ADVANCED R. Apply. The R program (as a text file) for the code on this page. In the example below, closures counter_one() and counter_two() each get their own enclosing environments when run, so they can maintain different counts. In addition to the base functionalities, there are more than 10,000 R packages created by users published in the official R repository. Can you do it It works for any number of columns. Apart from a different function name, each function is almost identical. Each time new_counter is run, it creates an environment, initialises the counter i in this environment, and then creates a new function. What if different columns used different codes for missing values? It would be good to get an array instead. What base R function is closest to being a predicate version of If one also wants to return non-numeric input columns, these can be supplied to the else argument of the if() “function”: Q: Use both for loops and lapply() to fit linear models to the The search term – can be a text fragment or a regular expression. At least we are not aware of sth. Go to Sign Up arrow_forward. We think the array functions just need a dimension and an argument. # levels occur, f is a factor and some levels don't occur. A better approach would be to modify our lapply() call to include the extra argument: From time to time you may create a list of functions that you want to be available without having to use a special syntax. In relations: One can see this easily by intuition from examples: We think the only paste version that is not implemented in base R is an array version. Instead we could use closures, functions that make and return functions. Extra challenge: get rid of the anonymous function by using [[ directly. Are there any paste variants that don’t have existing R implementations? # we preallocate a logical vector and save the result, # of the predicate function applied to each element of the list, # we return NA, if the output of pred is always FALSE. Function factories are particularly well suited to maximum likelihood problems, and you’ll see a more compelling use of them in mathematical functionals. (The existing name is a bit of a hint.). This makes it easier to work with groups of related functions, in the same way a data frame makes it easier to work with groups of related vectors. It should take a function and a vector of inputs, apply() arranges its output columns (or list elements) according to the order of the margin. A few of the solutions inherit from the work of Peter Hurford & Robert Krzyzanowski. What does the following statistical function do? © Hadley Wickham. positional matching, since mean()’s first argument is supplied via name A: which() returns all indices of true entries from a logical vector. You can undo this by deleting the functions after you’re done. Another important use is to create closures, functions written by functions. of the input object. We can see, that the vectorised and reduced numerical functions are all consistent. What base R function is closest Use smaller and larger to implement equivalents of min(), max(), A: As a numeric data.frame we choose cars: And as a mixed data.frame we choose iris: Q: Why is using sapply() to get the class() of each element in A In the following table we can see the requested base R functions, that we are aware of: Notice that we were relatively strict about the binary row. Sean C. Anderson already has done this based on a presentation from Hadley Wickham and provided the following result here. sequential run of elements where the predicate is true. In seems relatively hard to find an easy rule for all cases and especially the different behaviour for NULL is relatively confusing. might find rle() helpful.). In R the data frame is considered a list and the variables in the data frame are the elements of the list. When you print a closure, you don’t see anything terribly useful: That’s because the function itself doesn’t change. We don’t know how we would name them, but sth. To conclude this chapter, I’ll develop a simple numerical integration tool using first-class functions. To avoid this, set check.names = FALSE. In R, almost every function is a closure. A As we understand this exercise, it is about working with a list of lists, like in the following example: So we can get the same result with a more specialized function: Q: Implement mcsapply(), a multicore version of sapply(). For sin() in the range [0, π], determine the number of pieces needed so that each rule will be equally accurate. you can make your own functions in R), 4. A: In the first statement each element of trims is explicitly supplied to mean()’s second argument. I recommend the first option, using with(), because it makes it very clear when code is being executed in a special context and what that context is. | download | Z-Library. Replacement term – usually a text fragment 3. available on github. Specifically, we’ll talk about the apply family of functions, starting with sapply.To show what sapply does, let’s look at the following function: Breaking down the components: 1. Q: Use Filter() and vapply() to create a function that applies a summary In the follwing table, we return the output of `f`(x, 1), where f is the function in the first column and x is the special input in the header (the named functions also have an argument, which is FALSE by default). It applies the function to each element of the list and returns a new list. The vapply() version could be useful, if you want to control the structure of the output to get an error according to some logic of a specific usecase or you want typestable output to build up other functions on top of it. What option allows you to suppress this behaviour? You could do this by storing each approach (function) in a list: Calling a function from a list is straightforward. