# What are the advantages of financial modelling in R? [closed]

Recently I've found out that quantitative department in my company uses mostly R software for modeling in general.

What is the advantage of modeling financial data in R instead of Excel, or some statistical packages like SPSS and SAS? (Apart from the obvious merit that it is an open source software.)

## closed as primarily opinion-based by Dirk Eddelbuettel, Joshua Ulrich, olaker♦Oct 5 '15 at 0:31

Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.

It is very hard to answer this quiz as people might be good at different at tools. For example, if you are good at VBA, then you can achieve the same effect compared to R in most cases. The following parts are the reasons why I prefer to R based on my own situation.

1. 'package'. This is the most obvious strength of R over Excel in terms of convenience. You may only need one line code to solve a problem which is impossible in Excel;
2. Speed. Once I tried to select some data with 200,000 items with VBA, it takes more than half day, may be I do not have an efficient algorithm. However, it only takes less than half hour by calling C++ script in R.
3. I am not sure if this could be a strength as I never tried but just read it from some materials that it is very convenient to integrate R and broker with some packages.

• Yes, packages are great help. Calling C++ scripts in R is something new, never used it before. – Ascorpio Oct 4 '15 at 17:22

Some advantages of R over Excel:

1. R is a scripting language, which allows to record a data manipulation script once and reuse it multiple times.
2. R, as a [scripting] programming language is much more flexible than very limited Excel's GUI. In fact, R has become a de facto statistical programming environment, which delivers most recent statistical techniques.
3. R can handle much bigger data sets, even out of memory with the help of extra packages.
4. R is fast with the help of data.table, and fast and very flexible with the help of dplyr
5. Community, literature, and existing knowledge base on internet/stackoverlflow.
6. R has a superb quality, easy to learn graphing libraries like ggplot2 and many special case others