Stacked Bars Within Grouped Bar Chart

How to produce stacked bars within grouped barchart in R

Here is what I came up with, similar to a solution proposed here: stacked bars within grouped bar chart

1. Melt data.frame and add a new column cat

   library(reshape2) # for melt
   
   melted <- melt(test, "person")
   
   melted$cat <- ''
   melted[melted$variable == 'value1',]$cat <- "first"
   melted[melted$variable != 'value1',]$cat <- "second"
   

2. Plot a stacked chart cat vs value, faceting by person. You may need to adjust the labels to get what you want:

   ggplot(melted, aes(x = cat, y = value, fill = variable)) + 
     geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ person)
   

stacked bars within grouped bar chart

You can put cat on the x-axis and use facet_grid with year:

ggplot() +
  geom_bar(data=test, aes(y = value, x = cat, fill = cond), stat="identity",
           position='stack') +
  theme_bw() + 
  facet_grid( ~ year)

How to create grouped and stacked bars

• As an FYI, stacked bars are not the best option, because they can make it difficult to compare bar values and can easily be misinterpreted. The purpose of a visualization is to present data in an easily understood format; make sure the message is clear. Side-by-side bars are often a better option.
• Side-by-side stacked bars are a difficult manual process to construct, it's better to use a figure-level method like seaborn.catplot, which will create a single, easy to read, data visualization.
• Bar plot ticks are located by 0 indexed range (not datetimes), the dates are just labels, so it is not necessary to convert them to a datetime dtype.
• Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2

seaborn

import seaborn as sns

sns.catplot(kind='bar', data=df, col='subsidiary', x='date', y='value', hue='business')

Create grouped and stacked bars

• See Stacked Bar Chart and Grouped bar chart with labels
• The issue with the creation of the stacked bars in the OP is bottom is being set on the entire dataframe for that group, instead of only the values that make up the bar height.
• do I really need to create three sub-dfs per subsidiary. Yes, a DataFrame is needed for every group, so 6, in this case.
  
  • Creating the data subsets can be automated using a dict-comprehension to unpack the .groupby object into a dict.
    
    • data = {''.join(k): v for k, v in df.groupby(['subsidiary', 'business'])} to create a dict of DataFrames
    • Access the values like: data['EUCORP'].value
• Automating the plot creation is more arduous, as can be seen x depends on how many groups of bars for each tick, and bottom depends on the values for each subsequent plot.

import numpy as np
import matplotlib.pyplot as plt

labels=df['date'].drop_duplicates()  # set the dates as labels
x0 = np.arange(len(labels))  # create an array of values for the ticks that can perform arithmetic with width (w)

# create the data groups with a dict comprehension and groupby
data = {''.join(k): v for k, v in df.groupby(['subsidiary', 'business'])}

# build the plots
subs = df.subsidiary.unique()
stacks = len(subs)  # how many stacks in each group for a tick location
business = df.business.unique()

# set the width
w = 0.35

# this needs to be adjusted based on the number of stacks; each location needs to be split into the proper number of locations
x1 = [x0 - w/stacks, x0 + w/stacks]

fig, ax = plt.subplots()
for x, sub in zip(x1, subs):
    bottom = 0
    for bus in business:
        height = data[f'{sub}{bus}'].value.to_numpy()
        ax.bar(x=x, height=height, width=w, bottom=bottom)
        bottom += height
        
ax.set_xticks(x0)
_ = ax.set_xticklabels(labels)

• As you can see, small values are difficult to discern, and using ax.set_yscale('log') does not work as expected with stacked bars (e.g. it does not make small values more readable).

Create only stacked bars

• As mentioned by @r-beginners, use .pivot, or .pivot_table, to reshape the dataframe to a wide form to create stacked bars where the x-axis is a tuple ('date', 'subsidiary').
  
  • Use .pivot if there are no repeat values for each category
  • Use .pivot_table, if there are repeat values that must be combined with aggfunc (e.g. 'sum', 'mean', etc.)

# reshape the dataframe
dfp = df.pivot(index=['date', 'subsidiary'], columns=['business'], values='value')

# plot stacked bars
dfp.plot(kind='bar', stacked=True, rot=0, figsize=(10, 4))

grouped (twice) and stacked bar chart with facet wrapping

Updated following OP's comment about wanting dodged and stacked bars: dodged by majortype; stacked by type.

Combining dodged and stacked bars is not a feature of ggplot: https://github.com/tidyverse/ggplot2/issues/2267

However, with help from this link: ggplot2 - bar plot with both stack and dodge and a bit of additional tinkering you could try this...

library(ggplot2)
library(dplyr)

