Connect stack bar charts with multiple groups with lines or segments using ggplot 2

Question

I am conducting a study of a number of patients with a disease, and using an ordinal scale assessment of functional status at 3 different time points. I want to connect multiple groups in stacked bar charts across these time points.

I looked at these topics and havent gotten it to work using these suggestions:

How to position lines at the edges of stacked bar charts

Is there an efficient way to draw lines between different elements in a stacked bar plot using ggplot2?

Draw lines between different elements in a stacked bar plot

Please see the graphical representation of how I ultimately want this figure to look from R (generated in PRISM) of the frequencies of each of these 6 ordinal values across the three time points (top group has no patients with ordinal score 3,5,6):

Intended FIGURE using PRISM

Data:

library(tidyverse)

mrs <-tibble(
  Score = c(0,1,2,3,4,5,6),
  pMRS = c(17,  2,   1,  0,  1,  0,   0),
  dMRS = c(2,  3,   2,  6,  4,  2,  2),
  fMRS = c(4,  4,  5,  4,  1,  1,  2)

And this is the code that ive tried so far before I run in to issues using either geom_line or geom_segment (left out thse lines because it just distorts the figure currently)

mrs <- mrs %>% mutate(across(-Score,~paste(round(prop.table(.) * 100, 2)))) %>%
   pivot_longer(cols = c("pMRS", "dMRS", "fMRS"), names_to = "timepoint") %>% 
   mutate(Score=as.character(Score),
          value=as.numeric(value)) %>% 
   mutate(timepoint = factor(timepoint, 
                             levels= c("fMRS", 
                              "dMRS",
                              "pMRS"))) %>% 
   mutate(Score = factor(Score,
                         levels = c("6","5","4","3","2","1","0")))
mrs %>% ggplot(aes(y= timepoint, x= value, fill= Score))+
  geom_bar(color= "black", width = 0.6, stat= "identity") +
  scale_fill_manual(name= NULL,
                    breaks = c("6","5","4","3","2","1","0"), values=  c("#000000","#294e63", "#496a80","#7c98ac", "#b3c4d2","#d9e0e6","#ffffff"))+
  scale_y_discrete(breaks=c("pMRS",
                            "dMRS",
                            "fMRS"),
                   labels=c("Pre-mRS,  (N=21)",
                            "Discharge mRS,  (N=21)",
                            "Followup mRS,  (N=21)"))+
  theme_classic()

Answer 1

You're essentially creating an alluvial diagram. You could make use of the ggalluvial package. Below the desired look - I kept it in horizontal fashion, because it's more natural to read time points from left to right (at least in Western societies). But you can simply add coord_flip if you really want to.

Also - please see below a suggestion of what I personally find a more compelling visualisation.

Check the following sources for more info on alluvial charts

• https://corybrunson.github.io/2019/09/13/flow-taxonomy/
• https://matthewdharris.com/2017/11/11/a-brief-diversion-into-static-alluvial-sankey-diagrams-in-r/

library(tidyverse)
library(ggalluvial)

# I personally prefer to create a new object when you do data modifications
mrs_long <- 
  mrs %>% mutate(across(-Score,~paste(round(prop.table(.) * 100, 2)))) %>%
  pivot_longer(cols = c("pMRS", "dMRS", "fMRS"), names_to = "timepoint") %>% 
  mutate(Score=as.character(Score),
         value=as.numeric(value),
         ## I've reversed the level order
         timepoint = factor(timepoint, levels= rev(c("fMRS", "dMRS", "pMRS"))),
         Score = factor(Score, levels = 6:0))

ggplot(mrs_long,
       aes(y = value, x = timepoint)) +
  geom_flow(aes(alluvium = Score), alpha= .9, 
            lty = 2, fill = "white", color = "black",
            curve_type = "linear", 
            width = .5) +
  geom_col(aes(fill = Score), width = .5, color = "black") +
  scale_fill_manual(NULL, breaks = 6:0,
                    values=  c("#000000","#294e63", "#496a80","#7c98ac", "#b3c4d2","#d9e0e6","#ffffff"))+
  scale_y_continuous(expand = c(0,0)) +
  cowplot::theme_minimal_hgrid()
#> Warning: The `.dots` argument of `group_by()` is deprecated as of dplyr 1.0.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Arguably more compelling - I find the message gets across better by making the full use of the "alluvial look". For example this could look like this:

ggplot(mrs_long,
       aes(y = value, x = timepoint, fill = Score)) +
  geom_alluvium(aes(alluvium = Score), alpha= .9, color = "black") +
  scale_fill_manual(NULL, breaks = 6:0,
                    values=  c("#000000","#294e63", "#496a80","#7c98ac", "#b3c4d2","#d9e0e6","#ffffff"))+
  scale_y_continuous(expand = c(0,0)) +
  cowplot::theme_minimal_hgrid()