MS Patterson, [email protected] June 14, 2019
Set working directory to where data is being stored.
#setwd("~/R/GIA/")library(tidyverse)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
## Registered S3 method overwritten by 'rvest':
## method from
## read_xml.response xml2
## -- Attaching packages ------------------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.1.1 v purrr 0.3.2
## v tibble 2.1.1 v dplyr 0.8.0.1
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts ---------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(knitr)
source("00_GIA_functions.R")dataPath <- c("data/Vietnam")
files <- dir(dataPath)
test <- files %>%
map_dfr(~ read_csv(file.path(dataPath, .), col_types = "ddddldc_dcdddcdccccc"))
# remove unneeded columns
dataNames <- colnames(test)
keep <- c(1,2, 5, 7, 10, 11, 13:19)
test <- test[,keep]
# remove Potapov plots
test <- subset(test, PL_STRATUM != 3)
# Clean up the data
test <- clean_data(test, c(9:13))# Test presence, using Tea
result <- presence(test, lblfld = "LAND_COVER", cmmdty = 'Tea')$Tea
# determine frequency of Tea
sum(result)## [1] 248
# Test question table builder
metaNames <- c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID",
"PL_COUNTRY", "PL_STRATUM")
landCover <- build_question(test, mtdt = metaNames, qstn = "LAND_COVER")
# Test analysis table builder
commodityNames <- c("Rubber", "Tea", "Coffee", "Pulpwood", "Coconut", "Oil_Palm")
groupList <- c("PL_COUNTRY", "PL_STRATUM", "PLOT_ID")
toAnalyze <- build_yc(landCover, lblfld = 'LAND_COVER',
cmmdtylst = commodityNames)
# Test plot level summarization
pSummary <- plot_means(toAnalyze, grplst = groupList, cmmdtylst = commodityNames)
# Stratum summarization
sSummary <- stratum_means(toAnalyze, grplst = groupList, cmmdtylst = commodityNames)
sError <- stratum_SE(toAnalyze, grplst = groupList, cmmdtylst = commodityNames)
stratumAreas <- c("Strata1 Area" = 348394, "Strata2 Area" = 1319799,
"Strata3 Area" = 1604577)
#stratumAreas <- c("Strata1 Area" = 348394, "Strata2 Area" = 1319799)
sampSize <- c(312, 576, 425)
#sampSize <- c(312, 576)
# Overall proportion of cover and variance
p_hat_sub_c <- overall_prop(sSummary, areas = stratumAreas)
se_hat_sub_c <- overall_SE(pSummary, c(1,2), areas = stratumAreas, ns = sampSize)
p_hat_sub_c * 100## Rubber Tea Coffee Pulpwood Coconut Oil_Palm
## 7.3854942 0.6875607 1.3663654 11.4130939 0.0000000 0.0000000
se_hat_sub_c * 100## Rubber Tea Coffee Pulpwood Coconut Oil_Palm
## 0.6050383 0.1958464 0.2286939 0.7321189 0.0000000 0.0000000
# Doing full analysis
# Extra tidbits needed for totals
ntotal <- nrow(test)/24
nstrata1 <- length(which(test$PL_STRATUM == 1))/24
nstrata2 <- length(which(test$PL_STRATUM == 2))/24
