R：分组散点图

rm(list = ls())
setwd("C:\\Users\\Administrator\\Desktop\\machine learning\\分组散点图")
data("mpg")
library(ggplot2)

data <- read.table("data.txt", sep = "\t", header = TRUE, row.names = 1)

# 自定义刻度和标签
custom_x_breaks <- c(28, 42, 56, 70)  # 根据数据自定义刻度位置
custom_x_labels <- c("DAS28", "DAS42", "DAS56", "DAS70")  # 自定义刻度标签

# 自定义颜色
custom_colors <- c("B73" = "#8FC9E2", "Mo17" = "#ECC97F")  # 定义颜色

p1 <- ggplot(data = data, aes(x = time, y = abundance)) +
  theme_minimal(base_size = 12) +
  geom_point(aes(colour = gene, shape = gene), size = 4) +  # 点的大小
  geom_smooth(aes(colour = gene), method = "glm", size = 1.5) +  # 曲线的大小
  labs(x = "Growth Stage", y = "Relative Abundance", title = "Sphingobium") +
  theme(plot.title = element_text(hjust = 0.5, size = 20),  # 自定义标题大小
        legend.position = c(0.85, 0.8),
        legend.title = element_blank(),  # 去掉图例标题
        legend.key.size = unit(1, "cm"),  # 自定义图例大小
        legend.text = element_text(size = 12),  # 自定义图例标签大小
        panel.grid.major = element_blank(),  # 去掉主要网格线
        panel.grid.minor = element_blank(),  # 去掉次要网格线
        axis.line = element_line(size = 1.2, colour = "black"),  # 自定义坐标轴线粗细
        axis.text.x = element_text(size = 18),  # 自定义横坐标刻度标签大小
        axis.text.y = element_text(size = 18),  # 自定义纵坐标刻度标签大小
        axis.title.x = element_text(size = 18),  # 自定义横坐标标题大小
        axis.title.y = element_text(size = 18),  # 自定义纵坐标标题大小
        axis.ticks = element_line(size = 1.2)) +  # 自定义刻度线粗细
  scale_x_continuous(breaks = custom_x_breaks, labels = custom_x_labels) +  # 使用自定义的横坐标刻度位置和标签
  scale_y_continuous() +  # 使用默认的纵坐标刻度标签
  scale_color_manual(values = custom_colors)  # 自定义点和线的颜色

p1

# 保存图形
ggsave('p.png', width = 8, height = 8, bg = 'white', dpi = 1200)