library(modelr)
library(tidyverse)
library(gapminder)
# nest data by continent and label test/train data
nested_gap <- gapminder %>%
mutate(test_train = ifelse(year < 1992, "train", "test")) %>%
group_by(continent) %>%
nest()
# make a linear model function than only trains on training set
cont_model <- function(df) {
lm(lifeExp ~ year, data = df %>% filter(test_train == "train"))
}
# fit a model and add predictions to all data
fitted_gap <- nested_gap %>%
mutate(model = map(data, cont_model)) %>%
mutate(pred = map2(data, model, add_predictions))
# unnest predictions and filter only the test rows
fitted_gap %>%
unnest(pred) %>%
filter(test_train == "test")
