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 list
as.data.set(x,row.names=NULL,...)
is.data.set(x)
# S3 method for data.set
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S4 method for data.set
within(data, expr, ...)
dsView(x)
# S4 method for data.set
head(x,n=20,...)
# S4 method for 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 *Answer refused England 3084.2687
#> 2 *Answer refused England 1892.9557
#> 3 *Not applicable England 1156.5737
#> 4 *Answer refused *Not asked in survey 2917.6312
#> 5 *Not applicable Wales 2867.5674
#> 6 *Answer refused England 1311.5318
#> 7 *Don't know England 325.2048
#> 8 *Answer refused England 1102.6051
#> 9 *Not applicable Scotland 759.3739
#> 10 *Answer refused *Not asked in survey 4143.9835
#> 11 Conservatives Scotland 879.8037
#> 12 Liberal Democrats *Not asked in survey 940.6910
#> 13 *Not asked in survey Wales 782.6869
#> 14 *Not asked in survey Scotland 4149.1683
#> 15 Liberal Democrats *Not asked in survey 2667.0581
#> 16 Labour England 892.5669
#> 17 Labour Wales 696.1730
#> 18 Labour England 1324.5828
#> 19 *Not applicable England 2691.7366
#> 20 Conservatives England 3043.3218
#> 21 *Not asked in survey Wales 4037.6429
#> 22 Conservatives England 1462.9063
#> 23 *Not asked in survey England 7149.0187
#> 24 Conservatives England 2522.0721
#> 25 *Not asked in survey England 5090.6997
#> .. .................... .................... .........
#> (25 of 300 observations shown)
if (FALSE) {
# 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 9 9 97 9 97 9 8 9 97 9 ...
#> $ region: Nmnl. item w/ 5 labels for 1,2,3,... + ms.v. num 1 1 1 99 3 1 1 1 2 99 ...
#> $ income: Rto. item num 3084 1893 1157 2918 2868 ...
summary(Data)
#> vote region income
#> Conservatives :51 England :128 Min. : 315.5
#> Labour :30 Scotland : 82 1st Qu.: 1244.5
#> Liberal Democrats :49 Wales : 44 Median : 2113.7
#> *Don't know :44 *Not asked in survey: 46 Mean : 2746.1
#> *Answer refused :40 3rd Qu.: 3432.0
#> *Not applicable :39 Max. :12913.8
#> *Not asked in survey:47
if (FALSE) {
# 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] *Answer refused *Answer refused *Not applicable *Answer refused ...
Data[1,]
#>
#> Data set with 1 observations and 3 variables
#>
#> vote region income
#> 1 *Answer refused England 3084.269
head(as.data.frame(Data))
#> vote region income
#> 1 <NA> England 3084.269
#> 2 <NA> England 1892.956
#> 3 <NA> England 1156.574
#> 4 <NA> <NA> 2917.631
#> 5 <NA> Wales 2867.567
#> 6 <NA> England 1311.532
EnglandData <- subset(Data,region == "England")
EnglandData
#>
#> Data set with 128 observations and 3 variables
#>
#> vote region income
#> 1 *Answer refused England 3084.2687
#> 2 *Answer refused England 1892.9557
#> 3 *Not applicable England 1156.5737
#> 4 *Answer refused England 1311.5318
#> 5 *Don't know England 325.2048
#> 6 *Answer refused England 1102.6051
#> 7 Labour England 892.5669
#> 8 Labour England 1324.5828
#> 9 *Not applicable England 2691.7366
#> 10 Conservatives England 3043.3218
#> 11 Conservatives England 1462.9063
#> 12 *Not asked in survey England 7149.0187
#> 13 Conservatives England 2522.0721
#> 14 *Not asked in survey England 5090.6997
#> 15 *Not asked in survey England 1507.5981
#> 16 *Don't know England 2023.7406
#> 17 Conservatives England 1493.3919
#> 18 Labour England 3029.6432
#> 19 *Not asked in survey England 2465.3593
#> 20 *Don't know England 4519.6520
#> 21 Conservatives England 984.0079
#> 22 *Answer refused England 2054.7803
#> 23 Conservatives England 7624.3885
#> 24 *Answer refused England 638.0687
#> 25 Labour England 2945.0936
#> .. .................... ....... .........
#> (25 of 128 observations shown)
xtabs(~vote+region,data=Data)
#> region
#> vote England Scotland Wales
#> Conservatives 22 13 6
#> Labour 14 7 6
#> Liberal Democrats 18 14 7
xtabs(~vote+region,data=within(Data, vote <- include.missings(vote)))
#> region
#> vote England Scotland Wales
#> Conservatives 22 13 6
#> Labour 14 7 6
#> Liberal Democrats 18 14 7
#> *Don't know 22 8 8
#> *Answer refused 20 12 4
#> *Not applicable 12 12 8
#> *Not asked in survey 20 16 5