My solution with some explanation below.
library(gapminder)
library(purrr)
library(tidyr)
library(broom)
nest_data <- gapminder %>% group_by(continent) %>% nest(.key = by_continent)
First question was: how to nest by_country inside the nested by_continent
Great solution by @aosmith on the comments
nested_again<-
nest_data %>% mutate(by_continent = map(by_continent, ~.x %>%
group_by(country) %>%
nest(.key = by_country)))
# Level 1
nested_again
# # A tibble: 5 × 2
# continent by_continent
# <fctr> <list>
# 1 Asia <tibble [33 × 2]>
# 2 Europe <tibble [30 × 2]>
# 3 Africa <tibble [52 × 2]>
# 4 Americas <tibble [25 × 2]>
# 5 Oceania <tibble [2 × 2]>
# Level 2
nested_again %>% unnest %>% slice(1:2)
# # A tibble: 2 × 3
# continent country by_country
# <fctr> <fctr> <list>
# 1 Asia Afghanistan <tibble [12 × 4]>
# 2 Asia Bahrain <tibble [12 × 4]>
Second question: how to apply a regression model at a deeper level (and save the models on the tibble, I suppose)
Solution by @aosmith (which I'm calling sol1)
sol1<-mutate(nested_again, models = map(by_continent, "by_country") %>%
at_depth(2, ~lm(lifeExp ~ year, data = .x)))
sol1
# # A tibble: 5 × 3
# continent by_continent models
# <fctr> <list> <list>
# 1 Asia <tibble [33 × 2]> <list [33]>
# 2 Europe <tibble [30 × 2]> <list [30]>
# 3 Africa <tibble [52 × 2]> <list [52]>
# 4 Americas <tibble [25 × 2]> <list [25]>
# 5 Oceania <tibble [2 × 2]> <list [2]>
sol1 %>% unnest(models)
# Error: Each column must either be a list of vectors or a list of data frames [models]
sol1 %>% unnest(by_continent) %>% slice(1:2)
# # A tibble: 2 × 3
# continent country by_country
# <fctr> <fctr> <list>
# 1 Asia Afghanistan <tibble [12 × 4]>
# 2 Asia Bahrain <tibble [12 × 4]>
The solution is doing what it is supposed to, but there's no easy way to filter by country, because that information is nested in the level 2.
I propose the solution 2, based on @aosmith's solution to the first question:
sol2<-nested_again %>% mutate(by_continent = map(by_continent, ~.x %>%
mutate(models = map(by_country, ~lm(lifeExp ~ year, data = .x) )) ))
sol2
# # A tibble: 5 × 2
# continent by_continent
# <fctr> <list>
# 1 Asia <tibble [33 × 4]>
# 2 Europe <tibble [30 × 4]>
# 3 Africa <tibble [52 × 4]>
# 4 Americas <tibble [25 × 4]>
# 5 Oceania <tibble [2 × 4]>
sol2 %>% unnest %>% slice(1:2)
# # A tibble: 2 × 4
# continent country by_country models
# <fctr> <fctr> <list> <list>
# 1 Asia Afghanistan <tibble [12 × 4]> <S3: lm>
# 2 Asia Bahrain <tibble [12 × 4]> <S3: lm>
sol2 %>% unnest %>% unnest(by_country) %>% colnames
# [1] "continent" "country" "year" "lifeExp" "pop"
# [6] "gdpPercap"
# get model by specific country
sol2 %>% unnest %>% filter(country == "Brazil") %$% models %>% extract2(1)
# Call:
# lm(formula = lifeExp ~ year, data = .x)
#
# Coefficients:
# (Intercept) year
# -709.9427 0.3901
# summary with broom::tidy
sol2 %>% unnest %>% filter(country == "Brazil") %$% models %>%
extract2(1) %>% tidy
# term estimate std.error statistic p.value
# 1 (Intercept) -709.9426860 10.801042821 -65.72909 1.617791e-14
# 2 year 0.3900895 0.005456243 71.49417 6.990433e-15
We can tidy all the models and save in the data to use for plotting or filter
sol2 %<>% mutate(by_continent = map(by_continent, ~.x %>%
mutate(tidymodels = map(models, tidy )) ))
sol2 %>% unnest %>% unnest(tidymodels) %>%
ggplot(aes(country,p.value,colour=continent))+geom_point()+
facet_wrap(~continent)+
theme(axis.text.x = element_blank())
selc <- sol2 %>% unnest %>% unnest(tidymodels) %>% filter(p.value > 0.05) %>%
select(country) %>% unique %>% extract2(1)
gapminder %>% filter(country %in% selc ) %>%
ggplot(aes(year,lifeExp,colour=continent))+geom_line(aes(group=country))+
facet_wrap(~continent)
aaaaand, we can use the models
m1 <- sol2 %>% unnest %>% slice(1) %$% models %>% extract2(1)
x <- sol2 %>% unnest %>% slice(1) %>% unnest(by_country) %>% select(year)
pred1 <- data.frame(year = x, lifeExp = predict.lm(m1,x))
sol2 %>% unnest %>% slice(1) %>% unnest(by_country) %>%
ggplot(aes(year, lifeExp )) + geom_point() +
geom_line(data=pred1)
In this case there's really no good reason to use this double nesting (besides learning how to to it, of course), but I found a case in my work where it is extremely valuable, specifically when you need a function to work on a 3rd level, grouped by levels 1 and 2, and save in level 2 - of course for this we could also use a for loop on level 1, but what's the fun in that ;) I'm not really sure how this "nested" map performs compared to for loop + map, but I'll test it next.
Benchmark
It looks like they do not differ much
# comparison map_map with for_map
map_map<-function(nested_again){
nested_again %>% mutate(by_continent = map(by_continent, ~.x %>%
mutate(models = map(by_country, ~lm(lifeExp ~ year, data = .x) )) )) }
for_map<-function(nested_again){ for(i in 1:length(nested_again[[1]])){
nested_again$by_continent[[i]] %<>%
mutate(models = map(by_country, ~lm(lifeExp ~ year, data = .x) )) }}
res<-microbenchmark::microbenchmark(
mm<-map_map(nested_again), fm<-for_map(nested_again) )
res
# Unit: milliseconds
# expr min lq mean median uq max neval cld
# mm <- map_map(nested_again) 121.0033 144.5530 160.6785 155.2389 174.2915 240.2012 100 a
# fm <- for_map(nested_again) 131.4312 148.3329 164.7097 157.6589 173.6480 455.7862 100 a
autoplot(res)
