A matrixset is a container of matrices, each having the same number of
rows and columns and the same dimnames. Moreover, each dimname must
uniquely identify elements.
While there is minimal support for NULL dimnames (and that is bound to
change at some point in the future), it is strongly recommended to
provide meaningful dimnames. One of the main reason for this is that
annotation is impossible with NULL dimnames.
In addition, as alluded above, a matrixset can store independent row
and column annotations. This meta information is stored, and available,
in the form of data frames - one for row information and one for column.
The annotation names are referred to as traits.
This latter feature makes matrixset especially attractive even if it
stores only a single matrix, because several methods have been
developped to manipulate matrixsets, accounting for annotations.
Many problems that matrixset can tackle could be solved via a
data.frame and more specifically using the tidyverse suite.
Two reasons for which you may want to use a matrixset instead are:
- object size. The
data.frameneeded to store the same information as amatrixsetcan be significantly bigger - You actually need a matrix format, for example for running a PCA.
The easiest way to install matrixset is from CRAN:
install.packages("matrixset")Or you can install the development version of matrixset from GitHub with:
# install.packages("devtools")
devtools::install_github("pascalcroteau/matrixset")In addition to store multiple matrices that share the same attributes, a
matrixset object’s strength is it’s annotation feature. You create an
object from existing matrices and annotation data.frame.
library(MASS)
library(tidyverse)
animals <- as.matrix(Animals)
head(animals)
#> body brain
#> Mountain beaver 1.35 8.1
#> Cow 465.00 423.0
#> Grey wolf 36.33 119.5
#> Goat 27.66 115.0
#> Guinea pig 1.04 5.5
#> Dipliodocus 11700.00 50.0
animal_info <- MASS::Animals %>%
rownames_to_column("Animal") %>%
mutate(is_extinct = case_when(Animal %in% c("Dipliodocus", "Triceratops", "Brachiosaurus") ~ TRUE,
TRUE ~ FALSE),
class = case_when(Animal %in% c("Mountain beaver", "Guinea pig", "Golden hamster", "Mouse", "Rabbit", "Rat") ~ "Rodent",
Animal %in% c("Potar monkey", "Gorilla", "Human", "Rhesus monkey", "Chimpanzee") ~ "Primate",
Animal %in% c("Cow", "Goat", "Giraffe", "Sheep") ~ "Ruminant",
Animal %in% c("Asian elephant", "African elephant") ~ "Elephantidae",
Animal %in% c("Grey wolf") ~ "Canine",
Animal %in% c("Cat", "Jaguar") ~ "Feline",
Animal %in% c("Donkey", "Horse") ~ "Equidae",
Animal == "Pig" ~ "Sus",
Animal == "Mole" ~ "Talpidae",
Animal == "Kangaroo" ~ "Macropodidae",
TRUE ~ "Dinosaurs")) %>%
select(-body, -brain)
animal_info
#> Animal is_extinct class
#> 1 Mountain beaver FALSE Rodent
#> 2 Cow FALSE Ruminant
#> 3 Grey wolf FALSE Canine
#> 4 Goat FALSE Ruminant
#> 5 Guinea pig FALSE Rodent
#> 6 Dipliodocus TRUE Dinosaurs
#> 7 Asian elephant FALSE Elephantidae
#> 8 Donkey FALSE Equidae
#> 9 Horse FALSE Equidae
#> 10 Potar monkey FALSE Primate
#> 11 Cat FALSE Feline
#> 12 Giraffe FALSE Ruminant
#> 13 Gorilla FALSE Primate
#> 14 Human FALSE Primate
#> 15 African elephant FALSE Elephantidae
#> 16 Triceratops TRUE Dinosaurs
#> 17 Rhesus monkey FALSE Primate
#> 18 Kangaroo FALSE Macropodidae
#> 19 Golden hamster FALSE Rodent
#> 20 Mouse FALSE Rodent
#> 21 Rabbit FALSE Rodent
#> 22 Sheep FALSE Ruminant
#> 23 Jaguar FALSE Feline
#> 24 Chimpanzee FALSE Primate
#> 25 Rat FALSE Rodent
#> 26 Brachiosaurus TRUE Dinosaurs
#> 27 Mole FALSE Talpidae
#> 28 Pig FALSE SusYou can create the object and then do some operations.
library(matrixset)
animals_ms <- matrixset(msr = animals, row_info = animal_info, row_key = "Animal")
animals_ms %>%
apply_row_dfl(rg = range(.i),
qt = quantile(.i, probs = c(.25, .75)))
#> $msr
#> # A tibble: 56 × 5
#> .rowname rg.name rg qt.name qt
#> <chr> <chr> <dbl> <chr> <dbl>
#> 1 Mountain beaver ..1 1.35 25% 3.04
#> 2 Mountain beaver ..2 8.1 75% 6.41
#> 3 Cow ..1 423 25% 434.
#> 4 Cow ..2 465 75% 454.
#> 5 Grey wolf ..1 36.3 25% 57.1
#> 6 Grey wolf ..2 120. 75% 98.7
#> 7 Goat ..1 27.7 25% 49.5
#> 8 Goat ..2 115 75% 93.2
#> 9 Guinea pig ..1 1.04 25% 2.16
#> 10 Guinea pig ..2 5.5 75% 4.38
#> # ℹ 46 more rows
animals_ms %>%
row_group_by(class) %>%
apply_column_dfl(avr = mean)
#> $msr
#> # A tibble: 22 × 3
#> class .colname avr
#> <chr> <chr> <dbl>
#> 1 Canine body 36.3
#> 2 Canine brain 120.
#> 3 Dinosaurs body 36033.
#> 4 Dinosaurs brain 91.5
#> 5 Elephantidae body 4600.
#> 6 Elephantidae brain 5158.
#> 7 Equidae body 354.
#> 8 Equidae brain 537
#> 9 Feline body 51.6
#> 10 Feline brain 91.3
#> # ℹ 12 more rows
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