Data Set Objects
dataSets.Rd"data.set" objects are collections of "item" objects,
with similar semantics as data frames. They are distinguished
from data frames so that coercion by as.data.fame
leads to a data frame that contains only vectors and factors.
Nevertheless most methods for data frames are inherited by
data sets, except for the method for the within generic
function. For the within method for data sets, see the details section.
Thus data preparation using data sets retains all informations about item annotations, labels, missing values etc. While (mostly automatic) conversion of data sets into data frames makes the data amenable for the use of R's statistical functions.
dsView is a function that displays data sets in a similar
manner as View displays data frames. (View works
with data sets as well, but changes them first into data frames.)
Usage
data.set(...,row.names = NULL, check.rows = FALSE, check.names = TRUE,
stringsAsFactors = FALSE, document = NULL)
as.data.set(x, row.names=NULL, ...)
# S4 method for class 'list'
as.data.set(x,row.names=NULL,...)
is.data.set(x)
# S3 method for class 'data.set'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S4 method for class 'data.set'
within(data, expr, ...)
dsView(x)
# S4 method for class 'data.set'
head(x,n=20,...)
# S4 method for class 'data.set'
tail(x,n=20,...)Arguments
- ...
For the
data.setfunction several vectors or items, forwithinfurther, ignored arguments.- row.names, check.rows, check.names, stringsAsFactors, optional
arguments as in
data.frameoras.data.frame, respectively.- document
NULL or an optional character vector that contains documenation of the data.
- x
for
is.data.set(x), any object; foras.data.frame(x,...)anddsView(x)a "data.set" object.- data
a data set, that is, an object of class "data.set".
- expr
an expression, or several expressions enclosed in curly braces.
- n
integer; the number of rows to be shown by
headortail
Details
The as.data.frame method for data sets is just a copy
of the method for list. Consequently, all items in the data set
are coerced in accordance to their measurement setting,
see as.vector,item-method and measurement.
The within method for data sets has the same effect as
the within method for data frames, apart from two differences:
all results of the computations are coerced into items if
they have the appropriate length, otherwise, they are automatically
dropped.
Currently only one method for the generic function as.data.set
is defined: a method for "importer" objects.
Value
data.set and the within method for
data sets returns a "data.set" object, is.data.set
returns a logical value, and as.data.frame returns
a data frame.
Examples
Data <- data.set(
vote = sample(c(1,2,3,8,9,97,99),size=300,replace=TRUE),
region = sample(c(rep(1,3),rep(2,2),3,99),size=300,replace=TRUE),
income = exp(rnorm(300,sd=.7))*2000
)
Data <- within(Data,{
description(vote) <- "Vote intention"
description(region) <- "Region of residence"
description(income) <- "Household income"
wording(vote) <- "If a general election would take place next tuesday,
the candidate of which party would you vote for?"
wording(income) <- "All things taken into account, how much do all
household members earn in sum?"
foreach(x=c(vote,region),{
measurement(x) <- "nominal"
})
measurement(income) <- "ratio"
labels(vote) <- c(
Conservatives = 1,
Labour = 2,
"Liberal Democrats" = 3,
"Don't know" = 8,
"Answer refused" = 9,
"Not applicable" = 97,
"Not asked in survey" = 99)
labels(region) <- c(
England = 1,
Scotland = 2,
Wales = 3,
"Not applicable" = 97,
"Not asked in survey" = 99)
foreach(x=c(vote,region,income),{
annotation(x)["Remark"] <- "This is not a real survey item, of course ..."
})
missing.values(vote) <- c(8,9,97,99)
missing.values(region) <- c(97,99)
# These to variables do not appear in the
# the resulting data set, since they have the wrong length.
junk1 <- 1:5
junk2 <- matrix(5,4,4)
})
#> Warning: Variables 'junk1','junk2' have wrong length, removing them.
# Since data sets may be huge, only a
# part of them are 'show'n
Data
#>
#> Data set with 300 observations and 3 variables
#>
#> vote region income
#> 1 *Not applicable *Not asked in survey 3384.5617
#> 2 *Not asked in survey Scotland 2228.3955
#> 3 Conservatives England 1597.7063
#> 4 *Don't know England 5972.8596
#> 5 Labour England 670.5351
#> 6 Labour England 988.8037
#> 7 Conservatives *Not asked in survey 1758.9525
#> 8 Liberal Democrats *Not asked in survey 1291.6376
#> 9 *Don't know *Not asked in survey 2876.3122
#> 10 Liberal Democrats England 2738.8012
#> 11 Labour *Not asked in survey 1349.7776
#> 12 Conservatives England 2850.4797
#> 13 *Not asked in survey England 1439.1641
#> 14 Liberal Democrats England 4178.9150
#> 15 Labour *Not asked in survey 1362.5640
#> 16 Liberal Democrats Wales 561.1009
#> 17 *Not applicable Wales 975.9246
#> 18 *Not asked in survey England 697.2254
#> 19 Labour Scotland 4468.6364
#> 20 Conservatives *Not asked in survey 1696.1056
#> 21 Labour England 3972.9354
#> 22 *Don't know England 1241.3736
#> 23 Conservatives Scotland 1374.9910
#> 24 *Not applicable England 2958.4366
#> 25 *Don't know Scotland 6693.7717
#> .. .................... .................... .........
