The data

data <- read_xlsx("2019-03-20-reykjavik_index.xlsx")

data %>% kable()

The file is quite small, with the score for the seven countries and the average over the G7 countries. There is not much cleaning to do. I improve the column name, make countries a factor and order it by index, and extract the average value to remove it from the dataframe.

G7_average <- data[data$Country == "G7 Average", 2]
G7_average2 <- data[data$Country == "G7 Average", 2][[1]]

data <- data %>% 
  rename(reykjavik_index = `Reykjavik Index `) %>% 
  mutate(Country = fct_reorder(Country, reykjavik_index)) %>% 
  filter(Country != "G7 Average") %>% 
  mutate(to_colour = ifelse(reykjavik_index < G7_average2,
                            "under", "over"))

The colourfull barplot

My instinctive first graph, a barplot. I colour the country, because I think it’s prettier.

data %>% 
  ggplot(aes(x = Country, y = reykjavik_index,
             fill = Country)) +
  
  geom_hline(yintercept = G7_average2,
             size = 1, linetype = "dashed", 
             colour = "grey50")  +
  geom_col() +
  coord_flip() +
  annotate(geom = "text",
           label = "G7 average",
           x = "Italy",
           y = 80,
           size = 5,
           colour = "grey50",
           #fontface = "bold",
           family = "Roboto Condensed") +
  
  ylim(0, 100) +
  
  labs(title = "Reykjavik index per country",
       subtitle = "Higher values represent less gendered views about leadership",
       fill = "",
       x = "",
       y = "Reykjavik index for leadership",
       caption = "\nSource: https://data.world/makeovermonday") +
  my_theme +
  theme(legend.position = "none") +
  scale_fill_manual(values = wes_palette("Zissou1", n = 8, type = "continuous"))

As first drafts go, I think it is actually not that bad: we can clearly see which countries are slightly above and under the average. We could split the countries in two groups and plot the difference to the mean, but I am not sure it is that pertinent: of course some are going to be over and some under, that’s a mean after all.

The less colourfull barplot

I don’t want to spend a lot of time on these plots, so I will stick with barplots tonight and not go into wild creations. Tonight I believe in simplicity (and laziness).

But I will try to highlight the two groups, and add some annotations to convey the message a bit better.

data %>% 
  ggplot(aes(x = Country, y = reykjavik_index,
             fill = to_colour,
             #colour = to_colour,
             label = reykjavik_index)) + 
  
  geom_hline(yintercept = G7_average2,
             size = 1, linetype = "dashed", 
             colour = "grey50")  +
    geom_col() +
  geom_text(aes(y = reykjavik_index - 3),
            colour = "white") +
  coord_flip() +
  annotate(geom = "text",
           label = "G7 average (66)",
           x = "Italy",
           y = 82,
           size = 5,
           colour = "grey50",
           family = "Roboto Condensed") +
  
  annotate(geom = "text",
           label = "Countries with higher\nagreement that women\nare as suitable as men\nat leadership positions",
           x = "France",
           y = 87,
           size = 3,
           colour = bar_col1,
           family = "Roboto Condensed") +
  
  annotate(geom = "text",
           label = "Countries wich hold\nmore prejudices against\nwomen leaders",
           x = "Japan",
           y = 87,
           size = 3,
           colour = bar_col2,
           family = "Roboto Condensed") +
  
  geom_hline(yintercept = 100,
             colour = "black",
             size = 1) +
  
  ylim(0, 100) +
  
  labs(title = "Reykjavic Index - What people really think of women leaders",
       subtitle = "\nIn the best cases there is still more than 1/4 of the road to travel to reach\nequal confidence in men and women abilities to lead\n",
       fill = "",
       x = "",
       y = "Reykjavik index for leadership",
       caption = "\nSource: Kantar, via  https://data.world/makeovermonday") +
  my_theme +
  theme(legend.position = "none",
        axis.ticks.y = element_blank()) +
  scale_fill_manual(values = c(bar_col1, bar_col2)) -> plot1

plot1

I want a slightly interactive version of the plot, so I use the ggplotly() function from the plotly package.

ggplotly(plot1,
         tooltip = c("x", "y"))

Here, this is it. Less than two hours from data download to online post, that was my aim for a quick first exploration. Additional bonus: now, I know how to write Reykjavic!