And if so, how?
I use RStudio. I know I can fork a project in order to perform calculations over two copies of the same environment (as described here). Although, it doesn't fit my needs because the environment I'm currently using is very big, and I don't have enough RAM for duplicating it.
Therefore, I am wondering if there is some way in which I can open two (or more) consoles using the same one (in particular, I would be particularly interested on not having to replicate the very big data frames).
Is there a way in which I can use RStudio this way, or is there any other IDE or tool which supports it?
Thank you for your help.
EDIT:
I will explain what I'm trying to do: I'm developing some machine learning models based on a large dataset.
I load the dataset into a data frame.
Then I perform different treatments over the data in order to transform them into ML-friendly data.
I perform these two steps in one R script, and I end up with an environment loaded with a heavy data frame, libraries and some other objects.
Then I'm using this dataset to feed several ML models: those models are of different classes, and within each class I'm trying several models with different parameters.
I have one R script for each class of models, and I would like to run and score each class parallel. Each model within each class will run sequentially.
The key here is: I know I can use different projects in order to do this, but that would suppose having to load several times the same environment, and for me that is problematic because it would mean having to load to RAM several times the same big data frame. Therefore I would like to know if there is a way to have several R scripts run in parallel while using the same environment.
Then I will use another script to rank all the models.
I would like to define a slightly more general version of a complex number in R. This should be a vector that has more than one component, accessible in a similar manner to using Re() and Im() for complex numbers. Is there a way to do this using S3/S4 classes?
I have read through the OO field guide among other resources, but most solutions seem focused around the use of lists as fundamental building objects. However, I need vectors for use in data.frames and matrices. I was hoping to use complex numbers as a template, but they seem to be implemented largely in C. At this point, I don't even know where to start.
We are building an r package to assess income inequality in survey data https://github.com/DjalmaPessoa/convey
I have about ten different public use microdata sets here - https://github.com/ajdamico/asdfree - that I would like to provide usage examples with dataset-specific vignettes. For the most part, the vignettes will be exactly the same, I just want a distinct vignette available for the different surveys. I only want a handful of things to vary across the vignette, like:
title/vignette name, survey load script, survey design object name, income variable within dataset
The package we are writing has about a dozen different functions that I would like to demonstrate in a (seemingly for the user) dataset-specific manner across ten different public use files-- but I don't want to write ten nearly-identical vignettes
Is there a reasonable way to write a single document that will then auto-generate specific vignettes? I am thinking about something like a Microsoft mail merge, but am not sure what the equivalent would be here. Thanks!
I would like to find out what the "R way" would be to let users the following with R: I have a file that can contain the data of one or more analysis runs of some other software. My R package should provide additional ways to calculate statistics or produce plots for those analyses. So the first step a user would have to do, is read in the file (with one or more analyses), then select the analysis and work with it.
An analysis is uniquely identified by two names (an analysis name and an analysis type where the type should later correspond to an S3 class).
What I am not sure about is how to best represent the collection of analyses that is returned when reading in the file: should this be an object or simply a list of lists (since there are two ids for identifying an analysis, the first list could be indexed by name and the second by type). Using a list feels very low-level and clumsy though.
If the read function returns a special kind of container object what would be a good method to access one of the contained objects based on name and type?
There are probably many ways how to do this, but since I only started to work with R in a way where others should eventually use my code, I am not sure how to best follow existing R-conventions for how to design this.
Much has been written here about developing a workflow in R for statistical projects. The most popular workflow seems to be Josh Reich's LCFD model. With a main.R containing code:
source('load.R')
source('clean.R')
source('func.R')
source('do.R')
so that a single source('main.R') runs the entire project.
Q: Is there a reason to prefer this workflow to one in which the line-by-line interpretive work done in load.R, clean.R, and do.R is replaced by functions which are called by main.R?
I can't find the link now, but I had read somewhere on SO that when programming in R one must get over their desire to write everything in terms of function calls---that R was MEANT to be written is this line-by-line interpretive form.
Q: Really? Why?
I've been frustrated with the LCFD approach and am going to probably write everything in terms of function calls. But before doing this, I'd like to hear from the good folks of SO as to whether this is a good idea or not.
