Which syntax r

If you've got a vector with lots of values so the printout runs across multiple lines, each line will start with a number in brackets, telling you which vector item number that particular line is starting with. See the screen shot, below.

If you don't create a list, you may be unpleasantly surprised that your variable containing 3, 8, "small" was turned into a vector of characters "3", "8", "small". And by the way, R assumes that 3 is the same class as 3.

If you want the integer 3, you need to signify it as 3L or with the as. In a situation where this matters to you, you can check what type of number you've got by using the class function:. There are several as functions for converting one data type to another, including as. R also has special vector and list types that are of special interest when analyzing data, such as matrices and data frames. A matrix has rows and columns; you can find a matrix dimension with dim such as.

Data frames are like matrices except one column can have a different data type from another column, and each column must have a name. If you've got data in a format that might work well as a database table or well-formed spreadsheet table , it will also probably work well as an R data frame. In a data frame, you can think of each row as similar to a database record and each column like a database field.

There are lots of useful functions you can apply to data frames, some of which I've gone over in earlier sections, such as summary and the psych package's describe. And speaking of quirks: There are several ways to find an object's underlying data type, but not all of them return the same value. For example, class and str will return data.

If you'd like to learn more details about data types in R, you can watch this video lecture by Roger Peng, associate professor of biostatistics at the Johns Hopkins Bloomberg School of Public Health:.

One more useful concept to wrap up this section -- hang in there, we're almost done: factors. These represent categories in your data. So, if you've got a data frame with employees, their department and their salaries, salaries would be numerical data and employees would be characters strings in many other languages ; but you'd likely want department to be a factor -- in other words, a category you may want to group or model your data by. Factors can be unordered, such as department, or ordered, such as "poor", "fair", "good" and "excellent.

Here are the latest Insider stories. More Insider Sign Out. The most important example of a class method for [ is that used for data frames. It is not described in detail here see the help page for [. If a single index is supplied, it is interpreted as indexing the list of columns—in that case the drop argument is ignored, with a warning.

Only character indices are allowed and no partial matching is done. Assignment to subsets of a structure is a special case of a general mechanism for complex assignment:.

Note that the index is first converted to a numeric index and then the elements are replaced sequentially along the numeric index, as if a for loop had been used. The same mechanism can be applied to functions other than [. Its last argument, which must be called value , is the new value to be assigned. For example,. It sets e in the outer environment to. These two candidate interpretations differ only if there is also a local variable x. It is a good idea to avoid having a local variable with the same name as the target variable of a superassignment.

As this case was handled incorrectly in versions 1. Almost every programming language has a set of scoping rules, allowing the same name to be used for different objects.

This allows, e. R uses a lexical scoping model, similar to languages like Pascal. However, R is a functional programming language and allows dynamic creation and manipulation of functions and language objects, and has additional features reflecting this fact.

The global environment is the root of the user workspace. An assignment operation from the command line will cause the relevant object to belong to the global environment. Its enclosing environment is the next environment on the search path, and so on back to the empty environment that is the enclosure of the base environment. Every call to a function creates a frame which contains the local variables created in the function, and is evaluated in an environment, which in combination creates a new environment.

Notice the terminology: A frame is a set of variables, an environment is a nesting of frames or equivalently: the innermost frame plus the enclosing environment. Environments may be assigned to variables or be contained in other objects.

However, notice that they are not standard objects—in particular, they are not copied on assignment. A closure mode "function" object will contain the environment in which it is created as part of its definition By default.

Notice that this is not necessarily the environment of the caller! Thus, when a variable is requested inside a function, it is first sought in the evaluation environment, then in the enclosure, the enclosure of the enclosure, etc. If the variable is not found there, the search will proceed next to the empty environment, and will fail. Every time a function is invoked a new evaluation frame is created. At any point in time during the computation the currently active environments are accessible through the call stack.

Each time a function is invoked a special construct called a context is created internally and is placed on a list of contexts. When a function has finished evaluating its context is removed from the call stack. Making variables defined higher up the call stack available is called dynamic scope. The binding for a variable is then determined by the most recent in time definition of the variable. This contradicts the default scoping rules in R, which use the bindings in the environment in which the function was defined lexical scope.

Some functions, particularly those that use and manipulate model formulas, need to simulate dynamic scope by directly accessing the call stack. They are listed briefly below. In addition to the evaluation environment structure, R has a search path of environments which are searched for variables not found elsewhere. This is used for two things: packages of functions and attached user data.

