zap-gapminder.r

library(gapminder)
library(tidyverse)
library(zap)
library(broom)

# gapminder data for Asia only
gap <- gapminder %>%
  filter(continent == "Asia") %>%
  mutate(yr1952 = year - 1952) %>% 
  mutate(country = fct_reorder2(country, .x = year, .y = lifeExp))

gap_nested <- gap %>%
  group_by(country) %>%
  nest()
gap_nested
#> # A tibble: 33 x 2
#>    country          data             
#>    <fct>            <list>           
#>  1 Afghanistan      <tibble [12 × 6]>
#>  2 Bahrain          <tibble [12 × 6]>
#>  3 Bangladesh       <tibble [12 × 6]>
#>  4 Cambodia         <tibble [12 × 6]>
#>  5 China            <tibble [12 × 6]>
#>  6 Hong Kong, China <tibble [12 × 6]>
#>  7 India            <tibble [12 × 6]>
#>  8 Indonesia        <tibble [12 × 6]>
#>  9 Iran             <tibble [12 × 6]>
#> 10 Iraq             <tibble [12 × 6]>
#> # … with 23 more rows

gap_fitted <- gap_nested %>% 
  zap(fit = ~lm(lifeExp ~ yr1952, data = data)) %>% 
  zap_dfr(coef = ~tibble(!!!coef(fit)) )
gap_fitted
#> # A tibble: 33 x 4
#>    country          data              fit      coef$`(Intercept)` $yr1952
#>    <fct>            <list>            <list>                <dbl>   <dbl>
#>  1 Afghanistan      <tibble [12 × 6]> <S3: lm>               29.9   0.275
#>  2 Bahrain          <tibble [12 × 6]> <S3: lm>               52.7   0.468
#>  3 Bangladesh       <tibble [12 × 6]> <S3: lm>               36.1   0.498
#>  4 Cambodia         <tibble [12 × 6]> <S3: lm>               37.0   0.396
#>  5 China            <tibble [12 × 6]> <S3: lm>               47.2   0.531
#>  6 Hong Kong, China <tibble [12 × 6]> <S3: lm>               63.4   0.366
#>  7 India            <tibble [12 × 6]> <S3: lm>               39.3   0.505
#>  8 Indonesia        <tibble [12 × 6]> <S3: lm>               36.9   0.635
#>  9 Iran             <tibble [12 × 6]> <S3: lm>               45.0   0.497
#> 10 Iraq             <tibble [12 × 6]> <S3: lm>               50.1   0.235
#> # … with 23 more rows