Below is given an introductory demonstration of the function ProtectTable() which enables an easy interface to the statistical disclosure control package ‘sdcTable’ (https://CRAN.R-project.org/package=sdcTable). To see the input and output to functions in sdcTable consult the function ProtectTable1() which is an underlying function of ProtectTable(). Note that ‘easySdcTable’ is not as general as ‘sdcTable’.
This package was originally developed as a part of the modernization of the production of the key figures on municipal activities in Norway (https://www.ssb.no/en/offentlig-sektor/kostra). The fictitious example data is generated to be similar to realistic data from Norwegian municipalities and the variable names are (unfortunately) in Norwegian.
The demonstration below is based on the data from example 2 in the package and first we will use the unstacked data.
Before demonstrating ProtectTable() a few words about other possibilities.
A graphical user interface based on ‘shiny’ can be started by:
To start the gui with example data and catch output:
To start the gui with possibility to run tau-argus:
exeArgus <- "C:/Tau/TauArgus4.1.4/TauArgus.exe" # Tau-argus executable
pathArgus <- "C:/Users/nnn/Documents" # Folder for (temporary) tau-argus files
PTgui(exeArgus = exeArgus, pathArgus = pathArgus)The interface to tau-argus make use of functionality in ‘sdcTable’. See the documentation of ProtectTable() for more information.
The function EasyData() in ‘easySdcTable’ returns example data.
region fylke kostragr annet arbeid soshjelp trygd
A 1 300 11 11 55 36
B 4 300 7 1 29 18
C 5 300 5 8 35 25
D 5 300 13 2 17 13
E 6 300 9 14 63 52
F 6 300 12 9 24 22
G 8 300 6 4 22 8
H 8 300 9 3 38 15
I 1 400 3 0 9 2
J 10 400 9 0 32 20
K 10 400 4 2 18 11By unstacked data we mean that counts (cell frequencies) are in more than a single column.
In this case we have counts in columns four to seven. Using the dimensional variable in the first column we can run ProtectTable by:
The output element freq contains the data with computed totals.
region annet arbeid soshjelp trygd Total
A 11 11 55 36 113
B 7 1 29 18 55
C 5 8 35 25 73
D 13 2 17 13 45
E 9 14 63 52 138
F 12 9 24 22 67
G 6 4 22 8 40
H 9 3 38 15 65
I 3 0 9 2 14
J 9 0 32 20 61
K 4 2 18 11 35
Total 88 54 342 222 706In the output element sdcStatus the cells are coded as “u” (primary suppressed), “x” (secondary suppression), and “s” (can be published).
region annet arbeid soshjelp trygd Total
A s s s s s
B x u s s s
C s s s s s
D s u s x s
E s s s s s
F s s s s s
G s s s s s
H x u s s s
I u u s u s
J x u s s s
K x u s s s
Total s s s s sThe output element suppressed is the same as freq with the exception that suppressed cells (“u” and “x”) are set to missing (NA).
region annet arbeid soshjelp trygd Total
A 11 11 55 36 113
B NA NA 29 18 55
C 5 8 35 25 73
D 13 NA 17 NA 45
E 9 14 63 52 138
F 12 9 24 22 67
G 6 4 22 8 40
H NA NA 38 15 65
I NA NA 9 NA 14
J NA NA 32 20 61
K NA NA 18 11 35
Total 88 54 342 222 706Now we specify the variables using names instead of numbers. Furthermore we use the “HITAS” method. The default method is “SIMPLEHEURISTIC” and other possibilities are “OPT” and “HYPERCUBE”. The latter is not possible in cases with two linked tables.
ex2wHITAS <- ProtectTable(z2w, dimVar = c("region"), freqVar = c("annet", "arbeid",
"soshjelp", "trygd"), method = "HITAS") region annet arbeid soshjelp trygd Total
A 11 11 55 36 113
B NA NA 29 18 55
C 5 8 35 25 73
D NA NA 17 13 45
E 9 14 63 52 138
F 12 9 24 22 67
G 6 4 22 8 40
H NA NA 38 15 65
I NA NA 9 NA 14
J NA NA 32 20 61
K NA NA 18 NA 35
Total 88 54 342 222 706Here we include the tree first variables as dimensional variables. It will be detected automatically that “fylke” and “kostragr” are hierarchically related to “region” and that they are not hierarchically related to each other. Zeros will not be suppressed and we will only primarily suppress ones and twos.
Now the output data will contain sub-totals of the additional variables and the secondary suppression has taken those sub-totals into account. Since addName is TRUE, sub-totals are named using “fylke” and “kostragr”.
region annet arbeid soshjelp trygd Total
A NA 11 55 NA 113
B NA NA 29 18 55
C 5 NA 35 NA 73
D 13 NA 17 NA 45
E 9 14 63 52 138
F 12 9 24 22 67
G 6 NA 22 NA 40
H 9 NA 38 NA 65
I NA 0 9 NA 14
J NA 0 32 NA 61
K NA NA 18 NA 35
fylke_1 14 11 64 38 127
fylke_4 NA NA 29 18 55
fylke_5 18 10 52 38 118
fylke_6 21 23 87 74 205
fylke_8 15 7 60 23 105
fylke_10 NA NA 50 31 96
kostragr_300 NA NA 283 189 596
kostragr_400 NA NA 59 33 110
Total 88 54 342 222 706The output element info contains three parts.
