## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, eval=FALSE, echo=TRUE ) ## ----message=FALSE, warning=FALSE--------------------------------------------- # library(itol.toolkit) # main package # library(dplyr) # data manipulation # library(data.table) # file read # library(ape) # tree operation # library(stringr) # string operation # library(tidyr) # data manipulation ## ----message=FALSE, warning=FALSE--------------------------------------------- # tree <- system.file("extdata","tree_of_itol_templates.tree",package = "itol.toolkit") # data("template_groups") # data("template_parameters_count") # hub <- create_hub(tree = tree) # # ## 1,7 data # df_group <- data.frame(id = unique(template_groups$group), # data = unique(template_groups$group)) # # ## 2 data # df_count <- cbind(template_groups,as.data.frame(rowSums(template_parameters_count))) # # ## 3 data # df_rename <- data.frame(id = template_groups$template, # new_label = str_to_title(str_replace_all(template_groups$template,"_"," "))) # # ## 5 data # tab_tmp_01 <- as.data.frame(t(template_parameters_count)) # tab_tmp_connect <- convert_01_to_connect(tab_tmp_01) # tab_tmp_connect <- full_join(tab_tmp_connect, template_groups, by=c("row" = "template")) # tab_tmp_connect <- tab_tmp_connect %>% filter(val > 10) %>% filter(row != col) # # ## 6 data # tab_tmp <- fread(system.file("extdata","parameter_groups.txt",package = "itol.toolkit")) # tab_id_group <- tab_tmp[,c(1,2)] # tab_tmp <- tab_tmp[,-c(1,2)] # tab_tmp_01 <- convert_01(object = tab_tmp) # tab_tmp_01 <- cbind(tab_id_group,tab_tmp_01) # # order <- c("type","separator","profile","field","common themes","specific themes","data") # # tab_tmp_01_long <- tab_tmp_01 %>% tidyr::gather(key = "variable",value = "value",c(-parameter,-group)) # # template_start_group <- tab_tmp_01_long %>% group_by(group,variable) %>% summarise(sublen = sum(value)) %>% tidyr::spread(key=variable,value=sublen) # template_start_group$group <- factor(template_start_group$group,levels = order) # template_start_group <- template_start_group %>% arrange(group) # start_group <- data.frame(Var1 = template_start_group$group, Freq = apply(template_start_group[,-1], 1, max)) # start_group$start <- 0 # for (i in 2:nrow(start_group)) { # start_group$start[i] <- sum(start_group$Freq[1:(i-1)]) # } # template_start_group[template_start_group == 0] <- NA # template_end_group <- template_start_group[,2:(ncol(template_start_group)-1)] + start_group$start # template_end_group <- data.frame(group = order,template_end_group) # template_end_group_long <- template_end_group %>% tidyr::gather(key = "variable",value = "value",-group) # names(template_end_group_long)[3] <- "end" # template_end_group_long$start <- rep(start_group$start,length(unique(template_end_group_long$variable))) # template_end_group_long <- template_end_group_long %>% na.omit() # template_end_group_long$length <- sum(start_group$Freq) # template_end_group_long <- template_end_group_long[,c(2,5,4,3,1)] # template_end_group_long$group <- factor(template_end_group_long$group,levels = order) # # ## 8 data # df_values <- fread(system.file("extdata","templates_frequence.txt",package = "itol.toolkit")) # names(df_values) <- c("id","Li,S. et al. (2022) J. Hazard. Mater.","Zheng,L. et al. (2022) Environ. Pollut.","Welter,D.K. et al. (2021) mSystems","Zhang,L et al. (2022) Nat. Commun.","Rubbens,P. et al. (2019) mSystems","Laidoudi,Y. et al. (2022) Pathogens","Wang,Y. et al. (2022) Nat. Commun.","Ceres,K.M. et al. (2022) Microb. Genomics","Youngblut,N.D. et al. (2019) Nat. Commun.","Balvín,O. et al. (2018) Sci. Rep.","Prostak,S.M. et al. (2021) Curr. Biol.","Dijkhuizen,L.W. et al. (2021) Front. Plant Sci.","Zhang,X. et al. (2022) Microbiol. Spectr.","Peris,D. et al. (2022) PLOS Genet.","Denamur,E. et al. (2022) PLOS Genet.","Dezordi,F.Z. et al. (2022) bioRxiv","Lin,Y. et al. (2021) Microbiome","Wang,Y. et al. (2022) bioRxiv","Qi,Z. et al. (2022) Food Control","Zhou,X. et al. (2022) Food Res. Int.","Zhou,X. et al. (2022) Nat. Commun.") # names(df_values) <- str_remove_all(names(df_values),"[()]") # names(df_values) <- str_replace_all(names(df_values),",","-") # # ## 9 data # df_value <- fread(system.file("extdata","templates_frequence.txt",package = "itol.toolkit")) # df_value <- df_value %>% tidyr::pivot_longer(-templates) %>% na.omit() %>% select(templates,value) %>% as.data.frame() # df_value$value <- log(df_value$value) ## ----tree colors clade in node, message=FALSE, warning=FALSE------------------ # unit_1 <- create_unit(data = df_group, # key = "E1_template_types", # type = "TREE_COLORS", # subtype = "clade", # line_type = c(rep("normal",4),"dashed"), # size_factor = 5, # tree = tree) # # unit_2 <- create_unit(data = df_count, # key = "E2_parameter_number", # type = "DATASET_SYMBOL", # position = 1, # tree = tree) # # unit_3 <- create_unit(data = df_rename, # key = "E3_template_rename", # type = "LABELS", # tree = tree) # # unit_4 <- create_unit(data = template_groups, # key = "E4_template_name_color", # type = "DATASET_STYLE", # subtype = "label", # position = "node", # size_factor = 1.5, # tree = tree) # # unit_5 <- create_unit(data = tab_tmp_connect[,1:4], # key = "E5_template_similarity", # type = "DATASET_CONNECTION", # tree = tree) # # unit_6 <- create_unit(data = template_end_group_long, # key = "E6_template_parameters_structure", # type = "DATASET_DOMAINS", # tree = tree) # # unit_7 <- create_unit(data = df_group, # key = "E7_template_types", # type = "DATASET_COLORSTRIP", # tree = tree) # # unit_8 <- create_unit(data = df_values, # key = "E8_usage_count_among_publications", # type = "DATASET_HEATMAP", # tree = tree) # # unit_9 <- create_unit(data = df_value, # key = "E9_log_transformed_usage_count", # type = "DATASET_BOXPLOT", # tree = tree) ## ----message=FALSE------------------------------------------------------------ # unit_2@specific_themes$basic_plot$size_max <- 40 # # unit_5@specific_themes$basic_plot$size_max <- 100 # # unit_8@specific_themes$heatmap$color$min <- "#ffd966" # unit_8@specific_themes$heatmap$color$max <- "#cc0000" # unit_8@specific_themes$heatmap$use_mid <- 0 # # unit_9@specific_themes$basic_plot$size_max <- 100 ## ----------------------------------------------------------------------------- # hub <- hub + # unit_1 + # unit_2 + # unit_3 + # unit_4 + # unit_5 + # unit_6 + # unit_7 + # unit_8 + # unit_9 # # write_hub(hub,getwd())