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DATASET_PIECHART

Longzhao Li1, Zhongyi Hua2, Tong Zhou3

Last compiled on 01 September, 2025

DATASET_PIECHART.Rmd

Introduction

The function of DATASET_PIECHART is to draw the pie chart and store drawing information. The DATASET_PIECHART template belongs to the “Basic graphics” class (refer to the Class for detail information).

Typically, users cannot save drawing information using iTOL, making reproduce pictures or share them with others difficultly. Here, we provide a convenient way to store radius and the proportion of different groups of pip charts. This section describes how to itol.toolkit to draw the pie chart and store corresponding information.

General process

This section uses dataset 1 as an example to show how to draw the line chart. (refer to the Dataset for detail information)

Load data

The first step is to load the newick format tree file tree_of_itol_templates.tree and its corresponding metadata df_frequence.

library(itol.toolkit)
library(data.table)
library(tidyr)
library(dplyr)
library(stringr)
library(ape)
tree <- system.file("extdata",
                    "tree_of_itol_templates.tree",
                    package = "itol.toolkit")
df_frequence <- system.file("extdata",
                            "templates_frequence.txt",
                             package = "itol.toolkit")
df_frequence <- fread(df_frequence)
names(df_frequence) <- 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_frequence) <- stringr::str_remove_all(names(df_frequence),"[()]")
names(df_frequence) <- stringr::str_replace_all(names(df_frequence),",","-")

Data processing and create the unit

Convert wide data to long data. After conversion, the input data fed to DATASET_PIECHART should have at least two columns: tree tip id and the radius. If there are more than two columns, If there are more than two columns, the proportion of different groups starts from the third column. The column name is the group name.

df_frequence_years <- df_frequence %>% 
  pivot_longer(-id)%>% 
  mutate(years = str_extract(name,"\\d{4}")) %>% 
  replace(is.na(.), 0) %>% 
  group_by(id,years) %>% 
  summarise(value = sum(value)) %>% 
  spread(years,value) %>% 
  replace(is.na(.), 0) %>% 
  mutate(size = log(sum(`2018`,`2019`, `2021`, `2022`)+2)) %>% 
  relocate(size, .after = id)
unit_41 <- create_unit(data = df_frequence_years, 
                    key = "E041_piechart_1",
                    type = "DATASET_PIECHART",
                    position = 0,
                    tree = tree)

Pie chart visualization example