r - 指定不同类型的缺失值 (NA)

Question

我有兴趣指定缺失值的类型。我有不同类型缺失的数据，我试图将这些值编码为 R 中缺失，但我正在寻找一种解决方案，我仍然可以区分它们。

假设我有一些看起来像这样的数据，

set.seed(667) 
df <- data.frame(a = sample(c("Don't know/Not sure","Unknown","Refused","Blue", "Red", "Green"),  20, rep=TRUE), b = sample(c(1, 2, 3, 77, 88, 99),  10, rep=TRUE), f = round(rnorm(n=10, mean=.90, sd=.08), digits = 2), g = sample(c("C","M","Y","K"),  10, rep=TRUE) ); df
#                      a  b    f g
# 1              Unknown  2 0.78 M
# 2              Refused  2 0.87 M
# 3                  Red 77 0.82 Y
# 4                  Red 99 0.78 Y
# 5                Green 77 0.97 M
# 6                Green  3 0.99 K
# 7                  Red  3 0.99 Y
# 8                Green 88 0.84 C
# 9              Unknown 99 1.08 M
# 10             Refused 99 0.81 C
# 11                Blue  2 0.78 M
# 12               Green  2 0.87 M
# 13                Blue 77 0.82 Y
# 14 Don't know/Not sure 99 0.78 Y
# 15             Unknown 77 0.97 M
# 16             Refused  3 0.99 K
# 17                Blue  3 0.99 Y
# 18               Green 88 0.84 C
# 19             Refused 99 1.08 M
# 20                 Red 99 0.81 C

如果我现在制作两个表，我的缺失值( "Don't know/Not sure","Unknown","Refused" 和 77, 88, 99 )将作为常规数据包含在内，

table(df$a,df$g)
#                     C K M Y
# Blue                0 0 1 2
# Don't know/Not sure 0 0 0 1
# Green               2 1 2 0
# Red                 1 0 0 3
# Refused             1 1 2 0
# Unknown             0 0 3 0

和

table(df$b,df$g)
#    C K M Y
# 2  0 0 4 0
# 3  0 2 0 2
# 77 0 0 2 2
# 88 2 0 0 0
# 99 2 0 2 2

我现在重新编码三个因子水平 "Don't know/Not sure","Unknown","Refused"进入 <NA>

is.na(df[,c("a")]) <- df[,c("a")]=="Don't know/Not sure"|df[,c("a")]=="Unknown"|df[,c("a")]=="Refused"

并删除空的级别

df$a <- factor(df$a) 

对数值 77, 88, 也是如此。和 99

is.na(df) <- df=="77"|df=="88"|df=="99"

table(df$a, df$g, useNA = "always")       
#       C K M Y <NA>
# Blue  0 0 1 2    0
# Green 2 1 2 0    0
# Red   1 0 0 3    0
# <NA>  1 1 5 1    0

table(df$b,df$g, useNA = "always")
#      C K M Y <NA>
# 2    0 0 4 0    0
# 3    0 2 0 2    0
# <NA> 4 0 4 4    0

现在缺失的类别被重新编码为 NA但它们都混为一谈。有没有办法将某些东西重新编码为丢失的东西，但保留原始值？我想要 R 线程 "Don't know/Not sure","Unknown","Refused"和 77, 88, 99丢失，但我希望能够仍然拥有变量中的信息。

Answer 1

据我所知，base R 没有内置的方法来处理不同的 NA类型。 ( 编辑器: 它确实: NA_integer_ 、 NA_real_ 、 NA_complex_ 和 NA_character 。见 ?base::NA 。)

一种选择是使用这样做的包，例如“memisc”。这是一些额外的工作，但它似乎可以满足您的需求。

下面是一个例子:

首先，您的数据。我制作了一份副本，因为我们将对数据集进行一些非常重要的更改，并且有备份总是很好的。

set.seed(667) 
df <- data.frame(a = sample(c("Don't know/Not sure", "Unknown", 
                              "Refused", "Blue", "Red", "Green"),
                            20, replace = TRUE), 
                 b = sample(c(1, 2, 3, 77, 88, 99), 10, 
                            replace = TRUE), 
                 f = round(rnorm(n = 10, mean = .90, sd = .08), 
                           digits = 2), 
                 g = sample(c("C", "M", "Y", "K"), 10, 
                            replace = TRUE))
df2 <- df

