Specify different types of missing values (NAs)

Question

I'm interested to specify types of missing values. I have data that have different types of missing and I am trying to code these values as missing in R, but I am looking for a solution were I can still distinguish between them.

Say I have some data that looks like this,

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

If I now make two tables my missing values ("Don't know/Not sure","Unknown","Refused" and 77, 88, 99) are included as regular data,

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

and

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

I now recode the three factor levels "Don't know/Not sure","Unknown","Refused" into <NA>

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

and remove the empty levels

df$a <- factor(df$a) 

and the same is done with the numeric values 77, 88, and 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

Now the missing categories are recode into NA but they are all lumped together. Is there a way in a to recode something as missing, but retain the original values? I want R to thread "Don't know/Not sure","Unknown","Refused" and 77, 88, 99 as missing, but I want to be able to still have the information in the variable.

Answer 1

If you are willing to stick to numeric values then NA, Inf, -Inf, and NaN could be used for different missing values. You can then use is.finite to distinguish between them and normal values:

> x <- c(NA, Inf, -Inf, NaN, 1)
> is.finite(x)
[1] FALSE FALSE FALSE FALSE  TRUE

You could have a special print function that displays them in a more meaningful way or even create a special class but even without that this would divide the data into finite and multiple non-finite values.

Answer 2

To my knowledge, base R doesn't have an in-built way to handle different NA types. (editor: It does: NA_integer_, NA_real_, NA_complex_, and NA_character. See ?base::NA.)

One option is to use a package which does so, for instance "memisc". It's a little bit of extra work, but it seems to do what you're looking for.

Here's an example:

First, your data. I've made a copy since we will be making some pretty significant changes to the dataset, and it's always nice to have a backup.

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

Let's factor variable "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))

Load the "memisc" library:

library(memisc)

Now, convert variables "a" and "b" to items in "memisc":

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))

By doing this, we have a new data type. Compare the following:

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

We can use this information to create tables behaving the way you describe, while retaining all the original information.

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

The tables for the numeric column with missing data behaves the same way.

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

---

As a bonus, you get the facility to generate nice codebooks:

> 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

---

However, I do also suggest you read the comment from @Maxim.K about what really constitutes missing values.

Answer 3

To retain the original values, you can create new columns where you code the NA information , for example :

df <- transform(df,b.na = ifelse(b %in% c('77','88','99'),NA,b))
df <- transform(df,a.na = ifelse(a %in% 
                        c("Don't know/Not sure","Unknown","Refused"),NA,a))

Then you can do something like this :

   table(df$b.na , df$g)
    C K M Y
  2 0 0 4 0
  3 0 2 0 2

Another option without creating new columns is to use ,exclude option like this , to set the non desired values to NULL,( different of missing values)

table(df$a,df$g,
      exclude=c('77','88','99',"Don't know/Not sure","Unknown","Refused")) 
       C K M Y
  Blue  0 0 1 2
  Green 2 1 2 0
  Red   1 0 0 3

You can define some global constants( even it is not recommnded ) to group your "missing values", and use them in the rest of your program. Something like this :

B_MISSING <- c('77','88','99')
A_MISSING <- c("Don't know/Not sure","Unknown","Refused")