[Feature Request] case statements needed for grouped regression

[alexhallam]

I use grouped regressions often (aka Many Models). Though your package makes it mostly painless to create grouped model sql code, maybe it would be nice to have a convenience function to do this.

Hopefully the code below illustrates what I am thinking.

# load libs
library(tidyverse)
library(tidypredict)
library(gapminder)

# make model function
country_model <- function(df) {
  lm(lifeExp ~ year, data = df)
}

# nest data for grouped regression
by_country <- gapminder %>% 
  group_by(country, continent) %>% 
  nest() %>% 
  mutate(model = map(data, country_model)) %>% 
  # use tidypredict to make sql strings by model
  mutate(sql_txt = map(model,~tidypredict_sql(., dbplyr::simulate_mssql()))) %>% 
  # add some case statements -- more work needed here
  mutate(case_string = paste0("WHEN COUNTRY = ", "'", country, "'", " THEN ")) %>% 
  # paste case statements to model sql
  mutate(full_string = paste0(case_string, sql_txt))
  
# paste all of the models together with case statements and return one big glob
# of text for sql
by_country %>% 
  unnest(full_string) %>% 
  summarise(full_sql_text = toString(full_string)) %>% 
  as.list()

The first few entries are shown below. I am sure there are many ways to implement this in a cleaner fashion than what I did.

WHEN COUNTRY = 'Afghanistan' THEN -507.534271561773 + (`year`) * (0.275328671328672), 
WHEN COUNTRY = 'Albania' THEN -594.072510955711 + (`year`) * (0.334683216783217),
WHEN COUNTRY = 'Algeria' THEN -1067.85903962704 + (`year`) * (0.569279720279723)
...

Would it be possible to add some convenience function to do some lifting for building case statements needed for grouped regression?

[edgararuiz-zz]

Hi Alex, thanks for the suggestion, let me take a look and see what we could do via the package.

[edgararuiz-zz]

Actually, I have another package called modeldb that is able to fit models inside databases, and it also respects grouped data:

library(tidyverse)
library(gapminder)
library(modeldb)
gapminder %>%
  group_by(country) %>%
  select(lifeExp, year) %>%
  linear_regression_db(lifeExp)
#> Adding missing grouping variables: `country`
#> # A tibble: 142 x 3
#>    country     `(Intercept)`  year
#>    <fct>               <dbl> <dbl>
#>  1 Afghanistan         -508. 0.275
#>  2 Albania             -594. 0.335
#>  3 Algeria            -1068. 0.569
#>  4 Angola              -377. 0.209
#>  5 Argentina           -390. 0.232
#>  6 Australia           -376. 0.228
#>  7 Austria             -406. 0.242
#>  8 Bahrain             -860. 0.468
#>  9 Bangladesh          -936. 0.498
#> 10 Belgium             -340. 0.209
#> # ... with 132 more rows

This package works with tidypredict but at this time, only with un-grouped data. If modeldb fits the models that you ultimately need, maybe we should focus on adding grouped model capability in tidypredict but based on the output from modeldb.

Thanks!

[alexhallam]

Does this translate to sql text?

[edgararuiz-zz]

Not yet, that's the portion I need to code, but wanted first to find out if it would be useful for anyone.