#> (25 of 300 observations shown)
if (FALSE) { # \dontrun{
# If we insist on seeing all, we can use 'print' instead
print(Data)
} # }
str(Data)
#> Data set with 300 obs. of 3 variables:
#> $ vote : Nmnl. item w/ 7 labels for 1,2,3,... + ms.v. num 97 99 1 8 2 2 1 3 8 3 ...
#> $ region: Nmnl. item w/ 5 labels for 1,2,3,... + ms.v. num 99 2 1 1 1 1 99 99 99 1 ...
#> $ income: Rto. item num 3385 2228 1598 5973 671 ...
summary(Data)
#> vote region income
#> Conservatives :38 England :141 Min. : 307.6
#> Labour :49 Scotland : 86 1st Qu.: 1348.5
#> Liberal Democrats :41 Wales : 39 Median : 1914.5
#> *Don't know :43 *Not asked in survey: 34 Mean : 2512.3
#> *Answer refused :44 3rd Qu.: 3046.3
#> *Not applicable :44 Max. :16272.5
#> *Not asked in survey:41
if (FALSE) { # \dontrun{
# If we want to 'View' a data set we can use 'dsView'
dsView(Data)
# Works also, but changes the data set into a data frame first:
View(Data)
} # }
Data[[1]]
#>
#> Item 'Vote intention' (measurement: nominal, type: double, length = 300)
#>
#> [1:300] *Not applicable *Not asked in survey Conservatives *Don't know ...
Data[1,]
#>
#> Data set with 1 observations and 3 variables
#>
#> vote region income
#> 1 *Not applicable *Not asked in survey 3384.562
head(as.data.frame(Data))
#> vote region income
#> 1 <NA> <NA> 3384.5617
#> 2 <NA> Scotland 2228.3955
#> 3 Conservatives England 1597.7063
#> 4 <NA> England 5972.8596
#> 5 Labour England 670.5351
#> 6 Labour England 988.8037
EnglandData <- subset(Data,region == "England")
EnglandData
#>
#> Data set with 141 observations and 3 variables
#>
#> vote region income
#> 1 Conservatives England 1597.7063
#> 2 *Don't know England 5972.8596
#> 3 Labour England 670.5351
#> 4 Labour England 988.8037
#> 5 Liberal Democrats England 2738.8012
#> 6 Conservatives England 2850.4797
#> 7 *Not asked in survey England 1439.1641
#> 8 Liberal Democrats England 4178.9150
#> 9 *Not asked in survey England 697.2254
#> 10 Labour England 3972.9354
#> 11 *Don't know England 1241.3736
#> 12 *Not applicable England 2958.4366
#> 13 Labour England 1137.4416
#> 14 Conservatives England 1144.7375
#> 15 *Don't know England 1237.6942
#> 16 Labour England 1706.2591
#> 17 Conservatives England 2678.4200
#> 18 Liberal Democrats England 3770.3668
#> 19 *Don't know England 1146.1415
#> 20 Liberal Democrats England 1071.7657
#> 21 Liberal Democrats England 1049.9579
#> 22 *Answer refused England 829.9630
#> 23 Liberal Democrats England 2676.8390
#> 24 Labour England 1802.0925
#> 25 *Not asked in survey England 4175.8977
#> .. .................... ....... .........
#> (25 of 141 observations shown)
xtabs(~vote+region,data=Data)
#> region
#> vote England Scotland Wales
#> Conservatives 16 13 4
#> Labour 23 16 4
#> Liberal Democrats 20 12 4
xtabs(~vote+region,data=within(Data, vote <- include.missings(vote)))
#> region
#> vote England Scotland Wales
#> Conservatives 16 13 4
#> Labour 23 16 4
#> Liberal Democrats 20 12 4
#> *Don't know 21 12 6
#> *Answer refused 19 14 7
#> *Not applicable 24 10 5
#> *Not asked in survey 18 9 9