# prepare data so that values are in effect stacked and in the right order

dat <- 
  mydata %>% 
  group_by(year, subject, student, majortype) %>% 
  arrange(type) %>% 
  mutate(val_cum = cumsum(value))

ggplot(dat, aes(fill = majortype, y = val_cum, x = year)) +
  geom_col(data = filter(dat, type == "low income"), position = position_dodge2(width = 0.9), alpha = 0.5)+
  geom_col(data = filter(dat, type == "high income"), position = position_dodge2(width = 0.9), alpha = 1) +
  geom_tile(aes(y = NA_integer_, alpha = type)) +
  scale_fill_manual(breaks = c("passed", "total"),
                    labels = c("High income - passed", "High income - total"),
                    values = c("red", "blue"))+
  guides(alpha = guide_legend(override.aes = list(fill = c("red", "blue"), alpha = c(0.5, 0.5))))+
  scale_alpha_manual(breaks = c("high income", "low income"),
                    labels = c("Low income - passed", "Low income - total"),
                    values = c(1, 0.5))+
  facet_wrap(student~subject)+
  labs(x = NULL, 
       y = "Number of students",
       fill = NULL,
       alpha = NULL)+
  theme_minimal() +
  theme(text = element_text(size=15),
        plot.title = element_text(size=20, face="bold"),
        axis.text  = element_text(size=9))

^{Created on 2022-05-09 by the reprex package (v2.0.1)}

Grouped stacked bar chart in R

You need position = "stack" instead of "dodge".

I reorganised your code slightly:

library(ggplot2)
library(dplyr)

data %>% 
  mutate(emp_class = cut(Count, 
                         breaks = c(0, 30, 40, 50, 60, 100), 
                         labels = c('(0-30)', '(31-40)', '(41-50)', '(51-60)', '(61-100)')
  )
  ) %>% 
  pivot_longer(c(Male, Female),
               names_to = "MF") %>% 
  group_by(Emp_group, MF) %>% 
  add_count() %>% 
  mutate(x_axis = as.factor(paste(Emp_group, n, sep = "\n"))) %>% 
  ggplot(aes(x = x_axis, fill = as.factor(emp_class))) +
  geom_bar(aes(y = value),
           position = "fill",
           stat = "identity") + 
  labs(x = "", y = "% Employes", fill = "Age group") + 
  theme(text = element_text(size = 20)) +
  facet_wrap(~MF) + 
  scale_y_continuous(labels = scales::percent_format())

This returns

Data

structure(list(Count = c(38, 38, 33, 34, 41, 45, 35, 39, 39, 
44, 42), Male = c(10, 20, 15, 17, 35, 25, 17, 30, 9, 22, 20), 
    Female = c(28, 18, 18, 17, 6, 20, 18, 9, 30, 22, 22), Emp_group = c("Staff", 
    "Teacher", "Teacher", "Teacher", "Staff", "Teacher", "Staff", 
    "Staff", "Teacher", "Staff", "Teacher")), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -11L), spec = structure(list(
    cols = list(Count = structure(list(), class = c("collector_double", 
    "collector")), Male = structure(list(), class = c("collector_double", 
    "collector")), Female = structure(list(), class = c("collector_double", 
    "collector")), Emp_group = structure(list(), class = c("collector_character", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1L), class = "col_spec"))

Combine grouped and stacked bars

I am not aware of a straightforward solution for this (read more here:
Combination of grouped and stacked bar chart ). But we can find a workaround by editing the data and modifying the axis. See below;

DT1 <- DT[variable =="example_var3", year := year + 0.4][]

my_plot <-
 plot_ly(
   DT1,
   x = ~ year,
   y = ~  value,
   type = "bar",
   name = ~ variable,
   text = ~ ratio,
   textposition = 'auto'
 ) %>%
 layout (barmode = "stack",
         xaxis = list(
           ticktext = list(2019, 2020, 2021),
           tickvals = lapply(list(2019, 2020, 2021), `+`, 0.2),
           tickmode = "array"
         ))

How to plot a Stacked and grouped bar chart in ggplot?

Suppose you want to plot id as x-axis, side by side for the month, and stack different types, you can split data frame by month, and add a bar layer for each month, shift the x by an amount for the second month bars so they can be separated:

barwidth = 0.35

month_one <- filter(df, month == 1) %>% 
    group_by(id) %>% arrange(-type) %>% 
    mutate(pos = cumsum(count) - count / 2)   # calculate the position of the label

month_two <- filter(df, month == 2) %>% 
    group_by(id) %>% arrange(-type) %>% 
    mutate(pos = cumsum(count) - count / 2)

ggplot() + 
    geom_bar(data = month_one, 
             mapping = aes(x = id, y = count, fill = as.factor(type)), 
             stat="identity", 
             position='stack', 
             width = barwidth) + 
    geom_text(data = month_one, 
              aes(x = id, y = pos, label = count )) + 
    geom_bar(data = filter(df, month==2), 
             mapping = aes(x = id + barwidth + 0.01, y = count, fill = as.factor(type)), 
             stat="identity", 
             position='stack' , 
             width = barwidth) + 
    geom_text(data = month_two, 
              aes(x = id + barwidth + 0.01, y = pos, label = count )) + 
    labs(fill  = "type")

gives:

---

dput(df)
structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L), month = c(1L, 1L, 1L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L), type = c(1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L), count = c(10L, 9L, 26L, 60L, 90L, 80L, 10L, 9L, 26L, 60L, 
90L, 80L, 10L, 9L, 26L, 60L, 90L, 80L)), .Names = c("id", "month", 
"type", "count"), class = "data.frame", row.names = c(NA, -18L
))

---

Related Topics