Converting nested list with missing values to data frame in R

Question

I have a list of geocode output from the googleway package (ggmap geocode wouldn't work with an API key) stored in a list, each element of which contains two lists. However, for addresses in which no result was found, the structure of the list is different, frustrating my attempts to convert the list to a dataframe.

The structure of a "non-missing" result (created with dput()) is as follows (ignore the gibberish, RStudio doesn't display Cyrillic correctly in the console):

structure(list(results = structure(list(address_components = list(
    structure(list(long_name = c("11À", "óëèöà Ãîãîëÿ", "Çåëåíîãðàäñêèé àäìèíèñòðàòèâíûé îêðóã", 
    "Çåëåíîãðàä", "Ìîñêâà", "Ìîñêâà", "Ðîññèÿ", "124575"), short_name = c("11À", 
    "óë. Ãîãîëÿ", "Çåëåíîãðàäñêèé àäìèíèñòðàòèâíûé îêðóã", "Çåëåíîãðàä", 
    "Ìîñêâà", "Ìîñêâà", "RU", "124575"), types = list("street_number", 
        "route", c("political", "sublocality", "sublocality_level_1"
        ), c("locality", "political"), c("administrative_area_level_2", 
        "political"), c("administrative_area_level_1", "political"
        ), c("country", "political"), "postal_code")), .Names = c("long_name", 
    "short_name", "types"), class = "data.frame", row.names = c(NA, 
    8L))), formatted_address = "óë. Ãîãîëÿ, 11À, Çåëåíîãðàä, Ìîñêâà, Ðîññèÿ, 124575", 
    geometry = structure(list(location = structure(list(lat = 55.987567, 
        lng = 37.17152), .Names = c("lat", "lng"), class = "data.frame", row.names = 1L), 
        location_type = "ROOFTOP", viewport = structure(list(
            northeast = structure(list(lat = 55.9889159802915, 
                lng = 37.1728689802915), .Names = c("lat", "lng"
            ), class = "data.frame", row.names = 1L), southwest = structure(list(
                lat = 55.9862180197085, lng = 37.1701710197085), .Names = c("lat", 
            "lng"), class = "data.frame", row.names = 1L)), .Names = c("northeast", 
        "southwest"), class = "data.frame", row.names = 1L)), .Names = c("location", 
    "location_type", "viewport"), class = "data.frame", row.names = 1L), 
    place_id = "ChIJzXSgUeQUtUYREIohzQOG--A", types = list("street_address")), .Names = c("address_components", 
"formatted_address", "geometry", "place_id", "types"), class = "data.frame", row.names = 1L), 
    status = "OK"), .Names = c("results", "status"))

The structure of a "missing" result is as follows:

structure(list(results = list(), status = "ZERO_RESULTS"), .Names = c("results", 
"status"))

Basically, the issue appears to be that when the function doesn't get a result from the Google API, it creates an empty list, rather than a list with the same elements as the "non-missing" list with NA as values. This creates an error when you pass it these lists to data.frame(), because it cannot create a data frame from nothing.

I have tried the solution here after extracting the results sublists into a list of their own: Converting nested list (unequal length) to data frame. It is supposed to fill in NAs and create equal length lists, enabling a conversion to a data frame:

first100geocode.results.l <- vector("list", 100)
for(i in 1:length(first100geocode.results.l)){
     first100geocode.results.l[[i]] <- first100geocode[[i]]$results
}

indx <- sapply(first100geocode.results.l, length)
res <- as.data.frame(do.call(rbind,lapply(first100geocode.results.l, 
`length<-`, max(indx))))
colnames(res) <- names(first100geocode.results.l[[which.max(indx)]])

However, the line in which the "res" object is created throws an error: Error in rbind(deparse.level, ...) : invalid list argument: all variables should have the same length'.

Is there some other way to fill in NAs for the missing results, so that I can convert this to a data frame?

(Note: I can't just simply remove the missing results, I need to bind this back to the original list of addresses).

@Aurèle Yes, thank you for pointing that out - sorry I forgot originally. — Sean Norton
– Sean Norton, Commented Jun 9, 2017 at 18:07
So, I can't read through the jibberish enough to tell what row needs an NA and what row does not, but you can always add NA with c(NA, x) or c(x, NA) or c(x[1:4], NA, x[5:6])? — Evan Friedland
– Evan Friedland, Commented Jun 9, 2017 at 18:41
@EvanFriedland the gibberish should actually be irrelevant - the code represents a list which will form one row of the dataset, with the elements such as "formatted_address" becoming columns. The issue is that if you look at the second block of code for an "NA", results is created an empty list, (results = list(). In order for it to become a data frame, that empty list needs to be populated with the elements of the original list (i.e. the names) with the values set to "NA" OR changed to something that will return NA for all columns when cast to a data frame. — Sean Norton
– Sean Norton, Commented Jun 9, 2017 at 19:00
The package returns address_components because that's what Google returns, and I didn't want to try and second-guess what users would and would not find useful. — SymbolixAU
– SymbolixAU, Commented Jun 11, 2017 at 4:03