Since we have unstacked data an extra variable, named var1, is created. How the categories of this variable are related to the variable names are described. Here these categories are simply the variable names. In more advanced cases it is possible that more than a single variable are created from the variable names.
Secondly, it is described how the tables(s) are created from the variables. In this case the problem is solved using two linked tables. The first table involves “fylke” and the second table involves “kostragr”.
The last part contains summary output for each of the two linked tables.
var1
annet annet
arbeid arbeid
soshjelp soshjelp
trygd trygd
==========
Variables Table1 Table2
1 region, fylke 1 0
2 region, kostragr 0 1
3 var1 1 1
==========
######################################################
### Summary of the result object of class 'safeObj' ###
######################################################
--> The input data have been protected using algorithm SIMPLEHEURISTIC.
--> The algorithm ran for 1 second.
--> To protect 8 primary sensitive cells, 13 cells need to be additionally suppressed.
--> A total of 64 cells may be published.
--> Duplicated cells: Only 85 table cells are unique, the remaining 5 cells are duplicates.
###################################
### Structure of protected Data ###
###################################
Classes 'data.table' and 'data.frame':\t90 obs. of 4 variables:
$ region : chr "Total" "Total" "Total" "Total" ...
$ var1 : chr "Total" "annet" "arbeid" "soshjelp" ...
$ Freq : num 706 88 54 342 222 127 14 11 64 38 ...
$ sdcStatus: chr "s" "s" "s" "s" ...
- attr(*, ".internal.selfref")=<externalptr>
NULL
==========
######################################################
### Summary of the result object of class 'safeObj' ###
######################################################
--> The input data have been protected using algorithm SIMPLEHEURISTIC.
--> The algorithm ran for 1 second.
--> To protect 13 primary sensitive cells, 10 cells need to be additionally suppressed.
--> A total of 47 cells may be published.
###################################
### Structure of protected Data ###
###################################
Classes 'data.table' and 'data.frame':\t70 obs. of 4 variables:
$ region : chr "Total" "Total" "Total" "Total" ...
$ var1 : chr "Total" "annet" "arbeid" "soshjelp" ...
$ Freq : num 706 88 54 342 222 596 72 52 283 189 ...
$ sdcStatus: chr "s" "s" "s" "s" ...
- attr(*, ".internal.selfref")=<externalptr>
- attr(*, "index")= int(0)
NULL Now we will use a stacked variant of the same data. A single column (“ant”) holds cell counts and the variable “hovedint” contains the four categories “annet”, “arbeid”, “soshjelp” and “trygd”.
