Predict memory usage in R

Question

I have downloaded a huge file from the UCI Machine learning Dataset library. (~300mb).

Is there a way to predict the memory required to load the dataset, before loading it into R memory?

Googled a lot, but everywhere all I could find is how to calculate memory with R-profiler and several other packages, but after loading the objects into R.

Answer 1

based on "R programming" coursera course, U can calculate the proximate memory usage using number of rows and columns within the data" U can get that info from the codebox/meta file"

memory required = no. of column * no. of rows * 8 bytes/numeric

so for example if you have 1,500,00 rows and 120 column you will need more than 1.34 GB of spare memory required

U also can apply the same approach on other types of data with attention to number of bytes used to store different data types.