The first element of the search path is the global environment and the last is the base package. An Autoloads environment is used for holding proxy objects that may be loaded on demand. Other environments are inserted in the path using attach or library. Packages which have a namespace have a different search path. When a search for an R object is started from an object in such a package, the package itself is searched first, then its imports, then the base namespace and finally the global environment and the rest of the regular search path.

The effect is that references to other objects in the same package will be resolved to the package, and objects cannot be masked by objects of the same name in the global environment or in other packages.

While R can be very useful as a data analysis tool most users very quickly find themselves wanting to write their own functions.

This is one of the real advantages of R. Users can program it and they can, if they want to, change the system level functions to functions that they find more appropriate. R also provides facilities that make it easy to document any functions that you have created.

The first component of the function declaration is the keyword function which indicates to R that you want to create a function. An argument list is a comma separated list of formal arguments. The body can be any valid R expression. The value returned by the call to function is a function. If this is not given a name it is referred to as an anonymous function. Anonymous functions are most frequently used as arguments to other functions such as the apply family or outer.

So echo is a function that takes a single argument and when echo is invoked it prints its argument. The formal arguments to the function define the variables whose values will be supplied at the time the function is invoked. The names of these arguments can be used within the function body where they obtain the value supplied at the time of function invocation.

In this case, if the user does not specify a value for the argument when the function is invoked the expression will be associated with the corresponding symbol. When a value is needed the expression is evaluated in the evaluation frame of the function.

Default behaviours can also be specified by using the function missing. When missing is called with the name of a formal argument it returns TRUE if the formal argument was not matched with any actual argument and has not been subsequently modified in the body of the function. An argument that is missing will thus have its default value, if any. The missing function does not force evaluation of the argument. The special type of argument It is used for a variety of purposes. It allows you to write a function that takes an arbitrary number of arguments.

It can be used to absorb some arguments into an intermediate function which can then be extracted by functions called subsequently. Functions are first class objects in R. They can be used anywhere that an R object is required. In particular they can be passed as arguments to functions and returned as values from functions. See Function objects for the details.

When a function is called or invoked a new evaluation frame is created. In this frame the formal arguments are matched with the supplied arguments according to the rules given in Argument matching.

The statements in the body of the function are evaluated sequentially in this environment frame. The enclosing frame of the evaluation frame is the environment frame associated with the function being invoked. This may be different from S. While many functions have. GlobalEnv as their environment this does not have to be true and functions defined in packages with namespaces normally have the package namespace as their environment.

This subsection applies to closures but not to primitive functions. The latter typically ignore tags and do positional matching, but their help pages should be consulted for exceptions, which include log , round , signif , rep and seq. The first thing that occurs in a function evaluation is the matching of formal to the actual or supplied arguments.

This is done by a three-pass process:. Argument matching is augmented by the functions match. Access to the partial matching algorithm used by R is via pmatch. One of the most important things to know about the evaluation of arguments to a function is that supplied arguments and default arguments are treated differently. The supplied arguments to a function are evaluated in the evaluation frame of the calling function.

The default arguments to a function are evaluated in the evaluation frame of the function. The semantics of invoking a function in R argument are call-by-value.

In general, supplied arguments behave as if they are local variables initialized with the value supplied and the name of the corresponding formal argument. Changing the value of a supplied argument within a function will not affect the value of the variable in the calling frame.

R has a form of lazy evaluation of function arguments. Arguments are not evaluated until needed. It is important to realize that in some cases the argument will never be evaluated. Thus, it is bad style to use arguments to functions to cause side-effects. There is no guarantee that the argument will ever be evaluated and hence the assignment may not take place. It is possible to access the actual not default expressions used as arguments inside the function.

The mechanism is implemented via promises. When a function is being evaluated the actual expression used as an argument is stored in the promise together with a pointer to the environment the function was called from. When if the argument is evaluated the stored expression is evaluated in the environment that the function was called from. Since only a pointer to the environment is used any changes made to that environment will be in effect during this evaluation. The resulting value is then also stored in a separate spot in the promise.

Subsequent evaluations retrieve this stored value a second evaluation is not carried out. Access to the unevaluated expression is also available using substitute. When a function is called, each formal argument is assigned a promise in the local environment of the call with the expression slot containing the actual argument if it exists and the environment slot containing the environment of the caller.

