R中的因素：不仅仅是烦恼

你应该使用因子.是的，它们可能是一种痛苦，但我的理论是，90%的痛苦是因为在read.table和read.csv中，默认情况下，参数stringsAsFactors = TRUE(大多数用户忽略了这一微妙之处).我说它们很有用，因为lme4等模型拟合包使用因子和有序因子来差异拟合模型，并确定要使用的对比度类型.图形包也使用它们来分组.ggplot和大多数模型拟合函数将特征向量强制为因子，因此结果是相同的.但是，最终代码中会出现警告:

lm(Petal.Length ~ -1 + Species, data=iris)

# Call:
# lm(formula = Petal.Length ~ -1 + Species, data = iris)

# Coefficients:
#     Speciessetosa  Speciesversicolor   Speciesvirginica  
#             1.462              4.260              5.552  

iris.alt <- iris
iris.alt$Species <- as.character(iris.alt$Species)
lm(Petal.Length ~ -1 + Species, data=iris.alt)

# Call:
# lm(formula = Petal.Length ~ -1 + Species, data = iris.alt)

# Coefficients:
#     Speciessetosa  Speciesversicolor   Speciesvirginica  
#             1.462              4.260              5.552  

警告信息:在model.matrix.default(mt, mf, contrasts):

变量Species转换为factor

一件棘手的事情是整个drop=TRUE位.在向量中，这可以很好地go 除数据中没有的因素级别.例如:

s <- iris$Species
s[s == 'setosa', drop=TRUE]
#  [1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [11] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [21] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [31] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [41] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# Levels: setosa
s[s == 'setosa', drop=FALSE]
#  [1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [11] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [21] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [31] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [41] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# Levels: setosa versicolor virginica

However，对于data.frame，[.data.frame()的行为是不同的:见this email或?"[.data.frame".在data.frame上使用drop=TRUE并不像你想象的那样有效:

x <- subset(iris, Species == 'setosa', drop=TRUE)  # susbetting with [ behaves the same way
x$Species
#  [1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [11] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [21] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [31] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# [41] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
# Levels: setosa versicolor virginica

幸运的是，您可以轻松地使用droplevels()来删除单个因子或data.frame中每个因子的未使用因子级别(自R 2.12起):

x <- subset(iris, Species == 'setosa')
levels(x$Species)
# [1] "setosa"     "versicolor" "virginica" 
x <- droplevels(x)
levels(x$Species)
# [1] "setosa"

这是如何让你从ggplot个传奇人物中 Select 的等级保持不变.

在内部，factor是带有属性级字符向量的整数(参见attributes(iris$Species)和class(attributes(iris$Species)$levels))，这是干净的.如果您必须更改级别名称(并且使用字符串)，这将是一个效率较低的操作.我经常更改关卡名称，尤其是ggplot个传奇.如果你用角色向量伪造因子，你可能只会改变一个元素，并意外地创建一个单独的新关卡.