Extract Slopes by group, Broom? Dplyr?

tidyverse
2

Your code works for me, with the minor exception of the quotations in the filter statement not printing well.

If you're looking to do this for every tree, you need to look into tidyr, purrr and nesting your data into list-columns. What's going on below is:

  1. Grouping your data by Tree
  2. Nesting those groups into a list-column
  3. Create a model column that maps the lm function then tidy to the data list-column.
  4. Unnest the model list column.
  5. Filter it to circumference.
library(tidyverse) #for purrr, tidyr and dplyr
library(broom)

Orange %>% 
  group_by(Tree) %>% 
  nest() %>% 
  mutate(model = map(data, ~lm(age ~ 1 + circumference, data = .x) %>% 
                       tidy)) %>% 
  unnest(model) %>% 
  filter(term == 'circumference')

Which results in:

# A tibble: 5 x 6
   Tree          term  estimate std.error statistic      p.value
  <ord>         <chr>     <dbl>     <dbl>     <dbl>        <dbl>
1     1 circumference 11.919245 0.9188029  12.97258 4.851902e-05
2     2 circumference  7.795225 0.5595479  13.93129 3.425041e-05
3     3 circumference 12.038885 0.8353445  14.41188 2.901046e-05
4     4 circumference  7.169842 0.5719516  12.53575 5.733090e-05
5     5 circumference  8.787132 0.6211365  14.14686 3.177093e-05

You can then extract what you need using pull from dplyr or any other method you prefer.

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