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.
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.
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")
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.
来源：https://stackoverflow.com/questions/16074384/specify-different-types-of-missing-values-nas