If no actual argument for a formal argument is given in the call and there is a default expression, it is similarly assigned to the expression slot of the formal argument, but with the environment set to the local environment. A promise will only be forced once, the value slot content being used directly later on. A promise is forced when its value is needed. This usually happens inside internal functions, but a promise can also be forced by direct evaluation of the promise itself.

This is occasionally useful when a default expression depends on the value of another formal argument or other variable in the local environment. This is seen in the following example where the lone label ensures that the label is based on the value of x before it is changed in the next line.

The expression slot of a promise can itself involve other promises. This happens whenever an unevaluated argument is passed as an argument to another function. When forcing a promise, other promises in its expression will also be forced recursively as they are evaluated.

Scope or the scoping rules are simply the set of rules used by the evaluator to find a value for a symbol. Every computer language has a set of such rules. In R the rules are fairly simple but there do exist mechanisms for subverting the usual, or default rules.

R adheres to a set of rules that are called lexical scope. This means the variable bindings in effect at the time the expression was created are used to provide values for any unbound symbols in the expression.

Most of the interesting properties of scope are involved with evaluating functions and we concentrate on this issue. A symbol can be either bound or unbound. All of the formal arguments to a function provide bound symbols in the body of the function. Any other symbols in the body of the function are either local variables or unbound variables.

A local variable is one that is defined within the function. Because R has no formal definition of variables, they are simply used as needed, it can be difficult to determine whether a variable is local or not. Local variables must first be defined, this is typically done by having them on the left-hand side of an assignment.

During the evaluation process if an unbound symbol is detected then R attempts to find a value for it. The scoping rules determine how this process proceeds.

In R the environment of the function is searched first, then its enclosure and so on until the global environment is reached. The global environment heads a search list of environments that are searched sequentially for a matching symbol. The value of the first match is then used. When this set of rules is combined with the fact that functions can be returned as values from other functions then some rather nice, but at first glance peculiar, properties obtain.

A rather interesting question is what happens when h is evaluated. When a function body is evaluated there is no problem determining values for local variables or for bound variables. Scoping rules determine how the language will find values for the unbound variables.

When h 3 is evaluated we see that its body is that of g. Within that body x is bound to the formal argument and y is unbound. In a language with lexical scope x will be associated with the value 3 and y with the value 10 local to f so h 3 should return the value In R this is indeed what happens. In S, because of the different scoping rules one will get an error indicating that y is not found, unless there is a variable y in your workspace in which case its value will be used.

Object-oriented programming is a style of programming that has become popular in recent years. Much of the popularity comes from the fact that it makes it easier to write and maintain complicated systems. It does this through several different mechanisms.

Central to any object-oriented language are the concepts of class and of methods. A class is a definition of an object. Typically a class contains several slots that are used to hold class-specific information. An object in the language must be an instance of some class.

Programming is based on objects or instances of classes. Computations are carried out via methods. Methods are basically functions that are specialized to carry out specific calculations on objects, usually of a specific class.

This is what makes the language object oriented. In R, generic functions are used to determine the appropriate method. The generic function is responsible for determining the class of its argument s and uses that information to select the appropriate method. Another feature of most object-oriented languages is the concept of inheritance. In most programming problems there are usually many objects that are related to one another. The programming is considerably simplified if some components can be reused.

If a class inherits from another class then generally it gets all the slots in the parent class and can extend it by adding new slots. On method dispatching via the generic functions if a method for the class does not exist then a method for the parent is sought. In this chapter we discuss how this general strategy has been implemented in R and discuss some of the limitations within the current design.

One of the advantages that most object systems impart is greater consistency. This is achieved via the rules that are checked by the compiler or interpreter. Unfortunately because of the way that the object system is incorporated into R this advantage does not obtain. Users are cautioned to use the object system in a straightforward manner.

While it is possible to perform some rather interesting feats these tend to lead to obfuscated code and may depend on implementation details that will not be carried forward.

The greatest use of object oriented programming in R is through print methods, summary methods and plot methods. These methods allow us to have one generic function call, plot say, that dispatches on the type of its argument and calls a plotting function that is specific to the data supplied. In order to make the concepts clear we will consider the implementation of a small system designed to teach students about probability. In this system the objects are probability functions and the methods we will consider are methods for finding moments and for plotting.

