Integrating Flags into R Charts

library(tidyverse)
library(ggplot2)
library(ggflags)
library(countrycode)
mypalette<- c("#22457C",
              "#4A77B3",
              "#72A9EA",
              "#55A630",
              "#80B918",
              "#E9B310",
              "#F2D388",
              "#2E5E24")

datgun <- tibble(country = toupper(c("US", "Italy", "Canada", "UK", "Japan", "Germany", "France", "Russia")),
              count = c(3.2, 0.7, 0.5, 0.1, 0, 0.2, 0.1, 0),
              label = c(as.character(c(3.2, 0.7, 0.5, 0.1, 0, 0.2, 0.1)), "No Data"),
              code = c("us", "it", "ca", "gb", "jp", "de", "fr", "ru"))

datgun %>% mutate(country = reorder(country, -count)) %>%
  ggplot(aes(country, count, label = label)) +
  geom_bar(stat = "identity", fill = "darkred") +
  geom_text(nudge_y = 0.2, color = "darkred", size = 5) +
  geom_flag(y = -0.3, aes(country = code), size = 11) +
  scale_y_continuous(breaks = c(0, 1, 2, 3, 4), limits = c(0,4)) +   
  labs (caption = "",
        x ="",
        y = "# of gun-related homicides\nper 100,000 people",
        title = toupper("Homicide Per 100,000 in G-8 Countries")) + 
  ggthemes::theme_economist() +
  theme(axis.text.x = element_text(size = 8, vjust = -8),
        axis.ticks.x = element_blank(),
        axis.line.x = element_blank(),
        plot.caption = element_text(vjust = 4),
        plot.margin = unit(c(1,1,1,1), "cm")) 

Flags

Next, I want to incorporate flags in charts. What a fantastic way to add visual appeal and a layer of contextual information to your data stories in Posit/R. Imagine bringing a regular chart with data across various countries to life in R using ggplot2 and then strategically placing flag icons for countries - especially for flags that are easily recognisable. Suddenly, a boring bar or line chart become much more meaningful, instantly connecting the data.

Not Just Your Boring Chart Anymore

The beauty of using R and ggplot2 lies in their incredible flexibility. You are not limited to just basic lines and bars. With a bit of creative coding, you can integrate images, like your national flag or flags representing different countries of origin directly onto your plots. In this example, we reproduce a chart from The Economist. The chart shows homicide rates across advanced countries. Here, the flag of each country is displayed next to its corresponding homicide rate or data point on a bar chart. This not only makes the chart more visually engaging but also enhances understanding at a glance.

This capability truly underscores the versatility of R and ggplot2. It moves beyond simply presenting numbers and allows you to weave in visual narratives that resonate with your audience. Whether you’re highlighting local events with your national flag or comparing international data with respective country flags, this technique adds a unique and informative dimension to your data visualizations, making your insights more accessible and memorable.

Comparison

Let’s consider a comparison chart - this time. Without incorporating flags. It is a standard bar chart created in R with ggplot2, each bar representing a different country, at one period in time, and homicide rate per 100,000 people on the y-axis. While this chart effectively communicates the trends and allows for comparisons between countries, it remains bland.

dat_gun <- tibble(country = toupper(c("United States", "Canada", "Portugal", "Ireland", "Italy", "Belgium", "Finland", "France", "Netherlands", "Denmark", "Sweden", "Slovakia", "Austria", "New Zealand", "Australia", "Spain", "Czech Republic", "Hungry", "Germany", "United Kingdom", "Norway", "Japan", "Republic of Korea")),
              count = c(3.61, 0.5, 0.48, 0.35, 0.35, 0.33, 0.26, 0.20, 0.20, 0.20, 0.19, 0.19, 0.18, 0.16, 0.16, 0.15, 0.12, 0.10, 0.06, 0.04, 0.04, 0.01, 0.01))

dat_gun %>% 
  mutate(country = reorder(country, count)) %>%
  ggplot(aes(country, count, label = count)) +   
  geom_bar(stat = "identity", fill = "darkred", width = 0.5) +
  geom_text(nudge_y = 0.2,  size = 3) +
  xlab("") + ylab("") + 
  ggtitle(toupper("Gun Murders per 100,000 residents")) + 
  ggthemes::theme_economist() +
  theme(panel.grid.major =element_blank(), panel.grid.minor = element_blank(), 
        axis.text.x = element_blank(),
        axis.line = element_blank(),
        plot.caption.position = "plot",
        axis.ticks = element_blank())+
  coord_flip()