Are men more judgmental than women?

This visualization explores whether or not there are gender differences in airplane judgments. There appears to be a small difference in childcare judgments, such that women consider it less rude to bring a baby or unruly child on a plane than men do. Gender differences of judgments seemingly differed across generations.

Draft 1

figure_4 %>% 
  ggplot(aes(x = perc, y = fct_reorder(type, perc), fill = gender)) +
  geom_bar(stat="identity") +
  theme_minimal()+
  colorblindr::scale_fill_OkabeIto(name = "Gender") +
  scale_x_continuous(limits = c(0, 1))+
  facet_wrap(~gender) +
  labs(x = "Percent judging the action as rude",
       y = NULL,
       title = "Are men more judgmental than women?")+
  theme(legend.position = "none",
        axis.text.x=element_blank())+
  geom_text(x = figure_4$perc, y = figure_4$type, 
            label = paste0(round(figure_4$perc*100), "%"),
            hjust = -.2)

In the first draft, I am plotting the percent who judge each action as rude and facet wrapping by gender. The numbers make it easy to compare female and male judgments. However, for my next draft, I want to be able to make the comparison more easily.

Draft 2

figure_4 %>% 
  ggplot(aes(x = perc, y = fct_reorder(type, perc), fill = gender)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_minimal()+
  colorblindr::scale_fill_OkabeIto(name = "Gender") +
  scale_x_continuous(labels = scales::percent,
                     limits = c(0, 1))+
  labs(x = NULL,
       y = NULL,
       title = "Are men more judgmental than women?") +
  geom_vline(xintercept = 0.5, color = "gray40", linetype = "dashed")

In this draft, I used the position_dodge() argument to show the bars right next to each other. I also added a 50% vertical line so that you can see easily which actions are judged by rude by the majority of people. For the next draft, I am going to show this relation with a dumbbell plot/ connected dotplot. This will get rid of the unnecessary part of the bar that is not informative.

Draft 3

figure_4 %>% ggplot(aes(x = perc, y = fct_reorder(type, perc)))+
  geom_line(size = 3, color = "gray60") +
  geom_point(aes(color = gender), size = 4) +
  theme_minimal() +
  labs(x = "Percent judging the action as rude",
       y = NULL,
       title = "Are men more judgmental than women?")+
  theme(legend.direction = "horizontal",
        plot.title.position = "plot",
        legend.position = "bottom")+
  scale_x_continuous(labels = scales::percent,
                     limits = c(0,1))+
  colorblindr::scale_color_OkabeIto(name = "Gender") +
  geom_vline(xintercept = 0.5, color = "gray40", linetype = "dashed")

In my final draft, I wanted to color the bars so that it is really easy to see if one mean was greater than the other. Next, I wanted to facet wrap by age cohort because I noticed an interesting pattern in that women were less judgmental about bringing babies and children on planes. Since attitudes about gender roles are generational, I thought it would be interesting to see if this relationship occurred across different generations. I also created a theme and added a new font to the visualization.

Final draft

library(showtext)
font_add_google('Caveat Brush', "cb")
showtext_auto()

figure_4b %>% ggplot(aes(x = perc, y = fct_reorder(type, perc)))+
  geom_vline(xintercept = c(.25,.5, .75 ), color = "gray75")+
  geom_line(aes(color = greater_gender), size = 1.5) +
  scale_color_manual(values = c("#ffd780", "#8eccf0"), name = "Higher judgment") +
  ggnewscale::new_scale_color() + 
  geom_point(aes(color = gender), size = 3) +
  colorblindr::scale_color_OkabeIto(name = "Gender") +
  labs(x = "Percent judging the action as rude",
       y = NULL,
       title = "Are men more judgmental than women?",
       caption = "Data source: FiveThirtyEight")+
  theme_classic(base_size=15)+
  theme(legend.direction = "horizontal",
        plot.title.position = "plot",
        legend.position = "top",
        strip.text.x = element_text(face = "bold", size = 18, family = "cb"),
        strip.background = element_rect(colour = "black", fill = "#8eccf0", size = 1),
        panel.border = element_rect(linetype = "solid", size = 1, fill = NA),
        panel.background = element_rect(fill = "white"),
        plot.title = element_text(face = "bold", size = 40, family = "cb"),
        legend.key=element_blank(),
        axis.ticks = element_blank(),
        axis.text.y = element_text(face = c("bold", "plain", "bold", "plain", 'bold', 'plain', 'bold', 'plain', 'bold')))+
  scale_x_continuous(labels = scales::percent,
                     limits = c(0,1),
                     expand = c(0,0),
                     breaks = c(.25, .5, .75))+
  facet_wrap(~age, ncol = 2)