Using dataframe name as a column in a model table - r

Question

I'm confused as to why the following doesn't work. I'm trying to use the name of a data frame/tibble as a column in a multiple models data frame, but keep running up against the following error. Here's an example:
library(tidyverse)
library(rlang)
set.seed(666)
df1 <- tibble(
x = 1:10 + rnorm(10),
y = seq(20, 38, by=2) + rnorm(10),
z = 2*x + 3*y
)
df2 <- tibble(
x = 1:10 + rnorm(10),
y = seq(20, 38, by=2) + rnorm(10),
z = 4*x + 5*y
)
results <- tibble(dataset = c('df1','df2'))
Notice that the following all work:
lm(z ~ x + y, data=df1)
lm(z ~ x + y, data=df2)
lm(z ~ x + y, data=eval(sym('df1')))
But when I try the following:
results <- results %>% mutate(model = lm(z ~ x + y, data = eval(sym(dataset))))
I get the error
Error in mutate_impl(.data, dots) :
Evaluation error: Only strings can be converted to symbols.
Can someone figure out how to make this work?

Answer 1

We can use the map function and specify the lm function as the following.
library(tidyverse)
library(rlang)
results2 <- results %>%
mutate(model = map(dataset, ~lm(z ~ x + y, data = eval(sym(.)))))
results2
# # A tibble: 2 x 2
# dataset model
# <chr> <list>
# 1 df1 <S3: lm>
# 2 df2 <S3: lm>
results2$model[[1]]
# Call:
# lm(formula = z ~ x + y, data = eval(sym(.)))
#
# Coefficients:
# (Intercept) x y
# 6.741e-14 2.000e+00 3.000e+00
results2$model[[2]]
# Call:
# lm(formula = z ~ x + y, data = eval(sym(.)))
#
# Coefficients:
# (Intercept) x y
# 9.662e-14 4.000e+00 5.000e+00

Answer 2

I'd recommend a slightly different route where you bind all the data and skip the eval and sym calls. This follows the "Many Models" chapter of R for Data Science.
purrr::lst creates a list of the data frames with the names of those variables as the list's names, and the .id argument to bind_rows uses those names to create a column marking data as coming from df1 or df2. Nesting creates a column data which is a list-column of data frames. Then you can build the models of each dataset. I used the tilde shortcut notation to build the anonymous function.
The result: you have a column model that is a list of models.
library(tidyverse)
library(rlang)
results <- lst(df1, df2) %>%
bind_rows(.id = "dataset") %>%
group_by(dataset) %>%
nest() %>%
mutate(model = map(data, ~lm(z ~ x + y, data = .)))
results$model[[1]]
#>
#> Call:
#> lm(formula = z ~ x + y, data = .)
#>
#> Coefficients:
#> (Intercept) x y
#> 6.741e-14 2.000e+00 3.000e+00
You also still have a column of that nested data. If you don't want it, you can drop it:
select(results, -data)
#> # A tibble: 2 x 2
#> dataset model
#> <chr> <list>
#> 1 df1 <lm>
#> 2 df2 <lm>