Data Set Objects¶
Description¶
"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 signature 'list'
as.data.set(x,row.names=NULL,...)
is.data.set(x)
## S4 method for signature 'data.set'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S4 method for signature 'data.set'
within(data, expr, ...)
dsView(x)
## S4 method for signature 'data.set'
head(x,n=20,...)
## S4 method for signature '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
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.
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.
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 in within(Data, { :
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 asked in survey England 2364.9365
2 Labour Scotland 1488.7954
3 *Answer refused England 1217.8677
4 Labour England 1778.9219
5 Liberal Democrats Scotland 1568.2350
6 *Don't know Scotland 2428.9049
7 *Answer refused *Not asked in survey 2093.2721
8 Liberal Democrats Wales 3380.5580
9 *Don't know *Not asked in survey 1347.5785
10 *Answer refused Scotland 1686.1971
11 Conservatives *Not asked in survey 5538.3095
12 *Not applicable England 2526.3227
13 Labour England 882.3118
14 Liberal Democrats England 3044.6825
15 *Answer refused Scotland 2095.6941
16 Conservatives England 921.6308
17 Labour England 1024.6084
18 Liberal Democrats Wales 2336.0998
19 Labour Wales 2528.7734
20 Labour Scotland 1896.8119
21 *Don't know England 1918.5024
22 *Not asked in survey *Not asked in survey 1536.7924
23 Conservatives England 4405.8723
24 Labour Wales 3772.3101
25 Liberal Democrats England 576.5559
(25 of 300 observations shown)
# 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 99 2 9 2 3 8 9 3 8 9
...
$ region: Nmnl. item w/ 5 labels for 1,2,3,... + ms.v. num 1 2 1 1 2 2 99 3 99
2 ...
$ income: Rto. item num 2365 1489 1218 1779 1568 ...
summary(Data)
vote region income
Conservatives :42 England :140 Min. : 321.5
Labour :45 Scotland : 72 1st Qu.: 1263.1
Liberal Democrats :45 Wales : 37 Median : 1972.5
*Don't know :47 *Not asked in survey: 51 Mean : 2556.0
*Answer refused :47 3rd Qu.: 2950.7
*Not applicable :43 Max. :20562.5
*Not asked in survey:31
# 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 asked in survey Labour *Answer refused Labour Liberal Democrats
*Don't know ...
Data[1,]
Data set with 1 observations and 3 variables
vote region income
1 *Not asked in survey England 2364.936
head(as.data.frame(Data))
vote region income
1 <NA> England 2364.936
2 Labour Scotland 1488.795
3 <NA> England 1217.868
4 Labour England 1778.922
5 Liberal Democrats Scotland 1568.235
6 <NA> Scotland 2428.905
EnglandData <- subset(Data,region == "England")
EnglandData
Data set with 140 observations and 3 variables
vote region income
1 *Not asked in survey England 2364.9365
2 *Answer refused England 1217.8677
3 Labour England 1778.9219
4 *Not applicable England 2526.3227
5 Labour England 882.3118
6 Liberal Democrats England 3044.6825
7 Conservatives England 921.6308
8 Labour England 1024.6084
9 *Don't know England 1918.5024
10 Conservatives England 4405.8723
11 Liberal Democrats England 576.5559
12 Liberal Democrats England 776.6125
13 *Not applicable England 2361.1979
14 *Don't know England 1901.7334
15 *Not applicable England 1123.0880
16 *Don't know England 1134.5998
17 *Not asked in survey England 2153.2450
18 *Don't know England 1105.9471
19 *Not applicable England 2423.6032
20 Conservatives England 957.6335
21 *Not asked in survey England 3875.7992
22 *Answer refused England 2751.8808
23 Labour England 2542.8886
24 Conservatives England 4762.5405
25 *Not applicable England 1503.1837
(25 of 140 observations shown)
xtabs(~vote+region,data=Data)
region
vote England Scotland Wales
Conservatives 14 12 4
Labour 21 14 6
Liberal Democrats 19 10 8
xtabs(~vote+region,data=within(Data, vote <- include.missings(vote)))
region
vote England Scotland Wales
Conservatives 14 12 4
Labour 21 14 6
Liberal Democrats 19 10 8
*Don't know 30 6 4
*Answer refused 17 15 5
*Not applicable 22 10 6
*Not asked in survey 17 5 4