让我们分解变量“a”:

df2$a <- factor(df2$a, 
                levels = c("Blue", "Red", "Green", 
                           "Don't know/Not sure",
                           "Refused", "Unknown"),
                labels = c(1, 2, 3, 77, 88, 99))

加载“memisc”库:

library(memisc)

现在，将变量“a”和“b”转换为 item在“memisc”中:

df2$a <- as.item(as.character(df2$a), 
                  labels = structure(c(1, 2, 3, 77, 88, 99),
                                     names = c("Blue", "Red", "Green", 
                                               "Don't know/Not sure",
                                               "Refused", "Unknown")),
                  missing.values = c(77, 88, 99))
df2$b <- as.item(df2$b, 
                 labels = c(1, 2, 3, 77, 88, 99), 
                 missing.values = c(77, 88, 99))

通过这样做，我们有一个新的数据类型。比较以下内容:

as.factor(df2$a)
#  [1] <NA>  <NA>  Red   Red   Green Green Red   Green <NA>  <NA>  Blue 
# [12] Green Blue  <NA>  <NA>  <NA>  Blue  Green <NA>  Red  
# Levels: Blue Red Green
as.factor(include.missings(df2$a))
#  [1] *Unknown             *Refused             Red                 
#  [4] Red                  Green                Green               
#  [7] Red                  Green                *Unknown            
# [10] *Refused             Blue                 Green               
# [13] Blue                 *Don't know/Not sure *Unknown            
# [16] *Refused             Blue                 Green               
# [19] *Refused             Red                 
# Levels: Blue Red Green *Don't know/Not sure *Refused *Unknown

我们可以使用这些信息来创建表现出您所描述方式的表格，同时保留所有原始信息。

table(as.factor(include.missings(df2$a)), df2$g)
#                       
#                        C K M Y
#   Blue                 0 0 1 2
#   Red                  1 0 0 3
#   Green                2 1 2 0
#   *Don't know/Not sure 0 0 0 1
#   *Refused             1 1 2 0
#   *Unknown             0 0 3 0
table(as.factor(df2$a), df2$g)
#        
#         C K M Y
#   Blue  0 0 1 2
#   Red   1 0 0 3
#   Green 2 1 2 0
table(as.factor(df2$a), df2$g, useNA="always")
#        
#         C K M Y <NA>
#   Blue  0 0 1 2    0
#   Red   1 0 0 3    0
#   Green 2 1 2 0    0
#   <NA>  1 1 5 1    0

具有缺失数据的数字列的表的行为方式相同。

table(as.factor(include.missings(df2$b)), df2$g)
#      
#       C K M Y
#   1   0 0 0 0
#   2   0 0 4 0
#   3   0 2 0 2
#   *77 0 0 2 2
#   *88 2 0 0 0
#   *99 2 0 2 2
table(as.factor(df2$b), df2$g, useNA="always")
#       
#        C K M Y <NA>
#   1    0 0 0 0    0
#   2    0 0 4 0    0
#   3    0 2 0 2    0
#   <NA> 4 0 4 4    0

作为奖励，您可以获得生成漂亮的工具 codebook s:

> codebook(df2$a)
========================================================================

   df2$a

------------------------------------------------------------------------

   Storage mode: character
   Measurement: nominal
   Missing values: 77, 88, 99

            Values and labels    N    Percent 

    1   'Blue'                   3   25.0 15.0
    2   'Red'                    4   33.3 20.0
    3   'Green'                  5   41.7 25.0
   77 M 'Don't know/Not sure'    1         5.0
   88 M 'Refused'                4        20.0
   99 M 'Unknown'                3        15.0

但是，我也建议您阅读 the comment来自@Maxim.K 关于什么真正构成缺失值。