In case a textual argument allows for selecting the result, for
instance, if plot() allows for several charts that you can choose with a
type= or which=, making the function 'subsettable' also allows to
indicate fun$variant(). The subsettable_type2 variant is faster for only
internal implementation of various types, while subsettable_type first
searches for a function with name.<generic>$type(). See examples.
# S3 method for class 'subsettable_type'
x$name
# S3 method for class 'subsettable_type2'
x$name
# S3 method for class 'subsettable_which'
x$name
name_function_type(fun, method = NULL, type)
list_types(fun, method = NULL)
get_type(fun, method = NULL, type, stop.if.missing = TRUE)
args_type(fun, method = NULL, type)
# S3 method for class 'subsettable_type'
.DollarNames(x, pattern = "")Arguments
- x
A
subsettable_typefunction.- name
The value to use for the
type=argument.- fun
The name of the function (as a scalar string).
- method
An optional method name (as a scalar string).
- type
The type to select (as a scalar string).
- stop.if.missing
If
TRUE(default), an error is raised if the requested type is not found. IfFALSE,NULLis returned instead.- pattern
A regular expression. Only matching names are returned.
Examples
# Simple selection of type with a switch inside the function itself
foo <- structure(function(x, type = c("histogram", "boxplot"), ...) {
type <- match.arg(type, c("histogram", "boxplot"))
switch(type,
histogram = hist(x, ...),
boxplot = boxplot(x, ...),
stop("unknow type")
)
}, class = c("function", "subsettable_type2"))
foo
#> function (x, type = c("histogram", "boxplot"), ...)
#> {
#> type <- match.arg(type, c("histogram", "boxplot"))
#> switch(type, histogram = hist(x, ...), boxplot = boxplot(x,
#> ...), stop("unknow type"))
#> }
#> <environment: 0x55926882d7d0>
#> attr(,"class")
#> [1] "function" "subsettable_type2"
# This function can be used as usual:
foo(rnorm(50), type = "histogram")
# ... but also this way:
foo$histogram(rnorm(50))
foo$boxplot(rnorm(50))
# A more complex use, where it is possible to define additional types easily.
# It also allow for completion after fun$... and completion of functions
# arguments, depending on the selected type (to avoid putting all arguments
# for all types together, otherwise, it is a mess)
head2 <- structure(function(data, n = 10, ..., type = "default") {
# This was the old (static) aaproach: not possible to add a new type
# without modifying the function head2()
#switch(type,
# default = `.head2$default`(data, n = n, ...),
# fun = `.head2$fun`(data, n = n, ...)
#)
# This is the new (dynamic) approach
get_type("head2", type = type)(data, n = n, ...)
}, class = c("subsettable_type", "function", "head2"))
# We define two types for head2(): default and fun
`head2_default` <- function(data, n = 10, ...) {
head(data, n = n)
}
# Apply a fun on head() - just an example, not necessarily useful
`head2_fun` <- function(data, n = 10, fun = summary, ...) {
head(data, n = n) |> fun(...)
}
head2(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
head2(iris, type = "default") # Idem
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
head2$default(iris) # Idem
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
head2$fun(iris) # The other type, with fun = summary()
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> Min. :4.400 Min. :2.900 Min. :1.30 Min. :0.10 setosa :10
#> 1st Qu.:4.625 1st Qu.:3.100 1st Qu.:1.40 1st Qu.:0.20 versicolor: 0
#> Median :4.900 Median :3.300 Median :1.40 Median :0.20 virginica : 0
#> Mean :4.860 Mean :3.310 Mean :1.45 Mean :0.22
#> 3rd Qu.:5.000 3rd Qu.:3.475 3rd Qu.:1.50 3rd Qu.:0.20
#> Max. :5.400 Max. :3.900 Max. :1.70 Max. :0.40
head2$fun(iris, fun = str)
#> 'data.frame': 10 obs. of 5 variables:
#> $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9
#> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1
#> $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5
#> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1
#> $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1
# Now, the completion (e.g., in RStudio or Positron)
# 1. Type head2$ and you got the list of available types
# 2. Select "default" then hit <tab>, you got the list of args for default
# 3. Do the same but select "fun", now you got the arguments for the fun type
# 4. Just write a new `.head2_<type>` function and <type> is automatically
# integrated!