tests/testthat/test-valueCalculator.R
In bcgov/BCForestGroundSample: BC forest ground sample compilation
test_that("valueCalculator.R: tree value is not correctly calculated.", {
library(data.table)
library(testthat)
## check the error
# 1. number of row is right, ncol is not right
logvolmatrix <- data.table(log_v_0 = 1:5,
log_v_1 = 5:9,
log_v_2 = c(5:7, NA, NA)) ## has 5 rows and 3 columns
merchlogmatrix <- data.table(log_vm_1 = 1:5,
log_vm_2 = 5:9,
log_vm_3 = 7:11) ## has 5 rows and 3 columns
soundtable <- data.table(log_s_1 = rep(100, 5),
log_s_2 = rep(80, 5),
log_s_3 = rep(70, 5),
log_s_4 = rep(60, 5)) ## has 5 rows and 4 columns
expect_error(valueCalculator(species = "AC",
grossVolMatrix = logvolmatrix,
grossMerchVolMatrix = merchlogmatrix,
callGradeMatrix = soundtable))
# 2. number of row is wrong and number of col is right
logvolmatrix <- data.table(log_v_0 = 1:5,
log_v_1 = 5:9,
log_v_2 = c(5:7, NA, NA)) ## has 5 rows and 3 columns
merchlogmatrix <- data.table(log_vm_1 = 1:3,
log_vm_2 = 5:7) ## has 5 rows and 3 columns
soundtable <- data.table(log_s_1 = rep(100, 5),
log_s_2 = rep(80, 5)) ## has 5 rows and 4 columns
expect_error(valueCalculator(species = "AC",
grossVolMatrix = logvolmatrix,
grossMerchVolMatrix = merchlogmatrix,
callGradeMatrix = soundtable))
## check the outputs along with sas compiler
##
randomsasoutputs <- data.table(CLSTR_ID = c("0071-0079-NO1", "CMI2-0425-FR1",
"0172-1911-MO1", "0242-0086-NO1", "0091-0118-DO1", "0221-0070-TO1",
"DPG1-0010-NO1", "DPG1-0124-DO1", "DME1-0032-QR1", "011M-0082-MR1"),
PLOT = c("I", "I", "I", "N", "I", "W", "W", "N",
"I", "I"),
TREE_NO = c("007",
"060", "194", "001", "001", "004", "002", "005", "004", "201"),
SPECIES = c("SE", "FDI", "BL", "SX", "BL", "PY", "SE", "AT",
"PY", "FDI"),
LOG_V_0 = c(0.115653255733009,
0.0683438028704155, 0.0203226570323606, 0.199506032292159,
0.0316514639234829, 0.238202640191485, 0.0920843487373005,
0.0279606601119462, 0.120399650145645, 0.0767425813663651),
LOG_V_1 = c(1.99186066371631, 1.31566450371338,
0.270473606918279, 1.83203738785607, 0.787142840916972, 4.3374748058904,
1.37638078511191, 0.136673116479434, 1.67919378465936, 1.1048730845388),
LOG_V_2 = c(0.723002021451179,
0.445775066064147, 0.136142504066935, 1.66396381054031, 0.308896881360363,
2.00638726831731, 0.424587812779612, 0.585443272877791, 0.700402139851381,
1.13831789282572),
LOG_V_3 = c(0.722456018914301,
0.501792009737348, 0.0737936007500049, 1.74683186506489,
0.0239241721384739, 1.21782510878648, 0.344345381186035,
0.0996147915024272, 0.414942323557599, 0.461132223528808),
LOG_V_4 = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
LOG_VM_1 = c(1.99186066371631,
1.31566450371338, 0.270473606918279, 1.83203738785607, 0.787142840916972,
4.3374748058904, 1.37638078511191, 0.136673116479434, 1.67919378465936,
1.1048730845388),
LOG_VM_2 = c(0.723002021451179,
