如何将 plot 与 ggplot2 组合成多个区域？

Question

I am trying to achieve a complex data viz like in the picture bellow. But with R and ggplot2.

As observed:

1. there are 6 different groups "Africa", "Asia", "Europe", etc, above each sets of data visualisation;
2. 1 set comprising of 3 area plots per each continent;
3. the x axis appears only to one set, last row of Oceania
4. the legend appears only once, above.
5. There are two legends, above the plot - risk groups and conditions
6. as you can see, Africa has population in million (one chart), risk groups and conditions.

I am trying to achieve same results with 2 of my datasets. For India for example, I want in one line, a chart for symptoms and the second a chart for comorbidities. The same for UK and Pakistan. Here are some fake datasets created:

1. https://github.com/gabrielburcea/stackoverflow_fake_data/blob/master/fake_symptoms.csv
2. https://github.com/gabrielburcea/stackoverflow_fake_data/blob/master/fake_comorbidities%202.csv

I have tried to get something by creating small datasets per each country and then created 2 plots, one for symptoms and the other for comorbities, and then adding them together. But this is heavy work with so many other issues coming up. Problems may emerge taking this approach. One example it is here:

india_count_symptoms <- count_symptoms %>%
  dplyr::filter(Country == "India")

india_count_symptoms$symptoms <- as.factor(india_count_symptoms$symptoms)
india_count_symptoms$Count <- as.numeric(india_count_symptoms$Count)

library(viridis)

india_sympt_plot <- ggplot2::ggplot(india_count_symptoms, ggplot2::aes(x = age_band, y = Count, group = symptoms, fill = symptoms)) +
  ggplot2::geom_area(position = "fill", color = "white") + 
  ggplot2::scale_x_discrete(limits = c("0-19", "20-39", "40-59","60+"), expand = c(0, 0)) +
  ggplot2::scale_y_continuous(expand = expansion(mult = c(0, 0.1))) + 
  viridis::scale_fill_viridis(discrete = TRUE)

india_sympt_plot  

this is what I got:

And as you can see:

a. the age bands aren't nicely aligned

b. I end up with legends for each plot for each country, if I take this approach

c. y axis does not give me the counts, it goes all the way to 1. and does not come intuitively right.

d. do the same for comorbidites and then get the same problems expressed in the above 3 points.

Thus, I want to follow an easier approach in order to get similar plot as in the first picture, with conditions expressed: from 1 to 5 points but for my 3 countries and for symptoms and comorbidities. However, my real dataset is bigger, with 5 countries but with same plotting - symptoms and comorbidities.

Is there a better way of achieving this with ggplot2, in RStudio?

Answer 1

This is a good start - I'm not clear on some of your goals, but this answer should get you over the immediate obstacles.

## read in your data
count_symptoms = readr::read_csv("https://github.com/gabrielburcea/stackoverflow_fake_data/raw/master/fake_symptoms.csv")

## as mentioned in comments, removing `position = 'fill'` lets your chart show counts.
## (I'm skipping the unnecessary data conversions)
## And I'm removing the `ggplot2::` to make the code more readable...
## No other changes are made

india_count_symptoms <- count_symptoms %>%
  dplyr::filter(Country == "India")

india_sympt_plot <- ggplot(india_count_symptoms, aes(x = age_band, y = Count, group = symptoms, fill = symptoms)) +
  geom_area(color = "white") + 
  scale_x_discrete(limits = c("0-19", "20-39", "40-59","60+"), expand = c(0, 0)) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1))) + 
  viridis::scale_fill_viridis(discrete = TRUE)

Now, instead of making individual plots for each country, let's use facets:

## same plot code as above, but we give it the whole data set
## and add the `facet_grid` on
ggplot(count_symptoms, aes(x = age_band, y = Count, group = symptoms, fill = symptoms)) +
  geom_area(color = "white") + 
  scale_x_discrete(limits = c("0-19", "20-39", "40-59","60+"), expand = c(0, 0)) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +  
  viridis::scale_fill_viridis(discrete = TRUE) + 
  facet_grid(Country ~ .)

Notice we have a single legend. You can re-position it easily as shown here . Probably the next change I'd make is adding the argument labels = scales::comma_format in your scale_y_continuous . I have no idea what your issue is with the x-axis labels.

For the complete figure, I'd suggest doing one facet_grid plot for each column, and then use the patchwork package to combine them into one image. See how far you can get based on this, and if you continue to have issues ask a new question focused on the next step.