R可视化：基础图形可视化之whole or part（六）

作者: 生信学习者2 | 来源:发表于2021-03-02 09:12 被阅读0次

R可视化：基础图形可视化之whole or part（六）
R实战 | 山脊图(ridgeline plot)
【ggplot2绘图一：ggplot2原理】
[R语言可视化-精美图形绘制系列]--组内相关性分析
R可视化之美之科研绘图-01.R可视化基础
R可视化：基础图形可视化
[R语言可视化-精美图形绘制系列]--散点图+箱线图组合图
[R语言可视化-精美图形绘制系列]--散点图+箱线图组合图
R可视化：基础图形可视化（二）
[R语言可视化]--箱线图不同的画法及参数设置 | 学习笔记

数据分析的图形可视化是了解数据分布、波动和相关性等属性必不可少的手段。数据整体和部分组成可视化图形：堆积图、矩形树图和组成图等等。更多知识分享请到 https://zouhua.top/。

分组堆积图 grouped stacked barplot

library(ggplot2)
library(viridis)
library(hrbrthemes)

specie <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition <- rep(c("normal" , "stress" , "Nitrogen") , 4)
value <- abs(rnorm(12 , 0 , 15))
data <- data.frame(specie,condition,value)

ggplot(data, aes(fill=condition, y=value, x=specie)) + 
    geom_bar(position="stack", stat="identity") +
    scale_fill_viridis(discrete = T) +
    ggtitle("Studying 4 species..") +
    theme_ipsum() +
    xlab("")

矩形树图 Treemap

library(treemap)

group <- c(rep("group-1",4),rep("group-2",2),rep("group-3",3))
subgroup <- paste("subgroup" , c(1,2,3,4,1,2,1,2,3), sep="-")
value <- c(13,5,22,12,11,7,3,1,23)
data <- data.frame(group,subgroup,value)
 
treemap(data,
        index=c("group","subgroup"),
        vSize="value",
        type="index")

圆圈图 doughhut

library(ggplot2)

data <- data.frame(
  category=c("A", "B", "C"),
  count=c(10, 60, 30))

data$fraction <- data$count / sum(data$count)
data$ymax <- cumsum(data$fraction)
data$ymin <- c(0, head(data$ymax, n=-1))
data$labelPosition <- (data$ymax + data$ymin) / 2
data$label <- paste0(data$category, "\n value: ", data$count)

ggplot(data, aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=category)) +
  geom_rect() +
  geom_label( x=3.5, aes(y=labelPosition, label=label), size=6) +
  scale_fill_brewer(palette=4) +
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none")

饼图 pie

library(ggplot2)
library(dplyr)

data <- data.frame(
  group=LETTERS[1:5],
  value=c(13,7,9,21,2))

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )

ggplot(data, aes(x="", y=prop, fill=group)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() + 
  theme(legend.position="none") +
  geom_text(aes(y = ypos, label = group), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

系统树图 dendrogram

library(ggraph)
library(igraph)
library(tidyverse)

theme_set(theme_void())

d1 <- data.frame(from="origin", to=paste("group", seq(1,7), sep=""))
d2 <- data.frame(from=rep(d1$to, each=7), to=paste("subgroup", seq(1,49), sep="_"))
edges <- rbind(d1, d2)

name <- unique(c(as.character(edges$from), as.character(edges$to)))
vertices <- data.frame(
  name=name,
  group=c( rep(NA,8) ,  rep( paste("group", seq(1,7), sep=""), each=7)),
  cluster=sample(letters[1:4], length(name), replace=T),
  value=sample(seq(10,30), length(name), replace=T))

mygraph <- graph_from_data_frame( edges, vertices=vertices)

ggraph(mygraph, layout = 'dendrogram') + 
  geom_edge_diagonal() +
  geom_node_text(aes( label=name, filter=leaf, color=group) , angle=90 , hjust=1, nudge_y=-0.1) +
  geom_node_point(aes(filter=leaf, size=value, color=group) , alpha=0.6) +
  ylim(-.6, NA) +
  theme(legend.position="none")

sample <- paste(rep("sample_",24) , seq(1,24) , sep="")
specie <- c(rep("dicoccoides" , 8) , rep("dicoccum" , 8) , rep("durum" , 8))
treatment <- rep(c(rep("High",4 ) , rep("Low",4)),3)
data <- data.frame(sample,specie,treatment)
for (i in seq(1:5)){
  gene=sample(c(1:40) , 24 )
  data=cbind(data , gene)
  colnames(data)[ncol(data)]=paste("gene_",i,sep="")
 }
data[data$treatment=="High" , c(4:8)]=data[data$treatment=="High" , c(4:8)]+100
data[data$specie=="durum" , c(4:8)]=data[data$specie=="durum" , c(4:8)]-30
rownames(data) <- data[,1]    

dist <- dist(data[ , c(4:8)] , diag=TRUE)
hc <- hclust(dist)
dhc <- as.dendrogram(hc)
specific_leaf <- dhc[[1]][[1]][[1]]

i=0
colLab<<-function(n){
    if(is.leaf(n)){
        a=attributes(n)
        ligne=match(attributes(n)$label,data[,1])
        treatment=data[ligne,3];
            if(treatment=="Low"){col_treatment="blue"};if(treatment=="High"){col_treatment="red"}
        specie=data[ligne,2];
            if(specie=="dicoccoides"){col_specie="red"};if(specie=="dicoccum"){col_specie="Darkgreen"};if(specie=="durum"){col_specie="blue"}
        attr(n,"nodePar")<-c(a$nodePar,list(cex=1.5,lab.cex=1,pch=20,col=col_treatment,lab.col=col_specie,lab.font=1,lab.cex=1))
        }
    return(n)
}

