Coo: Coo "super" class

Description

Coo class is the 'parent' or 'super' class of Out, Opn and Ldk classes.

Usage

Coo(...)

Value

a list of class Coo

Arguments

...: anything and, anyway, this function will simply returns a message.

Details

Useful shortcuts are described below. See browseVignettes("Momocs") for a detail of the design behind Momocs' classes.

Coo class is the 'parent' class of the following 'child' classes

Out for closed outlines
Opn for open outlines
Ldk for configuration of landmarks

Since all 'child classes' of them handle $(x; y)$ coordinates among other generic methods, but also all have their specificity, this architecture allow to recycle generic methods and to use specific methods.

In other words, Out, Opn and Ldk classes are all, primarily, Coo objects on which we define generic and specific methods. See their respective help pages for more help.

Coo objects all have the following components:

$coo which is a list of matrices for coordinates
$fac a data_frame for covariates (if any). You can provide this data_frame directly, as long as it has as many rows as there are matrices in $coo (see examples), or use an helper function such as lf_structure.

You can access all the methods available for Coo objects with methods(class=Coo).

Examples

Run this code

# to see all methods for Coo objects.
methods(class='Coo')

# to see all methods for Out objects.
methods(class='Out') # same for Opn and Ldk

# Let's take an Out example. But all methods shown here
# work on Ldk (try on 'wings') and on Opn ('olea')
bot

# Primarily a 'Coo' object, but also an 'Out'
class(bot)
inherits(bot, "Coo")
panel(bot)
stack(bot)

# Getters (you can also use it to set data)
bot[1] %>% coo_plot()
bot[1:5] %>% str()

# Setters
bot[1] <- shapes[4]
panel(bot)

bot[1:5] <- shapes[4:8]
panel(bot)

# access the different components
# $coo coordinates
head(bot$coo)
# $fac grouping factors
head(bot$fac)
# or if you know the name of the column of interest
bot$type
# table
table(bot$fac)
# an internal view of an Out object
str(bot)

# subsetting
# see ?filter, ?select, and their 'see also' section for the
# complete list of dplyr-like verbs implemented in Momocs

length(bot) # the number of shapes
names(bot) # access all individual names
bot2 <- bot
names(bot2) <- paste0('newnames', 1:length(bot2)) # define new names

# Add a $fac from scratch
coo <- bot[1:5] # a list of five matrices
length(coo)
sapply(coo, class)

fac <- data.frame(name=letters[1:5], value=c(5:1))
# Then you have to define the subclass using the right builder
# here we have outlines, so we use Out
x <- Out(coo, fac)
x$coo
x$fac