EDIT: The project I'm working on right now is to (1) read in a set of financial data, (2) clean it (quite involved), (3) Estimate some quantity associated with the data using my estimator (4) Estimate that same quantity using traditional estimators (5) Report results. My programs should be written in such a way that it's a cinch to do the work (1) for different empirical data sets, (2) for simulation data, or (3) using different estimators. ALSO, it should follow literate programming and reproducible research guidelines so that it's simple for a newcomer to the code to run the program, understand what's going on, and how to tweak it.
I think that any temporary stuff created in source'd files won't get cleaned up. If I do:
x=matrix(runif(big^2),big,big)
z=sum(x)
and source that as a file, x hangs around although I don't need it. But if I do:
ff=function(big){
x = matrix(runif(big^2),big,big)
z=sum(x)
return(z)
}
and instead of source, do z=ff(big) in my script, the x matrix goes out of scope and so gets cleaned up.
Functions enable neat little re-usable encapsulations and don't pollute outside themselves. In general, they don't have side-effects. Your line-by-line scripts could be using global variables and names tied to the data set in current use, which makes them unre-usable.
I sometimes work line-by-line, but as soon as I get more than about five lines I see that what I have really needs making into a proper reusable function, and more often than not I do end up re-using it.
I don't think there is a single answer. The best thing to do is keep the relative merits in mind and then pick an approach for that situation.
1) functions. The advantage of not using functions is that all your variables are left in the workspace and you can examine them at the end. That may help you figure out what is going on if you have problems.
On the other hand, the advantage of well designed functions is that you can unit test them. That is you can test them apart from the rest of the code making them easier to test. Also when you use a function, modulo certain lower level constructs, you know that the results of one function won't affect the others unless they are passed out and this may limit the damage that one function's erroneous processing can do to another's. You can use the debug facility in R to debug your functions and being able to single step through them is an advantage.
2) LCFD. Regarding whether you should use a decomposition of load/clean/func/do regardless of whether its done via source or functions is a second question. The problem with this decomposition regardless of whether its done via source or functions is that you need to run one just to be able to test out the next so you can't really test them independently. From that viewpoint its not the ideal structure.
On the other hand, it does have the advantage that you may be able to replace the load step independently of the other steps if you want to try it on different data and can replace the other steps independently of the load and clean steps if you want to try different processing.
3) No. of Files There may be a third question implicit in what you are asking whether everything should be in one or multiple source files. The advantage of putting things in different source files is that you don't have to look at irrelevant items. In particular if you have routines that are not being used or not relevant to the current function you are looking at they won't interrupt the flow since you can arrange that they are in other files.
On the other hand, there may be an advantage in putting everything in one file from the viewpoint of (a) deployment, i.e. you can just send someone that single file, and (b) editing convenience as you can put the entire program in a single editor session which, for example, facilitates searching since you can search the entire program using the editor's functions as you don't have to determine which file a routine is in. Also successive undo commands will allow you to move backward across all units of your program and a single save will save the current state of all modules since there is only one. (c) speed, i.e. if you are working over a slow network it may be faster to keep a single file in your local machine and then just write it out occasionally rather than having to go back and forth to the slow remote.
Note: One other thing to think about is that using packages may be superior for your needs relative to sourcing files in the first place.
No one has mentioned an important consideration when writing functions: there's not much point in writing them unless you're repeating some action again and again. In some parts of an analysis, you'll being doing one-off operations, so there's not much point in writing a function for them. If you have to repeat something more than a few times, it's worth investing the time and effort to write a re-usable function.
Workflow:
I use something very similar:
Base.r: pulls primary data, calls on other files (items 2 through 5)
Functions.r: loads functions
Plot Options.r: loads a number of general plot options I use frequently
Lists.r: loads lists, I have a lot of them because company names, statements and the like change over time
Recodes.r: most of the work is done in this file, essentially it's data cleaning and sorting
No analysis has been done up to this point. This is just for data cleaning and sorting.
