itol.toolkit (version 1.1.7)

learn_theme_label: Learn label

Description

learn label paramters as list

Usage

learn_theme_label(lines, sep)

Value

a list of label parameters containing

display

1/0 specifying display or hide the text labels above each field column

size

a number specifying the size factor for the text labels

top

1/0 specifying the labels position. If 0, label text which does not fit into the shape will be hidden

below

1/0 specifying the labels position. By default, internal labels will be placed above the branches. If 1, labels will be below the branches

rotation

a number specifying text label rotation angle

straight

1/0 specifying tree rotation. If set to 1, tree rotation will not influence the individual label rotation

vertical

a number specifying the label vertical shift. Shift internal labels vertically by this amount of pixels (positive or negative)

shift

a number specifying the label shift. text label shift in pixels (positive or negative)

external_shift

1/0 specifying label external shift that add extra horizontal shift to the external labels. Useful in unrooted display mode to shift text labels further away from the node labels.

Arguments

lines

a vector of character strings from template file.

sep

a character specifying the separator.

Examples

library(dplyr)
  tree <- system.file("extdata",
                      "tree_of_itol_templates.tree",
                      package = "itol.toolkit")
  tab_tmp <- data.table::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)
  unit <- create_unit(data = template_end_group_long,
                      key = "Quickstart",
                      type = "DATASET_DOMAINS",
                      tree = tree)
  file <- tempfile()
  write_unit(unit,file)
  lines <- line_clean(file=file)
  sep = learn_separator(file = file)
  learn_theme_label(lines,sep)