Cognitive specialization for learning faces is associated with shifts in the brain transcriptome of a social wasp
This venn diagram is from this paper
library(tidyverse) library(cowplot) knitr::opts_chunk$set(fig.path = './', echo = T, message = F) GOvenn <- read.csv(file = "./GOvenn.csv") #head(GOvenn) GOvenn$species <- factor(GOvenn$species, levels = c("P.fusatus", "P.metricus", "both")) levels(GOvenn$GO) <- c("biological processes", " molecular functions") GOvenn %>% spread(species, count) %>% select(GO, pattern,P.fusatus, both, P.metricus) ## GO pattern P.fusatus both P.metricus ## 1 biological processes expected 52 0 30 ## 2 biological processes observed 42 10 20 ## 3 molecular functions expected 43 1 37 ## 4 molecular functions observed 31 13 25 p <- ggplot(data=GOvenn, aes(x=pattern, y = count, fill = species)) + geom_bar(stat="identity") + theme_minimal() + scale_x_discrete(name = NULL, expand = c(0,0)) + coord_flip() + facet_wrap(~GO, nrow = 2) + geom_text(position = "stack", aes(x=pattern, y = count, label = count, hjust = 0.5)) + theme(legend.position = "bottom", legend.title = element_blank()) + labs(x = NULL, y = "total GO terms") + guides(fill = guide_legend(reverse = TRUE)) p
p2 <- ggdraw() + draw_image("GOvenn-original.png") plot_grid(p2, p, rel_widths = c(0.5,0.5))
In this example, the circles do not represent a meaningful quantity. A stacked bar plot can use color, space, and text to highlight patterns in the data, which in this case appears to be a greater overlap than expected. I wanted to make the bar plot mirror the Venn diagram as closely as possible, but I changed the order of the factors so that “both” category was plotted first. This was necessary for adding text to the bar chart because the values for P. metricus and both were overlapping when I kept the “both” category in the middle.