In statisticsnorway/SmallCountRounding: Small Count Rounding of Tabular Data

---

title: "Introduction to 'SmallCountRounding'" author: "Øyvind Langsrud and Johan Heldal" date: "r Sys.Date()" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Introduction to SmallCountRounding} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} keywords: statistical disclosure control, statistical confidentiality, rounding, official statistics

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library(knitr) 
library(kableExtra)
library(SmallCountRounding)

cell_background = function(x,row,col,background){
  backGround = rep("white", 100)
  backGround[row] = background
  suppressWarnings(column_spec(x,col, background = backGround))
}
yellow = "#FFFF88"
green = "#F0FFE9"
green2 = "#88FF88"
z <- SmallCountData("exPSD")
a <- PLSrounding(z, "freq", 5)
k <- PLS2way(a, "original") 
ka <- PLS2way(a)
b <- PLSrounding(z, "freq", 5, formula = ~rows + cols)
kb <- PLS2way(b)


e6  <-  SmallCountData("e6")

eDimList <- SmallCountData("eDimList")

e6a <-  PLSrounding(e6, "freq", 5)
e6b <-  PLSrounding(e6, "freq", 5, formula = ~eu * year + geo * year)
e6c <-  PLSrounding(e6[, -2], "freq", 5, hierarchies = eDimList)
e6d <-  PLSrounding(e6[, -2], "freq", 5, hierarchies = eDimList, formula = ~geo * year)




options(knitr.kable.NA = '')

Introductory example

Example without code

First some data to be rounded.

kable(k, "html", caption = "**Table 1**: Original data in tabular form with row and column totals") %>%
  kable_styling(full_width = F, bootstrap_options = c("bordered"),font_size = 16,  position = "left")  %>%
  add_indent(1:4,1.6,all_cols =TRUE) %>%
  column_spec(1, bold = T,background = green)  %>%
  #cell_background(2,2,"#FFFF33") %>%
  #cell_background(2,5,"orange") %>%
  #column_spec(3, background = c("#FFFFFF","#FFFFFF","#FF1111","white"))  %>%
  column_spec(7, bold = T)  %>%
  row_spec(0, bold = T,background = green)  %>%
  row_spec(4, bold = T) 

Some of the inner cells are rounded. Thereafter new totals are computed. The underlying algorithm tries to keep the values of these totals close to the original ones.

kable(ka, "html", caption = '**Table 2**: All small inner cell values (1-4) are rounded using 5 as rounding base.') %>%
  kable_styling(full_width = F, bootstrap_options = c("bordered"),font_size = 16,  position = "left")  %>%
  add_indent(1:4,1.5,all_cols =TRUE) %>%
  column_spec(1, bold = T,background = green)  %>%
  cell_background(2,2,yellow) %>%
  cell_background(2:3,3,yellow) %>%
  cell_background(1:3,4,yellow) %>%
  cell_background(1:2,5,yellow) %>%
  cell_background(1:3,6,yellow) %>%
  #cell_background(2,5,"orange") %>%
  #column_spec(3, background = c("#FFFFFF","#FFFFFF","#FF1111","white"))  %>%
  column_spec(7, bold = T)  %>%
  row_spec(0, bold = T,background = green)  %>%
  row_spec(4, bold = T) 

When the inner cells are not going to be published, the number of cells to be rounded can be limited.

kable(kb, "html", caption = '**Table 3**: Assuming only row and column totals to be published, necessary small inner cell values (1-4) are rounded using 5 as rounding base.') %>%
  kable_styling(full_width = F, bootstrap_options = c("bordered"),font_size = 16,  position = "left")  %>%
  add_indent(1:4,1.5,all_cols =TRUE) %>%
  column_spec(1, bold = T,background = green)  %>%
  cell_background(2:3,3,yellow) %>%
  cell_background(1:3,4,yellow) %>%
  cell_background(1:2,5,yellow) %>%
  cell_background(3,6,yellow) %>%
  #cell_background(2,5,"orange") %>%
  #column_spec(3, background = c("#FFFFFF","#FFFFFF","#FF1111","white"))  %>%
  column_spec(7, bold = T)  %>%
  row_spec(0, bold = T,background = green)  %>%
  row_spec(4, bold = T) 

---

The example dataset in Table 1

library(SmallCountRounding)
z <- SmallCountData("exPSD")
z

Rounding all small cells (Table 2)

To avoid any small values in the range 1-4 we can use 5 as rounding base.

a <- PLSrounding(z, freqVar = "freq", roundBase = 5)

The result is given in Table 2 and can bee seen in the output elements below.

a$inner

a$publish

The output element publish contains the original and rounded versions of the all the 24 values in Table 2. The corresponding element inner contains only the 15 inner cells and is similar to the input data. The values in publish are additive. That is, marginal cells (Totals) can be computed straightforwardly from inner for both original and rounded counts.

Rounding necessary small inner cell (Table 3)

Assuming only row and column totals to be published, the publishable cells can be defined by the formula ~rows+cols. Rounding can now be performed by:

b <- PLSrounding(z, "freq", 5, formula = ~rows + cols)

The result is given in Table 3 and can bee seen in the output elements below.

b$inner

b$publish

Unique output obtained by local random generator seed

The underlying algorithm is sequential. Within a loop, the next cell to be given the rounding base value is selected according to a criterion. Random draw is used when draw criterion. To ensure unique output, a fixed random generator seed is used locally within the function without affecting the random value stream in R. See the documentation of rndSeed, a parameter to RoundViaDummy.

