2020-12-16 第四章基本数据管理2.0
2020-12-16 本文已影响0人
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建立数据框并改变其中的元素
> manager<-c(1,2,3,4,5)
> data<-c("10/24/08","10/28/08","10/1/08","10/12/08","5/1/09")
> country<-c("US","US","UK","UK","UK")
> gender<-c("M","F","F","M","F")
> age<-c(32,45,25,39,99)
> q1<-c(5,3,3,3,2)
> q2<-c(4,5,5,3,2)
> q3<-c(5,2,5,4,1)
> q4<-c(5,5,5,NA,2)
> q5<-c(5,5,2,NA,1)
> date<-c("10/24/08","10/28/08","10/1/08","10/12/08","5/1/09")
> leadership<-data.frame(manager,date,country,gender,age,q1,q2,q3,q4,q5)
> leadership
manager date country gender age q1 q2 q3 q4 q5
1 1 10/24/08 US M 32 5 4 5 5 5
2 2 10/28/08 US F 45 3 5 2 5 5
3 3 10/1/08 UK F 25 3 5 5 5 2
4 4 10/12/08 UK M 39 3 3 4 NA NA
5 5 5/1/09 UK F 99 2 2 1 2 1
> leadership$age[leadership$age==99]<-NA
> leadership$agecat[leadership$age>75]<-"Elder"
> leadership$agecat[leadership$age>=55&leadership$age<=75]<"Middle Aged"
[1] NA
> leadership$agecat[leadership$age<55]<-"Young"
> library(reshape)
> leadership<-rename(leadership,
+ c(manager="managerID",date="testDate")
+ )
> names(leadership)
[1] "managerID" "testDate" "country" "gender" "age" "q1" "q2"
[8] "q3" "q4" "q5" "agecat"
> names(leadership)[2]<-"testDate"
> leadership
managerID testDate country gender age q1 q2 q3 q4 q5 agecat
1 1 10/24/08 US M 32 5 4 5 5 5 Young
2 2 10/28/08 US F 45 3 5 2 5 5 Young
3 3 10/1/08 UK F 25 3 5 5 5 2 Young
4 4 10/12/08 UK M 39 3 3 4 NA NA Young
5 5 5/1/09 UK F NA 2 2 1 2 1 <NA>
> names(leadership)[6:10]<-c("item1","item2","item3","item4","item5")
> leadership
managerID testDate country gender age item1 item2 item3 item4 item5 agecat
1 1 10/24/08 US M 32 5 4 5 5 5 Young
2 2 10/28/08 US F 45 3 5 2 5 5 Young
3 3 10/1/08 UK F 25 3 5 5 5 2 Young
4 4 10/12/08 UK M 39 3 3 4 NA NA Young
5 5 5/1/09 UK F NA 2 2 1 2 1 <NA>
>
在第95页
变量的重命名
> names(leadership)
[1] "managerID" "testDate" "country" "gender" "age" "q1" "q2"
[8] "q3" "q4" "q5" "agecat"
> names(leadership)[2]<-"testDate"
> leadership
managerID testDate country gender age q1 q2 q3 q4 q5 agecat
1 1 10/24/08 US M 32 5 4 5 5 5 Young
2 2 10/28/08 US F 45 3 5 2 5 5 Young
3 3 10/1/08 UK F 25 3 5 5 5 2 Young
4 4 10/12/08 UK M 39 3 3 4 NA NA Young
5 5 5/1/09 UK F NA 2 2 1 2 1 <NA>
> names(leadership)[6:10]<-c("item1","item2","item3","item4","item5")
> leadership
managerID testDate country gender age item1 item2 item3 item4 item5 agecat
1 1 10/24/08 US M 32 5 4 5 5 5 Young
2 2 10/28/08 US F 45 3 5 2 5 5 Young
3 3 10/1/08 UK F 25 3 5 5 5 2 Young
4 4 10/12/08 UK M 39 3 3 4 NA NA Young
5 5 5/1/09 UK F NA 2 2 1 2 1 <NA>
> y<-c(1,2,3,NA)
> is.na(y)
[1] FALSE FALSE FALSE TRUE
> is.na(leadership[,6:10])
item1 item2 item3 item4 item5
[1,] FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE TRUE TRUE
[5,] FALSE FALSE FALSE FALSE FALSE
> leadership$age[leadership$age==99]<-NA
> leadership
managerID testDate country gender age item1 item2 item3 item4 item5 agecat
1 1 10/24/08 US M 32 5 4 5 5 5 Young
2 2 10/28/08 US F 45 3 5 2 5 5 Young
3 3 10/1/08 UK F 25 3 5 5 5 2 Young
4 4 10/12/08 UK M 39 3 3 4 NA NA Young
5 5 5/1/09 UK F NA 2 2 1 2 1 <NA>
使用na.omit()删除不完整的观测
> manager<-c(1,2,3,4,5)
> country<-c("US","US","UK","UK","UK")
> gender<-c("M","F","F","M","F")
> age<-c(32,45,25,39,99)
> q1<-c(5,3,3,3,2)
> q2<-c(4,5,5,3,2)
> q3<-c(5,2,5,4,1)
> q4<-c(5,5,5,NA,2)
> q5<-c(5,5,2,NA,1)
> date<-c("10/24/08","10/28/08","10/1/08","10/12/08","5/1/09")
> leadership<-data.frame(manager,date,country,gender,age,q1,q2,q3,q4,q5)
> leadership
manager date country gender age q1 q2 q3 q4 q5
1 1 10/24/08 US M 32 5 4 5 5 5
2 2 10/28/08 US F 45 3 5 2 5 5
3 3 10/1/08 UK F 25 3 5 5 5 2
4 4 10/12/08 UK M 39 3 3 4 NA NA
5 5 5/1/09 UK F 99 2 2 1 2 1
> newdata<-na.omit(leadership)
> newdata
manager date country gender age q1 q2 q3 q4 q5
1 1 10/24/08 US M 32 5 4 5 5 5
2 2 10/28/08 US F 45 3 5 2 5 5
3 3 10/1/08 UK F 25 3 5 5 5 2
5 5 5/1/09 UK F 99 2 2 1 2 1
关于日期的函数在第99页
第四章主要学习了如何确定一个对象的数据类型，以及它如何转换成其他类型，存储缺失值，日期值的方式。用公式创建新变量并重编码了现有变量，如何对数据进行排序对变量进行重命名，对数据集进行了横向合并和纵向合并
