process_list

library(tidyverse)
library(purrr)


# Make a list that looks a bit like yours
# It has two entries
# Each entry has two "normal fields (application, user) - which have different lengths
# And a third field "custom" which contains the weird, staggered dataframe

data <- list()
df <- data.frame(a=c(1,NA,NA),
                 b=c(NA,"hello",NA),
                 c=c(NA,NA,14.6))

data[[1]] <- list(application = list("AppName"),
                 user = list(c("somebody","sometime")),
                 custom = list(dimensions = df))
data[[2]] <-  list(application = list(c("something","something else","and again")),
                 user = list("else"),
                 custom = list(dimensions = df))

# Look at the structure of data to see what it looks like
str(data)


# This is a utility function that turns that weird 23x23 data frame
# into a single row with containing all the data
# with no NA values
proc <- function(x){
  frst <- function(x){first(na.omit(x))}
  y <- x %>% summarise_all(lst(frst))
  names(y)<- names(x)
  y
}

# This is the main function
# This takes one list entry and looks at all the fields
# If it's a "normal" field, it adds it as a column to the output
# If it is that nested data frame, then it takes it apart using the 
# "proc" function above and then adds all the columns to the output

proc_row <- function(x){
  out = list() # List to contain our output
  for(i in 1:length(x)){ # For each field in this entry
    y=x[i] # Get that field
    this_name <-  names(y) # Get the name of that field
    this_data <-  y[[1]] # Get the contents of that field
    if(this_name != "custom"){  # Is this the weird field with the data frame?
      out[this_name]<-this_data # If not - just add the data as a column
    }else{  # But if it IS the data frame
      this_df <- this_data$dimensions # Get the contents
      this_df <- as.list(proc(this_df))  # Take it apart so it's just ONE row and not 23
      for(j in 1:length(this_df)){  # For each column
        this_name2 <-   names(this_df[j])
        this_data2 <-  this_df[[j]]
        out[this_name2]<- this_data2 # Add it to our output
      }
    }
  }
  as_tibble(out)  # Turn our output list into a little data frame
}


# And here we run it all - apply that "proc_row" function over the input list
final <- data %>% map_df(proc_row)