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Functions such as are called aggregate operators, and they are powerful constructs, especially when combined with the other features of F#. Here is a longer example that uses the aggregate operators List.filter and to count the number of URL links in an HTML page and then collects stats on a group of pages (this sample uses the function http defined in 2): let delimiters = [ ' '; '\n'; '\t'; '<'; '>'; '=' ] let getWords s = String.split delimiters s let getStats site = let url = "http://" + site let html = http url let hwords = html |> getWords let hrefs = html |> getWords |> List.filter (fun s -> s = "href") (site,html.Length, hwords.Length, hrefs.Length) Here we use the function getStats with three web pages: > let sites = [ "";"";"" ];; val sites : string list > sites |> getStats;; val it : (string * int * int * int) list = [("", 7728, 1156, 10); ("", 2685, 496, 14); ("", 10715, 1771, 38)] The function getStats computes the length of the HTML for the given website, the number of words in the text of that HTML, and the approximate number of links on that page. The previous code sample extensively uses the |> operator to pipeline operations, discussed in the Pipelining with |> sidebar. The F# library design ensures that a common, consistent set of aggregate operations is defined for each structured type. Table 3-11 shows how the same design patterns occur for the map abstraction. code 39 generator open source

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: ('a -> 'b) -> 'a list : ('a -> 'b) -> 'a[] -> 'b list -> 'b[]

Relational databases work best when data is normalized. When you normalize your data, you eliminate redundant data to help protect your data against data entry errors. You also ensure that the information in each data table is correctly linked so that you can properly crossreference related data.

Frequently, types will also define methods such as Map that provide a slightly more succinct way of transforming data. For example, we could have written sites |> getStats like so: sites.Map(getStats) Both styles are used in F# programming, depending on the methods and properties that are available for a particular data type.


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The |> forward pipe operator is perhaps the most important operator in F# programming. Its definition is deceptively simple: let (|>) x f = f x Here is how to use the operator to compute the cubes of three numbers: [1;2;3] |> (fun x -> x * x * x) This produces [1;8;27], just as if you had written this: (fun x -> x * x * x) [1;2;3] In a sense, |> is just function application in reverse. However, using |> has distinct advantages: Clarity: When used in conjunction with operators such as, the |> operator allows you to perform the data transformations and iterations in a forward-chaining, pipelined style. Type inference: Using the |> operator allows type information to be flowed from input objects to the functions manipulating those objects. F# uses information collected from type inference to resolve some language constructs such as property accesses and method overloading. This relies on information being propagated left to right through the text of a program. In particular, typing information to the right of a position is not taken into account when resolving property access and overloads. For completeness, here is the type of the operator: val (|>) : 'a -> ('a -> 'b) -> 'b code 39 generator source code

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You normalize data when you have a lot of repetitive data in one or more data tables and you want to restructure the data to reduce data entry errors and possibly reduce data storage requirements. To normalize data, you should follow a set of well-established rules called normal forms. There are three common normal forms. There are also several less common normal forms that are beyond the scope of this book. The general strategies underlying the three common normal forms are the following: Eliminate repeating data in rows or data records. Eliminate repeating data in columns or data fields, moving the repeated data to other data tables. Use primary keys and foreign keys to cross-reference related data records among data tables. For example, examine the following nonnormalized data in Table 1-1. Table 1-1. Nonnormalized Weather Data for Three United States Cities

You saw earlier how to use the |> forward pipe operator to pipe values through a number of functions. This was a small example of the process of computing with functions, an essential and powerful programming technique in F#. In this section, we cover ways to compute new function values from existing ones using compositional techniques. First let s take a look at function composition. For example, consider the following code: let google = http "" google |> getWords |> List.filter (fun s -> s = "href") |> List.length code 39 generator

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