0.445775066064147, 0.136142504066935, 1.66396381054031, 0.308896881360363,
2.00638726831731, 0.424587812779612, 0.585443272877791, 0.700402139851381,
1.13831789282572),
LOG_VM_3 = c(0.708624127050974,
0.490191592317167, 0.0581736172535729, 1.73616458656735,
0.0062246980089177, 1.21110968435012, 0.333184581791034,
0.0859368095607724, 0.408667733071952, 0.449892147685851),
LOG_VM_4 = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
LOG_G_1 = c("H",
"I", "J", "H", "J", "H", "I", "Y", "I", "H"),
LOG_G_2 = c("J", "U", "U", "I", "U", "U", "U",
"U", "U", "J"),
LOG_G_3 = c("U", "X", "Y", "U", "Y", "Y", "Y",
"Y", "Y", "X"),
LOG_G_4 = c("", "", "", "", "", "", "", "", "", ""),
VAL_NET = c(302.22533536152,
177.327773624792, 25.675541131859, 404.928832382611, 62.205517862466,
421.514168354895, 137.830552611847, 28.7605913300878, 109.768589200405,
279.54065458192),
VAL_MER = c(301.60290022767, 177.037763189288,
24.9726418745196, 404.448804850222, 61.4090415266359, 421.379859866168,
137.328316639072, 28.2818619621299, 109.643097390692, 279.259652685847),
LOG_C_1 = c(219.104673008794,
144.723095408472, 16.2284164150967, 201.524112664168, 47.2285704550183,
346.997984471232, 103.228558883393, 4.7835590767802, 83.9596892329681,
171.255328103514),
LOG_C_2 = c(50.6101415015825,
20.0598779728866, 6.12641268301206, 124.797285790523, 13.9003596612163,
50.1596817079328, 19.1064515750825, 20.4905145507227, 17.5100534962845,
96.7570208901862),
LOG_C_3 = c(32.5105208511435,
12.5448002434337, 3.32071203375022, 78.60743392792, 1.07658774623133,
24.3565021757296, 15.4955421533716, 3.48651770258495, 8.29884647115197,
11.5283055882202),
LOG_C_4 = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_))
# CLSTR_ID PLOT TREE_NO LOG_V_0 LOG_V_1 LOG_V_2 LOG_V_3 LOG_V_4 LOG_VM_1
# 1: 0071-0079-NO1 I 007 0.11565326 1.9918607 0.7230020 0.72245602 NA 1.9918607
# 2: CMI2-0425-FR1 I 060 0.06834380 1.3156645 0.4457751 0.50179201 NA 1.3156645
# 3: 0172-1911-MO1 I 194 0.02032266 0.2704736 0.1361425 0.07379360 NA 0.2704736
# 4: 0242-0086-NO1 N 001 0.19950603 1.8320374 1.6639638 1.74683187 NA 1.8320374
# 5: 0091-0118-DO1 I 001 0.03165146 0.7871428 0.3088969 0.02392417 NA 0.7871428
# 6: 0221-0070-TO1 W 004 0.23820264 4.3374748 2.0063873 1.21782511 NA 4.3374748
# 7: DPG1-0010-NO1 W 002 0.09208435 1.3763808 0.4245878 0.34434538 NA 1.3763808
# 8: DPG1-0124-DO1 N 005 0.02796066 0.1366731 0.5854433 0.09961479 NA 0.1366731
# 9: DME1-0032-QR1 I 004 0.12039965 1.6791938 0.7004021 0.41494232 NA 1.6791938
# 10: 011M-0082-MR1 I 201 0.07674258 1.1048731 1.1383179 0.46113222 NA 1.1048731
# LOG_VM_2 LOG_VM_3 LOG_VM_4 LOG_G_1 LOG_G_2 LOG_G_3 LOG_G_4 VAL_NET VAL_MER
# 1: 0.7230020 0.708624127 NA H J U 302.22534 301.60290
# 2: 0.4457751 0.490191592 NA I U X 177.32777 177.03776
# 3: 0.1361425 0.058173617 NA J U Y 25.67554 24.97264
# 4: 1.6639638 1.736164587 NA H I U 404.92883 404.44880
# 5: 0.3088969 0.006224698 NA J U Y 62.20552 61.40904
# 