Appending multiple nested lists to a dataframe in R - r

Question

I have a list of ambiguous addresses that I need to return full geocode information for.
Only issue is that what I get is a large list of nested lists (JSON)
I want to be able to get a data frame that contains the key information, i.e.
IDEAL OUTPUT
Original_Address, StreetNum, StreetName, Suburb, town_city, locality, Postcode, geo_xCord, Country, Postcode
I almost wonder if this is just too difficult and if there is an easier method that I haven't considered.
I basically just need to be able to spit out the key address elements for each address I have.
# Stack Overflow Example -------------------------------------------
random_addresses <- c('27 Hall Street, Wellington',
'52 Ethan Street, New Zealand',
'13 Epsom Street, Auckland',
'42 Elden Drive, New Zealand')
register_google(key = "MYAPIKEY")
place_lookup <- geocode(random_addresses, output = "all")
print(place_lookup[1])
>>>
[[1]]$results
[[1]]$results[[1]]
[[1]]$results[[1]]$address_components
[[1]]$results[[1]]$address_components[[1]]
[[1]]$results[[1]]$address_components[[1]]$long_name
[1] "27"
[[1]]$results[[1]]$address_components[[1]]$short_name
[1] "27"
[[1]]$results[[1]]$address_components[[1]]$types
[[1]]$results[[1]]$address_components[[1]]$types[[1]]
[1] "street_number"
[[1]]$results[[1]]$address_components[[2]]
[[1]]$results[[1]]$address_components[[2]]$long_name
[1] "Hall Street"
[[1]]$results[[1]]$address_components[[2]]$short_name
[1] "Hall St"
[[1]]$results[[1]]$address_components[[2]]$types
[[1]]$results[[1]]$address_components[[2]]$types[[1]]
[1] "route"
[[1]]$results[[1]]$address_components[[3]]
[[1]]$results[[1]]$address_components[[3]]$long_name
[1] "Newtown"
[[1]]$results[[1]]$address_components[[3]]$short_name
[1] "Newtown"
[[1]]$results[[1]]$address_components[[3]]$types
[[1]]$results[[1]]$address_components[[3]]$types[[1]]
[1] "political"
[[1]]$results[[1]]$address_components[[3]]$types[[2]]
[1] "sublocality"
[[1]]$results[[1]]$address_components[[3]]$types[[3]]
[1] "sublocality_level_1"
[[1]]$results[[1]]$address_components[[4]]
[[1]]$results[[1]]$address_components[[4]]$long_name
[1] "Wellington"
[[1]]$results[[1]]$address_components[[4]]$short_name
[1] "Wellington"
[[1]]$results[[1]]$address_components[[4]]$types
[[1]]$results[[1]]$address_components[[4]]$types[[1]]
[1] "locality"
[[1]]$results[[1]]$address_components[[4]]$types[[2]]
[1] "political"
[[1]]$results[[1]]$address_components[[5]]
[[1]]$results[[1]]$address_components[[5]]$long_name
[1] "Wellington"
[[1]]$results[[1]]$address_components[[5]]$short_name
[1] "Wellington"
[[1]]$results[[1]]$address_components[[5]]$types
[[1]]$results[[1]]$address_components[[5]]$types[[1]]
[1] "administrative_area_level_1"
[[1]]$results[[1]]$address_components[[5]]$types[[2]]
[1] "political"
[[1]]$results[[1]]$address_components[[6]]
[[1]]$results[[1]]$address_components[[6]]$long_name
[1] "New Zealand"
[[1]]$results[[1]]$address_components[[6]]$short_name
[1] "NZ"
[[1]]$results[[1]]$address_components[[6]]$types
[[1]]$results[[1]]$address_components[[6]]$types[[1]]
[1] "country"
[[1]]$results[[1]]$address_components[[6]]$types[[2]]
[1] "political"
[[1]]$results[[1]]$address_components[[7]]
[[1]]$results[[1]]$address_components[[7]]$long_name
[1] "6021"
[[1]]$results[[1]]$address_components[[7]]$short_name
[1] "6021"
[[1]]$results[[1]]$address_components[[7]]$types
[[1]]$results[[1]]$address_components[[7]]$types[[1]]
[1] "postal_code"
[[1]]$results[[1]]$formatted_address
[1] "27 Hall Street, Newtown, Wellington 6021, New Zealand"
[[1]]$results[[1]]$geometry
[[1]]$results[[1]]$geometry$bounds
[[1]]$results[[1]]$geometry$bounds$northeast
[[1]]$results[[1]]$geometry$bounds$northeast$lat
[1] -41.31066
[[1]]$results[[1]]$geometry$bounds$northeast$lng
[1] 174.7768
[[1]]$results[[1]]$geometry$bounds$southwest
[[1]]$results[[1]]$geometry$bounds$southwest$lat
[1] -41.31081
[[1]]$results[[1]]$geometry$bounds$southwest$lng
[1] 174.7766
[[1]]$results[[1]]$geometry$location
[[1]]$results[[1]]$geometry$location$lat
[1] -41.31074
[[1]]$results[[1]]$geometry$location$lng
[1] 174.7767
[[1]]$results[[1]]$geometry$location_type
[1] "ROOFTOP"
[[1]]$results[[1]]$geometry$viewport
[[1]]$results[[1]]$geometry$viewport$northeast
[[1]]$results[[1]]$geometry$viewport$northeast$lat
[1] -41.30932
[[1]]$results[[1]]$geometry$viewport$northeast$lng
[1] 174.778
[[1]]$results[[1]]$geometry$viewport$southwest
[[1]]$results[[1]]$geometry$viewport$southwest$lat
[1] -41.31202
[[1]]$results[[1]]$geometry$viewport$southwest$lng
[1] 174.7753
[[1]]$results[[1]]$place_id
[1] "ChIJiynBCOOvOG0RMx429ZNDR3A"
[[1]]$results[[1]]$types
[[1]]$results[[1]]$types[[1]]
[1] "premise"
[[1]]$status
[1] "OK"
---

Answer 1

You can explore the nested lists with viewer in Rstudio or listviewer::jsonedit. You can then drill down to the desired information. Basically using unnest_wider to spread the list to columns to then select desired columns and unnest_longer to tease out nested lists to then iterate through.
library(tidyverse)
map(random_addresses, ~geocode(.x, output = "all") %>%
# results is name of list with desired information, create tibble for unnest
tibble(output = .$results) %>%
# Create tibble with address_components as column-list
unnest_wider(output) %>%
dplyr::select(address_components) %>%
# Get address_components as list of lists, each list to df
unnest_longer(., col = "address_components") %>%
map_dfr(., ~.x) %>%
# types is the type of information. It is listed so unlist
mutate(types = unlist(types)) %>%
# Choose the information to keep
filter(types %in% c("street_number", "route")) %>%
# Choose the format of data
select(long_name, types) %>%
# Put in wide form
pivot_wider(names_from = "types", values_from = "long_name")
) %>%
bind_rows # create master df
It will give you lists with your information (before filtering)
[[4]]
# A tibble: 13 × 3
long_name short_name types
<chr> <chr> <chr>
1 New Zealand NZ country
2 New Zealand NZ political
3 42 42 street_number
4 Elden Drive Elden Dr route
5 Saddle River Saddle River locality
6 Saddle River Saddle River political
7 Bergen County Bergen County administrative_area_level_2
8 Bergen County Bergen County political
9 New Jersey NJ administrative_area_level_1
10 New Jersey NJ political
11 United States US country
12 United States US political
13 07458 07458 postal_code