219 lines
7.9 KiB
R
219 lines
7.9 KiB
R
# tocID <- "RPR-Unit_testing.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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# ---------------------------------------------------------------------------- #
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the RPR-Unit_testing unit.
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#
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# Version: 1.1
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#
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# Date: 2017 10 - 2019 01
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.1 Change from require() to requireNamespace()
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# 1.0 New code
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#
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#
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# TODO:
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#
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#
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# == DO NOT SIMPLY source() THIS FILE! =======================================
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#
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# If there are portions you don't understand, use R's help system, Google for an
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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#
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# ==============================================================================
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#TOC> ==========================================================================
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#TOC>
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#TOC> Section Title Line
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#TOC> -------------------------------------------------
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#TOC> 1 Unit Tests with testthat 46
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#TOC> 2 Organizing your tests 165
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#TOC> 2.1 Testing scripts 189
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#TOC> 3 Task solutions 204
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#TOC>
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#TOC> ==========================================================================
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# = 1 Unit Tests with testthat ============================================
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# The testthat package supports writing and executing unit tests in many ways.
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if (! requireNamespace("testthat", quietly = TRUE)) {
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install.packages("testthat")
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}
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# Package information:
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# library(help = testthat) # basic information
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# browseVignettes("testthat") # available vignettes
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# data(package = "testthat") # available datasets
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# testthat is one of those packages that we either use A LOT in a script,
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# or not at all. Therfore it's more reasonable to depart from our usual
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# <package>::<function>() idiom, and load the entire library. In fact, if
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# we author packages, it is common practice to load testthat in the part
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# of the package that automates testing.
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library(testthat)
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# An atomic test consists of an expectation about the bahaviour of a function or
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# the existence of an object. testthat provides a number of useful expectations:
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# At the most basic level, you can use expect_true() and expect_false():
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expect_true(file.exists("./data/S288C_YDL056W_MBP1_coding.fsa"))
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expect_true(file.exists("NO-SUCH-FILE.txt"))
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expect_false(is.integer(NA))
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# More commonly, you will test for equality of an output with a given result.
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# But you need to consider what it means for two numbers to be "equal" on a
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# digital computer. Consider:
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49*(1/49) == 1 # Surprised? Read FAQ 7.31
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# https://cran.r-project.org/doc/FAQ/R-FAQ.html
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49*(1/49) - 1 # NOT zero (but almost)
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# This is really unpredictable ...
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0.1 + 0.05 == 0.15
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0.2 + 0.07 == 0.27
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# It's easy to be caught on the wrong foot with numeric comparisons, therefore
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# R uses the function all.equal() to test whether two numbers are equal for
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# practical puposes up to machine precision.
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49*(1/49) == 1
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all.equal(49*(1/49), 1)
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# The testthat function expect_equal() uses all.equal internally:
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expect_equal(49*(1/49), 1)
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# ... which is reasonable, or, if things MUST be exactly the same ...
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expect_identical(49*(1/49), 1)
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# ... but consider:
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expect_identical(2, 2L) # one is typeof() "double", the other is integer"
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# Some very useful expectations are expect_warning(), and expect_error(), for
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# constructing tests that check for erroneous output:
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as.integer(c("1", "2", "three"))
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expect_warning(as.integer(c("1", "2", "three"))) # Note that the warning is NOT
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# printed.
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1/"x"
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expect_warning(1/"x")
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expect_error(1/"x") # Again: note that the error is NOT printed, as well
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# code execution will continue.
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# Even better, you can check if the warning or error is what you expect it
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# to be - because it could actually have occured somewhere else in your code.
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v <- c("1", "x")
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log(v[1:2])
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expect_error(log(v[1:2]), "non-numeric argument to mathematical function")
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expect_error(log(v[1:2]), "non-numeric") # We can abbreviate the error message.
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expect_error(log(v[1,2])) # This appears oK, but ...
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expect_error(log(v[1,2]), "non-numeric") # ... it's actually a different error!
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# Producing unit tests simply means: we define a function, and then we check
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# whether all test pass. Consider a function that is loaded on startup:
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biCode
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# We could test it like so:
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expect_equal(biCode(""), ".....")
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expect_equal(biCode(" "), ".....")
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expect_equal(biCode("123 12"), ".....")
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expect_equal(biCode("h sapiens"), "H..SA")
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expect_equal(biCode("homo sapiens"), "HOMSA")
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expect_equal(biCode("[homo sapiens neanderthaliensis]"), "HOMSA")
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expect_equal(biCode(c("Phascolarctos cinereus", "Macropus rufus")),
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c("PHACI", "MACRU"))
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expect_error(biCode(), "argument \"s\" is missing, with no default")
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# The test_that() function allows to group related tests, include an informative
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# message which test is being executed, and run a number of tests that are
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# passed to the function inside a code block - i.e. {...}
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# test_that("<descriptive string>, {<code block>})
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test_that("NA values are preserved", {
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expect_true(is.na((biCode(NA))))
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expect_equal(biCode(c("first", NA, "last")),
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c("FIRST", NA, "LAST."))
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})
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# Task: Write a function calcGC() that calculates GC content in a sequence.
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# Hint: you could strsplit() the sequence into a vector, and count
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# G's and C's; or you could use gsub("[AT]", "", <sequence>) to remove
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# A's and T's, and use nchar() before and after to calculate the content
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# from the length difference.
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# Then write tests that:
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# confirm that calcGC("AATT") is 0;
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# confirm that calcGC("ATGC") is 0.5;
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# confirm that calcGC("AC") is 0.5;
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# confirm that calcGC("CGCG") is 1;
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# = 2 Organizing your tests ===============================================
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# Tests are only useful if they are actually executed and we need to make sure
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# there are no barriers to do that. The testthat package supports automatic
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# execution of tests:
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# - put your tests into an R-script,
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# - save your tests in a file called "test_<my-function-name>.R"
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# - execute the test with test_file("test_<my-function-name>.R") ...
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# ... or, if you are working on a project ...
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# - place the file in a test-directory (e.g. the directory "test" in this
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# project),
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# - execute all your tests with test_dir("<my-test-directory>")
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# For example I have provided a "tests" directory with this project, and
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# placed the file "test_biCode.R" inside.
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file.show("./tests/test_biCode.R")
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# Execute the file ...
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test_file("./tests/test_biCode.R")
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# .. or execute all the test files in the directory:
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test_dir("./tests")
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# == 2.1 Testing scripts ===================================================
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# Scripts need special consideration since we do not necessarily source() them
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# entirely. Therefore automated testing is not reasonable. What you can do
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# instead is to place a conditional block at the end of your script, that
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# never gets executed - then you can manually execute the code in the block
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# whenever you wish to test your functions. For example:
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if (FALSE) {
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# ... your tests go here
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}
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# = 3 Task solutions ======================================================
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calcGC <- function(s) {
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s <- gsub("[^agctAGCT]", "", s)
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return(nchar(gsub("[atAT]", "", s)) / nchar(s))
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}
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expect_equal(calcGC("AATT"), 0)
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expect_equal(calcGC("ATGC"), 0.5)
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expect_equal(calcGC("AC"), 0.5)
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expect_equal(calcGC("CGCG"), 1)
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# [END]
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