nstrata3 <- length(which(test$PL_STRATUM == 3))/24
nplots <- c("Total Plots" = ntotal, "Strata1 Plots" = nstrata1,
"Strata2 Plots" = nstrata2, "Strata3 Plots" = nstrata3)
kable(nplots) | x | |
|---|---|
| Total Plots | 888 |
| Strata1 Plots | 312 |
| Strata2 Plots | 576 |
| Strata3 Plots | 0 |
countryArea <- 32727700
metaNames <- c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID",
"PL_COUNTRY", "PL_STRATUM")
groupList <- c("PL_COUNTRY", "PL_STRATUM", "PLOT_ID")
stratumAreas <- c("Strata1 Area" = 348394, "Strata2 Area" = 1319799,
"Strata3 Area" = 1604577)
strata <- sort(unique(test$PL_STRATUM))
sampSize <- c(312, 576, 425)
questions <- colnames(test[,9:13])
results <- do_yc_analysis(table = test, mtdt = metaNames, strata = strata,
ns = sampSize, areas = stratumAreas, qstns = questions,
grplst = groupList)# Test analysis table builder
metaNames <- c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID",
"PL_COUNTRY", "PL_STRATUM")
commodityNames <- c("Coffee", "Fruit_Nut", "Pulpwood", "Rice", "Rubber", "Tea")
groupList <- c("PL_COUNTRY", "PL_STRATUM", "PLOT_ID")
questions <- colnames(test[,9:13])
stratumAreas <- c("Strata1 Area" = 348394, "Strata2 Area" = 1319799)
strata <- sort(unique(test$PL_STRATUM))
sampSize <- c(312, 576)
#Build tables for doing o_in_c analysis for use types in commodities
landcover <- build_question(test, mtdt = metaNames, qstn = c("LAND_COVER"))
landuse <- build_question(test, mtdt = metaNames, qstn = c("LAND_USE"))
conditions <- sort(unique(landcover$LAND_COVER))
covers <- sort(unique(landuse$LAND_USE))
y_cTable <- build_yc(landcover, lblfld = 'LAND_COVER', cmmdtylst = conditions)
y_ocTable <- build_yoc(table = test, mtdt = metaNames, qstns = questions,
grplst = groupList, cvrfld = "LAND_USE",
cndtnfld = "LAND_COVER", conditions = conditions,
covers = covers)## Joining, by = c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID", "PL_COUNTRY", "PL_STRATUM")
p_hat_o_in_c <- cond_prop(y_ocTable, y_cTable, areas = stratumAreas, grplst = groupList,
conditions = conditions)
se_p_hat_o_in_c <- cond_SE(yoctable = y_ocTable, yctable = y_cTable, strata = strata,
grplst = groupList, conditions = conditions, covers = covers,
areas = stratumAreas, ns = sampSize)
#cbind(p_hat_o_in_c, se_p_hat_o_in_c) * 100
use_in_rubber <- cbind(p_hat_o_in_c[seq(13, 120, 15)], se_p_hat_o_in_c[seq(13, 120, 15)])
#Build tables for doing o_in_c analysis for commodities in use types
covers2 <- sort(unique(landcover$LAND_COVER))
conditions2 <- sort(unique(landuse$LAND_USE))
y_cTable2 <- build_yc(landuse, lblfld = 'LAND_USE', cmmdtylst = conditions2)
y_ocTable2 <- build_yoc(table = test, mtdt = metaNames, qstns = questions,
grplst = groupList, cvrfld = "LAND_COVER",
