A minimal reproducible example consists of the following items:

a minimal dataset, necessary to reproduce the error
the minimal runnable code necessary to reproduce the error, which can be run on the given dataset.
the necessary information on the used packages, R version, and the system it is run on.
in the case of random processes, a seed (set by set.seed()) for reproducibility
Looking at the examples in the help files of the used functions is often helpful. In general, all the code given there fulfills the requirements of a minimal reproducible example: data is provided, minimal code is provided, and everything is runnable.

Producing a minimal dataset
For most cases, this can be easily done by just providing a vector/data frame with some values. Or you can use one of the built-in datasets, which are provided with most packages.
A comprehensive list of built-in datasets can be seen with the library(help = “datasets”). There is a short description of every dataset and more information can be obtained for example with?m cars where ‘mtcars’ is one of the datasets in the list. Other packages might contain additional datasets.

Making a vector is easy. Sometimes it is necessary to add some randomness to it, and there are a whole number of functions to make that. sample() can randomize a vector, or give a random vector with only a few values. a letter is a useful vector containing the alphabet. This can be used for making factors.

A few examples :

random values : x <- rnorm(10) for normal distribution, x <- runif(10) for uniform distribution, …
a permutation of some values : x <- sample(1:10) for vector 1:10 in random order.
a random factor : x <- sample(letters[1:4], 20, replace = TRUE)
For matrices, one can use matrix(), eg :

matrix(1:10, ncol = 2)
Making data frames can be done using data.frame(). One should pay attention to name the entries in the data frame, and to not make it overly complicated.

An example :

set.seed(1)
Data <- data.frame(
X = sample(1:10),
Y = sample(c("yes", "no"), 10, replace = TRUE)
)

For some questions, specific formats can be needed. For these, one can use any of the provided as.someType functions: as.factor, as. Date, as.xts, … These in combination with the vector and/or data frame tricks.

The most perfect answer to this question is using Promise.

function ajax(method, url, params) {
return new Promise(function(resolve, reject) {
var xhr = new XMLHttpRequest();
xhr.onload = function() {
resolve(this.responseText);
};
xhr.onerror = reject;
xhr.open(method, url);
xhr.send(params);
});
}


This means that you could use only empty() to determine if the variable is set, and in addition, it checks the variable against the following, 0,”, null.

Example:

$o = [];
@$var = ["",0,null,1,2,3,$foo,$o['myIndex']];
array_walk($var, function($v) {
echo (!isset($v) || $v == false) ? 'true ' : 'false';
echo ' ' . (empty($v) ? 'true' : 'false');
echo "\n";
});