dL <- dendrapply(dhc, colLab)
plot(dL , main="structure of the population")
legend("topright", 
     legend = c("High Nitrogen" , "Low Nitrogen" , "Durum" , "Dicoccoides" , "Dicoccum"), 
     col = c("red", "blue" , "blue" , "red" , "Darkgreen"), 
     pch = c(20,20,4,4,4), bty = "n",  pt.cex = 1.5, cex = 0.8 , 
     text.col = "black", horiz = FALSE, inset = c(0, 0.1))

library(dendextend)
d1 <- USArrests %>% dist() %>% hclust( method="average" ) %>% as.dendrogram()
d2 <- USArrests %>% dist() %>% hclust( method="complete" ) %>% as.dendrogram()

dl <- dendlist(
  d1 %>% 
    set("labels_col", value = c("skyblue", "orange", "grey"), k=3) %>%
    set("branches_lty", 1) %>%
    set("branches_k_color", value = c("skyblue", "orange", "grey"), k = 3),
  d2 %>% 
    set("labels_col", value = c("skyblue", "orange", "grey"), k=3) %>%
    set("branches_lty", 1) %>%
    set("branches_k_color", value = c("skyblue", "orange", "grey"), k = 3)
)
 
tanglegram(dl, 
           common_subtrees_color_lines = FALSE, highlight_distinct_edges  = TRUE, highlight_branches_lwd=FALSE, 
           margin_inner=7,
           lwd=2)

library(dendextend)
library(tidyverse)
 
dend <- mtcars %>% 
  select(mpg, cyl, disp) %>% 
  dist() %>% 
  hclust() %>% 
  as.dendrogram()
my_colors <- ifelse(mtcars$am==0, "forestgreen", "green")

par(mar=c(9,1,1,1))
dend %>%
  set("labels_col", value = c("skyblue", "orange", "grey"), k=3) %>%
  set("branches_k_color", value = c("skyblue", "orange", "grey"), k = 3) %>%
  set("leaves_pch", 19)  %>% 
  set("nodes_cex", 0.7) %>% 
  plot(axes=FALSE)
rect.dendrogram( dend, k=3, lty = 5, lwd = 0, x=1, col=rgb(0.1, 0.2, 0.4, 0.1) ) 
colored_bars(colors = my_colors, dend = dend, rowLabels = "am")

library(ggraph)
library(igraph)
library(tidyverse)
library(RColorBrewer) 


d1 <- data.frame(from="origin", to=paste("group", seq(1,10), sep=""))
d2 <- data.frame(from=rep(d1$to, each=10), to=paste("subgroup", seq(1,100), sep="_"))
edges <- rbind(d1, d2)
 
vertices <- data.frame(
  name = unique(c(as.character(edges$from), as.character(edges$to))) , 
  value = runif(111)) 
vertices$group <- edges$from[ match( vertices$name, edges$to ) ]
 
vertices$id <- NA
myleaves <- which(is.na( match(vertices$name, edges$from) ))
nleaves <- length(myleaves)
vertices$id[myleaves] <- seq(1:nleaves)
vertices$angle <- 90 - 360 * vertices$id / nleaves
 
vertices$hjust <- ifelse( vertices$angle < -90, 1, 0)
vertices$angle <- ifelse(vertices$angle < -90, vertices$angle+180, vertices$angle)
mygraph <- graph_from_data_frame( edges, vertices=vertices )
 
# Make the plot
ggraph(mygraph, layout = 'dendrogram', circular = TRUE) + 
  geom_edge_diagonal(colour="grey") +
  scale_edge_colour_distiller(palette = "RdPu") +
  geom_node_text(aes(x = x*1.15, y=y*1.15, filter = leaf, label=name, angle = angle, hjust=hjust, colour=group), size=2.7, alpha=1) +
  geom_node_point(aes(filter = leaf, x = x*1.07, y=y*1.07, colour=group, size=value, alpha=0.2)) +
  scale_colour_manual(values= rep( brewer.pal(9,"Paired") , 30)) +
  scale_size_continuous( range = c(0.1,10) ) +
  theme_void() +
  theme(
    legend.position="none",
    plot.margin=unit(c(0,0,0,0),"cm"),
  ) +
  expand_limits(x = c(-1.3, 1.3), y = c(-1.3, 1.3))

圆形图 Circular packing

library(ggraph)
library(igraph)
library(tidyverse)
library(viridis)
 
edges <- flare$edges %>% 
  filter(to %in% from) %>% 
  droplevels()
vertices <- flare$vertices %>% 
  filter(name %in% c(edges$from, edges$to)) %>% 
  droplevels()
vertices$size <- runif(nrow(vertices))
 
# Rebuild the graph object
mygraph <- graph_from_data_frame(edges, vertices=vertices)

ggraph(mygraph, layout = 'circlepack') + 
  geom_node_circle(aes(fill = depth)) +
  geom_node_label( aes(label=shortName, filter=leaf, size=size)) +
  theme_void() + 
  theme(legend.position="FALSE") + 
  scale_fill_viridis()

参考

The R Graph Gallery

参考文章如引起任何侵权问题，可以与我联系，谢谢。

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