cndtnfld = "LAND_USE", conditions = conditions2,
covers = covers2)## Joining, by = c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID", "PL_COUNTRY", "PL_STRATUM")
p_hat_o_in_c2 <- cond_prop(y_ocTable2, y_cTable2, areas = stratumAreas, grplst = groupList,
conditions = conditions2)
se_p_hat_o_in_c2 <- cond_SE(yoctable = y_ocTable2, yctable = y_cTable2, strata = strata,
grplst = groupList, conditions = conditions2, covers = covers2,
areas = stratumAreas, ns = sampSize)
#cbind(p_hat_o_in_c2, se_p_hat_o_in_c2) * 100
com_in_agslv <- cbind(p_hat_o_in_c2[seq(1, 120, 8)], se_p_hat_o_in_c2[seq(1, 120, 8)])
#Build tables for doing o_in_c analysis for commodities in year 2000 covers
landuse2000 <- build_question(test, mtdt = metaNames, qstn = c("LAND_USE_YEAR_2000"))
covers3 <- sort(unique(landcover$LAND_COVER))
conditions3 <- sort(unique(landuse2000$LAND_USE_YEAR_2000))
y_cTable3 <- build_yc(landuse2000, lblfld = 'LAND_USE_YEAR_2000', cmmdtylst = conditions3)
y_ocTable3 <- build_yoc(table = test, mtdt = metaNames, qstns = questions,
grplst = groupList, cvrfld = "LAND_COVER",
cndtnfld = "LAND_USE_YEAR_2000", conditions = conditions3,
covers = covers3)## Joining, by = c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID", "PL_COUNTRY", "PL_STRATUM")
p_hat_o_in_c3 <- cond_prop(y_ocTable3, y_cTable3, areas = stratumAreas, grplst = groupList,
conditions = conditions3)
se_p_hat_o_in_c3 <- cond_SE(yoctable = y_ocTable3, yctable = y_cTable3, strata = strata,
grplst = groupList, conditions = conditions3, covers = covers3,
areas = stratumAreas, ns = sampSize)
com_in_2000 <- cbind(p_hat_o_in_c3, se_p_hat_o_in_c3)
com_in_forest <- cbind(p_hat_o_in_c3[seq(1, 45, 3)], se_p_hat_o_in_c3[seq(1, 45, 3)])
do_yoc_analysis(test, mtdt = metaNames, qstns = questions,
grplst = groupList, strata = strata, ns = sampSize,
areas = stratumAreas, cover = "LAND_COVER",
condition = "LAND_USE_YEAR_2000")## Joining, by = c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID", "PL_COUNTRY", "PL_STRATUM")
## p_hat_o_in_c se_p_hat_o_in_c
## Bamboo-in-Forest_Commodity 0.0010141051 0.0010116916
## Bamboo-in-Natural_Forest 0.0201742973 0.0064405371
## Bamboo-in-Other_LAND_USE_YEAR_2000 0.0003551121 0.0003556735
## Built_up-in-Forest_Commodity 0.0029123421 0.0021594114
## Built_up-in-Natural_Forest 0.0104847053 0.0026551288
## Built_up-in-Other_LAND_USE_YEAR_2000 0.0269713801 0.0079429548
## Coffee-in-Forest_Commodity 0.0020282102 0.0020233833
## Coffee-in-Natural_Forest 0.0284097507 0.0071528905
## Coffee-in-Other_LAND_USE_YEAR_2000 0.0422619108 0.0103859850
## Fruit_Nut-in-Forest_Commodity 0.0149788829 0.0074250962
## Fruit_Nut-in-Natural_Forest 0.0243349327 0.0072940146
## Fruit_Nut-in-Other_LAND_USE_YEAR_2000 