的成績表現。#方式一：打印到屏幕for(iin1:6){if(class_grade[i,3]<60){cat("學生",class_grade[i,2],"成績不合格\n")}elseif(class_grade[i,3]<70&class_grade[i,3]>=60){cat("學生",class_grade[i,2],"成績合格\n")}elseif(class_grade[i,3]<80&class_grade[i,3]>=70){cat("學生",class_grade[i,2],"成績一般\n")}elseif(class_grade[i,3]<90&class_grade[i,3]>=80){cat("學生",class_grade[i,2],"成績良好\n")}elseif(class_grade[i,3]<=100&class_grade[i,3]>=90){cat("學生",class_grade[i,2],"成績優(yōu)秀\n")}}輸出：學生Peter成績一般學生Tony成績不合格學生Lucy成績優(yōu)秀學生Tina成績良好學生Bob成績合格學生Tom成績優(yōu)秀#方式二：在原數據的基礎上新建一列數據for(iinrange(1:6)){if(class_grade[i,3]<60){class_grade$newgrade[i]<-"成績不合格"}elseif(class_grade[i,3]<70&class_grade[i,3]>=60){class_grade$newgrade[i]<-"成績合格"}elseif(class_grade[i,3]<80&class_grade[i,3]>=70){class_grade$newgrade[i]<-"成績一般"}elseif(class_grade[i,3]<90&class_grade[i,3]>=80){class_grade$newgrade[i]<-"成績良好"}elseif(class_grade[i,3]<=100&class_grade[i,3]>=90){class_grade$newgrade[i]<-"成績優(yōu)秀"}}class_grade輸出：IDNameGradenewgrade11Peter78成績一般22Tony56成績一般33Lucy90成績一般44Tina85成績一般55Bob62成績一般66Tom98成績優(yōu)秀4.現有數據集schoolInfo如下表所示：IDScoreStatusSum12open88.772NAclose75.6634close89.4345NA78.365NAclose91.2662NA76.1973NA61.5482close74.269NAopen86.53返回數據集中有缺失數據的行#獲取schoolInfo中含有缺失數據的行schoolInfo[!complete.cases(schoolInfo),]輸出：IDScoreStatusSum22NAclose75.66445<NA>78.3655NAclose91.26662<NA>76.19773<NA>61.5499NAopen86.53從數據集中提取所有Score值大于3的行#刪除有缺省值的行schoolInfo_withoutNA<-na.omit(schoolInfo)#提取所有Score值大于3的行schoolInfo_withoutNA[schoolInfo_withoutNA$Score>3,]輸出：IDScoreStatusSum334close89.43新建一個字段NewScore，并將Score中的所有數據為5的值替換為文本字符串“five”賦予其中#新建一個字段NewScoreschoolInfo$NewScore[schoolInfo$Score==5]<-"five"schoolInfo輸出：IDScoreStatusSumNewScore112open88.77222NAclose75.66<NA>334close89.434445<NA>78.36five55NAclose91.26<NA>662<NA>76.192773<NA>61.543882close74.26299NAopen86.53<NA>一個字段ScoreCat，Score中小于3的值在ScoreCat中賦值為bad，Score中大于等于3的值在ScoreCat中賦值為Good，Score中缺失的值在ScoreCat中賦值為Unknown#新建一個字段ScoreCatschoolInfo$ScoreCat[schoolInfo$Score<3]<-"bad"schoolInfo$ScoreCat[schoolInfo$Score>=3]<-"Good"schoolInfo$ScoreCat[is.na(schoolInfo$Score)]<-"Unknown"schoolInfo輸出：IDScoreStatusSumScoreCat112open88.77bad22NAclose75.66Unknown334close89.43Good445<NA>78.36Good55NAclose91.26Unknown662<NA>76.19bad773<NA>61.54Good882close74.26bad99NAopen86.53Unknown從數據集中隨機抽取5行數據#從數據集中隨機抽取5行數據schoolInfo<-schoolInfo[sample(1:nrow(schoolInfo),5,replace=FALSE),]schoolInfo輸出：IDScoreStatusSum99NAopen86.53112open88.77773<NA>61.54445<NA>78.36334close89.43四舍五入字段Sum中的值（只保留整數）#四舍五入字段Sum中的值schoolInfo$S