Testing R code

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Possibly, if a certain error is very common it does not matter whether it is always larger or smaller than the correct solution.

Here, we continue to improve the exercise by randomly selecting only one of the two typical errors. Then, sometimes the error is smaller and sometimes larger than the correct solution. This has a couple of desirable features:.

Therefore, for consistency, the binomial4 version of the exercise uses math markup for all numbers in the question. Of course, there is still at least one number that is either larger or smaller than the correct solution so that the correct solution takes rank 1 or 5 less often than ranks 2 to 4. But this seems to be a reasonable compromise. When drawing many random versions from a certain exercise template, it is essential to thoroughly check that exercise before using it in real exams. Most importantly, of course, the authors should make sure that the story and setup is sensible, question and solution are carefully formulated, etc.

But then the technical aspects should be checked as well. This should include checking whether the exercise can be rendered correctly into PDF via exams2pdf But it is possible that some edge cases occur only very rarely so that, dependending on the complexity of the data-generating process, it is often useful to use much higher values of n. Then we could have asssessed directly which of these lead to ok solutions and which are too extreme.

However, when the data-generating process becomes more complex this might not be so easy. Motivation After a dynamic exercise has been developed, thorough testing is recommended before administering the exercise in a student assessment. You are currently waiting in the queue at the checkout of a grocery story with n customers in front of you.


  1. Using t-tests in R | Department of Statistics.
  2. Table of contents?
  3. Testing R Code | R-bloggers?
  4. Motivation.
  5. Basics of testing.
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What is the probability in percent that k or more of the other customers pay with their card? Exercise templates Type Description 1 binomial1. Rmd binomial1. Rnw num First attempt with poor parameter ranges. Rmd binomial2.

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Rnw num As in 1 but with some parameter ranges improved. Rmd binomial3. Rnw schoice Single-choice version based on 2 but with too many typical wrong solutions and poor random answers. Rmd binomial4. Rnw schoice As in 3 but with improved sampling of both typical errors and random solutions.

Why testing is important?

Rnw : for i in paste0 extra, , ". Rmd" download. Here, no errors occurred but further examination shows that parameters have clearly been too extreme: plot s1 The left panel shows the distribution run times which shows that this was fast without any problems. Median Mean 3rd Qu. Single-choice exercise: First attempt Building on the numeric example above we now move on to set up a corresponding single-choice exercise. Rmd" plot s3 The runtimes are still ok indicating that the while loop was rarely used as are the numeric solutions - and due to shuffling the position of the correct solution in the answer list is completely random.

Single-choice exercise: Improved version Here, we continue to improve the exercise by randomly selecting only one of the two typical errors.

This makes sure that even if the correct solution is not in the middle of the admissible range in our case: either close to 0 or to , it is not more likely to get greater vs. We can set a minimum distance delta between all answer options correct solution and distractors to make sure that answers are not too close.

Multiple (Linear) Regression

Shuffling is also carried out automatically so exshuffle does not have to be set. Rmd" plot s4 Of course, there is still at least one number that is either larger or smaller than the correct solution so that the correct solution takes rank 1 or 5 less often than ranks 2 to 4. Summary When drawing many random versions from a certain exercise template, it is essential to thoroughly check that exercise before using it in real exams.

According to a recent survey - p percent of all customers in grocery stores pay cash while the rest use their credit or cash card.

One-Sample t Test & Confidence Interval in R with Example - R Tutorial 4.1- MarinStatsLectures

The difference between having unit tests and not having them is the difference between the cost of writing the test and the cost of running it compared to the cost of testing by hand. If the cost of writing a unit test is 2 minutes and the cost of running the unit test is practically 0, but the cost of manually testing the code is 1 minute, then you break even when you have run the test twice.

Testing is often introduced as a last-minute thing, but most scientists who write code do an informal version of testing as they develop. Software testing is a process by which one or more expected behaviours and results from a piece of software are exercised and confirmed. Well chosen tests will confirm expected code behaviour for the extreme boundaries of the input domains, output ranges, parametric combinations, and other behavioural edge cases. Unless you write flawless, bug-free, perfectly accurate, fully precise,and predictable code every time , you must test your code in order to trust it enough to answer in the affirmative to at least a few of the following questions:.

The longer answer is that testing either before or after your software is written will improve your code, but testing after your program is used for something important is too late. Bugs appear very close to hard deadlines. Testing allows to quickly identify where the problem is and fix it.

Code that is easy to test is usually better designed. Tests sometimes make you see large complicated functions and break them down into smaller, more manageable chunks. Getting more serious about testing has totally changed my approach towards software development over the last year. I find that I now write programs that are better separated into component parts, that define their roles more clearly, that have fewer bugs or unexpected behaviours and that are easier to modify as I go along.

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We'll use the testthat package to make testing easy and intuitive. This is a brilliant package that scales up from one-off tests to detailed suites that are well suited to large packages. This is a simple function, and one that we could use elsewhere.

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But especially if we do use it elsewhere we want to know how it behaves. So we write tests partly to document how it will react in particular edge cases. It also means that if we depend on it, we are free to change how it is implemented internally adding a new argument, or changing the underlying algorithm, etc and if the tests still agree then the code that depends on the function will still behave correctly if we have written the tests well.

Note that this does not produce output! It will only produce output if the test fails, in which case it will appear as an error. Alternatively, when running non-interactively, we'll see indications that individual tests have passed.