How to group bars together in a stacked bar plot? ggplot R

August 25, 2022

How do I get the stacked bars in group KAPA, TAKARA and plasmid_DNA to group separately on this bar chart?

This is what I’ve tried, but each group contains all columns even if empty:

s %>%
  rownames_to_column(var = "id") %>%
  filter(id != "miseq_pDNA") %>%
  select(id, unmapped_multihit:mapped_correct) %>% 
  mutate(group = c(rep('KAPA', 4), rep('TAKARA', 4), 'plasmid_DNA')) %>%
  pivot_longer(unmapped_multihit:mapped_correct) %>% 
  ggplot(aes(x = id, y = value, fill = name)) + 
  geom_col(position = position_fill(reverse = T)) +
  scale_y_continuous(labels = scales::percent) +
  facet_wrap(~ group)

This is my data
………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

> dput(s)
structure(list(total_pairs = c(35894878L, 26257107L, 32170406L, 
17432039L, 46844537L, 29392398L, 41038453L, 33939207L, 24109293L, 
17881958L), pc_aberrant_match = c(911L, 656L, 775L, 1102L, 774L, 
2652L, 5217L, 213L, 128L, 80L), pc_ambiguous = c(1146L, 88L, 
3L, 952L, 115L, 9L, 1829L, 217L, 23L, 21L), pc_f_multi_3p = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), pc_f_multi_5p = c(59948L, 
47425L, 55476L, 24988L, 65678L, 36836L, 58160L, 55139L, 3517L, 
2803L), pc_f_open_3p = c(1930323L, 1471085L, 1761019L, 948152L, 
3718951L, 2282510L, 3143527L, 2399348L, 1205911L, 943323L), pc_f_open_5p = c(1031640L, 
751738L, 940874L, 504689L, 2628015L, 1615047L, 2172979L, 1560282L, 
604596L, 338024L), pc_match = c(24991561L, 18283812L, 22380688L, 
12160990L, 30266973L, 19213004L, 27026700L, 22573486L, 21743899L, 
16178486L), pc_no_match = c(172167L, 123538L, 144297L, 73969L, 
848697L, 385718L, 424367L, 392399L, 87832L, 65499L), pc_r_multi_3p = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), pc_r_multi_5p = c(37313L, 
26587L, 35617L, 20454L, 50475L, 36288L, 51256L, 42501L, 2874L, 
2164L), pc_r_open_3p = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L), pc_r_open_5p = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
    pc_swap = c(7669869L, 5552178L, 6851657L, 3696743L, 9264859L, 
    5820334L, 8154418L, 6915622L, 460513L, 351558L), unmapped_multihit = c(98407, 
    74100, 91096, 46394, 116268, 73133, 111245, 97857, 6414, 
    4988), unmapped_no_match = c(3134130, 2346361, 2846190, 1526810, 
    7195663, 4283275, 5740873, 4352029, 1898339, 1346846), mapped_incorrect = c(7670780L, 
    5552834L, 6852432L, 3697845L, 9265633L, 5822986L, 8159635L, 
    6915835L, 460641L, 351638L), mapped_correct = c(24991561L, 
    18283812L, 22380688L, 12160990L, 30266973L, 19213004L, 27026700L, 
    22573486L, 21743899L, 16178486L)), row.names = c("A01", "A02", 
"A03", "A06", "A10", "A11", "A12", "B03", "hiseq_pDNA", "miseq_pDNA"
), class = "data.frame")

>Solution :

Removed prior answers:

The key element is to use facet_grid(. ~ group, scales = "free", space = "free")

library(tidyverse)

df %>%
  rownames_to_column(var = "id") %>%
  filter(id != "miseq_pDNA") %>%
  select(id, unmapped_multihit:mapped_correct) %>% 
  mutate(group = c(rep('KAPA', 4), rep('TAKARA', 4), 'plasmid_DNA')) %>%
  pivot_longer(unmapped_multihit:mapped_correct) %>% 
  ggplot(aes(x = id, y = value, fill = name)) + 
  geom_col(position = position_fill(reverse = T)) +
  scale_y_continuous(labels = scales::percent)+
  facet_grid(. ~ group, scales = "free", space = "free")