With the idea of (finally!) coming up with a formal replacement.
Imaginary code:
gapminder %>% nest_by(country) %>% mutate(fit = lm(lifeExp ~ year, data = data)) %>% summarise(broom::tidy(fit))
Equivalent to:
library(gapminder)
library(tidyverse)
gapminder %>%
group_by(country) %>%
nest() %>%
mutate(
fit = map(data, ~ lm(lifeExp ~ year, data = .x)),
tidy = map(fit, broom::tidy)
) %>%
select(tidy) %>%
unnest(tidy)
#> Adding missing grouping variables: `country`
#> # A tibble: 284 x 6
#> # Groups: country [142]
#> country term estimate std.error statistic p.value
#> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Afghanistan (Intercept) -508. 40.5 -12.5 1.93e- 7
#> 2 Afghanistan year 0.275 0.0205 13.5 9.84e- 8
#> 3 Albania (Intercept) -594. 65.7 -9.05 3.94e- 6
#> 4 Albania year 0.335 0.0332 10.1 1.46e- 6
#> 5 Algeria (Intercept) -1068. 43.8 -24.4 3.07e-10
#> 6 Algeria year 0.569 0.0221 25.7 1.81e-10
#> 7 Angola (Intercept) -377. 46.6 -8.08 1.08e- 5
#> 8 Angola year 0.209 0.0235 8.90 4.59e- 6
#> 9 Argentina (Intercept) -390. 9.68 -40.3 2.14e-12
#> 10 Argentina year 0.232 0.00489 47.4 4.22e-13
#> # … with 274 more rows
Or
gapminder %>%
group_by(country) %>%
do(fit = lm(lifeExp ~ year, data = .)) %>%
do(data.frame(country = .$country, broom::tidy(.$fit)))
This requires:
mutate.rowwise() needs to automatically wrap in outputs in list where needed.rowwise() needs to be able to capture grouping variables; or grouped_df needs some way to activate row-wise magic?summarise.rowwise() would return grouped_df since might no longer have 1 row per group?nest_by() that works like group_nest() + rowwise(). Needs a lot of thinking about name.RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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