## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----echo=FALSE, message=FALSE------------------------------------------------ # Plotting Packages library(dplyr) library(tidyr) library(ggplot2) library(ggpubr) library(ggpmisc) library(yardstick) library(gt) ## ----echo=FALSE, results='hide', message=FALSE, warning=FALSE----------------- # Import Data file <- system.file("extdata/validation.csv", package = "rTwig") all_biomass <- read.csv(file) %>% rename(Version = version) # Distinct colors factors <- distinct(all_biomass, Version) cbPalette <- c("#009E73", "#CC79A7", "#56B4E9", "#999999", "#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00") cbPalette <- cbPalette[1:nrow(factors)] factors <- bind_cols(factors, cbPalette) %>% rename(colors = 2) # Filter Data all_biomass <- all_biomass %>% left_join(factors, by = join_by(Version)) %>% filter(Version %in% c("TreeQSM v2.4.1", "Real Twig (TreeQSM v2.4.1)")) %>% mutate(id = paste0(SpCode, Tree)) # Total Biomass p1 <- ggplot(data = all_biomass, aes(x = Mt.DS, y = Mt.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name), ) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "TLS Biomass (kg)", title = "Total", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Main Stem Biomass p2 <- ggplot(data = all_biomass, aes(x = Md.DS, y = Md.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "", # "TLS Biomass (kg)", title = "Main Stem", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Branch Biomass p3 <- ggplot(data = all_biomass, aes(x = Mb.DS, y = Mb.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "", # "TLS Biomass (kg)", title = "Branch", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Total Biomass % Error p4 <- ggplot(data = all_biomass, aes(x = Mt.DS, y = tperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "", # "Reference Biomass (kg)", y = "% TLS Biomass Error", title = "Total % Error", shape = "Speces", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Main Stem Biomass % Error p5 <- ggplot(data = all_biomass, aes(x = Md.DS, y = sperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "Reference Biomass (kg)", y = "", # "% TLS Biomass Error", title = "Main Stem % Error", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Branch Biomass % Error p6 <- ggplot(data = all_biomass, aes(x = Mb.DS, y = tperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "", # "Reference Biomass (kg)", y = "", # "% TLS Biomass Error", title = "Branch % Error", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) ## ----echo=FALSE, fig.width=7, fig.height=4, fig.align='center', warning=FALSE---- ggarrange(p1, p4, common.legend = TRUE, legend = "bottom") ggarrange(p2, p5, common.legend = TRUE, legend = "bottom") ggarrange(p3, p6, common.legend = TRUE, legend = "bottom") ## ----echo=FALSE, warning=FALSE------------------------------------------------ ### TOTALS ##################################################################### total_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(tperr), RMSE.kg = sqrt(mean(tdiff^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Mt.DS) * 100 ) CCC_total <- all_biomass %>% group_by(Version) %>% ccc(Mt.TLS, Mt.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) total_stats <- left_join(total_stats, CCC_total, by = "Version") %>% mutate(type = "total") ### MAIN STEM ################################################################## stem_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(sperr), RMSE.kg = sqrt(mean(sdif^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Md.DS) * 100 ) CCC_stem <- all_biomass %>% group_by(Version) %>% ccc(Md.TLS, Md.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) stem_stats <- left_join(stem_stats, CCC_stem, by = "Version") %>% mutate(type = "stem") ### BRANCHES ################################################################### branch_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(bperr), RMSE.kg = sqrt(mean(bdiff^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Mb.DS) * 100 ) CCC_branch <- all_biomass %>% group_by(Version) %>% ccc(Mb.TLS, Mb.