lect08_prompts

lect08.R

library(tidyverse)
library(repurrrsive)

df <- tibble(
  name = map_chr(got_chars, "name"),
  titles = map(got_chars, "titles")
)
df

df <- got_chars %>% {
  tibble(
    name = map_chr(., "name"),
    titles = map(., "titles")
  )
}


map_dfr(got_chars, `[`,
        c("name", "culture", "gender", "born"))

got_chars %>% {
  tibble(name = map_chr(., "name"),
         houses = map(., "allegiances"))
} %>% filter(lengths(houses) > 1) %>%
  unnest()

aliases <- set_names(map(got_chars, "aliases"), map_chr(got_chars, "name"))
(aliases <- aliases[c("Theon Greyjoy", "Asha Greyjoy", "Brienne of Tarth")])

my_fun <- function(x) paste(x, collapse = " | ")
map(aliases, my_fun)

map(aliases, function(x) paste(x, collapse = " | "))

map(aliases, paste, collapse = " | ")

map(aliases, ~ paste(.x, collapse = " | "))

aliases %>%
  map_chr(~ paste(.x, collapse = " | ")) %>%
  enframe(value = "aliases")


library(gapminder)
gap_nested <- gapminder %>%
  group_by(country) %>%
  nest()
gap_nested

gap_fits <- gap_nested %>%
  mutate(fit = map(data, ~ lm(lifeExp ~ year, data = .x)))
gap_fits %>% tail(3)

summary(gap_fits$fit[[1]])

library(broom)

tidy(gap_fits$fit[[1]])

gap_fits %>%
  mutate(coef = map(fit, tidy)) %>%
  unnest(coef)

glance(gap_fits$fit[[1]])

gap_fits %>%
  mutate(fitsum = map(fit, glance)) %>%
  unnest(fitsum)