The output object

The result of printing the output from PLSrounding is (a and b as above):

a
b

First some utility measures are printet. For example maxdiff is the maximum difference between an original and rounded cells within publish. Thereafter a table of frequencies of cell frequencies and absolute differences are printed. Summary of inner and publish are shown in the left and right parts of the table, respectively. For example, row rounded and column inn.6+ is the number of rounded inner cell frequencies greater than or equal to 6. The last row (absDiff) is based on the differences without signs.

It is possible to compute manually the printed utility measures by:

f <- b$publish$original
g <- b$publish$rounded
print(c(
  maxdiff        =  max(abs(g - f)), 
  HDutility      =  HDutility(f, g), 
  meanAbsDiff    =  mean(abs(g - f)), 
  rootMeanSquare =  sqrt(mean((g - f)^2))
))

These measures are also found in the output element metrics together with the same measures based on inner. See ?HDutility for more information about the utility measure based on the Hellinger distance.

Apart from printing, output is a usual list and summary works as usual.

summary(b)

The output element freqTable is the table seen when the output object is printed (frequencies of cell frequencies and absolute differences).

Hierarchical data

Example without code

Below is a small data set to be used as input.

kable(e6, "html", caption = "**Table 4**: Input data") %>%
  kable_styling(full_width = F, bootstrap_options = c("bordered"),font_size = 14,  position = "left") %>%
  add_indent(1:6,0.4,all_cols =TRUE)

The variables geo and eu is hierarchical related. This data set can be processed in several ways. In some cases, the entire table will be input and in other cases the eu column can be omitted. Then, the hierarchical information is sent as input in another way. One possibility is the table below, where the hierarchy is coded as in the r package sdcTable.

kable(SmallCountData("eDimList")$geo, "html", caption = "**Table 5**:  Hierarchy, `geo`") %>%
  kable_styling(full_width = F, bootstrap_options = c("bordered"),font_size = 14,  position = "left")  %>%
  add_indent(1:6,1.2,all_cols =TRUE)

Another possibility is TauArgus coding. More general coding is also possible. See ?AutoHierarchies for more information.

Below is output in the case were all possible combinations (including the inner cells) are to be published. Also in this example we use 5 as a rounding base. As can be seen below, this output can be generated in several ways. The inner cells are colored according to the rounding.

kable(e6a$publish, "html", caption = "**Table 6**: Ouput data (publish)") %>%
  kable_styling(full_width = F, bootstrap_options = c("bordered"),font_size = 14,  position = "left") %>%
  column_spec(1:2, background = green) %>%
  cell_background( Match(e6a$inner,e6a$publish),4,c(yellow,green2)[1+(e6a$inner$difference==0)]) 
#cell_background( Match(e6a$inner[e6a$inner$difference!=0 , ],e6a$publish),4,yellow) %>%
#cell_background( Match(e6a$inner[e6a$inner$difference==0 , ],e6a$publish),4,green2) 

Data (Table 4) and hierarchies (Table 5)

e6  <-  SmallCountData("e6")             # As Table 4 
eDimList <- SmallCountData("eDimList")
eDimList

As seen above, a hierarchy is specified for both variables. eDimList$geo is given in Table 5 and eDimList$year is a plain hierarchy with total code.

Five ways to produce Table 6

The five lines below produce the same results with element publish as in Table 6. Ordering of rows can be different.

PLSrounding(e6, "freq", 5)                                                      # a) 
PLSrounding(e6, "freq", 5, dimVar = c("geo", "eu", "year"))                     # b) 
PLSrounding(e6, "freq", 5, formula = ~eu * year + geo * year)                   # c)
PLSrounding(e6[, -2], "freq", 5, hierarchies = eDimList)                        # d)
PLSrounding(e6[, -2], "freq", 5, hierarchies = eDimList, formula = ~geo * year) # e)

• In a), b) and d), the function uses hierarchies for the calculations. In a) and b) the hierarchies are found automatically from the input data. In a), dimVar is assumed to be all variables except freq.
• In c) the cross-classifications are found from the formula. In addition, hierarchical relations in the input data are analysed so that geo and eu are combined into the same output column.
• In e), how to cross the hierarchies are defined by a formula.

Remarks and other parameters

A difference occur when all combinations are not contained in input data. Then c) above will limit output to combinations available in input. In the other cases zeroes will be added. The extra zeroes can be avoided by using removeEmpty=TRUE. Note also the parameter inputInOutput which can be used to specify whether to include codes from input. Below is an example with incomplete input data using both these parameters.

out <- PLSrounding(e6[-1, ], "freq", 5, removeEmpty = TRUE, inputInOutput = c(FALSE,TRUE))
out
out$inner
out$publish

In this case only a single inner cell needed to be rounded (Iceland, 2019). The original small value of (Portugal, 2018) could be retained.

statisticsnorway/SmallCountRounding documentation built on July 8, 2023, 7:24 p.m.