0.0570269254 0.0144524514
## Herbaceous-in-Forest_Commodity 0.0637248456 0.0143220122
## Herbaceous-in-Natural_Forest 0.0553930668 0.0096434849
## Herbaceous-in-Other_LAND_USE_YEAR_2000 0.1371363203 0.0220079477
## Non_vegetated-in-Forest_Commodity 0.0264068252 0.0103129793
## Non_vegetated-in-Natural_Forest 0.0439699621 0.0089126010
## Non_vegetated-in-Other_LAND_USE_YEAR_2000 0.0685450949 0.0149522656
## Other_Crop-in-Forest_Commodity 0.0174162726 0.0092777911
## Other_Crop-in-Natural_Forest 0.1133470929 0.0150287858
## Other_Crop-in-Other_LAND_USE_YEAR_2000 0.1881112786 0.0290312656
## Other_LAND_COVER-in-Forest_Commodity 0.0005070525 0.0005094052
## Other_LAND_COVER-in-Natural_Forest 0.0127023952 0.0044211655
## Other_LAND_COVER-in-Other_LAND_USE_YEAR_2000 0.0133686687 0.0065951878
## Other_Shrub-in-Forest_Commodity 0.0222461118 0.0086530115
## Other_Shrub-in-Natural_Forest 0.0848073636 0.0109651807
## Other_Shrub-in-Other_LAND_USE_YEAR_2000 0.0586271897 0.0110749308
## Other_Tree-in-Forest_Commodity 0.0904991848 0.0219148519
## Other_Tree-in-Natural_Forest 0.3204275266 0.0206527965
## Other_Tree-in-Other_LAND_USE_YEAR_2000 0.1049615888 0.0186711213
## Pulpwood-in-Forest_Commodity 0.4860913651 0.0655191655
## Pulpwood-in-Natural_Forest 0.1946025124 0.0237487265
## Pulpwood-in-Other_LAND_USE_YEAR_2000 0.0784421949 0.0168564314
## Rice-in-Forest_Commodity 0.0000000000 0.0000000000
## Rice-in-Natural_Forest 0.0003637117 0.0003640869
## Rice-in-Other_LAND_USE_YEAR_2000 0.0034612125 0.0029606719
## Rubber-in-Forest_Commodity 0.2655895391 0.0441095708
## Rubber-in-Natural_Forest 0.0823488058 0.0139600668
## Rubber-in-Other_LAND_USE_YEAR_2000 0.1497670866 0.0216424964
## Tea-in-Forest_Commodity 0.0053240517 0.0053463922
## Tea-in-Natural_Forest 0.0022389672 0.0019065835
## Tea-in-Other_LAND_USE_YEAR_2000 0.0358319523 0.0111482773
## Water-in-Forest_Commodity 0.0012612113 0.0010497643
## Water-in-Natural_Forest 0.0063949096 0.0042406036
## Water-in-Other_LAND_USE_YEAR_2000 0.0351320843 0.0105426669
#coverHA <- round(t(results$Cover$LAND_COVER) * countryArea, 0)
coverHA <- round(t(results$Cover$LAND_COVER) * sum(stratumAreas), 0)
colnames(coverHA) <- c("Area_ha", "SE_Area_ha")
#useHA <- round(t(results$Cover$LAND_USE) * countryArea, 0)
useHA <- round(t(results$Cover$LAND_USE) * sum(stratumAreas), 0)
colnames(useHA) <- c("Area_ha", "SE_Area_ha")
#use2000HA <- round(t(results$Cover$LAND_USE_YEAR_2000) * countryArea, 0)
use2000HA <- round(t(results$Cover$LAND_USE_YEAR_2000) * sum(stratumAreas), 0)
colnames(use2000HA) <- c("Area_ha", "SE_Area_ha")
coverHA## Area_ha SE_Area_ha
