如何在R中将数据帧行转换为字符向量?
要在R中创建字符向量,我们可以将向量值括在双引号中,但是如果我们要使用数据帧的行值来创建字符向量,则可以使用as.character函数。例如,如果我们有一个数据帧df,则df第一行中的值可以使用as.character(df [1,])形成一个字符向量。
示例
请看以下数据帧-
set.seed(3232)x<−sample(letters[1:26],25,replace=TRUE)
y<−sample(LETTERS[1:26],25,replace=TRUE)
z<−sample(c("Purity","Impurity","Crystal","Odorless","Chain Reaction"),25,replace=TRUE)
a<−sample(c("High","Medium","Low"),25,replace=TRUE)
df<−data.frame(x,y,z,a)
df
输出结果
x y z a1 w W Odorless Medium
2 r I Chain Reaction High
3 v T Impurity High
4 g H Impurity Medium
5 s I Purity Medium
6 i O Crystal Medium
7 y D Odorless High
8 m L Crystal Medium
9 m B Crystal Low
10 b E Crystal Medium
11 a G Chain Reaction Low
12 j R Odorless Low
13 d H Odorless Low
14 j V Impurity High
15 l Q Crystal High
16 j Z Impurity High
17 a F Chain Reaction Low
18 l L Odorless High
19 x I Odorless Low
20 y Z Crystal Medium
21 q K Chain Reaction High
22 t F Crystal Medium
23 j F Crystal High
24 y F Impurity Medium
25 l A Chain Reaction High
使用数据帧df的行创建字符向量-
as.character(df[1,])[1] "w" "W" "Odorless" "Medium"
as.character(df[2,])
[1] "r" "I" "Chain Reaction" "High"
as.character(df[3,])
[1] "v" "T" "Impurity" "High"
as.character(df[5,])
[1] "s" "I" "Purity" "Medium"
as.character(df[10,])
[1] "b" "E" "Crystal" "Medium"
as.character(df[12,])
[1] "j" "R" "Odorless" "Low"
as.character(df[13,])
[1] "d" "H" "Odorless" "Low"
as.character(df[15,])
[1] "l" "Q" "Crystal" "High"
as.character(df[18,])
[1] "l" "L" "Odorless" "High"
as.character(df[20,])
[1] "y" "Z" "Crystal" "Medium"
as.character(df[25,])
[1] "l" "A" "Chain Reaction" "High"
as.character(df[21,])
[1] "q" "K" "Chain Reaction" "High"
示例
让我们看另一个例子-
G1<−sample(1:4,20,replace=TRUE)G2<−sample(c("Lower","Upper","Middle"),20,replace=TRUE)
G3<−sample(c("Sensitive","Insenstive"),20,replace=TRUE)
df_G<−data.frame(G1,G2,G3)
df_G
输出结果
G1 G2 G31 2 Middle Sensitive
2 4 Middle Insenstive
3 2 Middle Sensitive
4 1 Upper Insenstive
5 3 Lower Insenstive
6 1 Upper Insenstive
7 3 Lower Sensitive
8 3 Lower Sensitive
9 3 Lower Sensitive
10 3 Lower Sensitive
11 1 Upper Insenstive
12 2 Lower Sensitive
13 4 Upper Insenstive
14 3 Middle Insenstive
15 2 Upper Sensitive
16 1 Upper Insenstive
17 4 Lower Insenstive
18 4 Middle Sensitive
19 2 Middle Sensitive
20 3 Middle Sensitive
as.character(df_G[1,])
[1] "2" "Middle" "Sensitive"
as.character(df_G[2,])
[1] "4" "Middle" "Insenstive"
as.character(df_G[5,])
[1] "3" "Lower" "Insenstive"
as.character(df_G[7,])
[1] "3" "Lower" "Sensitive"
as.character(df_G[9,])
[1] "3" "Lower" "Sensitive"
as.character(df_G[10,])
[1] "3" "Lower" "Sensitive"
as.character(df_G[12,])
[1] "2" "Lower" "Sensitive"
as.character(df_G[13,])
[1] "4" "Upper" "Insenstive"
as.character(df_G[15,])
[1] "2" "Upper" "Sensitive"
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