CODEWITHSUNDEEP
rtidyversepurrr
Lucas De Abreu Maia
Lucas De Abreu Maia

Reputation: 597

purrr::map_dfr binds by columns, not row as expected

I'm new to tidyverse and thus still struggling a bit to make it do stuff I knew how to do with base.

The issue: I want to loop through the columns of a data frame, input each of them separately into a lm call, and get the output as a tidy data frame. I don't care for the intercept, so all I want to save into the tidy output are the coefficients from the independent variable. I want the final output to look as follows: a data frame where the columns are the coefficients and the rows are each variable from the original data frame. I can do it with base using do.call("rbind", ...) but as I'm migrating to tidyverse, I wanted to see if there's a way to do it on tidyverse. purrr::map_dfr doesn't work on this case; a known issue.

Some reproducible code:

> library(tidyverse)
> 
> set.seed(62442)
> 
> iv <- rnorm(100)
> dvs <- as_tibble(replicate(5, iv + rnorm(100)), .name_repair = "universal")
New names:
* `` -> ...1
* `` -> ...2
* `` -> ...3
* `` -> ...4
* `` -> ...5
> 
> # This doesn't work
> dvs %>% map_dfr(~ summary(lm(.x ~ iv))$coefficients[2, ]) 
# A tibble: 4 x 5
      ...1     ...2     ...3     ...4     ...5
     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
1 8.78e- 1 1.09e+ 0 9.11e- 1 1.19e+ 0 8.80e- 1
2 1.05e- 1 1.17e- 1 9.86e- 2 9.33e- 2 1.16e- 1
3 8.34e+ 0 9.29e+ 0 9.24e+ 0 1.27e+ 1 7.60e+ 0
4 4.78e-13 4.16e-15 5.40e-15 1.97e-22 1.80e-11
> 
> # It behaves exactly like:
> dvs %>% map_dfc(~ summary(lm(.x ~ iv))$coefficients[2, ])
# A tibble: 4 x 5
      ...1     ...2     ...3     ...4     ...5
     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
1 8.78e- 1 1.09e+ 0 9.11e- 1 1.19e+ 0 8.80e- 1
2 1.05e- 1 1.17e- 1 9.86e- 2 9.33e- 2 1.16e- 1
3 8.34e+ 0 9.29e+ 0 9.24e+ 0 1.27e+ 1 7.60e+ 0
4 4.78e-13 4.16e-15 5.40e-15 1.97e-22 1.80e-11
> 
> # All is left for me to do is:
> res <- dvs %>% map(~ summary(lm(.x ~ iv))$coefficients[2, ])
> do.call("rbind", res)
      Estimate Std. Error   t value                       Pr(>|t|)
...1 0.8776895 0.10525549  8.338658 0.0000000000004779501411861117
...2 1.0911362 0.11742588  9.292127 0.0000000000000041631074216992
...3 0.9113473 0.09863111  9.239958 0.0000000000000054021858298938
...4 1.1852848 0.09330950 12.702724 0.0000000000000000000001970469
...5 0.8799633 0.11579113  7.599575 0.0000000000179548788283525966

Upvotes: 2

Views: 1143

Answers (2)

tmfmnk
tmfmnk

Reputation: 40171

With the addition of broom, you can try:

map_dfr(.x = dvs, ~ tidy(lm(.x ~ iv)), .id = "ID")

   ID    term          estimate std.error statistic   p.value
   <chr> <chr>            <dbl>     <dbl>     <dbl>     <dbl>
 1 ...1  (Intercept) -0.260        0.0999 -2.61      1.05e- 2
 2 ...1  iv           0.878        0.105   8.34      4.78e-13
 3 ...2  (Intercept) -0.0000159    0.111  -0.000142 10.00e- 1
 4 ...2  iv           1.09         0.117   9.29      4.16e-15
 5 ...3  (Intercept) -0.0383       0.0936 -0.410     6.83e- 1
 6 ...3  iv           0.911        0.0986  9.24      5.40e-15
 7 ...4  (Intercept) -0.131        0.0885 -1.48      1.41e- 1
 8 ...4  iv           1.19         0.0933 12.7       1.97e-22
 9 ...5  (Intercept) -0.0132       0.110  -0.120     9.05e- 1
10 ...5  iv           0.880        0.116   7.60      1.80e-11

And if you don't need the intercept, with the addition of dplyr:

map_dfr(.x = dvs, ~ tidy(lm(.x ~ iv)), .id = "ID") %>%
 filter(term != "(Intercept)")

  ID    term  estimate std.error statistic  p.value
  <chr> <chr>    <dbl>     <dbl>     <dbl>    <dbl>
1 ...1  iv       0.878    0.105       8.34 4.78e-13
2 ...2  iv       1.09     0.117       9.29 4.16e-15
3 ...3  iv       0.911    0.0986      9.24 5.40e-15
4 ...4  iv       1.19     0.0933     12.7  1.97e-22
5 ...5  iv       0.880    0.116       7.60 1.80e-11

Upvotes: 2

akrun
akrun

Reputation: 887881

map row bind works when the datasets are data.frame/tibble or lists. Here, it is a named vector. One option is to convert it to list with as.list

library(dplyr)
library(purrr)
dvs %>% 
    map_dfr(~ summary(lm(.x ~ iv))$coefficients[2, ] %>% as.list)
# A tibble: 5 x 4
#  Estimate `Std. Error` `t value` `Pr(>|t|)`
#*    <dbl>        <dbl>     <dbl>      <dbl>
#1    0.878       0.105       8.34   4.78e-13
#2    1.09        0.117       9.29   4.16e-15
#3    0.911       0.0986      9.24   5.40e-15
#4    1.19        0.0933     12.7    1.97e-22
#5    0.880       0.116       7.60   1.80e-11

Upvotes: 4

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