第 44 章 tidyverse中的purrr之美1

library(tidyverse)
library(broom)
penguins <- palmerpenguins::penguins %>% drop_na()

44.1 接、化、发

向马保国老师的武术精髓致敬。

f <- list(
  body_mass_g ~ bill_length_mm,
  body_mass_g ~ bill_length_mm + bill_depth_mm,
  body_mass_g ~ bill_length_mm + bill_depth_mm + flipper_length_mm
) 

f %>% 
  set_names(paste("model", 1:3)) %>%            # 接
  map( ~ tidy(lm(.x, data = penguins)) ) %>%    # 化
  list_rbind(names_to = "model")                # 发

model	term	estimate	std.error	statistic	p.value
model 1
	(Intercept)	388.845	289.817	1.342	0.181
	bill_length_mm	86.792	6.538	13.276	0.000
model 2
	(Intercept)	3,413.452	437.911	7.795	0.000
	bill_length_mm	74.813	6.076	12.313	0.000
	bill_depth_mm	-145.507	16.873	-8.624	0.000
model 3
	(Intercept)	-6,445.476	566.130	-11.385	0.000
	bill_length_mm	3.293	5.366	0.614	0.540
	bill_depth_mm	17.836	13.826	1.290	0.198
	flipper_length_mm	50.762	2.497	20.327	0.000

也可以不用broom::tidy(), 而使用gtsummary::tbl_regression()

library(gtsummary)

f %>%                                                    
  set_names() %>%                                       # 接       
  map(~ tbl_regression(lm(.x, data = penguins)) ) %>%   # 化
  tbl_merge(tab_spanner = paste("model", 1:3))          # 发

Characteristic	model 1			model 2			model 3
	Beta	95% CI1	p-value	Beta	95% CI1	p-value	Beta	95% CI1	p-value
bill_length_mm	87	74, 100	<0.001	75	63, 87	<0.001	3.3	-7.3, 14	0.5
bill_depth_mm				-146	-179, -112	<0.001	18	-9.4, 45	0.2
flipper_length_mm							51	46, 56	<0.001
1 CI = Confidence Interval