The problem presented involves creating a dataset that contains information about linear models, specifically focusing on their coefficients and R-squared values.

To approach this problem, we need to follow these steps:

1. Create the initial dataset: We have a dataset df with variables id, x, y, and year. The variable response is also included but not used in the model.

2. Use dplyr to group by id, x, and y: Since we want to create separate models for different combinations of x and y, we use group_by(id, x, y).

3. Create a new column ‘Model’ using map: Inside the group_by block, we use nest() to nest the remaining columns (including year) and then map over this nested data to create a linear model for each group using lm(response~year, data=.).

4. Extract the coefficient information and R-squared values: We use map again to extract the coefficient information (tidy) and R-squared values from each model.

5. Unnest the ‘Model’ column: After creating the models, we use ungroup() to remove the grouping and then nest the id, Model_Info, and a new r.squared column (generated using map_dbl) together.

6. Filter for the first model: Since there are two models in our dataset, we filter for the first one where id == 1.

7. Pull the R-squared value: Finally, we use pull(r.squared) to extract the R-squared value from each row of the dataset.

By following these steps, we can create a new dataset that contains information about linear models, including their coefficients and R-squared values for different combinations of x and y.

Here’s how the code looks like after applying the above steps:

library(dplyr)

df <- data.frame(
  id = rep(1:2, 2),
  x = rep(c(25, 30), 10),
  y = rep(c(100, 200), 10),
  year = rep(1980:1989, 2),
  response = rnorm(20)
)

df %>% 
  group_by(id, x, y) %>% 
  nest() %>% 
  mutate(Model = map(data, ~lm(response~year, data=.)), 
         Coeff_Info = map(Model, tidy), 
         Model_Info = map(Model, glance)) %>% 
  ungroup() %>% 
  unnest(id, Model_Info) %>% 
  filter(id == 1) %>% 
  pull(r.squared)

This code produces the desired dataset with information about linear models for different combinations of x and y, including their coefficients and R-squared values.

Last modified on 2024-12-07