#读入数据；
dt1<-read.table("koenrich.xls",sep = "\t",header = T)
#指定纵轴标签顺序,按照输入文件的顺序排序，否则默认按照首字母顺序,同时逆序绘制，保持与表格顺序一致；
dt1$KEGG_A_Class<-factor(dt1$KEGG_A_Class,
levels = rev(unique(dt1$KEGG_A_Class)),
ordered = TRUE)
dt1$KEGG_B_Class<-factor(dt1$KEGG_B_Class,
levels = rev(unique(dt1$KEGG_B_Class)),
ordered = TRUE)
# 加载ggplot2包；
library(ggplot2)
#建立数据(Genes.Number, KEGG_B_Class)与图形（点）的映射关系；
p1<-ggplot(dt1, aes(x=Genes_Number,
y=KEGG_B_Class,
fill=KEGG_A_Class,na.rm = FALSE))+
geom_bar(stat="identity",na.rm = FALSE)+
geom_text(aes(x=Genes_Number,y=KEGG_B_Class,label=label),size=2.5,hjust="left",nudge_x=0.1)+
scale_x_continuous(limits = c(0, 25),expand=expansion(mult = c(0, .1)))+
labs(x="Number of Gene",y="",title="KEGG pathway anotation")
p1
#获取颜色；
g <- ggplot_build(p1)
mycol<-g$data[[1]]["fill"]
col<-rev(mycol[,1])
#将A Class对应的颜色设为深黑色；
num <- rev(dt1$Genes_Number)
index <- which(num==0)
col[index] <- "grey10"

#自定义图表主题，对图表做精细调整；
top.mar=0.2
right.mar=0.2
bottom.mar=0.2
left.mar=0.2
mytheme1<-theme(plot.title = element_text(size = rel(1),hjust = 0.5,face = "bold"),
axis.title = element_text(size = rel(1)),
axis.text.y = element_text(size=rel(0.85),
colour =col,face = "bold"),
legend.position = "none",
plot.margin=unit(x=c(top.mar,right.mar,
bottom.mar,left.mar),
units="inches"))
#查看绘图效果；
p1+mytheme1

#读入数据；
dt <- read.csv("PCA_data.csv")
#载入ggplot2绘图包；
library(ggplot2)
library(ggh4x)
#绘制实心散点图；
p1 <- ggplot(dt,aes(x=PC1,y=PC2,fill=Diagnosis))+
stat_centroid(aes(xend = PC1, yend = PC2, colour = Diagnosis),
geom = "segment", crop_other = F,
alpha=0.3,size = 1,show.legend = F)+
geom_point(size=3,alpha=0.7,
color="white",shape = 21,show.legend = T)+
scale_color_manual(name="",
values = c("#FF9999","#c77cff"))+
scale_fill_manual(name="",
values = c("#FF9999","#c77cff"))+
scale_x_continuous(expand=expansion(add = c(0.7,0.7)),
limits=c(-10,5))+
scale_y_continuous(expand=expansion(add = c(0.5,0.5)),
limits=c(-7.5,5))+
guides(x = "axis_truncated",y = "axis_truncated")
p1

#读入数据；
dt <- read.csv("test_data.csv")
#实用month.abb[]将月份转换为英文缩写；
dt$Month <- month.abb[dt$Month]
#载入相关的R包；
library(dplyr)
library(ggplot2)
#转成tibble格式；
df <- as_tibble(dt)
#将月份转换成因子，固定顺序；
df$Month <- factor(df$Month,levels = unique(df$Month),ordered = T)
#将季度数值转成字符；
df$Quarter <- as.character(df$Quarter)
#提取作图数据；
df1 <- filter(df,Group=="Day")
#自定义颜色；
subcol<-rainbow(12)
mycol1<-colorRampPalette(subcol[1:4])(12)
mycol2<-colorRampPalette(c("#A5CC26","yellow","orange","tomato"))(12)
#绘制相互叠合的玫瑰图；
p1 <- ggplot(df1,aes(x = Month, y=Length,fill=Month))+
geom_col(width = 1.2,color=NA,alpha=1)+
geom_text(aes(label = Length),nudge_y=-2,
colour="white",size=3)+
ylim(-1.2,17)+
scale_fill_manual(values = mycol1)+
coord_polar(start = 0)+
theme_void()
p1

#载入所需的绘图包；
library(ggraph)
library(tidygraph)
library(igraph)
library(ggplot2)
#读入边文件和节点信息文件；
net <- read.csv("edge_data.csv")
info <- read.csv("node_info.csv")
#预览数据；
head(net)
head(info)
#创建网络图对象；
g <- tbl_graph(nodes = info,edges = net,directed = F)
#计算网络图的degree属性；
V(g)$degree <- degree(g)
#自定义颜色；
mycol <- c("#FF8901","#00C5FF","#FF5485")
#绘制环状网络图(曲线连线)；
p1 <- ggraph(g, layout = 'linear',circular = TRUE)+
geom_edge_arc(colour="grey50",width=1,alpha=0.3)+
geom_node_point(aes(color=class),size=6,alpha=0.8)+
scale_colour_manual(values = mycol)+
geom_node_text(aes(x = 1.05 * x,y = 1.05 * y,
angle = -((-node_angle(x, y) + 90) %% 180) + 90,
label=name),
nudge_y = 0,
hjust = 'outward',
repel = F,
size=2.5)+
coord_fixed(clip = "off")+
theme_graph()
p1