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This function is also available in pryr, where it does a little extra checking of arguments. make_function() is best used in conjunction with alist() , the a rgument list function. alist() doesn’t evaluate its arguments so that alist(x = a) is shorthand for list(x = quote(a)) .

make_function() has one advantage over using closures to construct functions: with it, you can easily read the source code. For example:

One useful application of make_function() is in functions like curve() . curve() allows you to plot a mathematical function without creating an explicit R function:

Here x is a pronoun. x doesn’t represent a single concrete value, but is instead a placeholder that varies over the range of the plot. One way to implement curve() would be with make_function() :

Functions that use a pronoun are called Professional Sale Online Bourgeois long dress Multicolour Temperley London Popular Online Largest Supplier HXto9i7v8
functions. They are used in (a lisp like language), , and Clojure .

How are alist(a) and alist(a = ) different? Think about both the input and the output.

Read the documentation and source code for pryr::partial() . What does it do? How does it work? Read the documentation and source code for pryr::unenclose() . What does it do and how does it work?

The actual implementation of curve() looks more like

How does this approach differ from curve2() defined above?

Sometimes code is represented as a string, rather than as an expression. You can convert a string to an expression with parse() . parse() is the opposite of deparse() : it takes a character vector and returns an expression object. The primary use of parse() is parsing files of code to disk, so the first argument is a file path. Note that if you have code in a character vector, you need to use the text argument:

Because there might be many top-level calls in a file, parse() doesn’t return just a single expression. Instead, it returns an expression object, which is essentially a list of expressions:

You can create expression objects by hand with expression() , but I wouldn’t recommend it. There’s no need to learn about this esoteric data structure if you already know how to use expressions.

With parse() and eval() , it’s possible to write a simple version of source() . We read in the file from disk, parse() it and then eval() each component in a specified environment. This version defaults to a new environment, so it doesn’t affect existing objects. source() invisibly returns the result of the last expression in the file, so simple_source() does the same.



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Enlarge / Royal graves at the site of Yinxu.
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During the final two centuries of the Shang Dynasty (1600-1046 BCE) in China, thousands of people were sacrificed at the state capital ofYinxu. Some were dispatched with great fanfare, buried with rich grave goods, while others appearto have been sacrificed with extreme prejudice and mutilated after death. Now, a new study sheds some light on these victims. Simon Fraser University bioarchaeologist Christina Cheung and her colleagues reconstructed these ancient people's' lives by discovering what they ate and when, based on chemical signatures left in their bones.

Human sacrifice was a common ritual among the people of almost every ancient civilization, from China and Europe, to Mesopotamia and the Americas. Though archaeologists have analyzed the graves of these sacrifices, they have many questions about the victims' lives. Were they revered and celebrated before death, or were they outcasts? Were they prisoners from far away, or were they the sons and daughters of their executioners?

Cheung and her team answered a number of these questions with a chemical analysis of the bones of 68 sacrificial victims at Yinxu, which werecomparedwith the bones of 39 locals. All of the victims were male, and most were young.

Sacrifices were buried in the royal cemetery across the Huan River from the palace. Archaeologists have been excavating at this site for almost a century, uncovering more than 3,000 sacrificial victims who appear to have been dispatched in groups of 50 to 350 at a time. In a recent paper for Journal of Anthropological Archaeology , Cheung and colleagues describe two distinct types of sacrificial victim.

In Shang, China, there were two main types of human sacrifice: rensheng (人牲) and renxun (人殉). Rensheng literally means "human offerings," and these victims were often buried in large groups, mutilated, and with little to no grave goods. Renxun can be loosely translated as "human companions." They were often buried with elaborate grave goods, individual coffins, and even their own rensheng.

Archaeologists typically find rensheng in mass graves that they divide into "skull pits," "headless pits," and "mutilated pits." As you might guess, these are pits full of skulls, decapitated bodies, and partial bodies, respectively. Unfortunately it's often hard to tell the difference between rensheng and renxun because there has been so much looting and excavation at Yinxu. The practice of mutilating the bodies also makes it difficult for scientists to match skulls with bodies, so they relied entirely on skeletons (headless or otherwise) to identify individuals.

Notice however, that there are different modes of NA —the literal constant is of mode "logical" , but it is frequently automatically coerced to other types. One effect of this is that x[NA] has the length of x , but x[c(1, NA)] has length 2. That is because the rules for logical indices apply in the former case, but those for integer indices in the latter.

Indexing with [ will also carry out the relevant subsetting of any names attributes.

Next: Indexing other structures , Previous: Indexing by vectors , Up: Indexing [ Contents ][ Index ]

Subsetting multi-dimensional structures generally follows the same rules as single-dimensional indexing for each index variable, with the relevant component of dimnames taking the place of names . A couple of special rules apply, though:

Normally, a structure is accessed using the number of indices corresponding to its dimension. It is however also possible to use a single index in which case the dim and dimnames attributes are disregarded and the result is effectively that of c(m)[i] . Notice that m[1] is usually very different from m[1, ] or m[, 1] .

It is possible to use a matrix of integers as an index. In this case, the number of columns of the matrix should match the number of dimensions of the structure, and the result will be a vector with length as the number of rows of the matrix. The following example shows how to extract the elements m[1, 1] and m[2, 2] in one operation.

Indexing matrices may not contain negative indices. NA and zero values are allowed: rows in an index matrix containing a zero are ignored, whereas rows containing an NA produce an NA in the result.

Both in the case of using a single index and in matrix indexing, a names attribute is used if present, as had the structure been one-dimensional.

If an indexing operation causes the result to have one of its extents of length one, as in selecting a single slice of a three-dimensional matrix with (say) m[2, , ] , the corresponding dimension is generally dropped from the result. If a single-dimensional structure results, a vector is obtained. This is occasionally undesirable and can be turned off by adding the ‘ drop = FALSE ’ to the indexing operation. Notice that this is an additional argument to the [ function and doesn’t add to the index count. Hence the correct way of selecting the first row of a matrix as a 1 by n matrix is m[1, , drop = FALSE] . Forgetting to disable the dropping feature is a common cause of failure in general subroutines where an index occasionally, but not usually has length one. This rule still applies to a one-dimensional array, where any subsetting will give a vector result unless ‘ drop = FALSE ’ is used.


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