r - Add Column of Predicted Values to Data Frame with dplyr

Question

I have a data frame with a column of models and I am trying to add a column of predicted values to it. A minimal example is :

exampleTable <- data.frame(x = c(1:5, 1:5),
                           y = c((1:5) + rnorm(5), 2*(5:1)),
                           groups = rep(LETTERS[1:2], each = 5))
                           
models <- exampleTable %>% group_by(groups) %>% do(model = lm(y ~ x, data = .))
exampleTable <- left_join(tbl_df(exampleTable), models)

estimates <- exampleTable %>% rowwise() %>% do(Est = predict(.$model, newdata = .["x"]))

How can I add a column of numeric predictions to exampleTable? I tried using mutate to directly add the column to the table without success.

exampleTable <- exampleTable %>% rowwise() %>% mutate(data.frame(Pred = predict(.$model, newdata = .["x"])))

Error: no applicable method for 'predict' applied to an object of class "list"

Now I use bind_cols to add the estimates to exampleTable but I am looking for a better solution.

estimates <- exampleTable %>% rowwise() %>% do(data.frame(Pred = predict(.$model, newdata = .["x"])))
exampleTable <- bind_cols(exampleTable, estimates)

How can it be done in a single step?

Answer 1

Using modelr, there is an elegant solution using the tidyverse.

The inputs

library(dplyr)
library(purrr)
library(tidyr)

# generate the inputs like in the question
example_table <- data.frame(x = c(1:5, 1:5),
                            y = c((1:5) + rnorm(5), 2*(5:1)),
                            groups = rep(LETTERS[1:2], each = 5))

models <- example_table %>% 
  group_by(groups) %>% 
  do(model = lm(y ~ x, data = .)) %>%
  ungroup()
example_table <- left_join(tbl_df(example_table ), models, by = "groups")

The solution

# generate the extra column
example_table %>%
  group_by(groups) %>%
  do(modelr::add_predictions(., first(.$model)))

The explanation

add_predictions adds a new column to a data frame using a given model. Unfortunately it only takes one model as an argument. Meet do. Using do, we can run add_prediction individually over each group.

. represents the grouped data frame, .$model the model column and first() takes the first model of each group.

Simplified

With only one model, add_predictions works very well.

# take one of the models
model <- example_table$model[[6]]

# generate the extra column
example_table %>%
  modelr::add_predictions(model)

Recipes

Nowadays, the tidyverse is shifting from the modelr package to recipes so that might be the new way to go once this package matures.