pmap_ variants operating on data.frames as lists - r

Question

I have a recollection that purrr::pmap_* can treat a data.frame as a list but the syntax eludes me.
Imagine we wanted to fit a separate lm object for each value of mtcars$vs and mtcars$am
library(tidyverse)
library(broom)
d1 <- mtcars %>%
group_by(
vs, am
) %>%
nest %>%
mutate(
coef = data %>%
map(
~lm(mpg ~ wt, data =.) %>%
tidy
)
)
If I wanted to extract the coefficient estimates as an un-nested data.frame, and append the values of am and vs, I might try
d1[, -3] %>%
pmap_dfr(
function(i, j, k)
k %>%
mutate(
vs = i,
am = j
)
)
But this results in an error. More explicitly declaring these variables as separate lists has the desired effect
list(
d1$vs,
d1$am,
d1$coef
) %>%
pmap_dfr(
function(i, j, k)
k %>%
mutate(
vs = i,
am = j
)
)
Is there a succinct way for pmap_* to treat a data.frame as a list?

Answer 1

We can use the standard option to extract the components (..1, ..2, etc)
d1[, -3] %>%
pmap_dfr(~ ..3 %>%
mutate(vs = ..1, am = ..2))
# A tibble: 8 x 7
# term estimate std.error statistic p.value vs am
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 (Intercept) 42.4 3.30 12.8 0.000213 0 1
#2 wt -7.91 1.14 -6.93 0.00227 0 1
#3 (Intercept) 44.1 6.96 6.34 0.00144 1 1
#4 wt -7.77 3.36 -2.31 0.0689 1 1
#5 (Intercept) 31.5 8.98 3.51 0.0171 1 0
#6 wt -3.38 2.80 -1.21 0.281 1 0
#7 (Intercept) 25.1 3.51 7.14 0.0000315 0 0
#8 wt -2.44 0.842 -2.90 0.0159 0 0

Answer 2

This is because the second list has no names attribute. If you unname d1 it works. The fact that you used the list function in the second example doesn't make a difference (except that it removed the names), because both objects are lists (data frames are lists).
d1[, -3] %>%
unname %>%
pmap_dfr(
function(i, j, k)
k %>%
mutate(
vs = i,
am = j
)
)
# # A tibble: 8 x 7
# term estimate std.error statistic p.value vs am
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 (Intercept) 42.4 3.30 12.8 0.000213 0 1
# 2 wt -7.91 1.14 -6.93 0.00227 0 1
# 3 (Intercept) 44.1 6.96 6.34 0.00144 1 1
# 4 wt -7.77 3.36 -2.31 0.0689 1 1
# 5 (Intercept) 31.5 8.98 3.51 0.0171 1 0
# 6 wt -3.38 2.80 -1.21 0.281 1 0
# 7 (Intercept) 25.1 3.51 7.14 0.0000315 0 0
# 8 wt -2.44 0.842 -2.90 0.0159 0 0
You can also name the arguments in your first code block's function to match (or use ..1 etc) for the same result
d1[, -3] %>%
pmap_dfr(
function(vs, am, coef)
coef %>%
mutate(
vs = vs,
am = am
)
)
# # A tibble: 8 x 7
# term estimate std.error statistic p.value vs am
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 (Intercept) 42.4 3.30 12.8 0.000213 0 1
# 2 wt -7.91 1.14 -6.93 0.00227 0 1
# 3 (Intercept) 44.1 6.96 6.34 0.00144 1 1
# 4 wt -7.77 3.36 -2.31 0.0689 1 1
# 5 (Intercept) 31.5 8.98 3.51 0.0171 1 0
# 6 wt -3.38 2.80 -1.21 0.281 1 0
# 7 (Intercept) 25.1 3.51 7.14 0.0000315 0 0
# 8 wt -2.44 0.842 -2.90 0.0159 0 0
You could also use wap from the experimental rap package
library(rap)
d1[, -3] %>%
wap( ~ coef %>%
mutate(
vs = vs,
am = am)) %>%
bind_rows
# # A tibble: 8 x 7
# term estimate std.error statistic p.value vs am
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 (Intercept) 42.4 3.30 12.8 0.000213 0 1
# 2 wt -7.91 1.14 -6.93 0.00227 0 1
# 3 (Intercept) 44.1 6.96 6.34 0.00144 1 1
# 4 wt -7.77 3.36 -2.31 0.0689 1 1
# 5 (Intercept) 31.5 8.98 3.51 0.0171 1 0
# 6 wt -3.38 2.80 -1.21 0.281 1 0
# 7 (Intercept) 25.1 3.51 7.14 0.0000315 0 0
# 8 wt -2.44 0.842 -2.90 0.0159 0 0