How Survey Items Are Converted into "Ordinary" Data Vectors
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.
# 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. ## S3 method for class 'character.item' as.data.frame(x, row.names = NULL, optional = FALSE, ...) ## S3 method for class 'double.item' as.data.frame(x, row.names = NULL, optional = FALSE, ...) ## S3 method for class 'integer.item' as.data.frame(x, row.names = NULL, optional = FALSE, ...) ## S3 method for class 'Date.item' as.data.frame(x, row.names = NULL, optional = FALSE, ...) ## S3 method for class 'datetime.item' as.data.frame(x, row.names = NULL, optional = FALSE, ...)
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 |
use.labels |
logical,should value labels be used for creating the character vector? |
include.missings |
logical; if |
row.names |
optional row names, see |
optional |
a logical value, see |
... |
other arguments, ignored. |
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.
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)
summary(x)
as.numeric(x)
test <- as.item(rep(1:6,2),labels=structure(1:6,
names=letters[1:6]))
as.factor(test)
as.numeric(test)
as.character(test)
as.character(test,include.missings=TRUE)
as.data.frame(test)[[1]]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.