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This book is accessible to readers without a background in data science. Pci Dss: An Integrated Bioinformatics And Biomedical Engineering Programming With bit Arm English French German Italian Spanish. Of course, subtraction, multiplication and division are also vectorized.
This way, we can do element-by-element operations on matrices without having to loop over every element. Dates are stored internally as the number of days since while times are stored internally as the number of seconds since I just thought those were fun facts.
Dates in R Watch a video of this section Dates are represented by the Date class and can be coerced from a character string using the as. Date function. This is a common way to end up with a Date object in R. POSIXct is just a very large integer under the hood. It use a useful class when you want to store times in something like a data frame.
This is useful when you need that kind of information. POSIXlt or as. POSIXct function. I can never remember the formatting strings. You can do comparisons too i. Date", "-. Date, as. POSIXlt, or as. Control structures allow you to respond to inputs or to features of the data and execute different R expressions accordingly. For starters, you can just use the if statement. If you have an action you want to execute when the condition is false, then you need an else clause.
This expression can also be written a different, but equivalent, way in R. Which one you use will depend on your preference and perhaps those of the team you may be working with. Of course, the else clause is not necessary.
You could have a series of if clauses that always get executed if their respective conditions are true. In R, for loops take an interator variable and assign it successive values from a sequence or vector.
For loops are most commonly used for iterating over the elements of an object list, vector, etc. The following three loops all have the same behavior. Nested for loops for loops can be nested inside of each other. Be careful with nesting though. If you find yourself in need of a large number of nested loops, you may want to break up the loops by using functions discussed later.
Control Structures 53 while Loops Watch a video of this section While loops begin by testing a condition. If it is true, then they execute the loop body. Once the loop body is executed, the condition is tested again, and so forth, until the condition is false, after which the loop exits. Use with care! Sometimes there will be more than one condition in the test.
For example, in the above code, if z were less than 3, the second test would not have been evaluated. These are not commonly used in statistical or data analysis applications but they do have their uses. The only way to exit a repeat loop is to call break. You could get in a situation where the values of x0 and x1 oscillate back and forth and never converge.
Better to set a hard limit on the number of iterations by using a for loop and then report whether convergence was achieved or not. Functions Writing functions is a core activity of an R programmer. Functions are often used to encapsulate a sequence of expressions that need to be executed numerous times, perhaps under slightly different conditions.
Functions are also often written when code must be shared with others or the public. The writing of a function allows a developer to create an interface to the code, that is explicitly specified with a set of parameters. This interface provides an abstraction of the code to potential users. In addition, the creation of an interface allows the developer to communicate to the user the aspects of the code that are important or are most relevant.
This is very handy for the various apply funtions, like lapply and sapply. However, they are really important in R and can be useful for data analysis. Your First Function Functions are defined using the function directive and are stored as R objects just like anything else.
The next thing we can do is create a function that actually has a non-trivial function body. The last aspect of a basic function is the function arguments. These are the options that you can specify to the user that the user may explicity set. Hello, world! Obviously, we could have just cut-and-pasted the cat "Hello, world!
But often it is useful if a function returns something that perhaps can be fed into another section of code. This next function returns the total number of characters printed to the console. In R, the return value of a function is always the very last expression that is evaluated. Because the chars variable is the last expression that is evaluated in this function, that becomes the return value of the function.
Note that there is a return function that can be used to return an explicity value from a function, but it is rarely used in R we will discuss it a bit later in this chapter. Finally, in the above function, the user must specify the value of the argument num.
If it is not specified by the user, R will throw an error. Any function argument can have a default value, if you wish to specify it. Sometimes, argument values are rarely modified except in special cases and it makes sense to set a default value for that argument. This relieves the user from having to specify the value of that argument every single time the function is called.
The formal arguments are the arguments included in the function definition. Because all function arguments have names, they can be specified using their name. Functions 60 Argument Matching Calling an R function with arguments can be done in a variety of ways. R functions arguments can be matched positionally or by name. Positional matching just means that R assigns the first value to the first argument, the second value to second argument, etc.
The following calls to the sd function which computes the empirical standard deviation of a vector of numbers are all equivalent. Note that sd has two arguments: x indicates the vector of numbers and na. In the example below, we specify the na. Below is the argument list for the lm function, which fits linear models to a dataset. NULL The following two calls are equivalent. Most of the time, named arguments are useful on the command line when you have a long argument list and you want to use the defaults for everything except for an argument near the end of the list.
Named arguments also help if you can remember the name of the argument and not its position on the argument list. For example, plotting functions often have a lot of options to allow for customization, but this makes it difficult to remember exactly the position of every argument on the argument list.
Function arguments can also be partially matched, which is useful for interactive work. The order of operations when given an argument is 1.
Check for exact match for a named argument 2. Check for a partial match 3. Check for a positional match Partial matching should be avoided when writing longer code or programs, because it may lead to confusion if someone is reading the code. However, partial matching is very useful when calling functions interactively that have very long argument names. In addition to not specifying a default value, you can also set an argument value to NULL.