region fylke kostragr hovedint ant
1 A 1 300 annet 11
2 B 4 300 annet 7
3 C 5 300 annet 5
4 D 5 300 annet 13
5 E 6 300 annet 9
6 F 6 300 annet 12
7 G 8 300 annet 6
8 H 8 300 annet 9
9 I 1 400 annet 3
10 J 10 400 annet 9
11 K 10 400 annet 4
12 A 1 300 arbeid 11
13 B 4 300 arbeid 1
14 C 5 300 arbeid 8
15 D 5 300 arbeid 2
16 E 6 300 arbeid 14
17 F 6 300 arbeid 9
18 G 8 300 arbeid 4
19 H 8 300 arbeid 3
20 I 1 400 arbeid 0
21 J 10 400 arbeid 0
22 K 10 400 arbeid 2
23 A 1 300 soshjelp 55
24 B 4 300 soshjelp 29
25 C 5 300 soshjelp 35
26 D 5 300 soshjelp 17
27 E 6 300 soshjelp 63
28 F 6 300 soshjelp 24
29 G 8 300 soshjelp 22
30 H 8 300 soshjelp 38
31 I 1 400 soshjelp 9
32 J 10 400 soshjelp 32
33 K 10 400 soshjelp 18
34 A 1 300 trygd 36
35 B 4 300 trygd 18
36 C 5 300 trygd 25
37 D 5 300 trygd 13
38 E 6 300 trygd 52
39 F 6 300 trygd 22
40 G 8 300 trygd 8
41 H 8 300 trygd 15
42 I 1 400 trygd 2
43 J 10 400 trygd 20
44 K 10 400 trygd 11We run ProtectTable with stacked data the same way as with unstacked data.
Instead of three output elements we now have the single element data:
region hovedint freq sdcStatus suppressed
1 A annet 11 s 11
2 B annet 7 x NA
3 C annet 5 s 5
4 D annet 13 s 13
5 E annet 9 s 9
6 F annet 12 s 12
7 G annet 6 s 6
8 H annet 9 x NA
9 I annet 3 u NA
10 J annet 9 x NA
11 K annet 4 x NA
12 A arbeid 11 s 11
13 B arbeid 1 u NA
14 C arbeid 8 s 8
15 D arbeid 2 u NA
16 E arbeid 14 s 14
17 F arbeid 9 s 9
18 G arbeid 4 x NA
19 H arbeid 3 u NA
20 I arbeid 0 u NA
21 J arbeid 0 u NA
22 K arbeid 2 u NA
23 A soshjelp 55 s 55
24 B soshjelp 29 s 29
25 C soshjelp 35 s 35
26 D soshjelp 17 s 17
27 E soshjelp 63 s 63
28 F soshjelp 24 s 24
29 G soshjelp 22 s 22
30 H soshjelp 38 s 38
31 I soshjelp 9 s 9
32 J soshjelp 32 s 32
33 K soshjelp 18 s 18
34 A trygd 36 s 36
35 B trygd 18 s 18
36 C trygd 25 s 25
37 D trygd 13 x NA
38 E trygd 52 s 52
39 F trygd 22 s 22
40 G trygd 8 x NA
41 H trygd 15 s 15
42 I trygd 2 u NA
43 J trygd 20 s 20
44 K trygd 11 x NA
45 A Total 113 s 113
46 B Total 55 s 55
47 C Total 73 s 73
48 D Total 45 s 45
49 E Total 138 s 138
50 F Total 67 s 67
51 G Total 40 s 40
52 H Total 65 s 65
53 I Total 14 s 14
54 J Total 61 s 61
55 K Total 35 s 35
56 300 annet 72 x NA
57 300 arbeid 52 x NA
58 300 soshjelp 283 s 283
59 300 trygd 189 s 189
60 300 Total 596 s 596
61 400 annet 16 x NA
62 400 arbeid 2 u NA
63 400 soshjelp 59 s 59
64 400 trygd 33 s 33
65 400 Total 110 s 110
66 Total annet 88 s 88
67 Total arbeid 54 s 54
68 Total soshjelp 342 s 342
69 Total trygd 222 s 222
70 Total Total 706 s 706Unlike above addName is FALSE (default) and therefore the sub-totals “300” and “400” are written without “kostragr”.
Below no columns holds cell counts (no freqVar input) and therefore it is assumed that each cell count is one. For this data set this is not realistic, but in other cases rows are replicated.
region hovedint freq sdcStatus suppressed
1 A annet 1 u NA
2 B annet 1 u NA
3 C annet 1 u NA
4 D annet 1 u NA
5 E annet 1 u NA
6 F annet 1 u NA
7 G annet 1 u NA
8 H annet 1 u NA
9 I annet 1 u NA
10 J annet 1 u NA
11 K annet 1 u NA
12 A arbeid 1 u NA
13 B arbeid 1 u NA
14 C arbeid 1 u NA
15 D arbeid 1 u NA
16 E arbeid 1 u NA
17 F arbeid 1 u NA
18 G arbeid 1 u NA
19 H arbeid 1 u NA
20 I arbeid 1 u NA
21 J arbeid 1 u NA
22 K arbeid 1 u NA
23 A soshjelp 1 u NA
24 B soshjelp 1 u NA
25 C soshjelp 1 u NA
26 D soshjelp 1 u NA
27 E soshjelp 1 u NA
28 F soshjelp 1 u NA
29 G soshjelp 1 u NA
30 H soshjelp 1 u NA
31 I soshjelp 1 u NA
32 J soshjelp 1 u NA
33 K soshjelp 1 u NA
34 A trygd 1 u NA
35 B trygd 1 u NA
36 C trygd 1 u NA
37 D trygd 1 u NA
38 E trygd 1 u NA
39 F trygd 1 u NA
40 G trygd 1 u NA
41 H trygd 1 u NA
42 I trygd 1 u NA
43 J trygd 1 u NA
44 K trygd 1 u NA
45 A Total 4 s 4
46 B Total 4 s 4
47 C Total 4 s 4
48 D Total 4 s 4
49 E Total 4 s 4
50 F Total 4 s 4
51 G Total 4 s 4
52 H Total 4 s 4
53 I Total 4 s 4
54 J Total 4 s 4
55 K Total 4 s 4
56 300 annet 8 x NA
57 300 arbeid 8 x NA
58 300 soshjelp 8 x NA
59 300 trygd 8 x NA
60 300 Total 32 s 32
61 400 annet 3 u NA
62 400 arbeid 3 u NA
63 400 soshjelp 3 u NA
64 400 trygd 3 u NA
65 400 Total 12 s 12
66 Total annet 11 s 11
67 Total arbeid 11 s 11
68 Total soshjelp 11 s 11
69 Total trygd 11 s 11
70 Total Total 44 s 44.