RSDA (version 3.2.1)

read.sym.table: Read a Symbolic Table

Description

It reads a symbolic data table from a CSV file.

Usage

read.sym.table(file, header = TRUE, sep, dec, row.names = NULL)

Value

Return a symbolic data table structure.

Arguments

file

The name of the CSV file.

header

As in R function read.table

sep

As in R function read.table

dec

As in R function read.table

row.names

As in R function read.table

Author

Oldemar Rodriguez Rojas

Details

The labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories) . In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.

The format is the CSV file should be like:

$C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4

Case1 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i

Case2 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d

Case3 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c

Case4 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a

Case5 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k

The internal format is:
$N
[1] 5
$M
[1] 4
$sym.obj.names
[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5'
$sym.var.names
[1] 'F1' 'F2' 'F3' 'F4'
$sym.var.types
[1] '$C' '$I' '$H' '$S'
$sym.var.length
[1] 1 2 3 4
$sym.var.starts
[1] 2 4 8 13
$meta
$C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4
Case1 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i
Case2 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d
Case3 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c
Case4 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a
Case5 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k
$data
F1 F2 F2.1 M1 M2 M3 E1 E2 E3 E4
Case1 2.8 1 2 0.1 0.7 0.2 e g k i
Case2 1.4 3 9 0.6 0.3 0.1 a b c d
Case3 3.2 -1 4 0.2 0.2 0.6 2 1 b c
Case4 -2.1 0 2 0.9 0.0 0.1 3 4 c a
Case5 -3.0 -4 -2 0.6 0.0 0.4 e i g k

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

See Also

display.sym.table

Examples

if (FALSE) {
data(example1)
write.sym.table(example1,
  file = "temp4.csv", sep = "|", dec = ".", row.names = TRUE,
  col.names = TRUE
)
ex1 <- read.sym.table("temp4.csv", header = TRUE, sep = "|", dec = ".", row.names = 1)
}