Probabilities can always be represented in terms of the cumulative distribution function but can often be represented in other ways. For example as a density, when it exists or as a moment generating function when it exists.

Rather than having a full-fledged object-oriented system R has a class system and a mechanism for dispatching based on the class of an object. The dispatch mechanism for interpreted code relies on four special objects that are stored in the evaluation frame. These special objects are. Generic ,.

Class ,. Method and. There is a separate dispatch mechanism used for internal functions and types that will be discussed elsewhere. The class system is facilitated through the class attribute. This attribute is a character vector of class names.

Thus, virtually anything can be turned in to an object of class "foo". The object system makes use of generic functions via two dispatching functions, UseMethod and NextMethod. The typical use of the object system is to begin by calling a generic function. This is typically a very simple function and consists of a single line of code.

The system function mean is just such a function,. When mean is called it can have any number of arguments but its first argument is special and the class of that first argument is used to determine which method should be called. The variable. Class is set to the class attribute of x ,. Generic is set to the string "mean" and a search is made for the correct method to invoke.

The class attributes of any other arguments to mean are ignored. Suppose that x had a class attribute that contained "foo" and "bar" , in that order. Then R would first search for a function called mean. If the last search is unsuccessful R reports an error. It is a good idea to always write a default method.

Note that the functions mean. NextMethod provides another mechanism for dispatching. A function may have a call to NextMethod anywhere in it. The determination of which method should then be invoked is based primarily on the current values of. Class and. This is somewhat problematic since the method is really an ordinary function and users may call it directly. If they do so then there will be no values for.

Generic or. If a method is invoked directly and it contains a call to NextMethod then the first argument to NextMethod is used to determine the generic function. An error is signalled if this argument has not been supplied; it is therefore a good idea to always supply this argument. In the case that a method is invoked directly the class attribute of the first argument to the method is used as the value of. Methods themselves employ NextMethod to provide a form of inheritance.

Commonly a specific method performs a few operations to set up the data and then it calls the next appropriate method through a call to NextMethod. Consider the following simple example. A point in two-dimensional Euclidean space can be specified by its Cartesian x-y or polar r-theta coordinates. Hence, to store information about the location of the point, we could define two classes, "xypoint" and "rthetapoint".

Now, suppose we want to get the x-position from either type of object. This can easily be achieved through generic functions. We define the generic function xpos as follows. The user simply calls the function xpos with either representation as the argument.

The internal dispatching method finds the class of the object and calls the appropriate methods. It is pretty easy to add other representations.

One need not write a new generic function only the methods. This makes it easy to add to existing systems since the user is only responsible for dealing with the new representation and not with any of the existing representations. The bulk of the uses of this methodology are to provide specialized printing for objects of different types; there are about 40 methods for print. The class attribute of an object can have several elements.

When a generic function is called the first inheritance is mainly handled through NextMethod. NextMethod determines the method currently being evaluated, finds the next class from th.

Generic functions should consist of a single statement. UseMethod "foo", x. When UseMethod is called, it determines the appropriate method and then that method is invoked with the same arguments, in the same order as the call to the generic, as if the call had been made directly to the method.

In order to determine the correct method the class attribute of the first argument to the generic is obtained and used to find the correct method. The name of the generic function is combined with the first element of the class attribute into the form, generic. If the function is found then it is used. If no such function is found then the second element of the class attribute is used, and so on until all the elements of the class attribute have been exhausted.

If no method has been found at that point then the method generic. If the first argument to the generic function has no class attribute then generic. Since the introduction of namespaces the methods may not be accessible by their names i. Any object can have a class attribute. This attribute can have any number of elements. Each of these is a string that defines a class. When a generic function is invoked the class of its first argument is examined.

UseMethod is a special function and it behaves differently from other function calls. The syntax of a call to it is UseMethod generic , object , where generic is the name of the generic function, object is the object used to determine which method should be chosen. UseMethod can only be called from the body of a function. UseMethod changes the evaluation model in two ways.

You can also input a list of values to the which function. Look at the example below where I am trying to find the position of two values from the dataframe. You can even use the which function to find the column names in a data which contains numerical data.

The output shows that the iris dataset has 5 columns in it. Among them, 4 are numerical columns and 1 is categorical Species. The which function has returned only the names of numerical columns as per the input condition. If you a data analyst, then which function will be invaluable for you. Finally, we have arrived at the matrix in R. Well, you can use the which function in R language to get the position of the values in a matrix.

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