【分析绘图】R语言实现一些常见的绘图
微生信-在线绘图网站
线性图
library(ggplot2)
x <- rnorm(100, 14, 5) # rnorm(n, mean = 0, sd = 1)
y <- x + rnorm(100, 0, 1)
ggplot(data = NULL, aes(x = x, y = y)) + # 开始绘图
geom_point(color = "darkred") + # 添加点
annotate(
"text",
x = 13, # 位置
y = 20,
parse = T,
label = "x[1] == x[2]"
) # 添加注释
频率分布直方图
yx = c(1, 2, 3, 5)
hist(
yx,
col = "PINK",
labels = TRUE,
ylim = c(0, 10),
main = "频率分布图",
xlab = "X",
ylab = "出现频数"
)
Venn图
library(VennDiagram)
library(grid)
venn.plot <- draw.pairwise.venn(
area1 = 754, # 区域1的数
area2 = 687, # 区域2的数
cross.area = 139, # 交叉数
category = c("A", "B"), # 分类名称
fill = c("red", "blue"), # 区域填充颜色
lty = "blank", # 区域边框线类型
cex = 2, # 区域内部数字的字体大小
cat.cex = 1.5, # 分类名称的字体大小
cat.col = c("red", "blue"), # 分类名称的显示颜色
cat.pos = c(185, 185), #分类名称在圆的位置，默认正上方，通过角度进行调整
# cat.dist = 0.09, #分类名称距离边的距离（可以为负数）
# cat.just = list(c(-1, -1), c(1, 1)), #分类名称的位置
# alpha = 0.7, # 透明度
)
# 将venn.plot通过grid.draw画到pdf文件中
pdf("venn.pdf")
grid.draw(venn.plot)
dev.off()
柱状累计分布图
data <- matrix(c(2587, 4576, 2457, 2946, 6670, 5790, 5862, 5421), ncol = 4, nrow = 2)
colnames(data) <- c('B-neg', 'T-neg', 'B-pos', 'T-pos')
barplot(
height = data,
main = "15minB和T", # 标题
col = c('green', 'white'), # 填充颜色
legend.text = c('Total','Match'),#设置图例的内容
args.legend = list(x = "topright", cex=0.7), #修改图例的位置
xlim = c(0, 9),
ylim = c(0, 12000), # Y轴范围
width = 1.5, # 必须指定xlim
)
箱体图
library(data.table)
library(ggplot2)
dat <- data.table(
Spring = c(runif(9, 0, 1), 2),
Summer = runif(10, 0, 1),
Autumn = runif(10, 0, 1),
Winter = runif(10, 0, 1)
)
dat1 <- melt(dat, measure.vars = c("Spring", "Summer", "Autumn", "Winter"))
ggplot(data = dat1, aes(x = variable, y = value, colour = variable)) + geom_boxplot()
热图
library(data.table)
library(pheatmap)
# 假设这是你的示例数据
b_data <- data.frame(
Gene = c("Gene1", "Gene2", "Gene3", "Gene4"),
Annotation = c("TypeA", "TypeB", "TypeA", "TypeB"),
Value1 = c(1.2, 2.3, 0.8, 1.5),
Value2 = c(0.9, 1.8, 1.1, 2.0),
Value3 = c(2.5, 1.1, 1.9, 1.7)
)
# b_data <- data.frame(data.table::fread("pvalue.csv", sep = ",", header = T))
# 设置行名和注释
rownames(b_data) <- b_data$Gene
annotation_row <- data.frame(Type = b_data$Annotation)
rownames(annotation_row) <- b_data$Gene
colnames(annotation_row) <- "Type "
# 提取数值矩阵
data <- as.matrix(b_data[, -(1:2)])
# 创建热图
pheatmap(
log2(data + 0.01),
cluster_rows = FALSE, # 对行聚类
cluster_cols = TRUE, # 对列聚类
treeheight_col = 0, # 列聚类树高度，默认为50
show_rownames = FALSE,
show_colnames = TRUE,
annotation_row = annotation_row, # 对行进行注释
main = "Heatmap (log2)"
)
