#加载包
> library(ggplot2)
> library(tidyverse)
> library(openxlsx)
> library(reshape2)
> library(RColorBrewer)
> library(randomcoloR)
> library(viridis)
> set.seed(1233) #设立种子数。
> palette <- distinctColorPalette(30) #生成随机30个颜色。
> write.table(palette, file = "30genus color.txt", sep = ",") #记录随机生成的颜色
> setwd("设立目录")
> mydata <- read.xlsx("读取.xlsx")
> data_draw<-melt(mydata,id.vars='species')#转换数据
> head(mydata)
species T1 T2 T3 T4 T5 T6 T7 T8 T9
1 Streptococcus salivarius 44092 121843 311183 486756 1156014 223453 163548 31383 47748
2 Rothia mucilaginosa 389451 347085 151944 159476 249279 92135 405152 99208 347259
3 Haemophilus influenzae 1051110 597710 0 0 0 0 0 0 2878
4 Streptococcus mitis 382851 246140 103352 211729 86955 18720 142086 416849 3792
5 Gemella haemolysans 109751 63687 15401 159620 15844 1537 194576 691594 64046
6 Streptococcus pneumoniae 180474 106915 78916 85142 49947 10830 313821 300134 0
> head(data_draw)
species variable value
1 Streptococcus salivarius T1 44092
2 Rothia mucilaginosa T1 389451
3 Haemophilus influenzae T1 1051110
4 Streptococcus mitis T1 382851
5 Gemella haemolysans T1 109751
6 Streptococcus pneumoniae T1 180474> data_drawspecies \<- factor(data_drawspecies, levels = c("Streptococcus salivarius", "Rothia mucilaginosa", "Haemophilus influenzae", "Streptococcus mitis",
"Gemella haemolysans", "Streptococcus pneumoniae", "Streptococcus parasanguinis", "Neisseria mucosa",
"Streptococcus oralis", "Haemophilus parainfluenzae", "Neisseria subflava", "Neisseria sicca", "Fusobacterium nucleatum",
"Prevotella melaninogenica", "Neisseria flavescens", "Veillonella parvula", "Veillonella dispar",
"Gemella sanguinis", "Streptococcus gordonii", "Leptotrichia wadei", "Streptococcus cristatus",
"Streptococcus anginosus", "Gemella morbillorum", "Neisseria meningitidis", "Veillonella atypica",
"Streptococcus sanguinis", "Corynebacterium argentoratense", "Streptococcus australis",
"Lactobacillus crispatus", "others"))#对种类图形元素排序,保证图形是从大向小排。
> ggplot(data_draw, aes(x = variable, y = value)) +
geom_bar(aes(fill = species), stat = "identity", color = NA, size = 0.4,
position = "fill", alpha = 0.90, width = 0.9) +
scale_fill_manual(values = palette) +
labs(y = "Relative abundance", x = "Sample") +
theme(
text = element_text(family = 'serif'),
axis.title = element_text(size = 14, face = "plain", color = "black"),
axis.text = element_text(size = 14, face = "plain", color = "black"),
legend.title = element_text(size = 14, face = "bold", color = "black"),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size = 0.4),
legend.position = "right", # 在这里设置图例位置
) +
scale_y_continuous(expand = c(0, 0)) +
theme(
plot.margin = unit(c(2, 1, 1, 1), "lines"),
axis.ticks.length = unit(0.1, "cm"),
axis.text.x = element_text(margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm")),
axis.text.y = element_text(margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"))
)
这种方法是直接将数据处理好，再导入R语言的。