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) branch_stats <- left_join(branch_stats, CCC_branch, by = "Version") %>% mutate(type = "branch") all_stats <- bind_rows(total_stats, stem_stats, branch_stats) ### TABLES ################################################################### data <- all_stats %>% mutate_if(is.numeric, round, 3) %>% rename( "Mean Relative Error (%)" = MRE.pct, "RMSE (kg)" = RMSE.kg, "Relative RMSE (%)" = RRMSE.pct ) %>% pivot_longer(cols = 2:5, names_to = "metric", values_to = "value") %>% pivot_wider(names_from = type, values_from = value) %>% group_by(Version) %>% group_split() tables <- vector(mode = "list", length(data)) data[[2]] %>% gt() %>% tab_header(title = "TreeQSM v2.4.1") %>% cols_hide(Version) %>% cols_label( metric = "Metric", total = "Total Woody AGB", stem = "Main Stem Biomass", branch = "Branch Biomass" ) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( table.border.top.style = "hidden", table.border.bottom.style = "hidden", table_body.hlines.color = "white", table.font.color = "black", heading.border.bottom.color = "black", column_labels.border.bottom.color = "black", table_body.border.bottom.color = "black" ) data[[1]] %>% gt() %>% tab_header(title = "Real Twig (TreeQSM v2.4.1)") %>% cols_hide(Version) %>% cols_label( metric = "Metric", total = "Total Woody AGB", stem = "Main Stem Biomass", branch = "Branch Biomass" ) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( table.border.top.style = "hidden", table.border.bottom.style = "hidden", table_body.hlines.color = "white", table.font.color = "black", heading.border.bottom.color = "black", column_labels.border.bottom.color = "black", table_body.border.bottom.color = "black" ) ## ----echo=FALSE, results='hide', message=FALSE, warning=FALSE----------------- # Import Data file <- system.file("extdata/validation.csv", package = "rTwig") all_biomass <- read.csv(file) %>% rename(Version = version) # Distinct colors factors <- distinct(all_biomass, Version) cbPalette <- c("#009E73", "#CC79A7", "#56B4E9", "#999999", "#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00") cbPalette <- cbPalette[1:nrow(factors)] factors <- bind_cols(factors, cbPalette) %>% rename(colors = 2) # Filter Data all_biomass <- all_biomass %>% left_join(factors, by = join_by(Version)) %>% filter(Version %in% c("TreeQSM v2.3.0", "Real Twig (TreeQSM v2.3.0)")) %>% mutate(id = paste0(SpCode, Tree)) # Total Biomass p1 <- ggplot(data = all_biomass, aes(x = Mt.DS, y = Mt.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name), ) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "TLS Biomass (kg)", title = "Total", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Main Stem Biomass p2 <- ggplot(data = all_biomass, aes(x = Md.DS, y = Md.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "", # "TLS Biomass (kg)", title = "Main Stem", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Branch Biomass p3 <- ggplot(data = all_biomass, aes(x = Mb.DS, y = Mb.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "", # "TLS Biomass (kg)", title = "Branch", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Total Biomass % Error p4 <- ggplot(data = all_biomass, aes(x = Mt.DS, y = tperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "", # "Reference Biomass (kg)", y = "% TLS Biomass Error", title = "Total % Error", shape = "Speces", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Main Stem Biomass % Error p5 <- ggplot(data = all_biomass, aes(x = Md.DS, y = sperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "Reference Biomass (kg)", y = "", # "% TLS Biomass Error", title = "Main Stem % Error", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Branch Biomass % Error p6 <- ggplot(data = all_biomass, aes(x = Mb.DS, y = tperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "", # "Reference Biomass (kg)", y = "", # "% TLS Biomass Error", title = "Branch % Error", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) ## ----echo=FALSE, fig.width=7, fig.height=4, fig.align='center', warning=FALSE, message=FALSE---- ggarrange(p1, p4, common.legend = TRUE, legend = "bottom") ggarrange(p2, p5, common.legend = TRUE, legend = "bottom") ggarrange(p3, p6, common.legend = TRUE, legend = "bottom") ## ----echo=FALSE, message=FALSE, warning=FALSE--------------------------------- ### TOTALS ##################################################################### total_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(tperr), RMSE.kg = sqrt(mean(tdiff^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Mt.DS) * 100 ) CCC_total <- all_biomass %>% group_by(Version) %>% ccc(Mt.TLS, Mt.