6: 2.0063873 1.211109684 NA H U Y 421.51417 421.37986
# 7: 0.4245878 0.333184582 NA I U Y 137.83055 137.32832
# 8: 0.5854433 0.085936810 NA Y U Y 28.76059 28.28186
# 9: 0.7004021 0.408667733 NA I U Y 109.76859 109.64310
# 10: 1.1383179 0.449892148 NA H J X 279.54065 279.25965
# LOG_C_1 LOG_C_2 LOG_C_3 LOG_C_4
# 1: 219.104673 50.610142 32.510521 NA
# 2: 144.723095 20.059878 12.544800 NA
# 3: 16.228416 6.126413 3.320712 NA
# 4: 201.524113 124.797286 78.607434 NA
# 5: 47.228570 13.900360 1.076588 NA
# 6: 346.997984 50.159682 24.356502 NA
# 7: 103.228559 19.106452 15.495542 NA
# 8: 4.783559 20.490515 3.486518 NA
# 9: 83.959689 17.510053 8.298846 NA
# 10: 171.255328 96.757021 11.528306 NA
netvols <- valueCalculator(species = randomsasoutputs$SPECIES,
grossVolMatrix = randomsasoutputs[, paste("LOG_V_", 0:4, sep = ""), with = FALSE],
grossMerchVolMatrix = randomsasoutputs[, paste("LOG_VM_", 1:4, sep = ""), with = FALSE],
callGradeMatrix = randomsasoutputs[, paste("LOG_G_", 1:4, sep = ""), with = FALSE])
## FOR MERCHANTABLE VALUE
expect_equal(netvols$VAL_MER, randomsasoutputs$VAL_MER)
## FOR VOLUME VALUE
expect_equal(netvols$VAL_NET, randomsasoutputs$VAL_NET)
## FOR EACH LOG
expect_equal(netvols$LOG_C_1, randomsasoutputs$LOG_C_1)
expect_equal(netvols$LOG_C_2, randomsasoutputs$LOG_C_2)
expect_equal(netvols$LOG_C_3, randomsasoutputs$LOG_C_3)
expect_equal(netvols$LOG_C_4, randomsasoutputs$LOG_C_4)
})
bcgov/BCForestGroundSample documentation built on May 25, 2019, 3:21 p.m.
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test_that("valueCalculator.R: tree value is not correctly calculated.", {
library(data.table)
library(testthat)
## check the error
# 1. number of row is right, ncol is not right
logvolmatrix <- data.table(log_v_0 = 1:5,
log_v_1 = 5:9,
log_v_2 = c(5:7, NA, NA)) ## has 5 rows and 3 columns
merchlogmatrix <- data.table(log_vm_1 = 1:5,
log_vm_2 = 5:9,
log_vm_3 = 7:11) ## has 5 rows and 3 columns
soundtable <- data.table(log_s_1 = rep(100, 5),
log_s_2 = rep(80, 5),
log_s_3 = rep(70, 5),
log_s_4 = rep(60, 5)) ## has 5 rows and 4 columns
expect_error(valueCalculator(species = "AC",
grossVolMatrix = logvolmatrix,
grossMerchVolMatrix = merchlogmatrix,
callGradeMatrix = soundtable))
# 2. number of row is wrong and number of col is right
logvolmatrix <- data.table(log_v_0 = 1:5,
log_v_1 = 5:9,
log_v_2 = c(5:7, NA, NA)) ## has 5 rows and 3 columns
merchlogmatrix <- data.table(log_vm_1 = 1:3,
log_vm_2 = 5:7) ## has 5 rows and 3 columns
soundtable <- data.table(log_s_1 = rep(100, 5),
log_s_2 = rep(80, 5)) ## has 5 rows and 4 columns
expect_error(valueCalculator(species = "AC",
grossVolMatrix = logvolmatrix,
grossMerchVolMatrix = merchlogmatrix,
callGradeMatrix = soundtable))
## check the outputs along with sas compiler
##
randomsasoutputs <- data.table(CLSTR_ID = c("0071-0079-NO1", "CMI2-0425-FR1",
"0172-1911-MO1", "0242-0086-NO1", "0091-0118-DO1", "0221-0070-TO1",
