ggplot geom_line on top of geom_bar

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I'm looking to simply display in one plot the production(geom_line) and the import(geom_bar) of wheat over years with data pulled from faostat. So basically on the x-axis = year, on the y-axis = production, and on the final axis = import. Adding to that I would like to have the bars split by the country for import since there is more import from a certain country, but I don't wish to display all of them, only the top 3 and the rest should be in a new category "other".

I'm quite bad with the code in R but once I have the foundation I can figure out how to adjust the looks of it just need to figure out how to start.

Here I'm providing a simplified dataset, as described, in the bar plot I want to show only the 3 highest values and have the rest combined into a new category "other". And a geom_line should be displayed on top of that with a separate axis on the right side, for some reason the geom_line is not working for me.

import <- data.frame(country = c("USA", "USA", "EU", "NZ", "EU", "Bulgaria", "Romania", "Serbia"),
                         date = c("1991", "1992", "1994", "1995", "1991", "1991", "1991", "1991"),
                         value = c(1000, 500, 2000, 1500, 1000, 100, 500, 500))

production <- data.frame(date = c("1991", "1992", "1994", "1995", "1995"),
                       value = c(50, 45, 40, 39, 38))

ggplot(data = import, mapping = aes(x=date, y=value, fill=country)) +
  geom_col()

ggplot(data = production, aes(x=date, y=value)) +
  geom_line()

Any help or guidance in the right direction will be appreciated. Thank you in advance! Love this community always great with the help <3

ggplot2 r
1 Answers

You are close but there are a few things to get you the plot you want:

  1. To connect observations with geom_line() you need to add a group aesthetic. So if you just add aes(group = 1) inside the geom_line() call you'll get an actual line.
  2. I strongly reccomend you treat the date as numeric instead of character. Time is generally continuous so it's best to treat it that way unless you have a good reason for using it as a discrete variable.
  3. There are probably multiple good ways to lump the low value countries together. I just used if_else. It wouldn't work here because but it's worth knowing about forcats::fct_lump_* if you haven't seen it already for doing something similar in a different setting.
  4. The order the colors appear in the stacked plot (and by deafult in the legend) is based on the levels in the factor supplied. It will default to alphabetical order but you can set it manulaly in a number of ways (although {forcats} has many great tools for this purpose. In this case, to put the "other" category at the end you can use forcats::reorder() and specify after = Inf to push that one level to the end no matter what else you have there.
  5. Finally, to get a second axis properly scaled, you need to provide a transformation factor that you use to divide the axis scale by inside sec_axis() and then multiply the data series you want to see on that axis by the same vactor.
library(tidyverse)

# create data with date as numeric
import <- data.frame(country = c("USA", "USA", "EU", "NZ", "EU", "Bulgaria", "Romania", "Serbia"),
                     date = c(1991, 1992, 1994, 1995, 1991, 1991, 1991, 1991),
                     value = c(1000, 500, 2000, 1500, 1000, 100, 500, 500))

# lump into 'other' if not in top 3 and move to end
import <- import %>% 
  mutate(country = if_else(value >= sort(value,decreasing = T)[3],
                           country, 
                           "other")) %>% 
  mutate(country = fct_relevel(country, "other", after = Inf))

# create data with date as numeric
production <- data.frame(date = c(1991, 1992, 1994, 1995, 1995),
                         value = c(50, 45, 40, 39, 38))

# calculate scling factor for second axis
trans_fct <- max(import$value)/max(production$value)

# plot with second axis, scaling data series that appears on that axis
ggplot() +
  geom_col(data = import,
           mapping = aes(x = date, y = value, fill = country)) +
  geom_line(data = production, aes(
    x = date,
    y = value * trans_fct,
    group = 1
  )) +
  scale_y_continuous(name = "Import",
                     sec.axis = sec_axis( ~ . / trans_fct, name = "Production"))

Created on 2022-01-17 by the reprex package (v2.0.1)

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