R绘图 | 气泡散点图+拟合曲线

rm(list = ls())
library(ggpubr)
library(ggprism)
library(paletteer)
plot_df = read.csv('plot_df.csv')
## 设置主题
rel_size <- 1
my_theme <- theme_prism(border = TRUE, 
                        base_size = 5) +
  theme(strip.text.x = element_text(size = rel(rel_size*2)),
        title = element_text(size = rel(rel_size*2)),
        legend.box.spacing = unit(1, "cm"),
        legend.text = element_text(size = rel(rel_size*1.5)),
        legend.title = element_text(size = rel(rel_size*0.5)),
        axis.text.y = element_text(size = rel(rel_size*2), angle = 0, vjust = 0.2),
        axis.text.x = element_text(size = rel(rel_size*1.6), angle = 45),
        panel.grid = element_line(color = "gray",
                                  size = 0.15,
                                  linetype = 2),
        panel.spacing = unit(1, "lines"),
        plot.caption = element_text(size = rel_size*8)) 
# 绘图
p <- ggplot(plot_df, aes(x = log10(mean_gdp_per_capita), y = mean_access_perc)) + 
  # coord_trans("log10") +
  geom_point(data = plot_df, aes(size = mean_death_perc, fill = continent), pch = 21) +
  geom_smooth(method = "loess") +
  scale_fill_paletteer_d("colorblindr::OkabeIto") +
  ggpubr::stat_cor(method = "spearman", 
                   aes(label = paste(..rr.label.., ..p.label.., sep = "~")), 
                   color = "red", geom = "label", label.x = 3.8, label.y = 5) +
  # facet_wrap(vars(continent),drop = TRUE) +
  scale_y_continuous(breaks = c(0, 25, 50, 75, 100)) + 
  my_theme +
  ggtitle("Access to clean energy is associated with GDP") +
  labs(x = "Log10 of Average GDP per capita",
       y = "Average % access to clean fuels/tech",
       fill = "Continent", 
       size = "Average % Death",
       caption = "Spearman correlation\nAveraged values over years 1990-2019\nData source: OurWorldInData.org")
p
ggsave(plot = p, filename = 'week1.pdf',width = 5,height = 4)