## Bamboo 8182 2495
## Built_up 11866 2321
## Coffee 22794 3815
## Fruit_Nut 27630 4830
## Herbaceous 70537 6782
## Non_vegetated 40582 5380
## Other_Crop 97939 8895
## Other_LAND_COVER 8638 2454
## Other_Shrub 53110 5290
## Other_Tree 170770 10324
## Pulpwood 190392 12213
## Rice 1067 805
## Rubber 123204 10093
## Tea 11470 3267
## Water 12129 3293
useHA## Area_ha SE_Area_ha
## Agrisiviculture 113017 9522
## Boundary_Agrisilviculture 9094 3012
## Mixed_Agrisilviculture 42741 6151
## Natural_Forest 173422 11006
## Other_LAND_USE 154905 10349
## Plantation 334258 14022
## Silvopastoral 17915 3732
## Terrace 4958 2342
use2000HA## Area_ha SE_Area_ha
## Forest_Commodity 191947 12615
## Natural_Forest 391228 14556
## Other_LAND_USE_YEAR_2000 267135 13842
# one commodity example
rubberAreas <- use_in_rubber[,1] * coverHA["Rubber", "Area_ha"]
rubberAreas_SE <- rubberAreas * sqrt((use_in_rubber[,2]/use_in_rubber[,1])^2 +
(coverHA["Rubber", "SE_Area_ha"]/coverHA["Rubber", "Area_ha"])^2)
use_in_rubber_ha <- round(cbind(rubberAreas, rubberAreas_SE), 2)
colnames(use_in_rubber_ha) <- c("Area_ha", "SE_Area_ha")
use_in_rubber_ha## Area_ha SE_Area_ha
## Agrisiviculture-in-Rubber 413.02 310.76
## Boundary_Agrisilviculture-in-Rubber 1665.95 1284.82
## Mixed_Agrisilviculture-in-Rubber 3747.09 1765.31
## Natural_Forest-in-Rubber 96.09 68.87
## Other_LAND_USE-in-Rubber 437.97 440.30
## Plantation-in-Rubber 114508.01 18094.57
## Silvopastoral-in-Rubber 1167.93 1166.43
## Terrace-in-Rubber 1167.93 1041.09
com_in_agslv## [,1] [,2]
## Bamboo-in-Agrisiviculture 0.000000000 0.0000000000
## Built_up-in-Agrisiviculture 0.000209842 0.0002097289
## Coffee-in-Agrisiviculture 0.108897327 0.0255869759
## Fruit_Nut-in-Agrisiviculture 0.021695617 0.0142550024
## Herbaceous-in-Agrisiviculture 0.020373813 0.0090133157
## Non_vegetated-in-Agrisiviculture 0.085806691 0.0258396098
## Other_Crop-in-Agrisiviculture 0.662755397 0.0988481501
## Other_LAND_COVER-in-Agrisiviculture 0.010764695 0.0104042730
## Other_Shrub-in-Agrisiviculture 0.051656740 0.0167109528
## Other_Tree-in-Agrisiviculture 0.021300411 0.0089578501
## Pulpwood-in-Agrisiviculture 0.003444702 0.0034719311
## Rice-in-Agrisiviculture 0.009440220 0.0072100758
## Rubber-in-Agrisiviculture 0.003654544 0.0027481100
## Tea-in-Agrisiviculture 0.000000000 0.0000000000
## Water-in-Agrisiviculture 0.000000000 0.0000000000
com_in_forest ## [,1] [,2]
## Bamboo-in-Forest_Commodity 0.0010141051 0.0010116916
## Built_up-in-Forest_Commodity 0.0029123421 0.0021594114
## Coffee-in-Forest_Commodity 0.0020282102 0.0020233833
## Fruit_Nut-in-Forest_Commodity 0.0149788829 0.0074250962
## Herbaceous-in-Forest_Commodity 0.0637248456 0.0143220122
## Non_vegetated-in-Forest_Commodity 0.0264068252 0.0103129793
## Other_Crop-in-Forest_Commodity 0.0174162726 0.0092777911
