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,  )) -> df3df4<-data.frame(  x = seq(0.5,6.5,1),  xend = seq(0.5,6.5,1),  y = -Inf,  yend = Inf)df4df5<-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_1p2_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))