为什么要写这篇文档，昨天在教一个朋友做数据可视化发现即使是16s分析中一个简单的物种组成图对新手来说也是挺难的，数据的导入，过滤，格式转换等；这一番操作是实在是繁琐，曾经有大佬指点过我如何制作R包，于是乎经过一番学习终于做出了自己的第一个R包 ggtest
名字随便写的，感兴趣的小伙伴可以联系我获取资料
曾经的你绘制物种组成图
可以看到如果纯自己写代码，需要如下步骤没有一定的基础是不太可能顺利完成的，那么使用我的R包该如何操作，请继续往下看
library(tidyverse)
library(magrittr)
colors <-c("#E41A1C","#1E90FF","#FF8C00","#4DAF4A","#984EA3",
"#40E0D0","#FFC0CB","#00BFFF","#FFDEAD","#90EE90",
"#EE82EE","#00FFFF","#F0A3FF", "#0075DC",
"#993F00","#4C005C","#2BCE48","#FFCC99",
"#808080","#94FFB5","#8F7C00","#9DCC00",
"#C20088","#003380","#FFA405","#FFA8BB",
"#426600","#FF0010","#5EF1F2","#00998F",
"#740AFF","#990000","#FFFF00")
computed_persent <- function(path) {
data <- path %>%
read.delim(check.names = FALSE, row.names = 1)
data2 <- data %>%
mutate(sum = rowSums(.), persent = sum / sum(sum) * 100,
sum = NULL,) %>%
rbind(filter(., persent < 1) %>% colSums()) %>%
mutate(OTU_ID = c(data %>% rownames(), "others"))
filter(data2[1:(nrow(data2) - 1),], persent > 1) %>%
rbind(data2[nrow(data2),]) %>%
select(ncol(.), 1:(ncol(.) - 2)) %>%
set_rownames(seq_len(nrow(.))) %>%
return()
}
path <- "phylumt.xls"
a1 <- computed_persent(path) %>% melt()
a2 <- "group.xls" %>% read.delim()
a4 <- NULL
for (i in seq_len(nrow(a1))) {
a4[i] <- a2[which(a2[, 1] == a1[i, 2]), 2] }
a1[, 4] <- a4
ggplot(a1,aes(variable,value,fill=OTU_ID))+
geom_bar(stat="identity",position = "fill")+
facet_grid(. ~ V4,scales = "free",space="free_x")+
labs(x="",y="Proportions")+
scale_fill_manual(values = colors)+labs(fill="")+
theme(legend.title=element_blank())+
scale_y_continuous(expand=c(0,0))+theme_bw()
现在的你绘制物种组成图
library(tidyverse)
library(magrittr)
library(ggtest)
otu_filter("genus.xls") %>% ggbar()
还是相同的代码，不过是对函数的封装，可以看到使用非常的便捷，如果您没有过高的要求完全可以满足需求
可以使用如下代码获取内置的示例数据进行绘图,只需要保证group文件的格式与示例文件一致即可
library(tidyverse)
library(magrittr)
library(ggtest)
data("genus")
data("group")
write.table(genus,file="genus.xls",sep="\t",row.names = F,quote = F)
write.table(group,file="group.xls",sep="\t",row.names = F,quote = F)
otu_filter("genus.xls") %>% ggbar()
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ggtest
R语言数据分析指南
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