Aurèle · Accepted Answer · 2017-06-09 19:41:06Z

We'll let jsonlite::flatten do most of the work:

Put your two example results in one list (hopefully this is faithful to your actual data structure):

first100geocode <- list(
  structure(list(results = structure(list(address_components = list(
    structure(list(long_name = c(
      "11À", "óëèöà Ãîãîëÿ", "Çåëåíîãðàäñêèé àäìèíèñòðàòèâíûé îêðóã", 
      "Çåëåíîãðàä", "Ìîñêâà", "Ìîñêâà", "Ðîññèÿ", "124575"), short_name = c(
        "11À", "óë. Ãîãîëÿ", "Çåëåíîãðàäñêèé àäìèíèñòðàòèâíûé îêðóã", "Çåëåíîãðàä", 
        "Ìîñêâà", "Ìîñêâà", "RU", "124575"), types = list(
          "street_number", "route", c("political", "sublocality", "sublocality_level_1"
          ), c("locality", "political"), c(
            "administrative_area_level_2", 
            "political"), c("administrative_area_level_1", "political"
            ), c("country", "political"), "postal_code")), .Names = c(
              "long_name", 
              "short_name", "types"), class = "data.frame", row.names = c(NA, 8L))), 
    formatted_address = "óë. Ãîãîëÿ, 11À, Çåëåíîãðàä, Ìîñêâà, Ðîññèÿ, 124575", 
    geometry = structure(list(location = structure(
      list(lat = 55.987567, lng = 37.17152), 
      .Names = c("lat", "lng"), class = "data.frame", row.names = 1L), 
      location_type = "ROOFTOP", viewport = structure(list(
        northeast = structure(list(
          lat = 55.9889159802915, lng = 37.1728689802915), .Names = c("lat", "lng"
          ), class = "data.frame", row.names = 1L), southwest = structure(list(
            lat = 55.9862180197085, lng = 37.1701710197085), .Names = c("lat", "lng"), 
            class = "data.frame", row.names = 1L)), .Names = c("northeast", "southwest"), 
        class = "data.frame", row.names = 1L)), 
      .Names = c("location", "location_type", "viewport"), 
      class = "data.frame", row.names = 1L), 
    place_id = "ChIJzXSgUeQUtUYREIohzQOG--A", types = list("street_address")), 
    .Names = c("address_components", 
               "formatted_address", "geometry", "place_id", "types"), 
    class = "data.frame", row.names = 1L), 
    status = "OK"), .Names = c("results", "status")),
  structure(list(results = list(), status = "ZERO_RESULTS"), 
            .Names = c("results", "status"))
)

Do the actual flattening (and filter out address_components and types that are a bit trickier and of no interest to you):

flatten_googleway <- function(df) {
  res <- jsonlite::flatten(df)
  res[, !names(res) %in% c("address_components", "types")]
}

Prepare the template data frame we'll use for "missing" results. And apply it to those:

template_res <- flatten_googleway(first100geocode[[1]]$results)[FALSE, ]
do.call(rbind, lapply(first100geocode, function(x) {
  if (length(x$results) == 0) template_res[1, ] else flatten_googleway(x$results)
}))
#                                      formatted_address                    place_id
# 1  óë. Ãîãîëÿ, 11À, Çåëåíîãðàä, Ìîñêâà, Ðîññèÿ, 124575 ChIJzXSgUeQUtUYREIohzQOG--A
# NA                                                <NA>                        <NA>
#    geometry.location_type geometry.location.lat geometry.location.lng
# 1                 ROOFTOP              55.98757              37.17152
# NA                   <NA>                    NA                    NA
#    geometry.viewport.northeast.lat geometry.viewport.northeast.lng
# 1                         55.98892                        37.17287
# NA                              NA                              NA
#    geometry.viewport.southwest.lat geometry.viewport.southwest.lng
# 1                         55.98622                        37.17017
# NA                              NA                              NA

Strangely, this works for the toy example I provided with two lists, but when used on the list as a whole returns the error `Error in rbind(deparse.level, ...). The wrinkle appears to be that some of the "non-missing" $results list are length 5, and some are length 6.
Yep, unfortunately some result lists contain an additional attribute, "partial_match". However, I was able to fix by selecting only the elements I want, and not the ones I do not with the flatten function: flatten_googleway <- function(df) { res <- flatten(df) res[, names(res) %in% c("geometry.location_type", "geometry.location.lat", "geometry.location.lng", "formatted_address")] } I appreciate your help, wouldn't have known how to flatten the data without you!

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