Convert/Export Googleway Output to Data Frame

Convert/export googleway output to data frame

Google's Distance API

is a service that provides travel distance and time for a matrix of origins and destinations.

That is, you will get a distance for all the possible origin & destination combinations.

Given your description

My desired output is distance and time of 1o pairs of lat/long (e.g., the 1st element of origins and 1st element of destinations, 2nd element of origins and 2nd element of destinations, etc.)

You actually want just one value per origin/destination pair.

Also, the API can only accept one request at a time, so if you want to iterate over all O/D pairs, and all transport modes, you need to use a loop

Example

library(googleway)

set_key("your_api_key")

## iterate over each row of origins/destinaions
lst <- lapply(1:nrow(origins), function(x) {

    google_distance(origins = c(origins[x, "V1"], origins[x,"V2"]), 
                    destinations = c(destinations[x, "V1"], destinations[x, "V2"]),
                    mode = "driving",  ## you can only do one mode at a time
    )

})

## in the above iteration, we used 'lapply', so our results are stored in a list
## you have to access the specific elements/results from that list
lst_elements <- lapply(lst, function(x){
    stats::setNames(
        cbind(
          distance_elements(x)[[1]][['duration']],
          distance_elements(x)[[1]][['distance']]
        )
        , c("duration_text", "duration_value", "distance_text", "distance_value")
    )
})

## then you can start to create your data.frames (or data.table in this case)
dt_durations <- data.table::rbindlist(lst_elements)

#      duration_text duration_value distance_text distance_value
#  1:        17 mins            993       17.6 km          17589
#  2: 1 hour 47 mins           6429        158 km         158198
#  3:        33 mins           2009       38.6 km          38630
#  4:         8 mins            504        2.5 km           2466
#  5:         4 mins            225        1.5 km           1486
#  6:          1 min              1           2 m              2
#  7:        22 mins           1312       19.5 km          19495
#  8:        27 mins           1630       27.1 km          27094
#  9:        47 mins           2845       61.0 km          61024
# 10:         6 mins            364        7.0 km           7001

You'll have to do a similar 'loop' for iterating over the different modes

Going Further

If you want, you can also use the directions API to get driving routes between them

lst <- lapply(1:nrow(origins), function(x) {

    google_directions(origin = c(origins[x, "V1"], origins[x,"V2"]), 
                    destination = c(destinations[x, "V1"], destinations[x, "V2"]),
                    mode = "driving",  ## you can only do one mode at a time
    )

})

lst_elements <- lapply(lst, function(x){
    data.frame(
        polyline = direction_polyline(x)
    )
})

dt_routes <- data.table::rbindlist(lst_elements)

df_distances <- cbind(origins, destinations)
df_distances <- stats::setNames(df_distances, c("origin_lat", "origin_lon", "destination_lat", "destination_lon"))
df_distances <- cbind(df_distances, dt_routes, dt_durations)
df_distances$colour <- "blue" ## for colouring some markers
df_distances$info <- paste0("<b>Duration:</b>", df_distances$distance_value, 
                            "<br><b>Distance:</b>", df_distances$duration_value)

set_key("your_api_key", api = "map")

google_map(data = df_distances) %>%
    add_markers(lat = "origin_lat", lon = "origin_lon") %>%
    add_markers(lat = "destination_lat", lon = "destination_lon", colour = "colour") %>%
    add_polylines(polyline = "polyline", info_window = "info")

Google Places API and R -- calling 2nd column in a data frame returns six separate columns

Akrun is right (as usual!). A data.frame can have lists as 'columns'. This is normal behaviour.

Your question seems to be a more general question about how to extract nested list data in R, but using Google's API response as an example. Given you're using googleway (I'm the author of the pacakge), I'm answering it in the context of Google's response. However, there are numerous other answers and examples online about how to work with lists in R.

Explanation

You're seeing the nested lists in your results because the data returned from Google's API is actually JSON. The google_places() function 'simplifies' this to a data.frame using jsonlite::fromJSON() internally.

If you set simplify = F in the function call you can see the raw JSON output

library(googleway)

set_key("GOOGLE_API_KEY")

HAVE_PLACES_JSON <- google_places(search_string = "grocery store",
                             location = c(35.4168, -80.5883),
                             radius = 10000, 
                             simplify = F)

## run this to view the JSON.
jsonlite::prettify(paste0(HAVE_PLACES_JSON))

You'll see the JSON can contain many nested objects. When converted to an R data.frame these nested objects are returned as list columns'

If you're not familiar with JSON it may be worth a bit of research to see what it's all about.

Extracting Data

I've written some functions to extract useful pieces of information from the API responses which may be of help here

locations <- place_location(HAVE_PLACES)
head(locations)
#        lat       lng
# 1 35.38690 -80.55993
# 2 35.42111 -80.57277
# 3 35.37006 -80.66360
# 4 35.39793 -80.60813
# 5 35.44328 -80.62367
# 6 35.37034 -80.54748

placenames  <- place_name(HAVE_PLACES)
head(placenames)
# "Food Lion" "Food Lion" "Food Lion" "Food Lion" "Food Lion" "Food Lion"

However, note that you will still get some list objects returned, because, in this case, a 'location' can have many 'types'

placetypes <- place_type(HAVE_PLACES)
str(placetypes)

# List of 20
# $ : chr [1:5] "grocery_or_supermarket" "store" "food" "point_of_interest" ...
# $ : chr [1:5] "grocery_or_supermarket" "store" "food" "point_of_interest" ...
# $ : chr [1:5] "grocery_or_supermarket" "store" "food" "point_of_interest" ...
# $ : chr [1:5] "grocery_or_supermarket" "store" "food" "point_of_interest" ...

Summary

With Google's API responses you will have to extract the specific data elemets you want and construct them into your required object

df <- cbind(
  place_name(HAVE_PLACES)
  , place_location(HAVE_PLACES)
  , place_type(HAVE_PLACES)[[1]]   ## only selecting the 1st 'type'
)

head(df)

#   place_name(HAVE_PLACES)      lat       lng place_type(HAVE_PLACES)[[1]]
# 1               Food Lion 35.38690 -80.55993       grocery_or_supermarket
# 2               Food Lion 35.42111 -80.57277                        store
# 3               Food Lion 35.37006 -80.66360                         food
# 4               Food Lion 35.39793 -80.60813            point_of_interest
# 5               Food Lion 35.44328 -80.62367                establishment
# 6               Food Lion 35.37034 -80.54748       grocery_or_supermarket

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