Managing Multi-Country Financial Data in R

# Need Guidance on Analyzing Financial Data Across Multiple CSV Files in R


# Need Guidance on Analyzing Financial Data Across Multiple CSV Files in R












I'm new to R but experienced in Stata. I'm working on a research project involving 39 countries, each with 100+ years of daily financial data (date, high, open, close, return), along with USD exchange rates in separate CSV files. I need to create plots, summary statistics, and perform bootstrapping. My main concerns are:

1.Data Management: Should I merge all data into one file or work with separate files?

2.Common R Codes: What are common and easy-to-use R codes for summary statistics?

3.Data Cleaning: How do I handle NA values and clean the data?

4.Range Selection: How can I select specific ranges of cells?

Any help on these topics would be greatly appreciated, especially for someone transitioning from Stata to R.

## Answer by blizzard16 (score 3)

https://quant.stackexchange.com/a/76452

I would suggest that you familiarize yourself with the concept of dataframe. I haven't used Stata yet, but dataframes are all over R and Python's Pandas package which is the main tool of data analytics on Python. But in R, the most useful way to handle dataframes is to learn to use package called Tidyverse. Tidyverse is a really extensive library as it consists of many "smaller" libraries out of which you could be using dplyr and tidyr when manipulating dataframes.

Those dataframes allow combining multiple csv files into one "file", that is the dataframe in the virtaul environment, without actually having the need to export that file as you can just stick to the dataframe.

If you need to select rows, columns, filter per multiple criteria, create rapidly new columns by combining other columns, transform panel data to a long-format table etc., the Tidyverse's dplyr and tidyr already contain readily available functions for these and many more tasks. Without even saying it, you can also do the bootstrapping with these Tidyverse's functions along some other tools and functions made by you, depending what you exactly aiming to do.

Also handling NAs is very trivial with Tidyverse: you can drop rows containing even one NA, you can drop rows that have NA on a given row etc. If you need more sophisticated NA handling from statistics point of view (Inverse Mill's ratios etc.) you really need to consider what you exactly want to model in order to select the correct packages and regression tools. In any case, R packages have really good documentation and in most cases you can find the right tools, or at least some suggestions with the documentation, straight from Google searh by giving some info what you are about to do, but if not, I would resort to Stats Stack Exchange that is the Cross Validated, or see Stack Overflow, or just scroll thourgh this Quant Stack Exchange and you should be fine.

Summary statistics can be calculated easily with Tidyverse as well, and you just calculate these values into dataframe once again, but if you want to export these results or regression tables for further examination, I would suggest you take a look at package called Stargazer. Stargazer actually allows you to calculate those summary statistics you see in journal articles (min, max, quantiles, median, mean) with maybe one or two lines of code, given that the dataframe contains the correct rawdata. By raw data I mean, a data frame that you would use for regressions. And if you want to get even fancier with the output, check Starpolishr from GitHub. Starpolishr is an extension for Stargazer. I use Stargazer to calculate summary statistics and to export regression tables for which I have specified the models and calculated the results earlier.

I really want to keep this message at the top level without going into too much detail since you didn't specify your needs too much nor I know what you are already capable of. Feel free to make further questions, I am happy to help.