row bind list columns using dplyr - r

Question

I would like to find a better way to bind together the results of any number of regressions after adding an identifier for each model. The code below is my current solution but is too manual for a large number of regressions. This is part of a larger tidy workflow so a solution inside of the tidyverse is preferred but whatever works is fine. Thanks
library(tidyverse)
library(broom)
model_dat=mtcars %>%
do(lm_1 = tidy(lm(disp~ wt*vs, data = .),conf.int=T),
lm_2=tidy(lm(cyl ~ wt*vs, data = .),conf.int=T ),
lm_3=tidy(lm(mpg ~ wt*vs, data = .),conf.int=T ))
df=model_dat %>%
select(lm_1) %>%
unnest(c(lm_1)) %>%
mutate(model="one") %>%
select(model,term,estimate,p.value:conf.high) %>%
bind_rows(
model_dat %>%
select(lm_2) %>%
unnest(c(lm_2)) %>%
mutate(model="two") %>%
select(model,term,estimate,p.value:conf.high)) %>%
bind_rows(
model_dat %>%
select(lm_3) %>%
unnest(c(lm_3)) %>%
mutate(model="three") %>%
select(model,term,estimate,p.value:conf.high))

Answer 1

It may be easier with map2 i.e. loop across the columns and the corresponding english word for the sequence of columns, pluck the list element, create the 'model' column with second argument i.e. engish words (.y), select the columns of interest, and create a single dataset by specifying _dfr in map
library(purrr)
library(english)
library(dplyr)
library(broom)
map2_dfr(model_dat, as.character(english(seq_along(model_dat))),
~ .x %>%
pluck(1) %>%
mutate(model = .y) %>%
select(model, term, estimate, p.value:conf.high) )
-output
# A tibble: 12 x 6
# model term estimate p.value conf.low conf.high
# <chr> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 one (Intercept) -70.0 1.55e- 1 -168. 28.2
# 2 one wt 102. 8.20e- 9 76.4 128.
# 3 one vs 31.2 6.54e- 1 -110. 172.
# 4 one wt:vs -36.7 1.10e- 1 -82.2 8.82
# 5 two (Intercept) 4.31 1.28e- 5 2.64 5.99
# 6 two wt 0.849 4.90e- 4 0.408 1.29
# 7 two vs -2.19 7.28e- 2 -4.59 0.216
# 8 two wt:vs 0.0869 8.20e- 1 -0.689 0.862
# 9 three (Intercept) 29.5 6.55e-12 24.2 34.9
#10 three wt -3.50 2.33e- 5 -4.92 -2.08
#11 three vs 11.8 4.10e- 3 4.06 19.5
#12 three wt:vs -2.91 2.36e- 2 -5.40 -0.419
Or use summarise with across, unclass and then bind with bind_rows
model_dat %>%
summarise(across(everything(), ~ {
# // get the column name
nm1 <- cur_column()
# // extract the list element (.[[1]])
list(.[[1]] %>%
# // create new column by extracting the numeric part
mutate(model = english(readr::parse_number(nm1))) %>%
# // select the subset of columns, wrap in a list
select(model, term, estimate, p.value:conf.high))
}
)) %>%
# // unclass to list
unclass %>%
# // bind the list elements
bind_rows
-output
# A tibble: 12 x 6
# model term estimate p.value conf.low conf.high
# <english> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 one (Intercept) -70.0 1.55e- 1 -168. 28.2
# 2 one wt 102. 8.20e- 9 76.4 128.
# 3 one vs 31.2 6.54e- 1 -110. 172.
# 4 one wt:vs -36.7 1.10e- 1 -82.2 8.82
# 5 two (Intercept) 4.31 1.28e- 5 2.64 5.99
# 6 two wt 0.849 4.90e- 4 0.408 1.29
# 7 two vs -2.19 7.28e- 2 -4.59 0.216
# 8 two wt:vs 0.0869 8.20e- 1 -0.689 0.862
# 9 three (Intercept) 29.5 6.55e-12 24.2 34.9
#10 three wt -3.50 2.33e- 5 -4.92 -2.08
#11 three vs 11.8 4.10e- 3 4.06 19.5
#12 three wt:vs -2.91 2.36e- 2 -5.40 -0.419