# Set working directory # Anyone using this script will need to adjust this line to their local path. working_directory = setwd("D:/SLR/Bonn_data_slr") library(readxl) # read excel file library(writexl) # export excel file library(dplyr) library(ggplot2) library(tidyverse) library(ggplot2) # ggplot2.org , website # visualization library library(RColorBrewer) # to select colors for graph & maps library(ggthemes) # to select the graph background design for example, theme_stata,theme_tufte, library(extrafont) # to change the font in the graph display.brewer.all() # to show the color sets # Read original data frame #### df <- read_excel("9.Dataset2_all_records_without_two_papers.xlsx") # explore the data in R dim(df) # shows the dimensions of the data frame by row and column str(df) #shows the structure of the data frame summary(df) #shows the descriptive statistics of each column in the data frame names(df) #shows the name of each column in the data frame colnames(df) #shows the name of each column in the data frame head(df) #shows the first 6 rows of the data frame tail(df) # shows the last 6 rows of the data frame View(df) #shows a spreadsheet-like display of the entire data frame in new window class(df) glimpse(df) # Read metadata #### metadata <- read_excel("7.Metadata_SLR_23012024.xlsx") names(metadata) view(metadata) glimpse(metadata) print(ncol(df)) print(length(metadata$Variable_name)) # Rename the head columns of the data as mapped in the metadata colnames(df) <- metadata$Variable_name glimpse(df) # 1. Create timeline graph #### ##sub-setting the data frame for timeline publication using the metadata mask<- metadata$Timeline timeline<- df[mask] head(timeline) View(timeline) is.data.frame(timeline) ## Create the stacked area chart library(tidyverse) library(patchwork) library(cowplot) # Ensure Participatory is a factor with correct levels timeline$Participatory <- factor(timeline$Participatory, levels = c("0", "1"), labels = c("Non_participatory", "Participatory")) # lablel with normal dash not underscore custom_labels <- c("Non_participatory" = "Non-participatory", "Participatory" = "Participatory") # group the data timeline2 <- timeline %>% group_by(Year, Participatory) %>% summarise(count = n(), .groups = 'drop') ### define the colors colors1 <- c("Non_participatory" = "#FF7F50" , "Participatory" = "#008080" ) colors2 <- c( "Participatory" = "#FF5733" , "Non_participatory" = "#87CEEB" ) # Create the stacked area chart timeline3 <- ggplot(timeline2, aes(x = Year, y = count, fill = Participatory)) + geom_area(position = "stack") + scale_fill_manual(values = colors2, name = "Valuation approach", labels = custom_labels )+ scale_x_continuous(breaks = seq(min(timeline2$Year), max(timeline2$Year), by = 1)) + labs(x = "Year of publication", y = "Number of papers") + theme_minimal() + theme(axis.title = element_text(size = 15), axis.text.x = element_text(angle = 45, hjust = 1, size = 10, colour ='black'), axis.text.y = element_text(size = 10, colour = 'black'), legend.title = element_text(size = 15), legend.text = element_text(size = 12)) # Pie chart data preparation pie_data <- timeline %>% count(Participatory) %>% mutate(percentage = n / sum(n) * 100) # Pie chart pie_plot <- ggplot(pie_data, aes(x = "", y = n, fill = Participatory)) + geom_bar(width = 1, stat = "identity") + coord_polar(theta = "y") + scale_fill_manual(values = colors2) + geom_text(aes(label = paste(n, "\n(", round(percentage, 1), "%)", sep = "")), position = position_stack(vjust = 0.5)) + theme_void()+ guides(fill = "none") ## Combine the two plots combined_plot <- ggdraw() + draw_plot(timeline3) + draw_plot(pie_plot, x = 0.05, y = 0.40, width = 0.50, height = 0.50) # Adjust margins of the pie chart combined_plot <- combined_plot + theme(plot.margin = unit(c(1, 0.25, 1, 1.2), "cm")) # Display the combined plot print(combined_plot) ## Save the plot # to specific directory file_timeline <- "D:/SLR/Analysis/Results/timeline.tiff" ggsave(filename = file_timeline , plot = combined_plot, width = 25, height = 15, units = "cm", dpi = 300, bg = "white") # Save the combined plot # to working directory ggsave("timeline2.tiff", combined_plot, width = 25, height = 15, units = "cm", dpi = 300, bg = "white") #### End of timeline graph # 2. Create venn graph #### # library library("ggvenn") library("VennDiagram") ##sub-setting the data frame for es using the metadata mask<- metadata$ES_venn es <- df[mask] head(es) View(es) # selecting data from the es data frame parti_es <- filter(es, Participatory == "1") non_parti_es <- filter(es, Participatory == "0") subset(non_parti_es, select = -(2)) ## remove ecosystem function column view(non_parti_es) # transforming data to logical values for participatory parti_es$Regulating <- as.logical(parti_es$Regulating_services) parti_es$Provisioning <- as.logical(parti_es$Provisioning_services) parti_es$Cultural<- as.logical(parti_es$Cultural_services) ## 2.1 Creating venn digram for services in participatory research #### venn_plot_parti<- ggvenn(parti_es, c("Regulating", "Provisioning", "Cultural")) print(venn_plot_parti) ## save it to specific directory venn_parti <- "D:/SLR/Analysis/Results/venn_parti.tiff" ggsave(filename = venn_parti , plot = venn_plot_parti , width = 13, height = 15, units = "cm", dpi = 300, bg = "white") ## or save it to working directory ggsave("venn_parti.tiff", venn_plot, width = 8, height = 8) # end of participatory venn diagram ## #transforming data to logical values for non participatory non_parti_es $Regulating<- as.logical(non_parti_es$Regulating_services) non_parti_es $Provisioning<- as.logical(non_parti_es$Provisioning_services) non_parti_es $Cultural<- as.logical(non_parti_es$Cultural_services) ## 2.2 Creating venn digram for services in non-participatory research #### venn_plot_nonparti<- ggvenn(non_parti_es, c("Regulating", "Provisioning", "Cultural")) ## save it to specific directory venn_nonparti <- "D:/SLR/Analysis/Results/venn_nonparti.tiff" ggsave(filename = venn_nonparti , plot = venn_plot_nonparti, width = 13, height = 15, units = "cm", dpi = 300, bg = "white") ## or save it to work directory ggsave("venn_nonparti.tiff", venn_plot, width = 8, height = 8) #### end of the venn diagram ## 2.3 Try to do upset graph to ecosystem services #### library(ComplexUpset) library(UpSetR) library(devtools) ### source from this video https://youtu.be/6nN9RC6Exus?t=610 ### https://cran.r-project.org/web/packages/ComplexUpset/vignettes/Examples_R.html ### https://krassowski.github.io/complex-upset/articles/Examples_R.html#fill-the-bars ### https://github.com/hms-dbmi/UpSetR/issues/59 ### https://stackoverflow.com/questions/54770795/stacked-barplot-in-upsetr/56704255#56704255 ### 2.3.1 Upset for participatory papers #### # Define your color variables main_bar_col <- "#FF5733" # Color for the main bars, "#800080" sets_bar_col <- "#FF5733" # Color for the set size bars matrix_col <- "#000000" # Color for the matrix, "#000000" shade_col <- "#D3D3D3" # Light gray color for the shade # Define your text scale options text_scale_options3 <- c(1.5, 1.5, 1.5, 1.5, 1.5) # Example scaling factors for various text elements # Define your main bar to set size bar ratio mb_ratio <- c(0.7, 0.3) # Example ratio; adjust as needed ## upset for participatory es set_var <- c ( "Regulating_services", "Provisioning_services", "Cultural_services", "Ecosystem_function") upset_parti<- upset(parti_es, sets= set_var, mb.ratio = mb_ratio, mainbar.y.label = "Number of papers", sets.x.label = "Number of papers", order_by( "freq"), show.numbers = TRUE, point.size= 1.5, line.size = 1, text.scale = text_scale_options3, main.bar.color = main_bar_col, sets.bar.color= sets_bar_col, matrix.color = matrix_col, shade.color = shade_col ) #saving the plot to a file, start the TIFF device again tiff("upset_plot_with_title.tiff", width = 2400, height = 2400, res = 300) print(upset_parti) dev.off() ### 2.3.2 Upset for nonparticipatory es #### non_parti_es2 <- filter(es, Participatory == "0") # Define your color variables main_bar_col <- "#87CEEB" # Color for the main bars, "#800080" sets_bar_col <- "#87CEEB" # Color for the set size bars matrix_col <- "#000000" # Color for the matrix, "#000000" shade_col <- "#D3D3D3" # Light gray color for the shade # Define your text scale options text_scale_options3 <- c(1.5, 1.5, 1.5, 1.5, 1.5) # Example scaling factors for various text elements # Define your main bar to set size bar ratio mb_ratio <- c(0.7, 0.3) # Example ratio; adjust as needed set_var2 <- c ( "Regulating_services", "Provisioning_services", "Cultural_services") upset_nonparti<- upset(non_parti_es2, sets= set_var2, mb.ratio = mb_ratio, mainbar.y.label = "Number of papers", sets.x.label = "Number of papers", order_by( "freq"), show.numbers = TRUE, point.size= 1.5, line.size = 1, text.scale = text_scale_options3, main.bar.color = main_bar_col, sets.bar.color= sets_bar_col, matrix.color = matrix_col, shade.color = shade_col ) #saving the plot to a file, start the TIFF device again tiff("upset_plot_nonparti.tiff", width = 2400, height = 2400, res = 300) print(upset_nonparti) dev.off() # end of the upset graph ## # 3. Create valuation unit graph #### ##sub-setting the data frame for es using the metadata mask<- metadata$Valuation_units valuation_units <- df[mask] View(valuation_units) head(valuation_units) names(valuation_units) <- gsub("_services$", "", names(valuation_units)) # remove the word services # First, mutation to create BN_SN, BN_SE, BN_H, etc. valuation_units2 <- valuation_units %>% mutate( BN_SN = as.numeric(BN == 1 & SN == 1), BN_SE = as.numeric(BN == 1 & SE == 1), BN_H = as.numeric(BN == 1 & Health == 1), BE_SN = as.numeric(BE == 1 & SN == 1), BE_SE = as.numeric(BE == 1 & SE == 1), BE_H = as.numeric(BE == 1 & Health == 1), SN_H = as.numeric(SN == 1 & Health == 1), SE_H = as.numeric(SE == 1 & Health == 1) ) # Second, mutation to create the Services column (Continue from valuation_units2, not redo from valuation_units) valuation_units2 <- valuation_units2 %>% mutate( Services = case_when( Regulating == 1 & Provisioning == 0 & Cultural == 0 ~ "Regulating", Regulating == 0 & Provisioning == 1 & Cultural == 0 ~ "Provisioning", Regulating == 0 & Provisioning == 0 & Cultural == 1 ~ "Cultural", Regulating == 1 & Provisioning == 1 & Cultural == 0 ~ "Regulating & Provisioning", Regulating == 1 & Provisioning == 0 & Cultural == 1 ~ "Regulating & Cultural", Regulating == 0 & Provisioning == 1 & Cultural == 1 ~ "Provisioning & Cultural", Regulating == 1 & Provisioning == 1 & Cultural == 1 ~ "All Services", TRUE ~ "No Service" ) ) # Third, mutation to adjust based on combinations (Continue from the last updated version of valuation_units2) df_adjusted <- valuation_units2 %>% mutate( BN_adj = ifelse(BN_SN + BN_SE + BN_H > 0, 0, BN), BE_adj = ifelse(BE_SN + BE_SE + BE_H > 0, 0, BE), SN_adj = ifelse(SN_H > 0, 0, SN), SE_adj = ifelse(SE_H > 0, 0, SE), Health_adj = ifelse(BN_H + BE_H + SN_H + SE_H > 0, 0, Health) ) # fourth ,Reshaping and summarizing the adjusted data, assuming code is a unique identifier for each paper. df_long <- df_adjusted %>% pivot_longer( cols = c(BN_adj, BE_adj, SN_adj, SE_adj, Health_adj, BN_SN, BN_SE, BN_H, BE_SN, BE_SE, BE_H, SN_H, SE_H), names_to = "valuation_unit", values_to = "presence" # Changed to 'presence' to reflect this is a binary indicator ) %>% filter(presence > 0) %>% distinct(Code, .keep_all = TRUE) %>% # Assuming you have a unique identifier column 'paper_id' group_by(Participatory, Services, valuation_unit) %>% summarize(paper_count = n_distinct(Code), .groups = 'drop') # Count distinct papers sum(df_long$paper_count) # Fifth, renaming the valuation unit columns by removing the "_adj" suffix library(stringr) df_long <- df_long %>% mutate(valuation_unit = str_replace(valuation_unit, "_adj", "")) # Checking the renamed columns head(df_long) sum(df_long$paper_count) df_long <- df_long %>% mutate(valuation_unit = str_replace_all(valuation_unit, "_", " ")) library(forcats) # for fct_recode str(df_long$Participatory) # Update the Participatory column to be a factor with readable levels df_long$Participatory <- factor(df_long$Participatory, levels = c("0", "1")) is.factor(df_long$Participatory) df_long$Participatory <- fct_recode(df_long$Participatory, "Non-Participatory" = "0", "Participatory" = "1") ## optional