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Guest post by Stefan Holst Milton Bache on the ensurer package.

If you use R in a production environment, you have most likely experienced that some circumstances change in ways that will make your R scripts run into trouble. Many things can go wrong; package updates, external data sources, daylight savings time, etc. There is a general increasing focus on this within the R community and words like “reproducibility”, “portability” and “unit testing” are buzzing big time. Many really neat solutions are already helping a lot: RStudio’s Packrat project, Revolution Analytic’s “snapshot” feaure, and Hadley Wickham’s testthat package to name a few. Another interesting package under development is Edwin de Jonge’s “validate” package.

I found myself running into quite a few annoying “runtime” moments, where some typically external factors break R software, and more often than not I spent just too much time tracking down where the bug originated. It made me think about how best to ensure that vulnarable statements behaves as expected and how to know exactly where and when things go wrong. My coding style is heaviliy influenced by the magrittr package’s pipe operator, and I am very happy with the workflow it generates:

 data <- read_external(...) %>% make_transformation(...) %>% munge_a_little(...) %>% summarize_somehow(...) %>% filter_relevant_records(...) %T>% maybe_even_store

It’s like a recipe. But the problem is that I found no existing way of tagging potentially vulnarable steps in the above process, leaving the choice of doing nothing, or breaking it up. So I decided to make “ensurer”, so I could do:

 data <- read_external(...) %>% ensure_that(all(is.good(.)) %>% make_transformation(...) %>% ensure_that(all(is.still.good(.))) %>% munge_a_little(...) %>% summarize_somehow(...) %>% filter_relevant_records(...) %T>% maybe_even_store

Now, I don’t have a blog, but Tal Galili has been so kind to accept the ensurer vignette as a post for r-bloggers.com. I hope that ensurer can help you write better and safer code; I know it has helped me. It has some pretty neat features, so read on and see if you agree!

# Introduction

The R programming language is becoming an integrated part of data solutions and analyses in many production environments. This is no surprise: it is very powerful and goals are often very quickly accomplished with R compared to many of its competitors, and it is therefore a primary tool of choice for many statisticians, data scientists, and the like.

A common argument against it, however, is the lack of e.g. type safety, and a compiler that guards against many potential code problems. To make the situation even more dangerous, it is very common in R to have functions that accept different types for the same argument, and they may even return different types depending on various inputs and circumstances. The very large community, which is one of R’s strengths, should also raise caution, as developers adhere to different principles, styles, and standards.

When putting an R program into production one should therefore be careful and aware of the various sources of risk. External data sources or schematics may change; packages on which you depend may change; resources may be temporarily unavailable, etc. While there is little hope to solve these kinds of problems altogether, it can be essential become aware of issues as soon as they arise and take proper action. In particular, unexpected behavior may not itself raise an error (due to the lack of type safety) but may result in “corrupted” data which may propagate, and when errors occur down the road, the initial source of the problem can be hard and/or time consuming to track down.

The ensurer package has one aim: to make it as simple as possible to ensure expected/needed/desired properties of your data, and to take proper action upon failure to comply.

# Basics

The ensurer package does not depend on other packages, but its semantics are designed with %>%, the magrittr pipe, in mind. This will make it very natural to attach an ensuring contract to the result of a data pipeline before assigning it to a name, or returning it from a function, etc. The examples below will use %>% but it should be clear how to proceed without.

Usage is simple. There are only two functions to remember:

 1. ensure_that: function(value., ..., fail_with, err_desc) [short-hand alias: ensure] 2. ensures_that: function(..., fail_with, err_desc) [short-hand alias: ensures]

The first (in imperative form) takes a value (value.) and a set of conditions (…) which are verified for the value. Upon success, the value itself is returned and upon failure an error is raised (default behavior, which can be modified.) The second (in present tense) takes only the conditions and creates a function which can be reused to ensure these. The arguments fail_with and err_desc can be (but need not be) specified to alter the default behavior.

Stating the conditions is simple, each is an expression which result in TRUE or FALSE and they are separated with commas (technical note: conditions are checked with isTRUE, and conditions should therefore not result in vectors as this will count as failure even if all entries are TRUE.) To reference the value itself, use the dot-placeholder (.).