"DPG1-0010-NO1", "DPG1-0124-DO1", "DME1-0032-QR1", "011M-0082-MR1"),
PLOT = c("I", "I", "I", "N", "I", "W", "W", "N",
"I", "I"),
TREE_NO = c("007",
"060", "194", "001", "001", "004", "002", "005", "004", "201"),
SPECIES = c("SE", "FDI", "BL", "SX", "BL", "PY", "SE", "AT",
"PY", "FDI"),
LOG_V_0 = c(0.115653255733009,
0.0683438028704155, 0.0203226570323606, 0.199506032292159,
0.0316514639234829, 0.238202640191485, 0.0920843487373005,
0.0279606601119462, 0.120399650145645, 0.0767425813663651),
LOG_V_1 = c(1.99186066371631, 1.31566450371338,
0.270473606918279, 1.83203738785607, 0.787142840916972, 4.3374748058904,
1.37638078511191, 0.136673116479434, 1.67919378465936, 1.1048730845388),
LOG_V_2 = c(0.723002021451179,
0.445775066064147, 0.136142504066935, 1.66396381054031, 0.308896881360363,
2.00638726831731, 0.424587812779612, 0.585443272877791, 0.700402139851381,
1.13831789282572),
LOG_V_3 = c(0.722456018914301,
0.501792009737348, 0.0737936007500049, 1.74683186506489,
0.0239241721384739, 1.21782510878648, 0.344345381186035,
0.0996147915024272, 0.414942323557599, 0.461132223528808),
LOG_V_4 = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
LOG_VM_1 = c(1.99186066371631,
1.31566450371338, 0.270473606918279, 1.83203738785607, 0.787142840916972,
4.3374748058904, 1.37638078511191, 0.136673116479434, 1.67919378465936,
1.1048730845388),
LOG_VM_2 = c(0.723002021451179,
0.445775066064147, 0.136142504066935, 1.66396381054031, 0.308896881360363,
2.00638726831731, 0.424587812779612, 0.585443272877791, 0.700402139851381,
1.13831789282572),
LOG_VM_3 = c(0.708624127050974,
0.490191592317167, 0.0581736172535729, 1.73616458656735,
0.0062246980089177, 1.21110968435012, 0.333184581791034,
0.0859368095607724, 0.408667733071952, 0.449892147685851),
LOG_VM_4 = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
LOG_G_1 = c("H",
"I", "J", "H", "J", "H", "I", "Y", "I", "H"),
LOG_G_2 = c("J", "U", "U", "I", "U", "U", "U",
"U", "U", "J"),
LOG_G_3 = c("U", "X", "Y", "U", "Y", "Y", "Y",
"Y", "Y", "X"),
LOG_G_4 = c("", "", "", "", "", "", "", "", "", ""),
VAL_NET = c(302.22533536152,
177.327773624792, 25.675541131859, 404.928832382611, 62.205517862466,
421.514168354895, 137.830552611847, 28.7605913300878, 109.768589200405,
279.54065458192),
VAL_MER = c(301.60290022767, 177.037763189288,
24.9726418745196, 404.448804850222, 61.4090415266359, 421.379859866168,
137.328316639072, 28.2818619621299, 109.643097390692, 279.259652685847),
LOG_C_1 = c(219.104673008794,
144.723095408472, 16.2284164150967, 201.524112664168, 47.2285704550183,
346.997984471232, 103.228558883393, 4.7835590767802, 83.9596892329681,
171.255328103514),
LOG_C_2 = c(50.6101415015825,
20.0598779728866, 6.12641268301206, 124.797285790523, 13.9003596612163,
50.1596817079328, 19.1064515750825, 20.4905145507227, 17.5100534962845,
96.7570208901862),
LOG_C_3 = c(32.5105208511435,
