Get_Start

library(itol.toolkit)
library(dplyr)
library(data.table)
library(ape)
library(stringr)
library(tidyr)
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.","BalvĂ­n,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)
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)
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,tempdir())