As an example, suppose you have a function get_matrix which is supposed to return a square matrix. The function may read an external text file, or otherwise be vulnerable to data corruption. Suppose that you need the matrix to be square, and you need it to be numeric. Here’s how:

 the_matrix <- get_matrix() %>% ensure_that(is.numeric(.), NCOL(.) == NROW(.))

If get_matrix returns valid data, everything is fine. But suppose a character somehow found its way in, coercing everything to the character type. In this case the default behavior is to raise an error with details on which conditions failed (all will be tested, even if previous ones fail). In this case the error is:

 Error: conditions failed for call 'get_matrix %>% ensu .. NCOL(.) == NROW(.))': * is.numeric(.)

The second function, ensures_that (note: present tense, not imperative form), is ideal for creating reusable contracts, so that the same conditions need not be specified several places with similar purpose. Using the above example, if several matrices need to be square and numeric, do:

 ensure_square_numeric <- ensures_that(NCOL(.) == NROW(.), is.numeric(.))   m1 <- get_matrix() %>% ensure_square_numeric m2 <- get_other_matrix() %>% ensure_square_numeric

Note how the present tense form, “ensures that”, and the imperative form, “ensure that” makes the statements very much like regular sentences.

It is straight forward to combine contracts made with ensures_that with on-the-fly additional conditions:

 m3 <- get_matrix() %>% ensure_square_numeric %>% ensure_that(all(. < 10))

However, note that conditions stated in the same ensure* call are all checked, i.e. in the following example the error received is not the same:

 # Only the first error is recorded. letters %>% ensure_that(length(.) == 10) %>% ensure_that(all(. == toupper(.)))   # Both errors are recorded. letters %>% ensure_that(length(.) == 10, all(. == toupper(.)))

To round off this section, here is how the first example is made without the pipe operator, %>%:

 the_matrix <- ensure_that(get_matrix(), NCOL(.) == NROW(.), is.numeric(.))

# Naming arguments

It is possible to assign names for use in the conditions, such that no external variable declaration is needed for additional information needed in the conditions. This can be used to make the code more readable, or to avoid the same computation twice. To assign a value in the ensure(s)_that call, simply use named arguments (and avoid fail_with and err_desc):

 some_object <- some_computation() %>% ensure_that(foo(a) == bar(a), a = baz(x, y, z))

# Adding existing contracts to new contracts

Sometimes it is useful to have a set of contracts for different purposes, and combine them in situations where more than one of them apply. As described above, one can chain together multiple ensure_that statements, but in that situation ensurer will not check all conditions from all contracts. Another option is to add existing contracts, constructed with ensures_that to a new ensure(s)_that call. To let ensurer know that the argument is an existing contract to be added, use a unary + operator:

 matrix_is_square <- ensures_that(NROW(.) == NCOL(.)) all_positive <- ensures_that(all(. > 0))     matrix(runif(16), 4, 4) %>% ensure(+matrix_is_square, +all_positive)

Any named arguments in an added contract will also be available in the new contract.

# Customizing individual condition messages

To make the description of each of the failed conditions more user-friendly and readable, ensurer lets you specify a description to conditions where the code is not transparent enough. To do this use a formula as condition: condition ~ “message if fails”. As an example:

 ensure_character <- ensures_that(is.character(.) ~ "vector must be of character type.")   1:10 %>% ensure_character   Error: conditions failed for call '1:10 %>% ensure_character': * vector must be of character type.

# Ensuring function return values

The ensures_that function also provides a useful mechanism for ensuring the characteristics of function return values. This will both make functions safer, but will also provide users with specific knowledge about what they can expect of your functions. Here are a few pseudo examples:

 : numeric <- ensures_that(is.numeric(.))   get_quote <- function(ticker) : numeric({ # some code that extracts latest quote for the ticker })     : square matrix <- ensures_that(is.numeric(.), is.matrix(.), NCOL(.) == NROW(.))   var_covar <- function(vec) : square matrix({ # some code that produces a variance-covariance matrix. })

While the naming convention used above is not necessary, it is expressive when used for this purpose; but there is nothing different about the contracts produced by ensures_that. Of course, another more standard option is to ensure the return value at the end of the function, yet this won’t be as transparent to the user of the function.

