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There are three main classes in the rimu
package: two implementations of multiple-response objects and one of multiple-score objects. Multiple-response objects generalise factors. Like factors, they have a set of permitted levels; unlike factors, it's possible for zero or more than one of the levels to be observed. Multiple responses have the same relationship to check boxes that factors have to radio buttons.
The main class represents multiple-response objects internally by a logical matrix. Each row has a logical vector, with an entry corresponding to each level of the factor: TRUE
if that level was observed, FALSE
if it was not (and NA
if we don't know). The objects behave as a single S3 vector in many ways: they can be a single data frame column, the length
function returns the number of rows, they print as a single character vector.
library(rimu)
data(ethnicity)
ethnicity
## [1] "European" "European+Maori" "Maori"
## [4] "Maori+Pacific" "?European+?Maori" "European+?Maori+Asian"
length(ethnicity)
## [1] 6
unclass(ethnicity)
## European Maori Pacific Asian MELAA
## [1,] TRUE FALSE FALSE FALSE FALSE
## [2,] TRUE TRUE FALSE FALSE FALSE
## [3,] FALSE TRUE FALSE FALSE FALSE
## [4,] FALSE TRUE TRUE FALSE FALSE
## [5,] NA NA FALSE FALSE FALSE
## [6,] TRUE NA FALSE TRUE FALSE
data.frame(ethnicity)
## ethnicity
## 1 European
## 2 European+Maori
## 3 Maori
## 4 Maori+Pacific
## 5
## 6 European+Asian
The class also creates a new S3 vector type, but does it using the package from the tidyverse. This is necessary (or at least the easiest way) to include multiple-response objects in tidyverse tbl_df
objects (tibbles). The class requires the package to work, and is only really useful if you have the and packages as well (as even a fairly minimal tidyverse installation will do). Internally, a object is a list of character vectors, plus an attribute specifying the permitted levels. It's built on the vctrs_list_of
class. Unfortunately, this representation does not allow for "don't know" membership.
eth<-as.vmr(ethnicity, na.rm=TRUE)
eth
## <vmultiresp[6]>
## [1] European European+Maori Maori Maori+Pacific
## [6] Asian+European
length(eth)
## [1] 6
unclass(eth)
## [[1]]
## [1] "European"
##
## [[2]]
## [1] "European" "Maori"
##
## [[3]]
## [1] "Maori"
##
## [[4]]
## [1] "Maori" "Pacific"
##
## [[5]]
## character(0)
##
## [[6]]
## [1] "European" "Asian"
##
## attr(,"levs")
## [1] "European" "Maori" "Pacific" "Asian" "MELAA"
## attr(,"ptype")
## character(0)
All the same functions are available for and objects, though most of the methods work by converting to and back again.
Finally, objects have a non-zero numeric score for each object. This might be a rank (list the first three birds you saw) or a monetary amount (how much did you spend on...). Internally, they are represented as numeric matrices.
data(nzbirds)
nzbirds
## kea ruru tui tauhou kaki
## [1,] 1 . 2 . .
## [2,] 1 2 . . 3
## [3,] . 1 . . .
## [4,] . 2 1 . .
## [5,] 2 3 1 . 4
## [6,] 2 <NA> . 1 .
d<-data.frame(nzbirds)
dim(d)
## [1] 6 1
d
## nzbirds
## 1 kea+tui
## 2 kea+ruru+kaki
## 3 ruru
## 4 ruru+tui
## 5 kea+ruru+tui+kaki
## 6 kea+tauhou
as.mr(nzbirds)
## [1] "kea+tui" "kea+ruru+kaki" "ruru"
## [4] "ruru+tui" "kea+ruru+tui+kaki" "kea+?ruru+tauhou"
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.