12.5448002434337, 3.32071203375022, 78.60743392792, 1.07658774623133,
24.3565021757296, 15.4955421533716, 3.48651770258495, 8.29884647115197,
11.5283055882202),
LOG_C_4 = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_))
# CLSTR_ID PLOT TREE_NO LOG_V_0 LOG_V_1 LOG_V_2 LOG_V_3 LOG_V_4 LOG_VM_1
# 1: 0071-0079-NO1 I 007 0.11565326 1.9918607 0.7230020 0.72245602 NA 1.9918607
# 2: CMI2-0425-FR1 I 060 0.06834380 1.3156645 0.4457751 0.50179201 NA 1.3156645
# 3: 0172-1911-MO1 I 194 0.02032266 0.2704736 0.1361425 0.07379360 NA 0.2704736
# 4: 0242-0086-NO1 N 001 0.19950603 1.8320374 1.6639638 1.74683187 NA 1.8320374
# 5: 0091-0118-DO1 I 001 0.03165146 0.7871428 0.3088969 0.02392417 NA 0.7871428
# 6: 0221-0070-TO1 W 004 0.23820264 4.3374748 2.0063873 1.21782511 NA 4.3374748
# 7: DPG1-0010-NO1 W 002 0.09208435 1.3763808 0.4245878 0.34434538 NA 1.3763808
# 8: DPG1-0124-DO1 N 005 0.02796066 0.1366731 0.5854433 0.09961479 NA 0.1366731
# 9: DME1-0032-QR1 I 004 0.12039965 1.6791938 0.7004021 0.41494232 NA 1.6791938
# 10: 011M-0082-MR1 I 201 0.07674258 1.1048731 1.1383179 0.46113222 NA 1.1048731
# LOG_VM_2 LOG_VM_3 LOG_VM_4 LOG_G_1 LOG_G_2 LOG_G_3 LOG_G_4 VAL_NET VAL_MER
# 1: 0.7230020 0.708624127 NA H J U 302.22534 301.60290
# 2: 0.4457751 0.490191592 NA I U X 177.32777 177.03776
# 3: 0.1361425 0.058173617 NA J U Y 25.67554 24.97264
# 4: 1.6639638 1.736164587 NA H I U 404.92883 404.44880
# 5: 0.3088969 0.006224698 NA J U Y 62.20552 61.40904
# 6: 2.0063873 1.211109684 NA H U Y 421.51417 421.37986
# 7: 0.4245878 0.333184582 NA I U Y 137.83055 137.32832
# 8: 0.5854433 0.085936810 NA Y U Y 28.76059 28.28186
# 9: 0.7004021 0.408667733 NA I U Y 109.76859 109.64310
# 10: 1.1383179 0.449892148 NA H J X 279.54065 279.25965
# LOG_C_1 LOG_C_2 LOG_C_3 LOG_C_4
# 1: 219.104673 50.610142 32.510521 NA
# 2: 144.723095 20.059878 12.544800 NA
# 3: 16.228416 6.126413 3.320712 NA
# 4: 201.524113 124.797286 78.607434 NA
# 5: 47.228570 13.900360 1.076588 NA
# 6: 346.997984 50.159682 24.356502 NA
# 7: 103.228559 19.106452 15.495542 NA
# 8: 4.783559 20.490515 3.486518 NA
# 9: 83.959689 17.510053 8.298846 NA
# 10: 171.255328 96.757021 11.528306 NA
netvols <- valueCalculator(species = randomsasoutputs$SPECIES,
grossVolMatrix = randomsasoutputs[, paste("LOG_V_", 0:4, sep = ""), with = FALSE],
grossMerchVolMatrix = randomsasoutputs[, paste("LOG_VM_", 1:4, sep = ""), with = FALSE],
callGradeMatrix = randomsasoutputs[, paste("LOG_G_", 1:4, sep = ""), with = FALSE])
## FOR MERCHANTABLE VALUE
expect_equal(netvols$VAL_MER, randomsasoutputs$VAL_MER)
## FOR VOLUME VALUE
expect_equal(netvols$VAL_NET, randomsasoutputs$VAL_NET)
## FOR EACH LOG
expect_equal(netvols$LOG_C_1, randomsasoutputs$LOG_C_1)
expect_equal(netvols$LOG_C_2, randomsasoutputs$LOG_C_2)
expect_equal(netvols$LOG_C_3, randomsasoutputs$LOG_C_3)
expect_equal(netvols$LOG_C_4, randomsasoutputs$LOG_C_4)
})
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