ggplot2散点图直方图条形图

生信喵

背景
       熟悉ggplot2绘图，有一本书，可以介绍大家使用，《R数据可视化手册》第二版

1. https://www.bookdown.org/

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      可以在上述网址中找到网页版本。
       书中的例子代码：

1. library(gcookbook)
   
2. uspop
   
3. colnames(uspopage)
   
4. ggplot(data = uspopage,mapping = aes(x=Year,y=Thousands,fill =AgeGroup)) +
   
5.   geom_area()

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一、散点图

1. x <- read.table("prok_representative.csv",sep = ",",header = T);  
   
2. head(x)
   
3. ggplot(data = x,aes(x=Size,y=Genes))+geom_point()
   
4. ggplot(data = x,aes(x=Size,y=Genes))+geom_point(size=1,color="blue")
   
5. fit <- lm(data = x,Genes~ Size)
   
6. summary(fit)
   
7. fit
   
8. ggplot(data = x,aes(x=Size,y=Genes))+geom_point(size=1,color="blue")+
   
9.   geom_abline(intercept = 286.6,slope = 843.7,col="red",lwd=1)
   
10. p <- ggplot(data = x,aes(x=Size,y=Genes))+geom_point(size=1,color="blue")+geom_abline(intercept = 286.6,slope = 843.7,col="red",lwd=1)
    
11. p+annotate(geom = "text",x=4,y=10000,label="y=286x+843.7\nR2=0.9676")
    
12. p+annotate(geom = "text",x=4,y=10000,label="y=286x+843.7\nR2=0.9676")+
    
13.   labs(title="Genome Size vs Gene Number",x="Genome Size",y="Genes")

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       ggplot2 绘制基因组大小与基因数目相关性图


二、直方图

1. x <- read.table("H37Rv.gff",sep = "\t",header = F,skip = 7,quote = "")  
   
2. x <- x[x$V3=="gene",]  
   
3. x <- abs(x$V5-x$V4+1)  
   
4. length(x)  
   
5. range(x)  
   
6. ggplot(data = NULL,aes(x=x))
   
7. ggplot(data = NULL,aes(x=x))+geom_histogram(bins = 80)
   
8. ggplot(data = NULL,aes(x=x))+geom_histogram(bins = 80)+geom_rug()
   
9. # library(dplyr)
   
10. # x <- read.table("H37Rv.gff",sep = "\t",header = F,skip = 7,quote = "")
    
11. # x %>% dplyr::filter(V3 == 'gene') %>% dplyr::mutate(gene_len = abs(V5-V4)+1)%>% ggplot(aes(x=gene_len))+geom_histogram(bins=80)
    
12. # x %>% dplyr::filter(V3 == 'gene') %>% dplyr::mutate(gene_len = abs(V5-V4)+1)%>% ggplot(aes(x=gene_len))+geom_histogram(bins=80,fill='cyan',color='black') + geom_rug()+theme_light()+labs(title='Histogram')
    

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       ggplot2 绘制基因长度分布直方图

三、条形图

1. # hg19_len <- read.csv(file = "homo_length.csv",header = T)
   
2. # x <- hg19_len[1:24,]   
   
3. # head(x)
   
4. # ggplot(data = x,aes(x=chr,y=length,fill=chr))+geom_bar(stat = "identity")
   
5. # p <- ggplot(data = x,aes(x=chr,y=length,fill=chr))+geom_bar(stat = "identity")
   
6. # p+scale_x_discrete(limits=x$chr)
   
7. # p+scale_x_discrete(limits=x$chr)+coord_flip()
   
8. # p+scale_x_discrete(limits=x$chr)+coord_flip()+guides(fill=FALSE)
   
9. 
   
10. x <- read.csv(file = "homo_length.csv",header = T)
    
11. x %>% dplyr::slice(1:24) %>% ggplot(aes(x=chr,y=length)) + geom_bar(stat = 'identity')+ scale_x_discrete(limits=x$chr[1:24])
    
12. x %>% dplyr::slice(1:24) %>% ggplot(aes(x=chr,y=length,fill=chr)) + geom_bar(stat = 'identity')+ scale_x_discrete(limits=x$chr[1:24]) + scale_fill_manual(values=rainbow(24))
    
13. x %>% dplyr::slice(1:24) %>% ggplot(aes(x=chr,y=length)) + geom_bar(stat = 'identity')+ scale_x_discrete(limits=x$chr[1:24]) +
    
14.   coord_flip()
    
15. x %>% dplyr::slice(1:24) %>% ggplot(aes(x=chr,y=length,fill=chr)) + geom_bar(stat = 'identity')+ scale_x_discrete(limits=x$chr[1:24]) +
    
16.   coord_flip() + scale_fill_manual(values = c(rep('red',24)))
    
17. library(RColorBrewer)
    
18. x %>% dplyr::slice(1:24) %>% ggplot(aes(x=chr,y=length,fill=chr)) + geom_bar(stat = 'identity')+ scale_x_discrete(limits=x$chr[1:24]) +
    
19.   coord_flip() + scale_fill_manual(values = c(rep(brewer.pal(4,'Set1'),6)))
    
20. x %>% dplyr::slice(1:24) %>% ggplot(aes(x=chr,y=length,fill=chr)) + geom_bar(stat = 'identity')+ scale_x_discrete(limits=x$chr[1:24]) +
    
21.   coord_flip() + scale_fill_manual(values = c(rep(brewer.pal(4,'Set1'),6))) +
    
22.   guides(fill='none')

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       ggplot2 绘制人染色体长度分布图