R语言数据可视化的实现方法是什么

 

df2<-read.csv("20210410/001.csv",header=T)
p1<-ggplot(data=df2,aes(x=value,y=variable))+
  geom_boxplot()+
  theme_bw()+
  scale_y_discrete(position = "right")+
  theme(panel.border = element_blank(),
        panel.grid = element_blank(),
        axis.line.x = element_line(),
        axis.line.y = element_line(),
        axis.text.y = element_blank(),
        axis.title.y = element_blank())+
  labs(x="Predicted \n copy number")+
  geom_hline(yintercept = 6.5,lty="dashed")+
  annotate(geom="text",
           x=-1.2,y=3,label="C degradation",angle=90)+
  annotate(geom="text",
           x=-1.2,y=12,label="N cycling",angle=90)
p1

 

df<-read.csv("20210410/002.csv",header=T)
head(df)
df1<-reshape2::melt(df,id.vars="Sample")
head(df1)
head(df1)
df1%>%
  #reshape2::melt(id.vars="Sample")%>%
  mutate(group_1 = case_when(
    value <= 0 ~ "A",
    TRUE ~ "B"
  ))%>%
  mutate(group_2=case_when(
    value >= -1 & value < -0.7 ~ "[-1,-0.7)",
    value >= -0.7 & value < -0.5 ~ "[-0.7,-0.5)",
    value >= -0.5 & value < -0.3 ~ "[-0.5,-0.3)",
    value >= -0.3 & value <= 0 ~ "[-0.3,0]",
    value > 0 & value <= 0.3 ~ "(0,0.3)",
    value > 0.3 & value <= 0.5 ~ "(0.3,0.5]",
    value > 0.5 & value <= 0.7 ~ "(0.5,0.7]",
    value > 0.7 & value <= 1 ~ "(0.7,1]",
  ))%>%
  mutate(value_1=case_when(
    value >= -1 & value < -0.7 ~ -0.8,
    value >= -0.7 & value < -0.5 ~ -0.6,
    value >= -0.5 & value < -0.3 ~ -0.4,
    value >= -0.3 & value <= 0 ~ -0.2,
    value > 0 & value <= 0.3 ~ 0.2,
    value > 0.3 & value <= 0.5 ~ 0.4,
    value > 0.5 & value <= 0.7 ~ 0.6,
    value > 0.7 & value <= 1 ~ 0.8,
  )) -> df3


df4<-data.frame(
  x = seq(0.5,6.5,1),
  xend = seq(0.5,6.5,1),
  y = -Inf,
  yend = Inf
)
df4
df5<-data.frame(
  x = -Inf,
  xend = Inf,
  y = seq(0.5,15.5,1),
  yend = seq(0.5,15.5,1)
)

p2<-ggplot(data=df3,aes(x=Sample,y=variable))+
  geom_point(aes(size=abs(value_1),
                 color=factor(value_1)),
             shape=15)+
  scale_color_manual(values = c(rep("#fe0000",4),
                                rep("#009ccc",4)))+
  theme_bw()+
  theme(panel.grid = element_blank(),
        panel.border = element_blank(),
        axis.ticks = element_blank(),
        legend.position = "none")+
  geom_segment(data=df4,aes(x=x,xend=xend,y=y,yend=yend),
               color="grey")+
  geom_segment(data=df5,aes(x=x,xend=xend,y=y,yend=yend),
               color="grey")+
  scale_size_continuous(range = c(2,10))+
  scale_y_discrete(position = "right",
                   expand = c(0,0))+
  labs(x=NULL,y=NULL)+
  scale_x_discrete(expand = c(0,0))

p2+
  geom_segment(x=7.3,xend=7.3,
           y=10,yend = 15)+
  geom_segment(x=7,xend=7,
               y=7,yend = 9)+
  geom_segment(x=7,xend=7,
               y=1,yend = 6)+
  annotate("text",x=6,y=13,label="group_A",
           angle=90,vjust=11)+
  annotate("text",x=6,y=8,label="group_B",
           angle=90,vjust=10)+
  annotate("text",x=6,y=4,label="group_C",
           angle=90,vjust=10)+
  theme(plot.margin = unit(c(0.2,2,0.2,0.3),'cm'))+
  coord_cartesian(clip = "off") -> p2_1
p2_1

 

p3<-ggplot(data = df6,aes(x=x,y=y))+
  geom_point(aes(size=value,color=group),shape=15)+
  geom_text(aes(x=x+0.1,label=label))+
  scale_size_continuous(range = c(2,10))+
  scale_color_manual(values = c("#fe0000","#009ccc"))+
  theme(panel.background = element_blank(),
        legend.position = "none",
        axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,1,0.2,0.2),"cm"))+
  ylim(0,15)+
  coord_cartesian(clip = "off")+
  theme(aspect.ratio = 11)
p3

 

p1+p2_1+p3+
  plot_layout(widths = c(1,2,0.4))