bstfunb: 各种生物统计函数汇总

R语言数据分析指南

发布于 2023-08-18 13:51:18

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发布于 2023-08-18 13:51:18

文章被收录于专栏：R语言数据分析指南R语言数据分析指南

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❝本节来介绍如何使用「bstfun」包的函数来自定义绘制各种图表，此款R包包含众多函数都非常的实用，下面小编来简单介绍几个案例来做展示，希望各位观众老爷能够喜欢，更多详细文档请参考作者官方文档。 ❞

官方文档

❝https://rdrr.io/github/ddsjoberg/bstfun/ ❞

加载R包

library(tidyverse)
devtools::install_github("MSKCC-Epi-Bio/bstfun")
library(bstfun)
library(gtsummary)
library(patchwork)
library(survival)

1. 将森林图添加进表格

lm(mpg ~ cyl + am + drat, mtcars) %>%
  tbl_regression() %>%
  add_inline_forest_plot()

2. 添加迷你图

trial %>% select(age, marker) %>%
  tbl_summary(missing = "no") %>%
  add_sparkline()

3. 将表格转化为ggplot2格式

tbl <- trial %>% select(age, marker, trt) %>%
  tbl_summary(by = trt, missing = "no") %>% as_ggplot()
 
gg <- trial %>% ggplot(aes(x = age, y = marker, color = trt)) +
  geom_point()

gg / tbl

4. 创建紧凑表格

head(trial) %>% gt::gt() %>% style_tbl_compact()

5. 创建森林图

tbl_uvregression(
  trial[c("response", "age", "grade")],
  method = glm,y = response,
  method.args = list(family = binomial),
  exponentiate = TRUE) %>% as_forest_plot()

tbl <- coxph(Surv(ttdeath, death) ~ age + marker, trial) %>%
  tbl_regression(exponentiate = TRUE) %>% add_n()

as_forest_plot(tbl, col_names = c("stat_n", "estimate", "ci", "p.value"))

tbl %>%
  modify_cols_merge(pattern = "{estimate} ({ci})",
    rows = !is.na(estimate)) %>%
  modify_header(estimate = "HR (95% CI)") %>%
  as_forest_plot(col_names = c("estimate", "p.value"),boxsize = 0.2,
    col = forestplot::fpColors(box = "darkred"))