Error: No glance method recognized for this list-CodePudding

I'm trying to write a function that can flexibly group by a variable number of arguments and fit a linear model to each subset. The output should be a table with each row showing the grouping variable(s) and corresponding lm call results that broom::glance provides. But I can't figure out how to structure the output. Code that produces the same error is as follows:

library(dplyr)
library(broom)

test_fcn <- function(var1, ...) {
  x <- unlist(list(...))
  mtcars %>% 
    group_by(across(all_of(c('gear', x)))) %>% 
    mutate(mod = list(lm(hp ~ !!sym(var1), data = .))) %>% 
    summarize(broom::glance(mod)) 
}

test_fcn('qsec', 'cyl', 'carb')

I'm pushing my R/dplyr comfort zone by mixing static and dynamic variable arguments, so I've left them here in case that's a contributing factor. Thanks for any input!

CodePudding user response：

You were nearly there.

test_fcn <- function(var1, ...) {
    x <- unlist(list(...))
    mtcars %>% 
        group_by(across(all_of(c('gear', x)))) %>% 
        summarise(
            mod = list(lm(hp ~ !!sym(var1), data = .)),
            mod = map(mod, broom::glance),
            .groups = "drop")
}

test_fcn('qsec', 'cyl', 'carb') %>% unnest(mod)
## A tibble: 12 × 15
#    gear   cyl  carb r.squared adj.r.sq…¹ sigma stati…² p.value    df logLik   AIC   BIC devia…³ df.re…⁴
#   <dbl> <dbl> <dbl>     <dbl>      <dbl> <dbl>   <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl>   <int>
# 1     3     4     1     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 2     3     6     1     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 3     3     8     2     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 4     3     8     3     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 5     3     8     4     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 6     4     4     1     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 7     4     4     2     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 8     4     6     4     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
# 9     5     4     2     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
#10     5     6     6     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
#11     5     8     4     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
#12     5     8     8     0.502      0.485  49.2    30.2 5.77e-6     1  -169.  344.  348.  72633.      30
## … with 1 more variable: nobs <int>, and abbreviated variable names ¹adj.r.squared, ²statistic,
##   ³deviance, ⁴df.residual
## ℹ Use `colnames()` to see all variable names

Because you are storing the lm fit objects in a list, you need to loop over the entries using purrr::map.

You might want to put the unnest into the test_fcn: a slightly more compact version would be

test_fcn <- function(var1, ...) {
    x <- unlist(list(...))
    mtcars %>% 
        group_by(across(all_of(c('gear', x)))) %>% 
        summarise(
            mod = map(list(lm(hp ~ !!sym(var1), data = .)), broom::glance),
            .groups = "drop") %>%
        unnest(mod)
}