ggplot左右两个y轴

两个ggplot 图合并到一起，一个x轴两个y轴

raw_data <- read_delim('./data/GO_pathway.txt', delim = '\t')head(raw_data)library(dplyr)library(Cairo)double_y_axis <- function(p1, p2){  g1 <- ggplot_gtable(ggplot_build(p1))  g2 <- ggplot_gtable(ggplot_build(p2))  # overlap the panel of 2nd plot on that of 1st plot  pp <- c(subset(g1$layout, name == "panel", se = t:r))  g <- gtable_add_grob(g1, g2$grobs[[which(g2$layout$name == "panel")]], pp$t, pp$l, pp$b, pp$l)  # axis tweaks  ia <- which(g2$layout$name == "axis-l")  ga <- g2$grobs[[ia]]  ax <- ga$children[[2]]  ax$widths <- rev(ax$widths)  ax$grobs <- rev(ax$grobs)  ax$grobs[[1]]$x <- ax$grobs[[1]]$x - unit(1, "npc") + unit(0.15, "cm")  g <- gtable_add_cols(g, g2$widths[g2$layout[ia, ]$l], length(g$widths) - 1)  g <- gtable_add_grob(g, ax, pp$t, length(g$widths) - 1, pp$b)  # draw it  grid.draw(g)  return(g)}split_fun <- function(x){  tmp_split <- strsplit(x, '~')  do.call('rbind', tmp_split)[,2]}go_data <- raw_data %>%  dplyr::filter(grepl('GO',Term) & PValue<0.01) %>%  select(c(1,2,3,5)) %>%  dplyr::mutate(term = split_fun(Term)) %>%  arrange(Category, PValue)go_data$term <- factor(go_data$term, levels = unique(go_data$term))# ggplotlibrary(ggplot2)library(gtable)library(grid)grid.newpage()# two plotsp1 <- ggplot(go_data)+  geom_bar(aes(x = term, y=Count,  fill=Category), stat = 'identity') +#   geom_line(aes(term, y=PValue, group=1), stat = 'identity', colour="#B00A13") +#   geom_point(aes(term, y=PValue, group=1),colour="red", size=3, alpha=0.4)+  theme_bw() +  scale_fill_manual(values = c('#6f359d', '#F28C42'),                    labels=c('Biological Process', 'Cellular Component'))+  theme(axis.text.x = element_text(angle = 40, hjust=1, vjust=1),        axis.ticks.x = element_blank(),        legend.position='left'       # axis.title.y=element_text(vjust=8)        )+  scale_y_continuous(expand = c(0,0), limits=c(0, 30))+  xlab('Go Items')+  ylab('The number of genes')  # coord_fixed(ratio = 0.6)# 控制长宽比p1 p1 <- p1+coord_fixed(ratio = 0.6)# 控制长宽比p2 <- ggplot(go_data, aes(term, PValue, group=1)) +  geom_line( stat = 'identity', colour="#B00A13") +  geom_point(colour="red", size=3, alpha=0.4)+  scale_y_continuous(limits = c(0, 0.02))+ # scale_y_reverse()+  scale_x_discrete(breaks=NULL)+  theme(axis.text.x = element_text(),        axis.ticks.x= element_blank(),  # panel.background = element_rect(fill = NA),   # panel.grid = element_blank(),  legend.position='top')+  ylab('')+  xlab('')p2  # extract gtableg <- double_y_axis(p1,p2)tiff("Plot600.tiff", type="cairo", width = 14, height = 8, units = 'in', res = 300)grid.draw(g)dev.off()# 在右下角的框里调试好图片, 然后采用# par('din') 获取宽高CairoPNG('plotCairo.png', width = 13.58, height = 8.2, units='in', dpi=700)grid.draw(g)dev.off()