context("evalMissingData") # setup horizon-level data: data are from lab sampled pedons # sprinkle-in some NA # added fake contact to the bottom via R, Cr, Cd d <- read.csv( textConnection('series,name,top,bottom,clay,frags,ph auburn,A,0,3,21,6,NA auburn,AB,3,15,21,13,5.6 auburn,Bw,15,25,20,9,5.8 auburn,R,25,47,21,28,5.8 dunstone,A,0,5,16,13,6 dunstone,AB,5,17,17,19,6.3 dunstone,Bt,17,31,20,6,6.3 dunstone,Cr,31,41,21,15,6.3 sobrante,A,0,5,18,0,5.8 sobrante,Ab,5,10,16,2,5.7 sobrante,Bt1,10,28,15,21,5.8 sobrante,Bt2,28,51,NA,NA,NA sobrante,Cd,51,74,20,12,6.2'), stringsAsFactors=FALSE) # establish site-level data s <- data.frame( series=c('auburn', 'dunstone', 'sobrante'), precip=c(24, 30, 32), stringsAsFactors=FALSE ) # upgrade to SoilProfile Collection object depths(d) <- series ~ top + bottom site(d) <- s ## tests test_that("runs as expected", { # does it run? e.rel <- evalMissingData(d, vars = c('clay', 'frags', 'ph'), name = 'name', method = 'relative') e.abs <- evalMissingData(d, vars = c('clay', 'frags', 'ph'), name = 'name', method = 'absolute') # result is a numeric vector expect_true(inherits(e.rel, 'numeric')) expect_true(inherits(e.abs, 'numeric')) # there should be no NA expect_true(all(is.na(e.rel) == FALSE)) expect_true(all(is.na(e.abs) == FALSE)) # check against hand-computed results # as.vector removes names attr # auburn expect_equal(as.vector(e.rel[1]), 22 / 25, tolerance=0.001) expect_equal(as.vector(e.abs[1]), 22 , tolerance=0.001) # dunstone expect_equal(as.vector(e.rel[2]), 1.000, tolerance=0.001) expect_equal(as.vector(e.abs[2]), 31, tolerance=0.001) # sobrante expect_equal(as.vector(e.rel[3]), 28 / 51, tolerance=0.001) expect_equal(as.vector(e.abs[3]), 28 , tolerance=0.001) }) test_that("expected errors", { # bad horizon name expect_error(evalMissingData(d, vars = c('clay', 'frags', 'ph'), name = 'namae')) # bad var spec expect_error(evalMissingData(d, vars = c('clay', 'frags', 'a'), name = 'name')) })