R - 因子_R 菜鸟教程

因子是用于对数据进行分类并将其存储为级别的数据对象。它们可以存储字符串和整数。它们在唯一值数量有限的列中非常有用。比如 “Male， ”Female“ 和 True， False 等。它们在统计建模的数据分析中很有用。

因子是使用 factor （） 函数通过采用矢量作为输入来创建的。

例子

# 创建一个矢量作为输入。
data <- c("East","West","East","North","North","East","West","West","West","East","North")

print(data)
print(is.factor(data))

# 应用因子函数。
factor_data <- factor(data)

print(factor_data)
print(is.factor(factor_data))

当我们执行上述代码时，它会产生以下结果——

[1] "East" "West" "East" "North" "North" "East" "West" "West" "West" "East" "North"
[1] FALSE
[1] East West East North North East West West West East North
Levels: East North West
[1] TRUE

数据帧中的因子

在创建任何包含文本数据列的数据框时，R 会将文本列视为分类数据，并在其上创建因子。

# 为数据帧创建矢量。
height <- c(132,151,162,139,166,147,122)
weight <- c(48,49,66,53,67,52,40)
gender <- c("male","male","female","female","male","female","male")

# 创建数据帧。
input_data <- data.frame(height,weight,gender)
print(input_data)

# 测试性别列是否是一个因子。
print(is.factor(input_data$gender))

# 打印性别栏，以便查看级别。
print(input_data$gender)

当我们执行上述代码时，它会产生以下结果——

height weight gender
1 132 48 male
2 151 49 male
3 162 66 female
4 139 53 female
5 166 67 male
6 147 52 female
7 122 40 male
[1] TRUE
[1] male male female female male female male
Levels: female male

更改级别顺序

可以通过再次应用因子函数和新的水平顺序来更改因子中水平的顺序。

data <- c("East","West","East","North","North","East","West",
	 	"West","West","East","North")
# 创造因子
factor_data <- factor(data)
print(factor_data)

# 应用具有所需级别顺序的因子函数。
new_order_data <- factor(factor_data,levels = c("East","West","North"))
print(new_order_data)

当我们执行上述代码时，它会产生以下结果——

[1] East West East North North East West West West East North
Levels: East North West
[1] East West East North North East West West West East North
Levels: East West North

生成因子水平

我们可以使用 gl（） 函数生成因子水平。它采用两个整数作为输入，表示级别数和每个级别多少次。

语法

gl(n, k, labels)

以下是所用参数的描述 -

n 是一个整数，给出了级别数。
k 是一个整数，给出了复制的数量。
labels 是结果因子水平的标签矢量。

例子

v <- gl(3, 4, labels = c("Tampa", "Seattle","Boston"))
print(v)

当我们执行上述代码时，它会产生以下结果——

Tampa Tampa Tampa Tampa Seattle Seattle Seattle Seattle Boston
[10] Boston Boston Boston
Levels: Tampa Seattle Boston

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