## Other_LAND_COVER-in-Forest_Commodity 0.0005070525 0.0005094052
## Other_Shrub-in-Forest_Commodity 0.0222461118 0.0086530115
## Other_Tree-in-Forest_Commodity 0.0904991848 0.0219148519
## Pulpwood-in-Forest_Commodity 0.4860913651 0.0655191655
## Rice-in-Forest_Commodity 0.0000000000 0.0000000000
## Rubber-in-Forest_Commodity 0.2655895391 0.0441095708
## Tea-in-Forest_Commodity 0.0053240517 0.0053463922
## Water-in-Forest_Commodity 0.0012612113 0.0010497643
p_hat_o_in_c3 * use2000HA[,1]## Bamboo-in-Forest_Commodity
## 194.65443
## Bamboo-in-Natural_Forest
## 7892.74998
## Bamboo-in-Other_LAND_USE_YEAR_2000
## 94.86286
## Built_up-in-Forest_Commodity
## 559.01533
## Built_up-in-Natural_Forest
## 4101.91027
## Built_up-in-Other_LAND_USE_YEAR_2000
## 7204.99961
## Coffee-in-Forest_Commodity
## 389.30886
## Coffee-in-Natural_Forest
## 11114.68996
## Coffee-in-Other_LAND_USE_YEAR_2000
## 11289.63555
## Fruit_Nut-in-Forest_Commodity
## 2875.15163
## Fruit_Nut-in-Natural_Forest
## 9520.50704
## Fruit_Nut-in-Other_LAND_USE_YEAR_2000
## 15233.88771
## Herbaceous-in-Forest_Commodity
## 12231.79293
## Herbaceous-in-Natural_Forest
## 21671.31873
## Herbaceous-in-Other_LAND_USE_YEAR_2000
## 36633.91093
## Non_vegetated-in-Forest_Commodity
## 5068.71088
## Non_vegetated-in-Natural_Forest
## 17202.28034
## Non_vegetated-in-Other_LAND_USE_YEAR_2000
## 18310.79393
## Other_Crop-in-Forest_Commodity
## 3343.00127
## Other_Crop-in-Natural_Forest
## 44344.55647
## Other_Crop-in-Other_LAND_USE_YEAR_2000
## 50251.10641
## Other_LAND_COVER-in-Forest_Commodity
## 97.32722
## Other_LAND_COVER-in-Natural_Forest
## 4969.53267
## Other_LAND_COVER-in-Other_LAND_USE_YEAR_2000
## 3571.23931
## Other_Shrub-in-Forest_Commodity
## 4270.07442
## Other_Shrub-in-Natural_Forest
## 33179.01525
## Other_Shrub-in-Other_LAND_USE_YEAR_2000
## 15661.37432
## Other_Tree-in-Forest_Commodity
## 17371.04702
## Other_Tree-in-Natural_Forest
## 125360.22039
## Other_Tree-in-Other_LAND_USE_YEAR_2000
## 28038.91404
## Pulpwood-in-Forest_Commodity
## 93303.77926
## Pulpwood-in-Natural_Forest
## 76133.95171
## Pulpwood-in-Other_LAND_USE_YEAR_2000
## 20954.65574
## Rice-in-Forest_Commodity
## 0.00000
## Rice-in-Natural_Forest
## 142.29419
## Rice-in-Other_LAND_USE_YEAR_2000
## 924.61101
## Rubber-in-Forest_Commodity
## 50979.11526
## Rubber-in-Natural_Forest
## 32217.15861
## Rubber-in-Other_LAND_USE_YEAR_2000
## 40008.03068
## Tea-in-Forest_Commodity
## 1021.93576
## Tea-in-Natural_Forest
## 875.94668
## Tea-in-Other_LAND_USE_YEAR_2000
## 9571.96856
## Water-in-Forest_Commodity
## 242.08573
## Water-in-Natural_Forest
## 2501.86769
## Water-in-Other_LAND_USE_YEAR_2000
## 9385.00933
p_hat_o_in_c3 * use2000HA[,1] * sqrt((se_p_hat_o_in_c3/p_hat_o_in_c3)^2 + (use2000HA[,2]/use2000HA[,1])^2)## Bamboo-in-Forest_Commodity
