modulartabler
modulartabler categorises records in a dataset and presents counts in each category in tabular format. Each table of counts is described by a mapping table, which can convert raw data to tabular format. Mapping tables can correspond to a single dimension, or be combined to form a multi-dimensional table.
When presenting tables, sometimes cells with small counts must be suppressed, and other cells might need to be suppressed to prevent back-calculation. Mapping tables in modulartabler record the relationships within its table, and the package implements an algorithm to solve the cell suppression problem.
Installation
You can install the development version of modulartabler like so:
remotes::install_github('inpowell/modulartabler')Example
In this example, we will use modulartabler to examine efficiency of
vehicles in the mtcars dataset.
library(modulartabler)
data("mtcars")First, we want to classify efficiency using the mpg column. To do
this, we construct a RangeMappingTable that lists the efficiency in
miles per galleon. We will categorise these as less than 20 mpg
(\<20.0), at least 20 but less than 25 mpg (20.0-24.9), and at least 25
mpg (25.0+). We will also add a subtotal for cars with at least 20 mpg
(20.0+).
MPG <- RangeMappingTable$new(
'Miles per galleon', 'mpg',
"<20.0" = c(0, 20),
"20.0-24.9" = c(20, 25),
"25.0+" = c(25, Inf),
"20.0+" = c(20, Inf),
.other = "Unknown",
.total = 'Total'
) Now we can use this mapping table to count the number of cars by their mileage.
MPG$count_aggregate(mtcars)
#> # A tibble: 6 × 2
#> `Miles per galleon` n
#> <fct> <int>
#> 1 <20.0 18
#> 2 20.0-24.9 8
#> 3 25.0+ 6
#> 4 20.0+ 14
#> 5 Unknown 0
#> 6 Total 32We may also be interested in whether cars in this dataset are more
efficient depending on whether the are automatic or manual. We can
construct a mapping table for transmission using BaseMappingTable.
am_map <- data.frame(
Transmission = factor(c('Automatic', 'Manual', rep('Total', 2L))),
am = c( 0, 1, 0, 1 )
)
Transmission <- BaseMappingTable$new(
am_map, raw_cols = 'am', table_cols = 'Transmission'
)Using this mapping table, we can see that a bit over half the cars in our data are automatic.
Transmission$count_aggregate(mtcars)
#> # A tibble: 3 × 2
#> Transmission n
#> <fct> <int>
#> 1 Automatic 19
#> 2 Manual 13
#> 3 Total 32However, to answer our question about whether efficiency is affected by
mileage, we need the cross-tabulation of these two dimensions. We can
create a mapping table to get this cross-tabulation using a
MultiMappingTable.
CrossMpgTransmission <- MultiMappingTable$new(Transmission, MPG)Then, we are able to calculate counts in each category.
crosstab <- CrossMpgTransmission$count_aggregate(mtcars)
crosstab
#> # A tibble: 18 × 3
#> Transmission `Miles per galleon` n
#> <fct> <fct> <int>
#> 1 Automatic <20.0 15
#> 2 Automatic 20.0-24.9 4
#> 3 Automatic 25.0+ 0
#> 4 Automatic 20.0+ 4
#> 5 Automatic Unknown 0
#> 6 Automatic Total 19
#> 7 Manual <20.0 3
#> 8 Manual 20.0-24.9 4
#> 9 Manual 25.0+ 6
#> 10 Manual 20.0+ 10
#> 11 Manual Unknown 0
#> 12 Manual Total 13
#> 13 Total <20.0 18
#> 14 Total 20.0-24.9 8
#> 15 Total 25.0+ 6
#> 16 Total 20.0+ 14
#> 17 Total Unknown 0
#> 18 Total Total 32While this table has all the information we need, it would be more
appropriate to have it in wide format for readablity. We can do this
using convert_tabular().
convert_tabular(crosstab, `Miles per galleon` ~ Transmission)
#> # A tibble: 6 × 4
#> `Miles per galleon` Automatic Manual Total
#> <fct> <int> <int> <int>
#> 1 <20.0 15 3 18
#> 2 20.0-24.9 4 4 8
#> 3 25.0+ 0 6 6
#> 4 20.0+ 4 10 14
#> 5 Unknown 0 0 0
#> 6 Total 19 13 32This shows all the information we need, including that manual cars tend to have better mileage.
If this table referred to people, then it might be prudent to suppress small cells. In particular, we might want to suppress the cell with count 3.
crosstab$suppress <- 0L < crosstab$n & crosstab$n <= 3L
crosstab$n.primary <- crosstab$n
crosstab$n.primary[crosstab$suppress] <- NAWhen we look at this table though, it is clear we can easily recover the value of the suppressed cell. Indeed, the cell’s row total is 18, and $18 - 15 = 3$. Also, its column total is 13, and the complementary cell for 20+ mpg is 10, and $13 - 10 = 3$ as well.
convert_tabular(
crosstab,
`Miles per galleon` ~ Transmission,
measures = 'n.primary'
)
#> # A tibble: 6 × 4
#> `Miles per galleon` Automatic Manual Total
#> <fct> <int> <int> <int>
#> 1 <20.0 15 NA 18
#> 2 20.0-24.9 4 4 8
#> 3 25.0+ 0 6 6
#> 4 20.0+ 4 10 14
#> 5 Unknown 0 0 0
#> 6 Total 19 13 32So, we need to conduct secondary suppression. The relationships of a
table are available from its MappingTable object in the nullspace
binding.
str(CrossMpgTransmission$nullspace)
#> num [1:10, 1:18] 1 0 0 0 1 0 0 0 0 0 ...Each row of this matrix describes a different relationship within the
table. We can use this information to conduct secondary suppression
using the suppress_secondary() function.
crosstab$suppress2ary <- suppress_secondary(
N = crosstab$n,
suppress = crosstab$suppress,
# NB crosstab must have same order as CrossMpgTransmission$mtab
nullspace = CrossMpgTransmission$nullspace
)When we look at the table following this suppression, there is no way to back-calculate the primary-suppressed cell exactly.
crosstab$n.secondary <- crosstab$n
crosstab$n.secondary[crosstab$suppress2ary] <- NA
convert_tabular(
crosstab,
`Miles per galleon` ~ Transmission,
measures = 'n.secondary'
)
#> # A tibble: 6 × 4
#> `Miles per galleon` Automatic Manual Total
#> <fct> <int> <int> <int>
#> 1 <20.0 NA NA 18
#> 2 20.0-24.9 NA NA 8
#> 3 25.0+ 0 6 6
#> 4 20.0+ NA NA 14
#> 5 Unknown 0 0 0
#> 6 Total 19 13 32Unfortunately, we have also lost a lot of information in our table. This highlights the need for care in designing tables so that they have sufficient data and small cells are rare.
Thanks
The authors would like to acknowledge the NSW Ministry of Health, which supported the initial development of this package while the authors (Ian Powell, Hafiz Khusyairi, and Peter Moritz) were employed there.