At the end of Recodes.r I save the environment to be reloaded into my actual analysis.
save(list=ls(), file="Cleaned.Rdata")
With the cleaning done, functions and plot options ready, I start getting into my analysis. Again, I continue to break it up into smaller files that are focused into topics or themes, like: demographics, client requests, correlations, correspondence analysis, plots, ect. I almost always run the first 5 automatically to get my environment set up and then I run the others on a line by line basis to ensure accuracy and explore.
At the beginning of every file I load the cleaned data environment and prosper.
load("Cleaned.Rdata")
Object Nomenclature:
I don't use lists, but I do use a nomenclature for my objects.
df.YYYY # Data for a certain year
demo.describe.YYYY ## Demographic data for a certain year
po.describe ## Plot option
list.describe.YYYY ## lists
f.describe ## Functions
Using a friendly mnemonic to replace "describe" in the above.
Commenting
I've been trying to get myself into the habit of using comment(x) which I've found incredibly useful. Comments in the code are helpful but oftentimes not enough.
Cleaning Up
Again, here, I always try to use the same object(s) for easy cleanup. tmp, tmp1, tmp2, tmp3 for example and ensuring to remove them at the end.
Functions
There has been some commentary in other posts about only writing a function for something if you're going to use it more than once. I'd like to adjust this to say, if you think there's a possibility that you may EVER use it again, you should throw it into a function. I can't even count the number of times I wished I wrote a function for a process I created on a line by line basis.
Also, BEFORE I change a function, I throw it into a file called Deprecated Functions.r, again, protecting against the "how the hell did I do that" effect.
I often divide up my code similarly to this (though I usually put Load and Clean in one file), but I never just source all the files to run the entire project; to me that defeats the purpose of dividing them up.
Like the comment from Sharpie, I think your workflow should depends a lot on the kind of work you're doing. I do mostly exploratory work, and in that context, keeping the data input (load and clean) separate from the analysis (functions and do), means that I don't have to reload and reclean when I come back the next day; I can instead save the data set after cleaning and then import it again.
I have little experience doing repetitive munging of daily data sets, but I imagine that I would find a different workflow helpful; as Hadley answers, if you're only doing something once (as I am when I load/clean my data), it may not be helpful to write a function. But if you're doing it over and over again (as it seems you would be) it might be much more helpful.
In short, I've found dividing up the code helpful for exploratory analyses, but would probably do something different for repetitive analyses, just like you're thinking about.
I've been pondering workflow tradeoffs for some time.
Here is what I do for any project involving data analysis:
Load and Clean: Create clean versions of the raw datasets for the project, as if I was building a local relational database. Thus, I structure the tables in 3n normal form where possible. I perform basic munging but I do not merge or filter tables at this step; again, I'm simply creating a normalized database for a given project. I put this step in its own file and I will save the objects to disk at the end using save.
Functions: I create a function script with functions for data filtering, merging and aggregation tasks. This is the most intellectually challenging part of the workflow as I'm forced to think about how to create proper abstractions so that the functions are reusable. The functions need to generalize so that I can flexibly merge and aggregate data from the load and clean step. As in the LCFD model, this script has no side effects as it only loads function definitions.
Function Tests: I create a separate script to test and optimize the performance of the functions defined in step 2. I clearly define what the output from the functions should be, so this step serves as a kind of documentation (think unit testing).
Main: I load the objects saved in step 1. If the tables are too big to fit in RAM, I can filter the tables with a SQL query, keeping with the database thinking. I then filter, merge and aggregate the tables by calling the functions defined in step 2. The tables are passed as arguments to the functions I defined. The output of the functions are data structures in a form suitable for plotting, modeling and analysis. Obviously, I may have a few extra line by line steps where it makes little sense to create a new function.
This workflow allows me to do lightning fast exploration at the Main.R step. This is because I have built clear, generalizable, and optimized functions. The main difference from the LCFD model is that I do not preform line-by-line filtering, merging or aggregating; I assume that I may want to filter, merge, or aggregate the data in different ways as part of exploration. Additionally, I don't want to pollute my global environment with lengthy line-by-line script; as Spacedman points out, functions help with this.