数据挖掘与数据分析 第一章课后习题及答案

第一章课后习题答案详解请读者分别创建数值型、字符型、复数型以及逻辑型变量，并给这些变量赋值。#1.创建数值型、字符型、复数型以及逻辑型变量，并给这些变量赋值。#数值型变量a<-c(1,2,3)aclass(a)#字符型变量b<-"ILikeR"bclass(b)#复数型c<-1+2icclass(c)#逻辑型变量d<-(1+1==3)dclass(d)2.分别用数据框、向量读入下表的数据。IDColorLogicFloat1blueTRUE1.042yellowFALSE5.153whiteTRUE8.434pinkTRUE6.72#使用数据框读入数据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输出：IDColorLogicFloat11blueTRUE1.0422yellowFALSE5.1533whiteTRUE8.4344pinkTRUE6.72#使用向量读入数据data1<-cbind(ID,Color,Logic,Float)names(data1)<-c("ID","Color","Logic","Float")data1输出：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.某班级的学生成绩如下表所示：IDNameGrade1Peter782Tony563Lucy904Tina855Bob626Tom98求该班级学生成绩的平均值#写入数据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)该班级学生平均成绩mean_grade<-mean(class_grade[,3])mean_grade输出：78.16667求该班级学生成绩的方差#标准差sd_grade<-sd(class_grade[,3])#方差DV<-sd_grade**2DV输出：266.5667（3）该班级规定，学生成绩Grade低于60分为不合格，成绩在[60,70)为合格，成绩在[70,80)为一般，成绩在[80,90)良好，成绩在[90,100]为优秀。请读者运用条件语句和循环语句判断该班级学生的成绩表现。#方式一：打印到屏幕for(iin1:6){if(class_grade[i,3]<60){cat("学生",class_grade[i,2],"成绩不合格\n")}elseif(class_grade[i,3]<70&class_grade[i,3]>=60){cat("学生",class_grade[i,2],"成绩合格\n")}elseif(class_grade[i,3]<80&class_grade[i,3]>=70){cat("学生",class_grade[i,2],"成绩一般\n")}elseif(class_grade[i,3]<90&class_grade[i,3]>=80){cat("学生",class_grade[i,2],"成绩良好\n")}elseif(class_grade[i,3]<=100&class_grade[i,3]>=90){cat("学生",class_grade[i,2],"成绩优秀\n")}}输出：学生Peter成绩一般学生Tony成绩不合格学生Lucy成绩优秀学生Tina成绩良好学生Bob成绩合格学生Tom成绩优秀#方式二：在原数据的基础上新建一列数据for(iinrange(1:6)){if(class_grade[i,3]<60){class_grade$newgrade[i]<-"成绩不合格"}elseif(class_grade[i,3]<70&class_grade[i,3]>=60){class_grade$newgrade[i]<-"成绩合格"}elseif(class_grade[i,3]<80&class_grade[i,3]>=70){class_grade$newgrade[i]<-"成绩一般"}elseif(class_grade[i,3]<90&class_grade[i,3]>=80){class_grade$newgrade[i]<-"成绩良好"}elseif(class_grade[i,3]<=100&class_grade[i,3]>=90){class_grade$newgrade[i]<-"成绩优秀"}}class_grade输出：IDNameGradenewgrade11Peter78成绩一般22Tony56成绩一般33Lucy90成绩一般44Tina85成绩一般55Bob62成绩一般66Tom98成绩优秀4.现有数据集schoolInfo如下表所示：IDScoreStatusSum12open88.772NAclose75.6634close89.4345NA78.365NAclose91.2662NA76.1973NA61.5482close74.269NAopen86.53返回数据集中有缺失数据的行#获取schoolInfo中含有缺失数据的行schoolInfo[!complete.cases(schoolInfo),]输出：IDScoreStatusSum22NAclose75.66445<NA>78.3655NAclose91.26662<NA>76.19773<NA>61.5499NAopen86.53从数据集中提取所有Score值大于3的行#删除有缺省值的行schoolInfo_withoutNA<-na.omit(schoolInfo)#提取所有Score值大于3的行schoolInfo_withoutNA[schoolInfo_withoutNA$Score>3,]输出：IDScoreStatusSum334close89.43新建一个字段NewScore，并将Score中的所有数据为5的值替换为文本字符串“five”赋予其中#新建一个字段NewScoreschoolInfo$NewScore[schoolInfo$Score==5]<-"five"schoolInfo输出：IDScoreStatusSumNewScore112open88.77222NAclose75.66<NA>334close89.434445<NA>78.36five55NAclose91.26<NA>662<NA>76.192773<NA>61.543882close74.26299NAopen86.53<NA>一个字段ScoreCat，Score中小于3的值在ScoreCat中赋值为bad，Score中大于等于3的值在ScoreCat中赋值为Good，Score中缺失的值在ScoreCat中赋值为Unknown#新建一个字段ScoreCatschoolInfo$ScoreCat[schoolInfo$Score<3]<-"bad"schoolInfo$ScoreCat[schoolInfo$Score>=3]<-"Good"schoolInfo$ScoreCat[is.na(schoolInfo$Score)]<-"Unknown"schoolInfo输出：IDScoreStatusSumScoreCat112open88.77bad22NAclose75.66Unknown334close89.43Good445<NA>78.36Good55NAclose91.26Unknown662<NA>76.19bad773<NA>61.54Good882close74.26bad99NAopen86.53Unknown从数据集中随机抽取5行数据#从数据集中随机抽取5行数据schoolInfo<-schoolInfo[sample(1:nrow(schoolInfo),5,replace=FALSE),]schoolInfo输出：IDScoreStatusSum99NAopen86.53112open88.77773<NA>61.54445<NA>78.36334close89.43四舍五入字段Sum中的值（只保留整数）#四舍五入字段Sum中的值schoolInfo$S