Vectorization to extract and bind very nested data - r

Question

I have some very nested data. Within my list-column-dataframes, there are some pieces I need to put together and I've done so in a single instance to get my desired dataframe:
a <- df[[2]][["result"]]#data
b <- df[[2]][["result"]]#coords
desired_df <- cbind(a, b)
My original Large list has 171 elements, meaning I have 1:171 (3.3 GB) to go inside those square brackets and would ideally end up with 171 desired dataframes (which I would then bind all together).
I haven't needed to write a loop in 10 years, but I don't see a tidyverse way to deal with this. I also no longer know how to write loops. There are definitely some elements in there that are junk and will fail.

Answer 1

You haven't provided any sort of minimal example of the data.
I've condensed it to mean something like this
base_data <- data.frame(group = c("a", "b", "c"), var1 = c(3, 1, 2),
var2 = c( 2, 4, 8))
base_data2 = matrix(
c(1, 2, 3, 4, 5, 6, 7, 8, 9),
nrow = 3,
ncol = 3,
byrow = TRUE
)
rownames(base_data2) = c("d", "e", "f")
methods::setClass(
"weird_object",
slots = c(data = "data.frame", coords = "matrix"),
prototype = list(data = base_data, coords = base_data2)
)
df <- list(
list(
result = new("weird_object")
),list(
result = new("weird_object")
),list(
result = new("weird_object")
),list(
result = new("weird_object")
)
)
And if I had such a list with these objects, then I could do
df %>%
map(. %>% {
list(data = .$result#data,
cooords = .$result#coords)
}) %>%
enframe() %>%
unnest_wider(value)
But the selecting / hoisting function might fail, thus
one can wrap it in a purrr::possibly, and
choose a reasonable default:
df %>%
map(possibly(. %>% {
list(data = .$result#data,
cooords = .$result#coords)
},
otherwise = list(data = NA, coords = NA))) %>%
enframe() %>%
unnest_wider(value)
Hopefully, this could be a step forward.
Next step is probably something resembling this:
df %>%
map(. %>% {
list(data = .$result#data,
coords = .$result#coords)
}) %>%
enframe() %>%
unnest_wider(value) %>%
mutate(coords = coords %>% map(. %>% as_tibble(rownames = "rowid"))) %>%
unnest(cols = c(data, coords)) %>%
#' rotating the thing now
pivot_longer(cols = c(group, rowid),
names_to = "var_name",
values_to = "var") %>%
select(-var_name) %>%
pivot_longer(cols = c(var1, var2, V1, V2, V3),
names_to = "var_name") %>%
pivot_wider(names_from = var, values_from = value) %>%
identity()

Answer 2

If I understand your data structure, which I probably don't, you could do:
library(tidyverse)
# Create dummy data
df <- mtcars
df$mpg <- list(result = I(list('test')))
df$mpg$result <- list("#data" = I(list('your data')))
df <- df %>% select(mpg, cyl)
df1 <- df
df2 <- df
# Pull data you're interested in.
# The index is 1 here, instead of 2, because it's fake data and not your data.
# Assuming the # is not unique, and is just parsed from JSON or some other format.
dont_at_me <- function(x){
a <- x[[1]][["result"]][["#data"]]
a
}
# Get a list of all of your data.frames
all_dfs <- Filter(function(x) is(x, "data.frame"), mget(ls()))
# Vectorize
purrr::map(all_dfs, ~dont_at_me(.))