Value Filters¶
Description¶
Value filters, that is objects that inherit from class “value.filter”, are a mechanism to distinguish between valid codes of a survey item and codes that are considered to be missing, such as the codes for answers like “don’t know” or “answer refused”.
Value filters are optional slot values of “item” objects. They determine which codes of
“item” objects are replaced by NA when they are coerced into a vector or a factor.
There are three (sub)classes of value filters: “missing.values”, which specify individual
missing values and/or a range of missing values; “valid.values”, which specify individual
valid values (that is, all other values of the item are considered as missing);
“valid.range”, which specify a range of valid values (that is, all values outside the
range are considered as missing). Value filters of class “missing.values” correspond to
missing-values declarations in SPSS files, imported by spss.fixed.file,
spss.portable.file, or spss.system.file.
Value filters also can be updated using the + and - operators.
Usage¶
value.filter(x)
missing.values(x)
missing.values(x)<-value
valid.values(x)
valid.values(x)<-value
valid.range(x)
valid.range(x)<-value
is.valid(x)
nvalid(x)
is.missing(x)
include.missings(x,mark="*")
Arguments¶
x,valueobjects of the appropriate class.
marka character string, used to pasted to value labels of
x(if present).
Value¶
value.filter(x), missing.values(x), valid.values(x), and valid.range(x),
return the value filter associated with x, an object of class “value.filter”, that
is, of class “missing.values”, “valid.values”, or “valid.range”, respectively.
is.missing(x) returns a logical vector indicating for each element of x whether
it is a missing value or not. is.valid(x) returns a logical vector indicating for
each element of x whether it is a valid value or not. nvalid(x) returns the
number of elements of x that are valid.
For convenience, is.missing(x) and is.valid(x) also work for atomic vectors and
factors, where they are equivalent to is.na(x) and !is.na(x). For atomic vectors
and factors, nvalid(x) returns the number of elements of x for which
!is.na(x) is TRUE.
include.missings(x,...) returns a copy of x that has all values declared as
valid.
Examples¶
x <- rep(c(1:4,8,9),2,length=60)
labels(x) <- c(
a=1,
b=2,
c=3,
d=4,
dk=8,
refused=9
)
missing.values(x) <- 9
missing.values(x)
9
missing.values(x) <- missing.values(x) + 8
missing.values(x)
9, 8
missing.values(x) <- NULL
missing.values(x)
NULL
missing.values(x) <- list(range=c(8,Inf))
missing.values(x)
8 - Inf
valid.values(x)
1, 2, 3, 4
print(x)
[1] a b c d *dk *refused a b c d
[11] *dk *refused a b c d *dk *refused a b
[21] c d *dk *refused a b c d *dk *refused
[31] a b c d *dk *refused a b c d
[41] *dk *refused a b c d *dk *refused a b
[51] c d *dk *refused a b c d *dk *refused
is.missing(x)
[1] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
FALSE FALSE FALSE
[17] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
TRUE FALSE FALSE
[33] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
FALSE TRUE TRUE
[49] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
is.valid(x)
[1] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
TRUE TRUE
[17] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
FALSE TRUE TRUE
[33] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
FALSE FALSE
[49] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
as.factor(x)
[1] a b c d <NA> <NA> a b c d <NA> <NA> a b c d <NA> <NA> a
[20] b c d <NA> <NA> a b c d <NA> <NA> a b c d <NA> <NA> a b
[39] c d <NA> <NA> a b c d <NA> <NA> a b c d <NA> <NA> a b c
[58] d <NA> <NA>
Levels: a b c d
as.factor(include.missings(x))
[1] a b c d *dk *refused a b c d
[11] *dk *refused a b c d *dk *refused a b
[21] c d *dk *refused a b c d *dk *refused
[31] a b c d *dk *refused a b c d
[41] *dk *refused a b c d *dk *refused a b
[51] c d *dk *refused a b c d *dk *refused
Levels: a b c d *dk *refused
as.integer(x)
[1] 1 2 3 4 NA NA 1 2 3 4 NA NA 1 2 3 4 NA NA 1 2 3 4 NA NA 1 2 3 4 NA NA 1 2
[33] 3 4 NA NA 1 2 3 4 NA NA 1 2 3 4 NA NA 1 2 3 4 NA NA 1 2 3 4 NA NA
as.integer(include.missings(x))
[1] 1 2 3 4 8 9 1 2 3 4 8 9 1 2 3 4 8 9 1 2 3 4 8 9 1 2 3 4 8 9 1 2 3 4 8 9 1 2
3 4 8 9 1 2 3 4 8 9
[49] 1 2 3 4 8 9 1 2 3 4 8 9