Reshaping a dataframe in R - r

Question

I need some help to re-design the output of a function that comes through an R package.
My scope is to reshape a dataframe called output_IMFData in a way that look very similar to the shape of output_imfr. The codes of a MWE reproducing these dataframes are:
library(imfr)
output_imfr <- imf_data(database_id="IFS", indicator="IAD_BP6_USD", country = "", start = 2010, end = 2014, freq = "A", return_raw =FALSE, print_url = T, times = 3)
and for output_IMFData
library(IMFData)
databaseID <- "IFS"
startdate <- "2010"
enddate <- "2014"
checkquery <- FALSE
queryfilter <- list(CL_FREA = "A", CL_AREA_IFS = "", CL_INDICATOR_IFS = "IAD_BP6_USD")
output_IMFData <- CompactDataMethod(databaseID, queryfilter, startdate, enddate,
checkquery)
the output from output_IMFData looks like this:
But, I want to redesign this dataframe to look like the output of output_imfr:
Sadly, I am not that advanced user and could not find something that can help me. My basic problem in converting the shape of output_IMFData to the shape of the second ``panel-data-looking" dataframework is that I don't know how to handle the Obs in output_IMFData in a way that cannot lose the "correspondence" with the reference code #REF-AREA in output_IMFData. That is, in column #REF-AREA there are codes of country names and the column in Obs has their respective time series data. This is very cumbersome way of working with panel data, and therefore I want to reshape that dataframe to the much nicer form of output_imfr dataframe.

Answer 1

The data of interest are stored in a list in the column Obs. Here is a dplyr solution to split the data, crack open the list, then stitch things back together.
longData <-
output_IMFData %>%
split(1:nrow(.)) %>%
lapply(function(x){
data.frame(
iso2c = x[["#REF_AREA"]]
, x$Obs
)
}) %>%
bind_rows()
head(longData)
gives:
iso2c X.TIME_PERIOD X.OBS_VALUE X.OBS_STATUS
1 FJ 2010 47.2107721901621 <NA>
2 FJ 2011 48.28347 <NA>
3 FJ 2012 51.0823499999999 <NA>
4 FJ 2013 157.015648875072 <NA>
5 FJ 2014 186.623232882226 <NA>
6 AW 2010 616.664804469274 <NA>

Answer 2

Here's another approach:
NewDataFrame <- data.frame(iso2c=character(),
year=numeric(),
IAD_BP6_USD=character(),
stringsAsFactors=FALSE)
newrow = 1
for(i in 1:nrow(output_IMFData)) { # for each row of your cludgy df
for(j in 1:length(output_IMFData$Obs[[i]]$`#TIME_PERIOD`)) { # for each year
NewDataFrame[newrow,'iso2c']<-output_IMFData[i, '#REF_AREA']
NewDataFrame[newrow,'year']<-output_IMFData$Obs[[i]]$`#TIME_PERIOD`[j]
NewDataFrame[newrow,'IAD_BP6_USD']<-output_IMFData$Obs[[i]]$`#OBS_VALUE`[j]
newrow<-newrow + 1 # increment down a row
}
}