## 194.61211
## Bamboo-in-Natural_Forest
## 2536.77266
## Bamboo-in-Other_LAND_USE_YEAR_2000
## 95.13991
## Built_up-in-Forest_Commodity
## 416.11757
## Built_up-in-Natural_Forest
## 1049.91206
## Built_up-in-Other_LAND_USE_YEAR_2000
## 2154.43528
## Coffee-in-Forest_Commodity
## 389.22421
## Coffee-in-Natural_Forest
## 2828.80070
## Coffee-in-Other_LAND_USE_YEAR_2000
## 2835.46142
## Fruit_Nut-in-Forest_Commodity
## 1437.69660
## Fruit_Nut-in-Natural_Forest
## 2875.52328
## Fruit_Nut-in-Other_LAND_USE_YEAR_2000
## 3940.62604
## Herbaceous-in-Forest_Commodity
## 2864.19419
## Herbaceous-in-Natural_Forest
## 3857.99843
## Herbaceous-in-Other_LAND_USE_YEAR_2000
## 6177.94906
## Non_vegetated-in-Forest_Commodity
## 2007.37901
## Non_vegetated-in-Natural_Forest
## 3545.11218
## Non_vegetated-in-Other_LAND_USE_YEAR_2000
## 4105.41645
## Other_Crop-in-Forest_Commodity
## 1794.34578
## Other_Crop-in-Natural_Forest
## 6106.78009
## Other_Crop-in-Other_LAND_USE_YEAR_2000
## 8180.71709
## Other_LAND_COVER-in-Forest_Commodity
## 97.98780
## Other_LAND_COVER-in-Natural_Forest
## 1739.53798
## Other_LAND_COVER-in-Other_LAND_USE_YEAR_2000
## 1771.49705
## Other_Shrub-in-Forest_Commodity
## 1684.46126
## Other_Shrub-in-Natural_Forest
## 4463.96698
## Other_Shrub-in-Other_LAND_USE_YEAR_2000
## 3067.78302
## Other_Tree-in-Forest_Commodity
## 4358.66002
## Other_Tree-in-Natural_Forest
## 9329.51550
## Other_Tree-in-Other_LAND_USE_YEAR_2000
## 5195.00782
## Pulpwood-in-Forest_Commodity
## 13991.53084
## Pulpwood-in-Natural_Forest
## 9713.37202
## Pulpwood-in-Other_LAND_USE_YEAR_2000
## 4632.00268
## Rice-in-Forest_Commodity
## NaN
## Rice-in-Natural_Forest
## 142.53936
## Rice-in-Other_LAND_USE_YEAR_2000
## 792.34888
## Rubber-in-Forest_Commodity
## 9105.50746
## Rubber-in-Natural_Forest
## 5591.56008
## Rubber-in-Other_LAND_USE_YEAR_2000
## 6141.90684
## Tea-in-Forest_Commodity
## 1028.41938
## Tea-in-Natural_Forest
## 746.62050
## Tea-in-Other_LAND_USE_YEAR_2000
## 3019.11446
## Water-in-Forest_Commodity
## 202.12625
## Water-in-Natural_Forest
## 1661.65216
## Water-in-Other_LAND_USE_YEAR_2000
## 2857.99197
com_in_2000_Areas <- calcError(com_in_2000, use2000HA)
contents <- str_split(rownames(com_in_2000_Areas), "-", 3, simplify = T)
data <- tibble(condition = contents[,3], cover = contents[,1],
area_ha = com_in_2000_Areas[,1], se_area = com_in_2000_Areas[,2])
conditions <- unique(data$condition)
prettyTable <- list()
for (c in seq_along(conditions)) {
prettyTable[[c]] <- filter(data, condition == conditions[c])
prettyTable[[c]] <- prettyTable[[c]][,-1]
names(prettyTable)[c] <- conditions[c]