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) total_stats <- left_join(total_stats, CCC_total, by = "Version") %>% mutate(type = "total") ### MAIN STEM ################################################################## stem_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(sperr), RMSE.kg = sqrt(mean(sdif^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Md.DS) * 100 ) CCC_stem <- all_biomass %>% group_by(Version) %>% ccc(Md.TLS, Md.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) stem_stats <- left_join(stem_stats, CCC_stem, by = "Version") %>% mutate(type = "stem") ### BRANCHES ################################################################### branch_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(bperr), RMSE.kg = sqrt(mean(bdiff^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Mb.DS) * 100 ) CCC_branch <- all_biomass %>% group_by(Version) %>% ccc(Mb.TLS, Mb.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) branch_stats <- left_join(branch_stats, CCC_branch, by = "Version") %>% mutate(type = "branch") all_stats <- bind_rows(total_stats, stem_stats, branch_stats) ### TABLES ################################################################### data <- all_stats %>% mutate_if(is.numeric, round, 3) %>% rename( "Mean Relative Error (%)" = MRE.pct, "RMSE (kg)" = RMSE.kg, "Relative RMSE (%)" = RRMSE.pct ) %>% pivot_longer(cols = 2:5, names_to = "metric", values_to = "value") %>% pivot_wider(names_from = type, values_from = value) %>% group_by(Version) %>% group_split() tables <- vector(mode = "list", length(data)) data[[2]] %>% gt() %>% tab_header(title = "TreeQSM v2.3.0") %>% cols_hide(Version) %>% cols_label( metric = "Metric", total = "Total Woody AGB", stem = "Main Stem Biomass", branch = "Branch Biomass" ) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( table.border.top.style = "hidden", table.border.bottom.style = "hidden", table_body.hlines.color = "white", table.font.color = "black", heading.border.bottom.color = "black", column_labels.border.bottom.color = "black", table_body.border.bottom.color = "black" ) data[[1]] %>% gt() %>% tab_header(title = "Real Twig (TreeQSM v2.3.0)") %>% cols_hide(Version) %>% cols_label( metric = "Metric", total = "Total Woody AGB", stem = "Main Stem Biomass", branch = "Branch Biomass" ) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( table.border.top.style = "hidden", table.border.bottom.style = "hidden", table_body.hlines.color = "white", table.font.color = "black", heading.border.bottom.color = "black", column_labels.border.bottom.color = "black", table_body.border.bottom.color = "black" ) ## ----echo=FALSE, results='hide', message=FALSE, warning=FALSE----------------- # Import Data file <- system.file("extdata/validation.csv", package = "rTwig") all_biomass <- read.csv(file) %>% rename(Version = version) # Distinct colors factors <- distinct(all_biomass, Version) cbPalette <- c("#009E73", "#CC79A7", "#56B4E9", "#999999", "#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00") cbPalette <- cbPalette[1:nrow(factors)] factors <- bind_cols(factors, cbPalette) %>% rename(colors = 2) # Filter Data all_biomass <- all_biomass %>% left_join(factors, by = join_by(Version)) %>% filter(Version %in% c("SimpleForest", "Real Twig (SimpleForest)")) %>% mutate(id = paste0(SpCode, Tree)) # Total Biomass p1 <- ggplot(data = all_biomass, aes(x = Mt.DS, y = Mt.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name), ) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "TLS Biomass (kg)", title = "Total", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Main Stem Biomass p2 <- ggplot(data = all_biomass, aes(x = Md.DS, y = Md.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "", # "TLS Biomass (kg)", title = "Main Stem", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Branch Biomass p3 <- ggplot(data = all_biomass, aes(x = Mb.DS, y = Mb.TLS, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_abline() + stat_poly_eq(aes( label = paste(after_stat(eq.label), after_stat(rr.label), sep = "~~~") )) + labs( x = "", # "Reference Biomass (kg)", y = "", # "TLS Biomass (kg)", title = "Branch", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Total Biomass % Error p4 <- ggplot(data = all_biomass, aes(x = Mt.DS, y = tperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "", # "Reference Biomass (kg)", y = "% TLS Biomass Error", title = "Total % Error", shape = "Speces", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Main Stem Biomass % Error p5 <- ggplot(data = all_biomass, aes(x = Md.DS, y = sperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "Reference Biomass (kg)", y = "", # "% TLS Biomass Error", title = "Main Stem % Error", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) # Branch Biomass % Error p6 <- ggplot(data = all_biomass, aes(x = Mb.DS, y = tperr, color = Version, fill = Version)) + geom_point(aes(shape = scientific.name)) + geom_smooth(method = "lm", formula = y ~ x) + geom_hline(yintercept = 0) + labs( x = "", # "Reference Biomass (kg)", y = "", # "% TLS Biomass Error", title = "Branch % Error", shape = "Species", color = "Method", fill = "Method" ) + theme_classic() + scale_color_manual(values = unique(all_biomass$colors)) + scale_fill_manual(values = unique(all_biomass$colors)) ## ----echo=FALSE, fig.width=7, fig.height=4, fig.align='center', warning=FALSE, message=FALSE---- ggarrange(p1, p4, common.legend = TRUE, legend = "bottom") ggarrange(p2, p5, common.legend = TRUE, legend = "bottom") ggarrange(p3, p6, common.legend = TRUE, legend = "bottom") ## ----echo=FALSE, warning=FALSE, message=FALSE--------------------------------- ### TOTALS ##################################################################### total_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(tperr), RMSE.kg = sqrt(mean(tdiff^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Mt.DS) * 100 ) CCC_total <- all_biomass %>% group_by(Version) %>% ccc(Mt.TLS, Mt.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) total_stats <- left_join(total_stats, CCC_total, by = "Version") %>% mutate(type = "total") ### MAIN STEM ################################################################## stem_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(sperr), RMSE.kg = sqrt(mean(sdif^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Md.DS) * 100 ) CCC_stem <- all_biomass %>% group_by(Version) %>% ccc(Md.TLS, Md.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) stem_stats <- left_join(stem_stats, CCC_stem, by = "Version") %>% mutate(type = "stem") ### BRANCHES ################################################################### branch_stats <- all_biomass %>% group_by(Version) %>% summarize( MRE.pct = mean(bperr), RMSE.kg = sqrt(mean(bdiff^2, na.rm = TRUE)), RRMSE.pct = RMSE.kg / mean(Mb.DS) * 100 ) CCC_branch <- all_biomass %>% group_by(Version) %>% ccc(Mb.TLS, Mb.DS) %>% select(Version, .estimate) %>% rename(CCC = 2) branch_stats <- left_join(branch_stats, CCC_branch, by = "Version") %>% mutate(type = "branch") all_stats <- bind_rows(total_stats, stem_stats, branch_stats) ### TABLES ################################################################### data <- all_stats %>% mutate_if(is.numeric, round, 3) %>% rename( "Mean Relative Error (%)" = MRE.pct, "RMSE (kg)" = RMSE.kg, "Relative RMSE (%)" = RRMSE.pct ) %>% pivot_longer(cols = 2:5, names_to = "metric", values_to = "value") %>% pivot_wider(names_from = type, values_from = value) %>% group_by(Version) %>% group_split() tables <- vector(mode = "list", length(data)) data[[2]] %>% gt() %>% tab_header(title = "SimpleForest") %>% cols_hide(Version) %>% cols_label( metric = "Metric", total = "Total Woody AGB", stem = "Main Stem Biomass", branch = "Branch Biomass" ) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( table.border.top.style = "hidden", table.border.bottom.style = "hidden", table_body.hlines.color = "white", table.font.color = "black", heading.border.bottom.color = "black", column_labels.border.bottom.color = "black", table_body.border.bottom.color = "black" ) data[[1]] %>% gt() %>% tab_header(title = "Real Twig (SimpleForest)") %>% cols_hide(Version) %>% cols_label( metric = "Metric", total = "Total Woody AGB", stem = "Main Stem Biomass", branch = "Branch Biomass" ) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( table.border.top.style = "hidden", table.border.bottom.style = "hidden", table_body.hlines.color = "white", table.font.color = "black", heading.border.bottom.color = "black", column_labels.border.bottom.color = "black", table_body.border.bottom.color = "black" ) ## ----echo=FALSE, warning=FALSE, message=FALSE--------------------------------- # Future Package Cleanup future::plan("sequential")