# Controlling failure

The main purpose of the ensurer is to ensure that values are as they are expected, and if not alert the stakeholder(s) immediately. The default behavior is therefore to raise an error. There could be several reasons to overrule this default, e.g. you may wish to send an email on error, or you might want to accept some default value if the desired value is not available.

The ensurer has a simple mechanism for changing the behavior by specifying the argument fail_with to ensure_that or ensures_that. It is possible to pass in a static value which will be returned (e.g. an empty data.frame with the correct columns, or maybe even just NA). More often, however, more dynamic behavior is needed, and for this on passes a function of a single argument, which will be applied to a simpleError object (the one that is raised by default):

 square_failure <- function(e) { # suppose you had an email function: email("[email protected]", subject = "error", body = e$message) stop(e) } m1 <- get_matrix() %>% ensure_that( NCOL(.) == NROW(.), is.numeric(.), fail_with = square_failure) m2 <- get_matrix() %>% ensure_that( NCOL(.) == NROW(.), is.numeric(.), fail_with = diag(10)) In some instances you may want to use the value itself in the error handler, although you don’t know anything about it, except that it does not comply with the conditions. Therefore this should be done with care, and currently there is not direct access to it (as you may need to think twice before doing it.) In the above example, the square_failure does not know about ., although you can fetch it using get:  square_failure <- function(e) { # fetch the dot. . <- get(".", parent.frame()) # compose a message detailing also the class of the object returned. msg <- sprintf("Here is what I know:n%snValue class: %s.", e$message, class(.) %>% paste(collapse = ", ")) # there could be several.   # suppose you had an email function: email("[email protected]", subject = "error", body = msg)   stop(e) }

It is also possible to use the dot, ., directly in anonymous error handlers defined directly in the call to e.g. ensure_that.

One can add a description to the error message without having to specify an error function, which can be useful if the same conditions are used several places in your code, or simply to add some information to the user about what could be the cause of the problem. This is done by specifying the named argument err_desc. For example, RODBC’s sqlQuery returns a data.frame on success and a character string on failure. Suppose you have a function which makes use of this to fetch some results, and you wish to make your function safe using the ensurer:

 : sql result <- ensures_that(is.data.frame(.), err_desc = "SQL error.")   daily_results <- function(day) : sql result({ sql <- sprintf("SELECT * FROM RESULTS WHERE DAY = '%s'", format(as.Date(day))) ch <- RODBC::odbcDriverConnect("connection_string") on.exit(RODBC::odbcClose(ch)) RODBC::sqlQuery(ch, sql) })

The error would look like this:

 daily_results("2014-10-01")   Error: conditions failed for call 'daily_results("2014-10-01")': * is.data.frame(.) Description: SQL error.

# Building on the ensurer functions

In this section it is shown how simple extensions to the functionality provided by the ensurer can be used to make flexible, yet strict, safety mechanisms.

A common task is to load data from one or more external sources, say from a SQL database, process the data, combine it various ways, and then produce some result or analysis. This may even be bundled up in a package and used by someone else who is unaware of the underlying data sources. If a connection is broken, a schema has changed, or if somehow the data does not come out as expected, the software will probably suffer from both lack of results and some useless error messages. One step towards avoid such a scenario is to ensure that all data that are pulled into the application are as expected; i.e. provide some “type safety” to the data objects, say data.frames.

Often you can gain a lot of security in your code if you can specify a template for risky objects which specifies the necessary details of such objects. An obvious example is a data.frame: you may need to require specific names and types of the columns and/or that it is non-empty. Maybe you have several important datasets that each are specific, yet all require the same type of safety. Here is an example of how to specify a “template” and an ensuring function that compares an object to such a template.

 iris_template <- data.frame( Sepal.Length = numeric(0), Sepal.Width = numeric(0), Petal.Length = numeric(0), Petal.Width = numeric(0), Species = factor(numeric(0), levels = c("setosa", "versicolor", "virginica")) )   ensure_as_template <- function(x, tpl) ensure_that(x, is.data.frame(.), identical(class(.), class(tpl)), identical(sapply(., class), sapply(tpl, class)), identical(sapply(., levels), sapply(tpl, levels)) )   iris %>% ensure_as_template(iris_template)

The ensure_as_template is general enough, to accept any other data.frame with a corresponding template. Packages that exposes functions which return data.frames could define such templates (internally or externally) and ensure data validity this way.