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法學如何選擇R資料框中不在某個向量中的列？
一個R資料框可能有很多列，我們可能想選擇除少數幾列外的所有列。在這種情況下，最好透過取消選擇不需要的列來提取列，而不是選擇需要的列，因為需要的列數多於不需要的列數。這可以透過!符號和單方括號輕鬆完成。
示例
考慮以下資料框：
> Age<-sample(20:50,20)
> Gender<-rep(c("Male","Female"),times=10)
> Salary<-sample(20000:40000,20)
> ID<-1:20
> Education<-rep(c("Grad","Post-Grad","PhD","Highschool"),times=5)
> Experience<-sample(1:5,20,replace=TRUE)
> df<-data.frame(ID,Gender,Age,Salary,Experience,Education)
> df輸出
ID Gender Age Salary Experience Education 1 1 Male 35 38245 4 Grad 2 2 Female 43 21995 5 Post-Grad 3 3 Male 21 38941 4 PhD 4 4 Female 26 36599 2 Highschool 5 5 Male 50 27477 2 Grad 6 6 Female 28 25281 2 Post-Grad 7 7 Male 33 20310 4 PhD 8 8 Female 24 30171 2 Highschool 9 9 Male 38 28779 3 Grad 10 10 Female 46 31213 3 Post-Grad 11 11 Male 36 27697 4 PhD 12 12 Female 41 36929 2 Highschool 13 13 Male 42 35367 2 Grad 14 14 Female 29 28711 1 Post-Grad 15 15 Male 22 29253 3 PhD 16 16 Female 30 28982 5 Highschool 17 17 Male 39 39458 4 Grad 18 18 Female 27 31891 2 Post-Grad 19 19 Male 48 29931 2 PhD 20 20 Female 31 34817 2 Highschool
選擇除“性別”和“年齡”列外的所有列：
> df[,!names(df)%in%c("Gender","Age")]輸出
ID Salary Experience Education 1 1 38245 4 Grad 2 2 21995 5 Post-Grad 3 3 38941 4 PhD 4 4 36599 2 Highschool 5 5 27477 2 Grad 6 6 25281 2 Post-Grad 7 7 20310 4 PhD 8 8 30171 2 Highschool 9 9 28779 3 Grad 10 10 31213 3 Post-Grad 11 11 27697 4 PhD 12 12 36929 2 Highschool 13 13 35367 2 Grad 14 14 28711 1 Post-Grad 15 15 29253 3 PhD 16 16 28982 5 Highschool 17 17 39458 4 Grad 18 18 31891 2 Post-Grad 19 19 29931 2 PhD 20 20 34817 2 Highschool
選擇除“ID”和“教育程度”列外的所有列：
> df[,!names(df)%in%c("ID","Education")]輸出
Gender Age Salary Experience 1 Male 35 38245 4 2 Female 43 21995 5 3 Male 21 38941 4 4 Female 26 36599 2 5 Male 50 27477 2 6 Female 28 25281 2 7 Male 33 20310 4 8 Female 24 30171 2 9 Male 38 28779 3 10 Female 46 31213 3 11 Male 36 27697 4 12 Female 41 36929 2 13 Male 42 35367 2 14 Female 29 28711 1 15 Male 22 29253 3 16 Female 30 28982 5 17 Male 39 39458 4 18 Female 27 31891 2 19 Male 48 29931 2 20 Female 31 34817 2
選擇除“ID”、“教育程度”和“性別”列外的所有列：
> df[,!names(df)%in%c("ID","Education","Gender")]輸出
Age Salary Experience 1 35 38245 4 2 43 21995 5 3 21 38941 4 4 26 36599 2 5 50 27477 2 6 28 25281 2 7 33 20310 4 8 24 30171 2 9 38 28779 3 10 46 31213 3 11 36 27697 4 12 41 36929 2 13 42 35367 2 14 29 28711 1 15 22 29253 3 16 30 28982 5 17 39 39458 4 18 27 31891 2 19 48 29931 2 20 31 34817 2
選擇除“ID”、“年齡”和“性別”列外的所有列：
> df[,!names(df)%in%c("ID","Age","Gender")]Salary Experience Education 1 38245 4 Grad 2 21995 5 Post-Grad 3 38941 4 PhD 4 36599 2 Highschool 5 27477 2 Grad 6 25281 2 Post-Grad 7 20310 4 PhD 8 30171 2 Highschool 9 28779 3 Grad 10 31213 3 Post-Grad 11 27697 4 PhD 12 36929 2 Highschool 13 35367 2 Grad 14 28711 1 Post-Grad 15 29253 3 PhD 16 28982 5 Highschool 17 39458 4 Grad 18 31891 2 Post-Grad 19 29931 2 PhD 20 34817 2 Highschool
更新於：2020年9月4日
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