跟着NaturePlants学作图：R语言ggplot2分组折线图完整示例

论文

The slow-evolving Acorus tatarinowii genome sheds light on ancestral monocot evolution

https://www.nature.com/articles/s41477-022-01187-x#Sec21

本地pdf s41477-022-01187-x.pdf

论文中的数据基本都公开了，我们可以利用论文中的数据模仿论文中的图，今天的推文模仿一下论文中Figure1c 和 figure1f

image.png

figure1c

部分示例数据截图

image.png

读取数据并作图

library(readxl)

datf3c<-read_excel("data/20220807/41477_2022_1187_MOESM4_ESM.xlsx",
                   sheet = "Fig.1C",
                   skip = 1)
head(datf3c)

library(tidyverse)
datf3c %>% 
  select(-'4dTV_1') %>% 
  pivot_longer(-'4dTV_2') %>% 
  mutate(name = fct_relevel(name,c("Acorus-Acorus",
                                   "Acorus-Zostera",
                                   "Acorus-Oryza",
                                   "Acorus-Asparagus"))) -> new.datf3c

#install.packages("latex2exp")
library(latex2exp)
ggplot(data=new.datf3c,
       aes(x=`4dTV_2`,y=value,color=name))+
  geom_line(size=1)+
  scale_color_manual(values = c("#ff0000","#7030a0",
                                "#00b050","#5b9bd5"),
                     name=NULL)+
  theme_classic()+
  theme(legend.position = c(0.8,0.8),
        legend.text = element_text(face = "italic"))+
  scale_x_continuous(expand = expansion(mult = c(0,0)),
                     limits = c(0,2),
                     breaks = seq(0,2,0.2))+
  scale_y_continuous(expand = expansion(mult = c(0,0)),
                     limits = c(0,300))+
  labs(x="4dTV",y="No.of gene pairs") -> p1
p1

image.png

figure1f

部分示例数据截图

image.png

读取数据并作图

datf3f<-read_excel("data/20220807/41477_2022_1187_MOESM4_ESM.xlsx",
                   sheet = "Fig.1F",
                   skip = 1)
head(datf3f)
datf3f %>% 
  pivot_longer(-"Distance to Gene Start") %>% 
  ggplot(aes(x=`Distance to Gene Start`,
             y=value,
             color=name))+
  geom_line()+
  scale_color_manual(values = c("#ff0000","#00b0f0"))+
  theme_classic()+
  theme(legend.position = c(0.9,0.2),
        plot.margin = unit(c(0.1,0.5,0.1,0.1),'cm'),
        legend.title = element_blank())+
  scale_x_continuous(expand = expansion(mult = c(0,0)),
                     labels = c("-2 kb","Start","",
                                "Stop","+2 kb"))+
  scale_y_continuous(expand = expansion(mult = c(0,0)),
                     limits = c(0,0.4))+
  labs(x=NULL,y="Ratio of TEs") -> p2
p2

image.png

最后是拼图

p1+p2+
  plot_annotation(tag_levels = list(c("c","f")))

image.png

示例数据可以在论文中去下载，代码直接在推文中复制，如果需要我整理好的数据和代码可以给推文打赏1元获取

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