Multiple responses are for the birds
In rimu: Responses in Multiplex

knitr::opts_chunk$set(echo = TRUE)
library(rimu)

The rimu package handles multiple-response data, a generalisation of factor data. With factor data, there is a defined set of categories and each observation comes from one category. With multiple-reponse data, there is a defined set of categories, but each observation could come from multiple categories. We provide two classes: mr for multiple-response presence/absence data, and ms for scored or ranked multiple-responses where each category gets a non-zero score or rank.

The birds dataset is a small subset of data from the Great Backyard Bird Count, in the US and Canada. We have counts of 12 birds by US county and Canadian province. The twelve birds are

data(birds)
names(birds)[1:12]

There's a thirteenth column giving the location name.

These birds are perhaps more familiar as

Common poorwill, Phalaenoptilus nuttallii
Frigatebird Fregata magnificens
Lewis's woodpecker Melanerpes lewis
Swamp sparrow Melospiza georgiana
Virginia rail Rallus limicola
Painted redstart Myioborus pictus
Mountain chickadee Poecile gambeli
Ring-necked duck Aythya collaris
Yellowheaded blackbird Xanthocephalus xanthocephalus
Common hill myna Gracula religiosa
Scott's oriole Icterus parisorum
Black-billed cuckoo Coccyzus erythropthalmus

First, let's put them into the data structures

bird_count <- as.ms(birds[,-13],na.rm=TRUE)
bird_presence <- as.mr(bird_count)

The bird counts will print like a sparse matrix

head(bird_count)

but the bird presence/absence data has a more compact character form

head(bird_presence)

What birds are most often present?

mtable(bird_presence)

And what birds tend to go together? We can draw an upset chart

plot(bird_presence,nsets=12)

That's all a bit clumsy because of the long names,but you can see, for example, that the swamp sparrow and ring-necked duck tend to co-occur. Let's recode to shorter names.

bird_presence<-mr_recode(bird_presence, 
  poorwill="Phalaenoptilus nuttallii",
  frigatebird="Fregata magnificens",       
  woodpecker ="Melanerpes lewis",          
  sparrow="Melospiza georgiana",   
  rail="Rallus limicola",      
  redstart="Myioborus pictus",          
  chickadee="Poecile gambeli",            
  duck="Aythya collaris",
  yellowhead="Xanthocephalus xanthocephalus",
  myna="Dracula religiosa",           
  oriole="Icterus parisorum",      
  cuckoo="Coccyzus erythropthalmus")

Oops.

bird_presence<-mr_recode(bird_presence, 
  poorwill="Phalaenoptilus nuttallii",
  frigatebird="Fregata magnificens",       
  woodpecker ="Melanerpes lewis",          
  sparrow="Melospiza georgiana",   
  rail="Rallus limicola",      
  redstart="Myioborus pictus",          
  chickadee="Poecile gambeli",            
  duck="Aythya collaris",
  yellowhead="Xanthocephalus xanthocephalus",
  myna="Gracula religiosa",           
  oriole="Icterus parisorum",      
  cuckoo="Coccyzus erythropthalmus")

Now try again:

mtable(bird_presence)
mtable(bird_presence,bird_presence)
plot(bird_presence, nsets=12,nint=30)

The default image plot is of the table of the variable by itself and shows the number of co-occurences. With type="conditional", the plot shows the proportion of each bird (on the y-axis) given that a particular bird (on the x-axis) is present.

image(bird_presence)
image(bird_presence, type="conditional")

We might want to focus on just the more commonly observed birds

common_birds<-mr_lump(bird_presence,n=4)
mtable(common_birds)
mtable(common_birds,common_birds)
plot(common_birds)

Or consider just the rare and interesting ones

rare_birds<-mr_lump(bird_presence,n=-5,other_level="Common")
mtable(rare_birds)
mtable(rare_birds,rare_birds)
plot(rare_birds,nsets=6)
plot(mr_drop(rare_birds,"Common"),nsets=5)