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)

gap <- gap %>%
  mutate(country = fct_reorder2(country, .x = year, .y = lifeExp))

gap_nested <- gap %>%
  group_by(country) %>%
  nest()

# gap_fitted <- gap_nested %>%
#   mutate(fit = map(data, ~ lm(lifeExp ~ yr1952, data = .x)))

gap_fitted <- zap(gap_nested, fit = ~lm(lifeExp ~ yr1952, data = data))

gap_fitted
#> # A tibble: 33 x 3
#>    country          data              fit     
#>    <fct>            <list>            <list>  
#>  1 Afghanistan      <tibble [12 × 6]> <S3: lm>
#>  2 Bahrain          <tibble [12 × 6]> <S3: lm>
#>  3 Bangladesh       <tibble [12 × 6]> <S3: lm>
#>  4 Cambodia         <tibble [12 × 6]> <S3: lm>
#>  5 China            <tibble [12 × 6]> <S3: lm>
#>  6 Hong Kong, China <tibble [12 × 6]> <S3: lm>
#>  7 India            <tibble [12 × 6]> <S3: lm>
#>  8 Indonesia        <tibble [12 × 6]> <S3: lm>
#>  9 Iran             <tibble [12 × 6]> <S3: lm>
#> 10 Iraq             <tibble [12 × 6]> <S3: lm>
#> # … with 23 more rows

# gap_fitted <- gap_fitted %>%
#   mutate(
#     intercept = map_dbl(fit, ~ coef(.x)[["(Intercept)"]]),
#     slope = map_dbl(fit, ~ coef(.x)[["yr1952"]])
#   )

gap_fitted %>%
  zap_dbl(intercept = ~coef(fit)[["(Intercept)"]]) %>%
  zap_dbl(slope = ~coef(fit)[["yr1952"]])
#> # A tibble: 33 x 5
#>    country          data              fit      intercept slope
#>    <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

# maybe even better
gap_fitted %>%
  zap(coefs = ~tidy(fit)) %>%
  unnest(coefs)
#> # A tibble: 66 x 6
#>    country     term        estimate std.error statistic  p.value
#>    <fct>       <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#>  1 Afghanistan (Intercept)   29.9      0.664      45.0  7.00e-13
#>  2 Afghanistan yr1952         0.275    0.0205     13.5  9.84e- 8
#>  3 Bahrain     (Intercept)   52.7      0.890      59.2  4.56e-14
#>  4 Bahrain     yr1952         0.468    0.0274     17.0  1.02e- 8
#>  5 Bangladesh  (Intercept)   36.1      0.530      68.1  1.13e-14
#>  6 Bangladesh  yr1952         0.498    0.0163     30.5  3.37e-11
#>  7 Cambodia    (Intercept)   37.0      3.06       12.1  2.69e- 7
#>  8 Cambodia    yr1952         0.396    0.0942      4.20 1.82e- 3
#>  9 China       (Intercept)   47.2      2.09       22.5  6.67e-10
#> 10 China       yr1952         0.531    0.0645      8.23 9.21e- 6
#> # … with 56 more rows

#' Created on 2018-11-24 by the [reprex
#' package](http://reprex.tidyverse.org) (v0.2.0).