How to merge a list of data frames from ggeffects R into one data frame?-CodePudding

I'm trying to convert a list of data frames from a ggeffects object into one data frame, so I can use it better in ggplot2. This is an simple example of what I'm trying:

library(ggeffects)
library(dplyr)

data(efc)

fit <- lm(barthtot ~ c12hour   neg_c_7   c161sex   c172code, data = efc)

full <- ggpredict(fit)

df <- bind_rows(full, .id="id")

But this gives me the following error:

Error: Can't recycle c12hour (size 35) to match neg_c_7 (size 12).

I'm new to R and Stackoverflow, so I hope this is all clear. Thank you!

CodePudding user response：

I don't fully understand your goal, but one way of binding the data frames in a list is using do.call(bind_rows, thelist):

do.call(bind_rows, full)

# Predicted values of Total score BARTHEL INDEX

# c12hour

c12hour | Predicted |         95% CI
------------------------------------
      0 |     75.44 | [73.26, 77.63]
     35 |     66.58 | [64.91, 68.25]
     70 |     57.71 | [55.81, 59.61]
    100 |     50.11 | [47.55, 52.68]
    170 |     32.38 | [27.73, 37.03]

# c161sex

c12hour | Predicted |         95% CI
------------------------------------
      1 |     63.96 | [60.57, 67.35]
      2 |     65.00 | [63.11, 66.90]

# c172code

c12hour | Predicted |         95% CI
------------------------------------
      1 |     64.06 | [61.01, 67.10]
      2 |     64.78 | [63.12, 66.43]
      3 |     65.49 | [62.32, 68.67]

# neg_c_7

c12hour | Predicted |         95% CI
------------------------------------
      6 |     78.17 | [75.11, 81.22]
     10 |     68.98 | [67.14, 70.81]
     14 |     59.79 | [57.88, 61.69]
     20 |     46.00 | [42.04, 49.97]
     28 |     27.63 | [20.31, 34.95]

Adjusted for:
*  neg_c_7 = 11.84
*  c161sex =  1.76
* c172code =  1.97

However, this form does not show all the data and the columns. To show all of them, you can use as.data.frame() or as_tibble().

do.call(bind_rows, full) |> as_tibble()

# A tibble: 52 × 6
       x predicted std.error conf.low conf.high group  
   <dbl>     <dbl>     <dbl>    <dbl>     <dbl> <chr>  
 1     0      75.4     1.12      73.3      77.6 c12hour
 2     5      74.2     1.06      72.1      76.3 c12hour
 3    10      72.9     1.01      70.9      74.9 c12hour
 4    15      71.6     0.965     69.8      73.5 c12hour
 5    20      70.4     0.925     68.6      72.2 c12hour
 6    25      69.1     0.893     67.4      70.9 c12hour
 7    30      67.8     0.868     66.1      69.5 c12hour
 8    35      66.6     0.851     64.9      68.2 c12hour
 9    40      65.3     0.842     63.7      67.0 c12hour
10    45      64.0     0.843     62.4      65.7 c12hour
# … with 42 more rows

This can then be used to create a plot by using ggplot. For example:

do.call(bind_rows, full) |> 
  ggplot(aes(x =x, y = predicted, col = group))   
  geom_point()

The resulted plot: