发布于 2016-01-02 09:29:37 | 471 次阅读 | 评论: 0 | 来源: 网络整理
因子是它们用于将数据进行分类并将其存储为级别的数据对象。它们可以同时存储字符串和整数。它们在具有唯一值的有限数目的列是有用的。 例如,"male, "Female" 和 True, False 等. 它们在统计建模的数据分析非常有用。
使用 factor() 函数通过采取向量作为输入来创建因子。
# Create a vector as input.
data <- c("East","West","East","North","North","East","West","West","West","East","North")
print(data)
print(is.factor(data))
# Apply the factor function.
factor_data <- factor(data)
print(factor_data)
print(is.factor(factor_data))
当我们上面的代码执行时,它产生以下结果:
[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
在创建任何数据帧文本数据的列,R语言对待文本列作为分类数据,并在其上创建因子。
# Create the vectors for data frame.
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")
# Create the data frame.
input_data <- data.frame(height,weight,gender)
print(input_data)
# Test if the gender column is a factor.
print(is.factor(input_data$gender))
# Print the gender column so see the levels.
print(input_data$gender)
当我们上面的代码执行时,它产生以下结果:
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
一个因素中的级别的顺序可以通过使用级别的新顺序,再次应用因子函数来改变。
data <- c("East","West","East","North","North","East","West","West","West","East","North")
# Create the factors
factor_data <- factor(data)
print(factor_data)
# Apply the factor function with required order of the level.
new_order_data <- factor(factor_data,levels = c("East","West","North"))
print(new_order_data)
当我们上面的代码执行时,它产生以下结果:
[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
我们可以通过使用 gl()函数生成因子的级别。它有两个整型输入,表示每个级别有多少水平和多少次。
gl(n, k, labels)
以下是所使用的参数的说明:
v <- gl(3, 4, labels = c("Tampa", "Seattle","Boston"))
print(v)
当我们上面的代码执行时,它产生以下结果:
Tampa Tampa Tampa Tampa Seattle Seattle Seattle Seattle Boston
[10] Boston Boston Boston
Levels: Tampa Seattle Boston