%?<-%: Assign if invalidWhen coding in R, the data checking is actually a headache. For
example, to check if a variable aa exists and not
NULL, otherwise set a default value to be 1, the check
looks like:
if( exists('aa') && !is.null(aa) ){
aa <- 1
}Most of time we are repeating ourselves. With %?<-%
operator, we just need:
aa %?<-% 1
print(aa)
#> [1] 1The powerful part is the left-hand side can be any expression. For example,
l %?<-% list()
l$aa %?<-% 1
print(l)
#> $aa
#> [1] 1If the value exists, then %?<-% does nothing (not
even evaluate the expressions on the right-hand side)
# e already exists
e <- list(aa = 1)
# %?<-% will not evaluate rhs, nor assign values
system.time({
e %?<-% { Sys.sleep(10); list(aa = 2) }
print(e)
})
#> $aa
#> [1] 1
#> user system elapsed
#> 0 0 0In modern JavaScript, function can be created via
(arg) => { ... }. For example,
const li = ['A', 'T', 'G', 'C'];
li.map((el, ii) => {
return(`The index for ${el} is ${ii}`);
});dipsaus provides functions iapply, and
%=>%, together with glue package, we can
apply elements like this:
# gl <- glue::glue
# `%>%` <- magrittr::`%>%`
li <- c('A', 'T', 'G', 'C')
li %>% iapply(c(el, ii) %=>% {
gl('The index for {el} is {ii}')
})
#> [1] "The index for A is 1" "The index for T is 2" "The index for G is 3"
#> [4] "The index for C is 4"%=>% collect the left-hand side elements as arguments
and right-hand side expression as body and create function:
c(a, b=a^2, ...) %=>% {
print(c(a , b,...))
}
#> function (a, b = a^2, ...)
#> {
#> print(c(a, b, ...))
#> }match_callsThe function match.call provided by base package let us
format calls with formals matched.
match.call(textInput, call = quote(textInput('inputId', 'label', 'aaa')))
#> textInput(inputId = "inputId", label = "label", value = "aaa")This is already powerful as we can parse the expressions using
as.list() to get the input parameters. However, when
encounter the nested calls like shiny UI components,
match.call does not work well. We can’t see the matched
results inside of the nested functions.
match.call(tagList, call = quote(tagList(
div(
tags$ul(
tags$li(textInput('inputId', 'label', 'aaa'))
)
)
)))
#> tagList(div(tags$ul(tags$li(textInput("inputId", "label", "aaa")))))match_calls solves this problem by recursively calling
match.call:
match_calls(call = tagList(
div(
tags$ul(
tags$li(textInput('inputId', 'label', 'aaa'))
)
)
), recursive = TRUE)
#> tagList(div(tags$ul(tags$li(textInput(inputId = "inputId", label = "label",
#> value = "aaa")))))It can also change modify the calls. For example, we want to add
ns to input ID in shiny modules, then the following
replave_args changes "inputId" to
ns("inputId")
match_calls(call = tagList(
div(
tags$ul(
tags$li(textInput('inputId', 'label', 'aaa'))
)
)
), recursive = TRUE, replace_args = list(
'inputId' = function(v, ...){
as.call(list(quote(ns), v))
}
))
#> tagList(div(tags$ul(tags$li(textInput(inputId = ns("inputId"),
#> label = "label", value = "aaa")))))Pipe functions can simplify the workflow and make R code more
readable. The most popular pipe %>% allows the left-hand
elements to be the first input of the right-hand side functions.
dipsaus provides several pipe-friendly functions.
no_opno_op takes whatever input in, and returns the input,
with side effects. For example, we want to plot the results from the
pipe and continue the analysis, usually this is what happens:
x %>%
do_something(...) ->
x_tmp
plot(x_tmp)
x_tmp %>%
do_others(...) ->
final_resultsWith no_op, the pipe becomes:
x %>%
do_something(...) %>%
no_op(plot, ylim = c(0,100)) %>%
do_others(...) ->
final_resultsHere’s an example
par(mfrow = c(1,2))
(1:10) %>%
iapply(c(el, ii) %=>% {
rnorm(20, el, ii)
}, simplify = FALSE) %>%
unlist %>%
# Begin no-ops, result will not change
no_op({
# Use expression and "." to refer the data
print(summary(.))
}) %>%
no_op(
# Use function and pass ... to function
plot, x = seq(0,1,length.out = 200),
type = 'p', ylim = c(-20,20), pch = 16,
xlab = 'Time', ylab = 'Value', las = 1
) %>%
no_op(hist, xlab = 'Values', main = 'Histogram') ->
result
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -7.767 1.383 4.474 6.230 9.483 41.505
str(result)
#> num [1:200] 0.409 0.237 1.98 0.918 -0.261 ...do_aggregateThis is a wrapper of aggregate function. When using
formula, aggregate requires the formula to be the first
element. If the pipe results are data.frame and we want to
use formula, it’s super inconvenient.
## S3 method for class 'formula'
aggregate(formula, data, FUN, ..., subset, na.action = na.omit)
do_aggregate allows the first element to be data frames
while using formula:
ToothGrowth %>%
do_aggregate(len ~ ., mean)
#> supp dose len
#> 1 OJ 0.5 13.23
#> 2 VC 0.5 7.98
#> 3 OJ 1.0 22.70
#> 4 VC 1.0 16.77
#> 5 OJ 2.0 26.06
#> 6 VC 2.0 26.14