library(SOCDRaH2) library(ggplot2) dataDir <- '~/Documents/Datasets/ISCN' knitr::opts_chunk$set(eval=FALSE)
library(SOCDRaH2) dataDir <- 'Your/Data/Dir'
keys.ls <- makeKeys() #ISCN.ls <- ISCN3(dataDir=dataDir, orginalFormat = FALSE) #ISCN.ls <- ISCN4(dataDir = dataDir, onlyNewData = FALSE) ISCN.ls <- ISCN5(dataDir = dataDir, orginalFormat = FALSE, newDataOnly = FALSE)
lapply(ISCN.ls, names) knitr::kable(keys.ls$ISCN[order(variable),][order(table),])
organic_carbon.dt <- ISCN.ls$layer[variable %in% c("oc", "loi", 'soc')] ##duplicate entries: merge(bulkDensity.df, bulkDensity.df[,.N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N>1][type =='value_num'], all.y=TRUE) ##Filter out duplicate entries doubleValueEntries <- organic_carbon.dt[type =='value_num', .N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N>1] singleValueEntries <- organic_carbon.dt[type =='value_num', .N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N==1] #unique(bulkDensity.df$type) organicCarbon_value.dt <- merge(organic_carbon.dt, singleValueEntries, all.y=TRUE)[, .(value = as.numeric(entry)), by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable)] organicCarbon_method.dt <- ISCN.ls$layer[variable %in% c("oc", "loi") & type == 'method', .(entry = paste0(header, ": ", entry, collapse = ';')), # header = paste0(header, collapse = '; '), # N = length(type)), by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable)] OC.dt <- merge(organicCarbon_value.dt, organicCarbon_method.dt, by=c('dataset_name_id', 'profile_name_id', 'site_name_id', 'layer_name_id', 'variable'), all.x=TRUE)
ggplot(OC.dt[value >= 0 & value < 100], aes(x=value)) + geom_histogram() + facet_wrap(~variable, scales='free') ggplot(OC.dt[value >= 0 & value < 25], aes(x=value)) + geom_histogram() + facet_wrap(~variable, scales='free')
bulkDensity.df <- ISCN.ls$layer[variable %in% c("bulk_density_other", "bulk_density_sample", "bulk_density_total", "bulk_density_whole" )] ##duplicate entries: merge(bulkDensity.df, bulkDensity.df[,.N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N>1][type =='value_num'], all.y=TRUE) ##Filter out duplicate entries singleValueEntries <- bulkDensity.df[type =='value_num', .N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N==1] #unique(bulkDensity.df$type) bulkDensity_value.df <- merge(bulkDensity.df, singleValueEntries, all.y=TRUE)[, .(value = as.numeric(entry)), by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable)] bulkDensity_method.dt <- ISCN.ls$layer[variable %in% c("bulk_density_other", "bulk_density_sample", "bulk_density_total", "bulk_density_whole" ) & type == 'method', .(entry = paste0(header, ": ", entry, collapse = ';')), # header = paste0(header, collapse = '; '), # N = length(type)), by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable)] bulkDensity.dt <- merge(bulkDensity_value.df, bulkDensity_method.dt, by=c('dataset_name_id', 'profile_name_id', 'site_name_id', 'layer_name_id', 'variable'), all.x=TRUE)
ggplot(bulkDensity.dt[(value > 0 & value < 3)], aes(x=value)) + geom_histogram() + facet_wrap(~variable, scales = 'free_y')
coarseFraction.df <- ISCN.ls$layer[variable %in% c("wpg2" )] ##duplicate entries: merge(coarseFraction.df, coarseFraction.df[,.N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N>1][type =='value_num'], all.y=TRUE) ##Filter out duplicate entries singleValueEntries <- coarseFraction.df[type =='value_num', .N, by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable, type)][N==1] #unique(coarseFraction.df$type) coarseFraction_value.df <- merge(coarseFraction.df, singleValueEntries, all.y=TRUE)[, .(value = as.numeric(entry)), by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable)] coarseFraction_method.dt <- ISCN.ls$layer[variable %in% c("wpg2" ) & type == 'method', .(entry = paste0(header, ": ", entry, collapse = ';')), # header = paste0(header, collapse = '; '), # N = length(type)), by=.(dataset_name_id, profile_name_id, site_name_id, layer_name_id, variable)] coarseFraction.dt <- merge(coarseFraction_value.df, coarseFraction_method.dt, by=c('dataset_name_id', 'profile_name_id', 'site_name_id', 'layer_name_id', 'variable'), all.x=TRUE)
ggplot(coarseFraction.dt[(value > 0 & value < 100)], aes(x=value)) + geom_histogram() + facet_wrap(~variable, scales = 'free_y')
ISCN.ls$study <- ISCN.ls$study[!is.na(entry)] knitr::kable(ISCN.ls$study[variable %in% c('reference', 'acknowledgement', 'citation'), .(entry = paste0(entry, collapse='\n\t\t', entry)), by=dataset_name_id])
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