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
[9] c d *dk *refused a b c d
[17] *dk *refused a b c d *dk *refused
[25] a b c d *dk *refused a b
[33] c d *dk *refused a b c d
[41] *dk *refused a b c d *dk *refused
[49] a b c d *dk *refused a b
[57] c d *dk *refused
is.missing(x)
[1] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[13] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[25] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[37] 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
[13] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
[25] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
[37] 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
[16] d <NA> <NA> a b c d <NA> <NA> a b c d <NA> <NA>
[31] a b c d <NA> <NA> a b c d <NA> <NA> a b c
[46] d <NA> <NA> a b c d <NA> <NA> a b c d <NA> <NA>
Levels: a b c d
as.factor(include.missings(x))
[1] a b c d *dk *refused a b
[9] c d *dk *refused a b c d
[17] *dk *refused a b c d *dk *refused
[25] a b c d *dk *refused a b
[33] c d *dk *refused a b c d
[41] *dk *refused a b c d *dk *refused
[49] a b c d *dk *refused a b
[57] 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
[26] 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
[51] 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
[39] 3 4 8 9 1 2 3 4 8 9 1 2 3 4 8 9 1 2 3 4 8 9