}
prettyTable## $Forest_Commodity
## # A tibble: 15 x 3
## cover area_ha se_area
## <chr> <dbl> <dbl>
## 1 Bamboo 195. 195.
## 2 Built_up 559. 416.
## 3 Coffee 389. 389.
## 4 Fruit_Nut 2875. 1438.
## 5 Herbaceous 12232. 2864.
## 6 Non_vegetated 5069. 2007.
## 7 Other_Crop 3343. 1794.
## 8 Other_LAND_COVER 97.3 98.0
## 9 Other_Shrub 4270. 1684.
## 10 Other_Tree 17371. 4359.
## 11 Pulpwood 93304. 13992.
## 12 Rice 0 NaN
## 13 Rubber 50979. 9106.
## 14 Tea 1022. 1028.
## 15 Water 242. 202.
##
## $Natural_Forest
## # A tibble: 15 x 3
## cover area_ha se_area
## <chr> <dbl> <dbl>
## 1 Bamboo 7893. 2537.
## 2 Built_up 4102. 1050.
## 3 Coffee 11115. 2829.
## 4 Fruit_Nut 9521. 2876.
## 5 Herbaceous 21671. 3858.
## 6 Non_vegetated 17202. 3545.
## 7 Other_Crop 44345. 6107.
## 8 Other_LAND_COVER 4970. 1740.
## 9 Other_Shrub 33179. 4464.
## 10 Other_Tree 125360. 9330.
## 11 Pulpwood 76134. 9713.
## 12 Rice 142. 143.
## 13 Rubber 32217. 5592.
## 14 Tea 876. 747.
## 15 Water 2502. 1662.
##
## $Other_LAND_USE_YEAR_2000
## # A tibble: 15 x 3
## cover area_ha se_area
## <chr> <dbl> <dbl>
## 1 Bamboo 94.9 95.1
## 2 Built_up 7205. 2154.
## 3 Coffee 11290. 2835.
## 4 Fruit_Nut 15234. 3941.
## 5 Herbaceous 36634. 6178.
## 6 Non_vegetated 18311. 4105.
## 7 Other_Crop 50251. 8181.
## 8 Other_LAND_COVER 3571. 1771.
## 9 Other_Shrub 15661. 3068.
## 10 Other_Tree 28039. 5195.
## 11 Pulpwood 20955. 4632.
## 12 Rice 925. 792.
## 13 Rubber 40008. 6142.
## 14 Tea 9572. 3019.
## 15 Water 9385. 2858.
# Test triple conditional
questions <- c("LAND_COVER", "LAND_USE", "LAND_USE_YEAR_2000")
crops <- c("Coffee", "Fruit_Nut" ,"Pulpwood", "Rubber", "Tea", "Other_Crop")
y_occ_test <- build_yocc(test, mtdt = metaNames, qstns = questions,
cvrfld = "LAND_COVER", cndtnfld1 = "LAND_USE",
cndtnfld2 = "LAND_USE_YEAR_2000", covers = crops, conditions1 = NULL,
conditions2 = "Natural_Forest")## Joining, by = c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID", "PL_COUNTRY", "PL_STRATUM")
## Joining, by = c("PLOT_ID", "SAMPLE_ID", "USER_ID", "PL_PLOTID", "PL_PACKETID", "PL_COUNTRY", "PL_STRATUM")
sum(test$LAND_COVER == "Pulpwood" & test$LAND_USE == "Plantation" &
test$LAND_USE_YEAR_2000 == "Natural_Forest")## [1] 1841
yoccaTest <- do_yocc_analysis(y_occ_test, mtdt = metaNames, strata = strata,
areas = stratumAreas, ns = sampSize, qstns = questions,
grplst = groupList)
cResults <- bind_rows(yoccaTest$Cover)
cResults %>%
select(cover, condition1, PercentCover) %>%
mutate(PercentCover = PercentCover * sum(stratumAreas)) %>%
spread(cover, PercentCover)## # A tibble: 8 x 7
## condition1 Coffee Fruit_Nut Other_Crop Pulpwood Rubber Tea
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Agrisiviculture 16466. 2710. 79457. 764. 8.10e2 0
## 2 Boundary_Agrisilvicul~ 46.5 0 0 0 9.77e2 286.
## 3 Mixed_Agrisilviculture 1697. 12234. 0 279. 1.62e3 0
## 4 Natural_Forest 0 233. 0 4678. 9.31e1 0
## 5 Other_LAND_USE 3596. 279. 4899. 477. 8.59e2 0
## 6 Plantation 0 3222. 1210. 143166. 5.88e4 0
## 7 Silvopastoral 0 0 1432. 0 0. 0
## 8 Terrace 0 0 0 0 0. 1432.