step #### ## if I want to know the total number of each bar ### Calculate total counts for each valuation unit within each Participatory group df_totals <- df_long %>% # df totals and df_long joined with total are not necessary for graph plot only to know the exact number of papers for each bar group_by(Participatory, valuation_unit) %>% summarize(paper_count = sum(paper_count), .groups = 'drop') ### Merge the total counts back into the long data frame for plotting ,careful with this step as not needed for the graph df_long <- df_long %>% left_join(df_totals, by = c("Participatory", "valuation_unit")) # continue in the code for the valuation unit ##### # Sixth, NICE stacked bar chart with readable facet labels and a nice color palette valuation_units_plot<- ggplot(df_long, aes(x = valuation_unit, y = paper_count, fill = Services)) + geom_bar(stat = "identity", position = "stack") + facet_grid(~ Participatory, scales = "free_x", space = "free_x") + scale_fill_brewer(palette = "Paired") + theme_minimal() + labs( title = "", ##I can add the title here x = "Valuation Units", y = "Number of Papers", fill = "Ecosystem Services" ) + scale_y_continuous ( breaks = seq(0, 40, by = 10), limits = c(NA,40)) + theme( axis.text.x = element_text(color = "black"), # Set x-axis label color to black panel.grid.major = element_line(size = 0.5, color = "grey80"), ### panel.grid.minor = element_blank(), ### strip.text.x = element_text(size = 12, face = "bold"), # Bold facet labels legend.position = "bottom", # Legend at the bottom panel.spacing = unit(1, "lines"), # Space between the facets panel.border = element_rect(colour = "black", fill = NA, size = 0.5), # Borders to separate facets axis.ticks.length = unit(0, "points"), # No axis ticks to clean up the look panel.background = element_rect(fill = "white", colour = NA) ) print(valuation_units_plot) ## now saving the graph ## setwd("D:/SLR/Analysis/Results") ggsave(filename = "valuation_units.tiff", plot = valuation_units_plot, device = "tiff", path = "D:/SLR/Analysis/Results", width = 19, height = 9, units = c("cm"), dpi = 700, bg = "white") ## save it as jpg image ggsave("valuation units.jpg", width = 19, height = 9, units = c("cm"),dpi = 700,bg = "white") ##### end of the code for the nice graph , simple and clear # 4. Drivers of change graph #### ## sub-setting the data frame for drivers using the metadata mask<- metadata$Drivers_change drivers <- df[mask] head(drivers) View(drivers) is.data.frame(drivers) # First, count the data for the graph drivers2 <- drivers %>% group_by(Drivers_of_change, Participatory) %>% count() %>% # Calculate the total count for each drivers of change category group_by(Drivers_of_change) %>% mutate(Total = sum(n)) %>% # Calculate percentage within each Participatory group mutate(Percentage = (n / Total) * 100) # Ensure 'Drivers_of_change' and 'Participatory' are factors, and rename it drivers2$Participatory <- factor(drivers2$Participatory, levels = c(0, 1), labels = c("Non-participatory", "Participatory")) drivers2$Drivers_of_change <- factor(drivers2$Drivers_of_change, levels = c(0, 1), labels = c("Without drivers of change", "With drivers of change")) # Reorder and rename the levels of the 'Drivers_of_change' factor variable drivers2$Participatory<- factor(drivers2$Participatory, levels = c("Participatory", "Non-participatory"), labels = c("Participatory", "Non-participatory")) drivers2$Drivers_of_change <- factor(drivers2$Drivers_of_change, levels = c("With drivers of change", "Without drivers of change"), labels = c("With drivers of change", "Without drivers of change")) ## coloring colors2 <- c( "Participatory" = "#FF5733" , "Non-participatory" = "#87CEEB" ) # Plotting the data driversplot <- ggplot(drivers2, aes(x = Drivers_of_change, y = n, fill = Participatory)) + geom_bar(stat = "identity", position = "stack") + # Use stacked bars geom_text(aes(label = n), vjust = -0.5, size = 4, position = position_stack(vjust = 0.5)) + # Show count of papers geom_text(aes(label = paste0("(", round(Percentage, 1), "%)"), y = n), vjust = 1.5, size = 4, position = position_stack(vjust = 0.5)) + # Show percentage with parentheses scale_y_continuous(breaks = seq(0, 90, by = 10), limits = c(0, 90)) + scale_fill_manual(values = colors2, name = "Valuation approach") + labs(x = "Drivers of change", y = "Number of papers")+ theme(axis.title=element_text(size=15), axis.text=element_text(size=10, colour = 'black'), legend.position = "right", legend.text=element_text(size=14), legend.title = element_text(size = 18), legend.key.size = unit(0.50, 'cm')) driversplot file_path <- "D:/SLR/Analysis/Results/drivers_of_change4.tiff" # Save the plot ggsave(filename = file_path, plot = driversplot, width = 8, height = 6, dpi = 300) #### colors options#### colors3 <- c("Without drivers of change" = "#fdae61", # A warm, soft orange "With drivers of change" = "#008080") # A cool, soothing blue colors4 <- c("Without drivers of change" = "#26A69A", # Vibrant teal "With drivers of change" = "#6A1B9A") # Deep purple colors5 <- c("With drivers of change" = "#80CBC4", # Gentle pastel teal "Without drivers of change" = "#B39DDB") colors6 <- c("Without drivers of change" = "#BCAAA4", # Muted brown "With drivers of change" = "#A5D6A7") # Soft pastel green colors7 <- c("Without drivers of change" = "#E6C17B", # Muted, earthy orange "With drivers of change" = "#A5D6A7") # Pastel green ####end of of valuation approach and drivers of change plot ## # 5. Create analysis methods #### #source: ## https://cran.r-project.org/web/packages/eulerr/vignettes/gallery.html ## http://127.0.0.1:27935/graphics/plot_zoom_png?width=1024&height=520 library(eulerr) library(grid) library(gridExtra) # subset the data from the df mask<- metadata$Analysis_methods methods <- df[mask] head(methods) # Filter for participatory papers df_participatory <- methods[methods$Participatory == 1, ] # Fit Euler diagrams fit_participatory <- euler(df_participatory[, c( "Statistical_modelling_equations", "Spatial_Remote_sensing", "Conceptual_framework", "Content_thematic_analysis", "Theory")]) # Custom labels for the diagram custom_labels <- c("Statistical Modelling and equations", "Spatial - remote sensing", "Conceptual and methodological framework", "Content and thematic analysis", "Theoretical analysis") # Set margins for additional space par(mar = c(6, 4, 4, 2)) # Adjust the top margin (third value) to make space for the title # Add a legend manually since eulerr's plot function doesn't have a built-in legend argument legend("topright", # Adjust the position as needed legend = custom_labels, col = c("lavenderblush2", "lightblue2", "lightsalmon", "plum2", "palegreen2"), lty = 1, cex = 2, pch = 19) # Create and save the plot for participatory plot_participatory <- plot(fit_participatory, labels = list(fontfamily = "serif", labels = c("Statistical modelling and equations", "Spatial - remote sensing", "Conceptual and methodological framework", "Content and thematic analysis", "Theoretical analysis"), cex = 1.2 ), quantities = list(type = c("counts", "percent"), font=3, round=2, cex= 0.95), edges = list(lty = 3), fills = c("lavenderblush2", "lightblue2", "lightsalmon", "plum2", "palegreen2")) # remove the title mtext("Participatory papers", side = 1, line = 5, col = "blue", font = 4, cex = 2) print(plot_participatory) file_path <- "D:/SLR/Analysis/Results/parti_methods3.tiff" # Save the plot ggsave(filename = file_path, plot = plot_participatory, width = 8, height = 6, dpi = 300) # removed line #Customize the title appearance if needed title(main = "Participatory papers", col.main = "blue", font.main = 5, cex.main = 2, mgp = c(3, 2, 0)) ## non participatory # Filter data for non-participatory df_non_participatory <- methods[methods$Participatory == 0, ] # Fit Euler diagram for non-participatory fit_non_participatory <- euler(df_non_participatory[, c( "Statistical_Modelling_equations", "Spatial_Remote_sensing", "Conceptual_framework", "Content_thematic_analysis", "Theory")]) plot_nonparticipatory <- plot(fit_non_participatory, labels = list(fontfamily = "serif", labels = c("Statistical modelling and equations", "Spatial - remote sensing", "Coceptual and methodological