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. Another good opportunity for sorting the functions would be to differentiate between “numerical” and “logical” operators first and then between binary, reduced and vectorised, like below (we left the last colum, which is redundant, because of coercion, as intended): The other point are the naming conventions. Closures allow us to make functions based on a template: In this case, you could argue that we should just add another argument: That’s a reasonable solution here, but it doesn’t always work well in every situation. One function, fix_missing(), knows how to fix a single vector; the other, lapply(), knows how to do something to each column in a data frame. This is a good choice for testing because it has a simple answer: 2. pandoc. For example, arg_max(-10:5, function(x) x ^ 2) should return -10. (Hint: you’ll need to use vapply() twice.). Imagine you’ve loaded a data file, like the one below, that uses −99 to represent missing values. 2018/06/13 Debugging, condition handling, and defensive programming. What does approxfun() do? As explained for Map() in the textbook, also every replicate() could have been written via lapply(). lapply(x, f, ...) is equivalent to the following for loop: The real lapply() is rather more complicated since it’s implemented in C for efficiency, but the essence of the algorithm is the same. So the default relation is Position(f, x) <=> min(which(f(x))). Take a minute or two to think about how you might tackle this problem before reading on. Putting these pieces together gives us: This code has five advantages over copy and paste: If the code for a missing value changes, it only needs to be updated in one place. lapply returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X.. sapply is a user-friendly version and wrapper of lapply by default returning a vector, matrix or, if simplify = "array", an array if appropriate, by applying simplify2array(). Use integrate() and an anonymous function to find the area under the curve for the following functions. Which of the following commands is equivalent to with(x, f(z))? Imagine you’ve loaded a data file, like the one below, that uses −99 to represent missing values. predicate function f, span returns the location of the longest What happens if you use <- instead of <<-? What arguments should the function A: Column names are often data, and the underlying make.names() transformation is non-invertible, so the default behaviour corrupts data. Some work only needs to be done once, when the function is generated. A closure can access its own arguments, and variables defined in its parent. 9. The lapply() function applies a ... (1 star), intermediate (2 stars) or advanced (3 stars) R user? subsetting.) ... (df, is.numeric) numeric_cols <- df[, numeric] data.frame(lapply(numeric_cols, mean)) } However, the function is not robust to unusual inputs. 跨領域知識型部落客,專注於數據分析、程式設計、數位行銷與知識管理。自許為個人型成長駭客,也是知識駭客。 SDcols is an useful but tricky method in data.table. Implement All() similarly. The apply() Family. Use lapply() and an anonymous function to find the coefficient of variation (the standard deviation divided by the mean) for all columns in the mtcars dataset. Q: Implement a combination of Map() and vapply() to create an lapply() mtcars using the formulas stored in this list: A: Like in the first exercise, we can create two lapply() versions: Note that all versions return the same content, but they won’t be identical, since the values of the “call” element will differ between each version. Implement na.rm = TRUE: what R allows to disclose scientific research by creating new packages. Anonymous functions shows you a side of functions that you might not have known about: you can use functions without giving them a name. However, if you do need mutable objects and your code is not very simple, it’s usually better to use reference classes, as described in RC. Q: What does replicate() do? data.table Advanced 1hr Tutorial Matthew Dowle R/Finance, Chicago May 2013 Make predictions about what will happen if you replace new_counter() with the variants below, then run the code and check your predictions. Illustrate your results with a graph. But in our opinion, there are two important parts. In contrast to the add() example from the book, we change two things at this step. Q: Implement Any(), a function that takes a list and a predicate function, Q: What’s the relationship between which() and Position()? Closures are useful for making function factories, and are one way to manage mutable state in R. A function factory is a factory for making new functions. This isn’t tremendously useful as lapply (x, "f") is almost always equivalent to lapply (x, f) and is more typing. For example, let’s create a sample dataset: data <- matrix(c(1:10, 21:30), nrow = 5, ncol = 4) data [,1] […] When we generalize from 3 to any real number this means that the identity has to be greater than any number, which leads us to infinity. E.g. data. Use Wolfram Alpha to check your answers. To find the identity value, we can apply the same argument as in the textbook, hence our functions are also associative and the following equation should hold: So the identidy has to be greater than 3. Volume 100%. R’s usual rules ensure that we get a data frame, not a list. Q: Why isn’t a predicate function? We can now add even better rules for integrating over smaller ranges: It turns out that the midpoint, trapezoid, Simpson, and Boole rules are all examples of a more general family called Newton-Cotes rules. Numerical integration concludes the chapter with a case study that uses anonymous functions, closures and lists of functions to build a flexible toolkit for numerical integration. The chapter starts by showing a motivating example, removing redundancy and duplication in code used to clean and summarise data. The idea behind numerical integration is simple: find the area under a curve by approximating the curve with simpler components. The discussion of functional programming continues in the following two chapters: functionals explores functions that take functions as arguments and return vectors as output, and function operators explores functions that take functions as input and return them as output. I’ve put the functions in a list because I don’t want them to be available all the time. These mistakes are inconsistencies that arose because we didn’t have an authorative description of the desired action (replace −99 with NA). (Hint: Each step in the development of the tool is driven by a desire to reduce duplication and to make the approach more general. Q: Implement the span() function from Haskell: given a list x and a The trade-off between integration rules is that more complex rules are slower to compute, but need fewer pieces. 1. Teams. What is the scalar binary Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Instead of assigning the results of lapply() to df, we’ll assign them to df[]. R doesn’t have a special syntax for creating a named function: when you create a function, you use the regular assignment operator to give it a name. R is known as a “functional” language in the sense that every operation it does can be be thought of a function that operates on arguments and returns a value. Want a physical copy of the second edition of this material? 8.4 Manipulating lists. User defined functions. Create a function that creates functions that compute the ith central moment of a numeric vector. What does it return? arguments to paste() equivalent to? and returns TRUE if the predicate function returns TRUE for any of You want to replace all the −99s with NAs. The following example shows a counter that records how many times a function has been called. To do that, we could store each summary function in a list, and then run them all with lapply(): What if we wanted our summary functions to automatically remove missing values? Why are functions created by other functions called closures? All functions remember the environment in which they were created, typically either the global environment, if it’s a function that you’ve written, or a package environment, if it’s a function that someone else has written. the supplied predicate function returns TRUE. 9.2.3 Passing arguments with... It’s often convenient to pass along additional arguments to … without an anonymous function? What sort of for loop does it eliminate? Should there be? R, at its heart, is a functional programming (FP) language. It is easy to generalise this technique to a subset of columns: The key idea is function composition. Together, a static parent environment and <<- make it possible to maintain state across function calls. You’ll learn more about them in functionals. We can start applying FP ideas by writing a function that fixes the missing values in a single vector: This reduces the scope of possible mistakes, but it doesn’t eliminate them: you can no longer accidentally type -98 instead of -99, but you can still mess up the name of variable. pmin(), pmax(), and new functions row_min() and row_max(). We’ll need either an anonymous function or a new named function, since there isn’t a built-in function to handle this situation. Writing simple functions that can be understood in isolation and then composed is a powerful technique. A: Because a predicate function always returns TRUE or FALSE. 6.3 Advanced Control Flow. R Library Advanced functions. Functionals are an important part of functional programming. 6. rapply function in R: rapply function in R is nothing but recursive apply, as the name suggests it is used to apply a function to all elements of a list recursively. How # (If f is a character, this has no effect. # This does not call the anonymous function. We can apply lapply() to this problem because data frames are lists. In R, functions are objects in their own right. Given a function, like "mean", lets you find a function. Where could you have used an anonymous function instead of a named function? Q: The following code simulates the performance of a t-test for non-normal smaller(NA, NA, na.rm = TRUE) must be bigger than any other value of x.) Function ‘aggregate’. The following example uses a function factory to create functions for the tags

