Loading an existing .RData file into an R program - r

I have a .RData file. I want to do some operations on the dataframe that this file contains. Can I load this file on my R program and convert it into a dataframe? The only option I know currently is to convert the ..RData file to a csv and convert that csv into a data frame again. I am looking for a neater solution. I got this file from a friend of mine and I cannot produce the dataframe from scratch.

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R export a data frame into a .gsheet file

I'm currently exporting my DataFrame into a .csv file with the following command
write.csv(MyDataFrame,file="MyFile.csv")
and desire to convert it so that it exports directly into a .gsheet file format (Google Sheet).
How may I achieve this?

Converting RData to CSV file returns incorrect CSV file

I do not have any expertise on R and I have to convert RData files to CSV to analyze the data. I followed the following links to do this: Converting Rdata files to CSV and "filename.rdata" file Exploring and Converting to CSV. The second option seemed to be a simpler as I failed to understand the first one. This is what I have tried till now and the results along with it:
>ddata <- load("input_data.RData")
>print(ddata)
[1] "input_data"
> print(ddata[[1]])
[1] "input_data"
> write.csv(ddata,"test.csv")
From the first link I learnt that we can see the RData type and when I did str(ddata) I found out that it is a List of size 1. Hence, I checked to see if print(ddata[[1]]) would print anything apart from just "input_data". With the write.csv I was able to write it to a csv without any errors but it has just the following 2 lines inside the CSV file:
"","x"
"1","input_data"
Can you please help me understand what am I doing wrong and show a way to get all the details in a csv?
The object ddata contains the name of the object(s) that load() created. Try typing the command ls(). That should give you the names of the objects in your environment. One of them should be input_data. That is the object. If it is a data frame (str(input_data)), you can create the csv file with
write.csv(input_data, "test.csv")

Vemco Acoustic Telemetry Data (vrl files) in R

Does anyone know a good way to read .vrl files from Vemco acoustic telemetry receivers directly into r as an object. Converting .vrl files to .csv files in the program VUE prior to analyzing the data in r seems like a waste of time if there is a way to bring them in directly. My internet searches have not turned up anything that worked for me.
I figured out a way using glatos to convert all .vrl files to .csv and then reading the .csv files in and binding them.
glatos has to be installed from github.
Convert all .vrl files to .csv files using vrl2csv. The help page has info on finding the path for vueExePath
library(glatos)
vrl2csv(vrl = "VRLFileInput",outDir = "VRLFilesToCSV", vueExePath = "C:/Program Files (x86)/VEMCO/VUE")
The following will pull in all .csv files in the output folder from vrl2csv and rbind them together. I had to add the paste0 function to create the full file path for each .csv in the list.
library(data.table)
AllDetections <- do.call(rbind, lapply(paste0("VRLFilesToCSV/", list.files(path = "VRLFilesToCSV")), read.csv))

Writing a csv file that is too large R

I currently saved some data as a csv file on my computer. It has 581 rows, but when I try to open the saved file on my mac, the dataframe has been altered and the numbers app from which I am looking at my csv from says some data was deleted. Is there a way to fix this? Or is there a different type of file I can save my data as that would adjust for the number of rows?
This is how I am writing the csv. I'm trying to manually add my file to a github repo after it has been saved to my computer.
write.csv(coords, 'Top_50_Distances.csv', row.names = FALSE)

R and zipped files

I have about ~1000 tar.gz files (about 2 GB/file compressed) each containing bunch of large .tsv (tab separated) files e.g. 1.tsv, 2.tsv, 3.tsv, 4.tsv etc.
I want to work in R on a subset of the .tsv files (say 1.tsv, 2.tsv) without extracting the .tar.gz files, in order to save space/time.
I tried looking around but couldn't find a library or a routine to stream the tar.gz files through memory and extracting data from them on the fly. In other languages there are ways of doing this efficiently. I would be surprised if one couldn't do this in R
Does anyone know of a way to accomplish this in R? Any help is greatly appreciated! Note: Unzipping/untarring the file is not an option. I want to extract relevant fields and save them in a data.frame without extracting the file

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