ggplot2是否有通用模板简化代码？求简化工具及替代绘图包

我是ggplot2新手，绘制如下生存曲线需编写30行代码，深感繁琐。现咨询以下问题：

1. 是否存在可创建类似下图的简单通用绘图模板？
2. 有无可简化ggplot2使用的工具包？
3. 有无对普通用户而言比ggplot2简单、但比base R绘图更具自定义性的替代绘图包？

原绘图代码 #

library(dplyr)
library(ggplot2)
library(MASS)
library(survival)

lung1 <- lung %>% 
  mutate(time1 = ifelse(time >= 500, 500, time)) %>% 
  mutate(status1 = ifelse(status == 2 & time >= 500, 1, status))

weibCurve <- function(time, survregCoefs) {exp(-(time/exp(survregCoefs[1]))^exp(-survregCoefs[2]))}

fit1 <- survreg(Surv(time1, status1) ~ 1, data = lung1)

lung1.survfit <- survfit(Surv(time1, status1) ~ 1, data = lung1)
lung1.df <- data.frame(time = lung1.survfit$time, 
                       survival = lung1.survfit$surv, 
                       upper_95 = lung1.survfit$upper, 
                       lower_95 = lung1.survfit$lower)

# generate simulation paths
n_simulations <- 50
simPaths <- data.frame(x = seq(from = 500, to = 1000, by = 5))
simPathList <- lapply(1:n_simulations, function(i) {
  newCoef <- MASS::mvrnorm(n = 1, fit1$icoef, vcov(fit1))
  y <- weibCurve(simPaths$x, newCoef)
  simPaths[[paste0("y", i)]] <- y
  simPaths
})

simPaths_df <- Reduce(function(x, y) merge(x, y, by = "x", all = TRUE), simPathList)
simPaths_df_long <- pivot_longer(simPaths_df, -x, names_to = "sim", values_to = "survival")

fit_km <- summary(survfit(Surv(time, status) ~ 1, data = lung))
plot_km <- data.frame(x = fit_km$time, y = fit_km$surv)

lung1.df %>%
  ggplot(aes(x = time, y = survival)) +
  geom_line(aes(x = x, group = sim, color = "Simulations"), data=simPaths_df_long) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = "Confidence Interval"), 
              alpha = 0.2) +
  scale_fill_manual(values = c("Confidence Interval" = "grey50"), name = NULL) +
  geom_line(aes(y = survival, color = "K-M actual data to period 500"), linewidth = 1) +
  scale_x_continuous(limits = c(0, 1500)) +
  scale_y_continuous(limits = c(0, 1), expand = c(0, 0.05)) +
  labs(x = "Time", y = "Survival probability", color = NULL) +
  theme_classic() +
  stat_function(fun = weibCurve, args = list(survregCoefs = fit1$icoef), 
                aes(color = "Weibull distribution fit"), size = 1, n = 1000) +
  scale_color_manual(
    values = c(
      "K-M actual data to period 500"="blue", 
      "K-M actual data after period 500"="black", 
      "Weibull distribution fit"="red", 
      "Simulations"="lightblue",
      "Confidence Interval"="grey50")
  ) +
  labs(color = NULL) +
  geom_step(data = plot_km %>% filter(x > 500), 
            aes(x = x, y = y, color = "K-M actual data after period 500"), 
            size = 1, alpha = 0.7) +
  theme(legend.position = c(0.95, 0.95), 
        legend.justification = c(1, 1),
        legend.title.align = 0.5, 
        legend.box.spacing = unit(0.3, "lines"), 
        legend.margin = margin(t = 0, r = 0, b = 0, l = 0), 
        legend.title = element_text(size = 12), 
        legend.text = element_text(size = 10))

1. 通用生存曲线绘图模板 #

可以把现有代码封装成可复用的函数模板，将数据处理、模型拟合、绘图逻辑打包，后续只需传入核心参数即可生成类似图表：

plot_custom_survival <- function(data, time_col, status_col, truncate_at = 500, n_sim = 50) {
  # 数据预处理
  df <- data %>%
    mutate(time1 = ifelse(.data[[time_col]] >= truncate_at, truncate_at, .data[[time_col]]),
           status1 = ifelse(.data[[status_col]] == 2 & .data[[time_col]] >= truncate_at, 1, .data[[status_col]]))
  