(paragraph), (bold), and (italics). Are called, 2. like sum_array(1, na.rm = TRUE) could be ok. We can also create vectorised versions as shown in the book. The following section discusses the third technique of functional programming in R: the ability to store functions in a list. This means that it provides many tools for the creation and manipulation of functions. use the simply2array to convert the results to an array. Function factories are most useful when: The different levels are more complex, with multiple arguments and complicated bodies. Note the column nothing, which is specifically for usecases, where sideeffects like plotting or writing data are intended. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2 ... Use lapply() and sapply() when working with lists and vectors. function below. In particular, R has what’s known as first class functions. But keeping them in a list makes code more verbose: Depending on how long we want the effect to last, you have three options to eliminate the use of html$: For a very temporary effect, you can use with(): For a longer effect, you can attach() the functions to the search path, then detach() when you’re done: Finally, you could copy the functions to the global environment with list2env(). These functions allow crossing the data in a number of ways and avoid explicit use of loop constructs. # With appropriate parenthesis, the function is called: #> [1] "

This is bold text.

". What this means should become clear by looking at the three and four dimensional cases of the following example: Q: There’s no equivalent to split() + vapply(). If you choose not to give the function a name, you get an anonymous function. FP tools are valuable because they provide tools to reduce duplication. If …. A closure is a function with data.” — John D. Cook. One way to see the contents of the environment is to convert it to a list: Another way to see what’s going on is to use pryr::unenclose(). In case of more than one longest sequenital, more than one first_index is returned. The risk of a conflict between an existing R function and an HTML tag is high. String searched – must be a string 4. Each takes the function we want to integrate, f, and a range of values, from a to b, to integrate over. some experiments. The community of R users is very large: numerous conferences, workshops and seminars are held where developers expose and present new applications. The behaviour for special inputs like NA, NaN, NULL and zero length atomics should be consistent and all versions should have a argument, for which the functions also behave consistent. should the identity be? We use the underscore suffix, to built up non suffixed versions on top, which will include the na.rm parameter. implement mcvapply(), a parallel version of vapply()? R Programming Cheat Sheet advanced Created By: arianne Colton and Sean Chen environments Access any environment on the search list as.environment('package:base') Find the environment where a name is defined pryr::where('func1') Function environments There are 4 environments for functions. Next, make your R code more efficient and readable using the apply functions. Closures are described in the next section. We’ll start with a simple benchmarking example. Find books like row_paste or paste_apply etc. outputs in a vector (or a matrix). Q: How does apply() arrange the output? Press shift question mark to access a list of keyboard shortcuts. Q: How does paste() fit into this structure? Why or why not? In the latter statement this happens via Advanced R | Hadley Wickham. #> Warning in mean.default(X[[i]], ...): argument is not numeric or logical: #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species, #> 5.843333 3.057333 3.758000 1.199333 NA, #> mpg cyl disp hp drat wt, #> 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250, #> qsec vs am gear carb, #> 17.848750 0.437500 0.406250 3.687500 2.812500, # for two dimensional cases everything is sorted by the other dimension, # there are three relevant cases for f. f is a character, f is a factor and all. Duplicating an action make… Make sure you’ve installed the pryr package with install.packages("pryr"). Q: What other types of input and output are missing? Finding errors | Using Functions |Creating and Formating Date/Time | Manupulating the Data as per the business requirements. frame. function that underlies paste()? Reproducible Research., Show how you define functions; Discuss parameters and arguments, and R's system for default values and Show how you can apply a function to every member of a list with lapply() , and give an actual example. Q&A for Work. (Hint: you Fixed – option which forces the sub function to treat the search term as a string, overriding any other instructions (useful when a search string can also be interpreted as a regular expre… We won’t include errorchecking, since this is done later at the top level and we return NA_integer_ if any of the arguments is NA (this is important, if na.rm is set to FALSE and wasn’t needed by the add() example, since + already returns NA in this case.). Ignore case – allows you to ignore case when searching 5. Modifying values in a parent environment is an important technique because it is one way to generate “mutable state” in R. Mutable state is normally hard because every time it looks like you’re modifying an object, you’re actually creating and then modifying a copy. Why doesn’t that make sense in R? To make them more accurate using the idea that underlies calculus: we’ll break up the range into smaller pieces and integrate each piece using one of the simple rules. Create a dataframe where you save the runtimes of sapply, lapply, parSapply, parLapply and doParallel Use the functions sapply and lapply to standardise the values of the download speed, sapply should also contain the initial values Acknowledgements. Note that this case often appears, wile working with the POSIXt types, POSIXct and POSIXlt. a. Now consider a related problem. How do they change for different functions? Where should you have used a named function instead of an anonymous function? To remove this source of duplication, you can take advantage of another functional programming technique: storing functions in lists. Lists of functions shows how to put functions in a list, and explains why you might care. Can you do it without a for loop? This function replaces variables defined in the enclosing environment with their values: The parent environment of a closure is the execution environment of the function that created it, as shown by this code: The execution environment normally disappears after the function returns a value. Related exercise sets: Optimize Data Exploration With Sapply() ... Go to your preferred site with resources on R, either within your university, the R community, or at work, and kindly ask the webmaster to add a link to in the following line we use mean() to aggregate these y values before they are used for the interpolation approxfun(x = c(1,1,2), y = 1:3, ties = mean).. Next, we focus on ecdf(). Can you create the list of functions from a list of coefficients for the Newton-Cotes formulae? Parse their arguments, 3. would you apply it to every column of a data frame? This can be useful for comparing observations to the mean of groups, where the group mean is not biased by the observation of interest. (Hint: use unique() and You might be tempted to copy-and-paste: As before, it’s easy to create bugs. Hence identity has to be Inf for smaller() (and -Inf for larger()), which we implement next: Like min() and max() can act on vectors, we can implement this easyly for our new functions. would it be useful? You’ll see many more closures in those two chapters. Intermediate R is the next stop on your journey in mastering the R programming language. # Since it might happen, that more than one maximum series of TRUE's appears, # we have to implement some logic, which might be easier, if we save the rle, # In the last line we calculated the first index in the original list for every encoding, # In the next line we calculate a column, which gives the maximum, # encoding length among all encodings with the value TRUE, # Now we just have to subset for maximum length among all TRUE values and return the. Brainstorm before you look up some answers in the plyr paper. The trapezoid rule uses a trapezoid. Can you Since the changes are made in the unchanging parent (or enclosing) environment, they are preserved across function calls. a data frame dangerous? You can do anything with functions that you can do with vectors: you can assign them to variables, store them in lists, pass them as arguments to other functions, create them inside functions, and even return them as the result of a function. A good rule of thumb is that an anonymous function should fit on one line and shouldn’t need to use {}. The closest in base that we are aware of is anyNA(), if one applies it elementwise. Having variables at two levels allows you to maintain state across function invocations. ... Use lapply() and sapply() when working with lists and vectors; Add your own functions into apply statements; A: Our span_r() function returns the first index of the longest sequential run of elements where the predicate is true. Filter(f, x) returns all elements of a list or a data frame, where Data Analytics, Data Science. Imagine you are comparing the performance of multiple ways of computing the arithmetic mean. From these specific functions you can extract a more general composite integration function: This function takes two functions as arguments: the function to integrate and the integration rule. In R, functions can be stored in lists. one column has more classes than the others: all columns have the same number of classes, which is more than one. fixed point algorithm. To time each function, we can combine lapply() and system.time(): Another use for a list of functions is to summarise an object in multiple ways. Q: The function below scales a vector so it falls in the range [0, 1]. For example, imagine you want to create HTML code by mapping each tag to an R function.

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