framework", "Content and thematic analysis", "Theoretical analysis"), cex = 1.2 ), quantities = list(type = c("counts", "percent"), font=3, round=2, cex= 0.95), edges = list(lty = 3), fills = c("lavenderblush2", "lightblue2", "lightsalmon", "plum2", "palegreen2")) # remove the title mtext("Non-participatory papers", side = 1, line = 5, col = "blue", font = 4, cex = 2) print(plot_nonparticipatory) file_path <- "D:/SLR/Analysis/Results/nonparti_methods3.tiff" # Save the plot ggsave(filename = file_path, plot = plot_nonparticipatory, width = 8, height = 6, dpi = 300) # removed line # Customize the title appearance if needed title(main = "Non-participatory papers", col.main = "blue", font.main = 4, cex.main = 2 , mgp = c(3, 2, 0)) #### end of analysis methods graph### # 6. Create table of descriptive statistics #### mask<- metadata$General_table general <- df[mask] head(general) View(general) is.data.frame(General) df_participatory <- general[general$Participatory == 1, ] df_non_participatory <- general[general$Participatory == 0,] str(df_non_participatory) ## count per variable table(df_non_participatory$Scenario) table(df_non_participatory$Ecosystem_services_tradeoff) table(df_non_participatory$Forest_ecosystem_alone) table(df_non_participatory$One_forest_type) #7 Economic valuation #### mask<- metadata$Economic_valuation economic <- df[mask] head(economic ) View(economic ) is.data.frame(economic ) str(economic) economic2 <- economic %>% group_by(Economic_valuation, Participatory) %>% count() %>% # Calculate the total count for each Participatory category group_by(Economic_valuation) %>% mutate(Total = sum(n)) %>% # Calculate percentage within each Participatory group mutate(Percentage = (n / Total) * 100) # Ensure 'Economic_valuation' and 'Participatory' are factors, and rename it economic2$Participatory <- factor(economic2$Participatory, levels = c(0, 1), labels = c("Non-participatory", "Participatory")) economic2$Economic_valuation <- factor(economic2$Economic_valuation, levels = c(0, 1), labels = c("Without economic valuation", "With economic valuation")) ## coloring colors2 <- c( "Participatory" = "#FF5733" , "Non-participatory" = "#87CEEB" ) # Plotting the data economicplot <- ggplot(economic2, aes(x = Economic_valuation , y = n, fill = Participatory )) + geom_bar(stat = "identity", position = "stack") + # Use stacked bars geom_text(aes(label = n), vjust = -0.5, size = 4, position = position_stack(vjust = 0.5)) + # Show count of papers geom_text(aes(label = paste0("(", round(Percentage, 1), "%)"), y = n), vjust = 1.5, size = 4, position = position_stack(vjust = 0.5)) + # Show percentage with parentheses scale_y_continuous(breaks = seq(0, 90, by = 15), limits = c(0, 90)) + scale_fill_manual(values = colors2, name = "Valuation approach") + labs(x = "Economic valuation", y = "Number of papers")+ theme(axis.title=element_text(size=15), axis.text=element_text(size=10, colour = 'black'), legend.position = "right", legend.text=element_text(size=14), legend.title = element_text(size = 18), legend.key.size = unit(0.50, 'cm')) economicplot file_path <- "D:/SLR/Analysis/Results/economic_valuation2.tiff" # Save the plot ggsave(filename = file_path, plot =economicplot, width = 8, height = 6, dpi = 300) ####end of of valuation approach and drivers of change plot ## # 8. Citation for r and packages #### # explanation # run the citation function , then cop the Bibtex entry in text file inside r # then save the text #file .Bib , after saving the file import it to reference # manager ar bibtex file and all reference #will be automatically added BINGOO :) #source: https://youtu.be/zuuOYjE8m98 citation() ## cite r version$version.string ## cite the version citation("ggplot2") citation("dplyr") citation("tidyverse") # visualization library citation("RColorBrewer") citation("ggthemes") citation("extrafont") ## timeline citation ("patchwork") citation ("cowplot") ## venn citation ("ggvenn") citation ("VennDiagram") ## upset citation ("ComplexUpset") citation ("UpSetR") citation ("devtools") ## eulerr citation ("eulerr") citation ("grid") citation ("gridExtra")