  # 模型拟合
  fit_weib <- survreg(Surv(time1, status1) ~ 1, data = df)
  surv_fit <- survfit(Surv(time1, status1) ~ 1, data = df)
  surv_df <- data.frame(time = surv_fit$time,
                        survival = surv_fit$surv,
                        upper_95 = surv_fit$upper,
                        lower_95 = surv_fit$lower)
  
  # 生成模拟路径
  weibCurve <- function(time, coefs) exp(-(time/exp(coefs[1]))^exp(-coefs[2]))
  sim_x <- seq(truncate_at, 1000, by = 5)
  sim_paths <- lapply(1:n_sim, function(i) {
    new_coef <- MASS::mvrnorm(1, fit_weib$icoef, vcov(fit_weib))
    data.frame(x = sim_x, survival = weibCurve(sim_x, new_coef), sim = i)
  }) %>% bind_rows()
  
  # 完整K-M曲线数据
  full_km <- summary(survfit(Surv(.data[[time_col]], .data[[status_col]]) ~ 1, data = data))
  full_km_df <- data.frame(x = full_km$time, y = full_km$surv)
  
  # 绘图
  ggplot(surv_df, aes(x = time, y = survival)) +
    geom_line(aes(x = x, group = sim, color = "Simulations"), data = sim_paths) +
    geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = "Confidence Interval"), alpha = 0.2) +
    geom_line(aes(color = "K-M actual data to period 500"), linewidth = 1) +
    stat_function(fun = weibCurve, args = list(coefs = fit_weib$icoef), aes(color = "Weibull distribution fit"), size = 1, n = 1000) +
    geom_step(data = full_km_df %>% filter(x > truncate_at), aes(x = x, y = y, color = "K-M actual data after period 500"), size = 1, alpha = 0.7) +
    scale_fill_manual(values = c("Confidence Interval" = "grey50"), name = NULL) +
    scale_color_manual(values = c(
      "K-M actual data to period 500" = "blue",
      "K-M actual data after period 500" = "black",
      "Weibull distribution fit" = "red",
      "Simulations" = "lightblue"
    ), name = NULL) +
    scale_x_continuous(limits = c(0, 1500)) +
    scale_y_continuous(limits = c(0, 1), expand = c(0, 0.05)) +
    labs(x = "Time", y = "Survival probability") +
    theme_classic() +
    theme(legend.position = c(0.95, 0.95),
          legend.justification = c(1, 1),
          legend.margin = margin(0,0,0,0),
          legend.text = element_text(size = 10))
}

# 使用示例
plot_custom_survival(lung, "time", "status")

该模板可直接复用，修改参数即可适配不同数据集。

2. 简化ggplot2的工具包 #

• survminer：专门针对生存分析可视化，核心函数ggsurvplot()可一键生成带置信区间、风险表的K-M曲线，还支持添加拟合分布曲线，大幅减少代码量。
• ggpubr：封装了ggplot2常用绘图逻辑，语法简洁，快速生成高质量统计图表。
• ggthemes：提供现成的美观主题，无需手动调整theme()参数。
• patchwork：简化多ggplot图的拼接，语法直观，替代复杂的gridExtra操作。

3. 替代绘图包 #

• plotly：交互式绘图工具，语法比ggplot2更直观，生成的生存曲线支持交互查看数值，自定义性强于base R，无需记忆复杂图层逻辑。
• lattice：老牌绘图包，语法简单，支持分面等自定义功能，输出图表比base R更美观规范。
• echarts4r：基于ECharts的R接口，代码简洁，生成交互式生存曲线，自定义选项丰富，适合需要交互展示的场景。
• ggvis：语法接近ggplot2但更简洁，支持交互式绘图，适合快速出图。

内容的提问来源于stack exchange，提问作者Village.Idyot