How Survey Items Are Converted into “Ordinary” Data Vectors¶
Description¶
Survey item objects in are numeric or character vectors with some extra information that
may helpful for for managing and documenting survey data, but they are not suitable for
statistical data analysis. To run regressions etc. one should convert item objects
into “ordinary” numeric vectors or factors. This means that codes or values declared as
“missing” (if present) are translated into the generial missing value NA, while value
labels (if defined) are translated into factor levels.
Usage¶
# The following methods can be used to covert items into
# vectors with a given mode or into factors.
## S4 method for signature 'item'
as.vector(x, mode = "any")
## S4 method for signature 'item'
as.numeric(x, ...)
## S4 method for signature 'item'
as.integer(x, ...)
## S4 method for signature 'item.vector'
as.factor(x)
## S4 method for signature 'item.vector'
as.ordered(x)
## S4 method for signature 'item.vector'
as.character(x, use.labels = TRUE, include.missings = FALSE, ...)
## S4 method for signature 'datetime.item.vector'
as.character()
## S4 method for signature 'Date.item.vector'
as.character()
# The following methods are unlikely to be useful in practice, other than
# that they are called internally by the 'as.data.frame()' method for "data.set"
# objects.
## S4 method for signature 'character.item'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S4 method for signature 'double.item'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S4 method for signature 'integer.item'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S4 method for signature 'Date.item'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S4 method for signature 'datetime.item'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
Arguments¶
x-
an object in class “item”,”item.vector”, etc., as relevant for the respective conversion method.
mode-
the mode of the vector to be returned, usually
"numeric","integer", or"charcater" use.labels-
logical,should value labels be used for creating the character vector?
include.missings-
logical; if
TRUE, declared missing values are not converted intoNA, but into character strings with"*"as the “missingness marker” added at the beginning. row.names-
optional row names, see
as.data.frame optional-
a logical value, see
as.data.frame ...-
other arguments, ignored.
Value¶
The function as.vector() returns a logical, numeric, or character depending on the
mode= argument. If mode="any", the vector has the mode that corresponds to the
(internal) mode of the item vector, that is, an item in class “integer.item” will become
an integer vector, an item in class “double.item” will become a double-precision numeric
vector, an item in class “character.item” will become a character vector; since the
internal mode of a “dateitem.item” or a “Date.item” vector is numeric, a numeric vector
will be returned.
The functions as.integer(), as.numeric(), as.character(), as.factor(),
and as.ordered() return an integer, numeric, or character vector, or an ordered or
unordered factor, respectively.
When as.data.frame() is applied to an survey item object, the result is a
single-column data frame, where the single column is a numeric vector or character vector
or factor depending on the measurement attribute of the item. In particular, if the
measurement attribute equals "ratio" or "interval" this column will be the
result of as.vector(), if the measurement attribute equals "ordinal" this
column will be an ordered factor (see ordered), and if the measurement attribute
equals "nominal" this column will be an unordered factor (see factor).
All these functions have in common that values declared as “missing” by virtue of the
value.filter attribute will be turned into NA.
See also¶
items annotation labels value.filter
Examples¶
x <- as.item(rep(1:5,4),
labels=c(
"First" = 1,
"Second" = 2,
"Third" = 3,
"Fourth" = 4,
"Don't know" = 5
),
missing.values=5,
annotation = c(
description="test"
))
str(x)
Nmnl. item w/ 5 labels for 1,2,3,... + ms.v. int [1:20] 1 2 3 4 5 1 2 3 4 5
...
summary(x)
First Second Third Fourth *Don't know
4 4 4 4 4
as.numeric(x)
[1] 1 2 3 4 NA 1 2 3 4 NA 1 2 3 4 NA 1 2 3 4 NA
attr(,"label")
[1] "test"
test <- as.item(rep(1:6,2),labels=structure(1:6,
names=letters[1:6]))
as.factor(test)
[1] a b c d e f a b c d e f
Levels: a b c d e f
as.numeric(test)
[1] 1 2 3 4 5 6 1 2 3 4 5 6
as.character(test)
[1] "a" "b" "c" "d" "e" "f" "a" "b" "c" "d" "e" "f"
as.character(test,include.missings=TRUE)
[1] "a" "b" "c" "d" "e" "f" "a" "b" "c" "d" "e" "f"
as.data.frame(test)[[1]]
[1] a b c d e f a b c d e f
Levels: a b c d e f