It is sometimes useful to allow an argument to take the NULL value, which might indicate that the function should take some specific action. Lazy Evaluation Arguments to functions are evaluated lazily, so they are evaluated only as needed in the body of the function.
In this example, the function f has two arguments: a and b. This behavior can be good or bad. This example also shows lazy evaluation at work, but does eventually result in an error. This is because b did not have to be evaluated until after print a. Once the function tried to evaluate print b the function had to throw an error. Functions 63 The Argument There is a special argument in R known as the Pass ' This is clear in functions like paste and cat.
So the first argument to either function is Arguments Coming After the Argument One catch with Take a look at the arguments to the paste function. When R tries to bind a value to a symbol, it searches through a series of environments to find the appropriate value. When you are working on the command line and need to retrieve the value of an R object, the order in which things occur is roughly 1.
Search the global environment i. Search the namespaces of each of the packages on the search list The search list can be found by using the search function. For better or for worse, the order of the packages on the search list matters, particularly if there are multiple objects with the same name in different packages. Users can configure which packages get loaded on startup so if you are writing a function or a package , you cannot assume that there will be a set list of packages available in a given order.
When a user loads a package with library the namespace of that package gets put in position 2 of the search list by default and everything else gets shifted down the list. The scoping rules of a language determine how a value is associated with a free variable in a function. R uses lexical scoping or static scoping. An alternative to lexical scoping is dynamic scoping which is implemented by some languages. Lexical scoping turns out to be particularly useful for simplifying statistical computations Related to the scoping rules is how R uses the search list to bind a value to a symbol Consider the following function.
In the body of the function there is another symbol z. In this case z is called a free variable. The scoping rules of a language determine how values are assigned to free variables. Free variables are not formal arguments and are not local variables assigned insided the function body. Lexical scoping in R means that the values of free variables are searched for in the environment in which the function was defined.
Okay then, what is an environment? An environment is a collection of symbol, value pairs, i. The only environment without a parent is the empty environment. A function, together with an environment, makes up what is called a closure or function closure.
How do we associate a value to a free variable? If a value for a given symbol cannot be found once the empty environment is arrived at, then an error is thrown. One implication of this search process is that it can be affected by the number of packages you have attached to the search list.
The more packages you have attached, the more symbols R has to sort through in order to assign a value.
Now things get interesting—in this case the environment in which a function is defined is the body of another function! Here is an example of a function that returns another function as its return value. Remember, in R functions are treated like any other object and so this is perfectly valid.
What is the value of n here? Well, its value is taken from the environment where the function was defined. When I defined the cube function it was when I called make. We can explore the environment of a function to see what objects are there and their values. Dynamic Scoping We can use the following example to demonstrate the difference between lexical and dynamic scoping rules. With dynamic scoping, the value of y is looked up in the environment from which the function was called sometimes referred to as the calling environment.
In R the calling environment is known as the parent frame. In this case, the value of y would be 2. When a function is defined in the global environment and is subsequently called from the global environment, then the defining environment and the calling environment are the same.
This can sometimes give the appearance of dynamic scoping. Consider this example. Lexical scoping in R has consequences beyond how free variables are looked up.
This is because all functions must carry a pointer to their respective defining environments, which could be anywhere. Application: Optimization Watch a video of this section NOTE: This section requires some knowledge of statistical inference and modeling. If you do not have such knowledge, feel free to skip this section. Why is any of this information about lexical scoping useful? Optimization routines in R like optim , nlm , and optimize require you to pass a function whose argument is a vector of parameters e.
However, an objective function that needst to be minimized might depend on a host of other things besides its parameters like data. When writing software which does optimization, it may also be desirable to allow the user to hold certain parameters fixed. The scoping rules of R allow you to abstract away much of the complexity involved in these kinds of problems.
Now w ecan generate some data and then construct our negative log-likelihood. We can also try to estimate one parameter while holding another parameter fixed. Here we fix sigma to be equal to 2. We can also try to estimate sigma while holding mu fixed at 1. Here is the function when mu is fixed.
Nevertheless, I will just give you the standards that I use and the rationale behind them. I think we can all agree on this one. Using text files and a text editor is fundamental to coding. Interactive development environments like RStudio have nice text editors built in, but there are many others out there.
Indent your code. Indenting is very important for the readability of your code. Some programming languages actually require it as part of their syntax, but R does not. Nevertheless, indenting is very important. How much you should indent is up for debate, but I think each indent should be a minimum of 4 spaces, and ideally it should be 8 spaces. Limit the width of your code. This limitation, along with the 8 space indentation, forces you to write code that is clean, readable, and naturally broken down into modular units.
In particular, this combination limits your ability to write very long functions with many different levels of nesting.
Limit the length of individual functions. The Data Science Design Manual. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.
The Data Science Design Manual OpenIntro Statistics. OpenIntro Statistics offers a traditional introduction to statistics at the college level. This textbook is widely used at the college level and offers an exceptional and accessible introduction for students from community colleges to the Ivy League.
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