I have a file run_experiment.rmd which performs an analysis on data using a bunch of .r scripts in another folder.
Every analysis is saved into its own timestamped folder. I save the outputs of the analysis, the inputs used, and if possible I would also like to save the code used to generate the analysis (including the contents of both the .rmd file and the .r files).
The reason for this is because if I make changes to the way my analyses are run, then if I re-run the analysis using the new updated file, I will get different results. If possible, I would like to keep legacy versions of the code so that I can always, if need be, re-run the original analysis.
Have you considered using a git repository to commit your code and output each time you update/run it? I think this is the optimal solution for what you are describing. Each commit would have a timestamp associated with it for you to rollback to a previous version when needed.
The best way to do this is to put all of those scripts into an R package, and in your Rmd file, print sessionInfo() to record the package version used.
You should change the version number of the package each time you make a non-trivial change to it (or even better, with every change).
Then when you want to reproduce the analysis, you use the sessionInfo() listing to work out which version of R and of the packages to install, and you'll get the same environment.
There are packages to help with this (pak and renv, maybe others), but I haven't used them, so I can't give details or recommendations.
For running an Rnw file in RStudio, one can compile or run all. Compiling does not see the variables in the current environment, and the current environment does not see the variables created while compiling. I would like to see how the output would look when I compile, and I debug the code using the environment. This requires me to compile and run, which performs the same calculations twice, which is very impractical for large projects. Is there a way to compile and have the output be seen in the environment?
When you knit a document, the work happens in a different R session, which is why you can't examine the results in the current session.
But you have a lot of choices besides run all. Take a look at the Run button: it allows you to run chunks one at a time, or run all previous chunks, etc.
If some of your chunks take too long to run, then you should consider organizing your work differently. Put the long computations into their own script, and save the results of that script using save(). Run it once, then spend time editing the display of those results in multiple runs in the main .Rnw document.
Finally, if you really want to see variables at the end of a run of your vignette, you can add save.image(file = 'vignette.RData') at the end, and in your interactive session, use load('vignette.RData') to load the values for examination. This won't necessarily give you an accurate view of the state of things at the end of the run, because it will load the values in addition to anything you've already got in your workspace, it won't load option settings or attach packages, but it might be enough for debugging.
Does anyone have any wisdom on workflows for data analysis related to custom report writing? The use-case is basically this:
Client commissions a report that uses data analysis, e.g. a population estimate and related maps for a water district.
The analyst downloads some data, munges the data and saves the result (e.g. adding a column for population per unit, or subsetting the data based on district boundaries).
The analyst analyzes the data created in (2), gets close to her goal, but sees that needs more data and so goes back to (1).
Rinse repeat until the tables and graphics meet QA/QC and satisfy the client.
Write report incorporating tables and graphics.
Next year, the happy client comes back and wants an update. This should be as simple as updating the upstream data by a new download (e.g. get the building permits from the last year), and pressing a "RECALCULATE" button, unless specifications change.
At the moment, I just start a directory and ad-hoc it the best I can. I would like a more systematic approach, so I am hoping someone has figured this out... I use a mix of spreadsheets, SQL, ARCGIS, R, and Unix tools.
Thanks!
PS:
Below is a basic Makefile that checks for dependencies on various intermediate datasets (w/ .RData suffix) and scripts (.R suffix). Make uses timestamps to check dependencies, so if you touch ss07por.csv, it will see that this file is newer than all the files / targets that depend on it, and execute the given scripts in order to update them accordingly. This is still a work in progress, including a step for putting into SQL database, and a step for a templating language like sweave. Note that Make relies on tabs in its syntax, so read the manual before cutting and pasting. Enjoy and give feedback!
http://www.gnu.org/software/make/manual/html_node/index.html#Top
R=/home/wsprague/R-2.9.2/bin/R
persondata.RData : ImportData.R ../../DATA/ss07por.csv Functions.R
$R --slave -f ImportData.R
persondata.Munged.RData : MungeData.R persondata.RData Functions.R
$R --slave -f MungeData.R
report.txt: TabulateAndGraph.R persondata.Munged.RData Functions.R
$R --slave -f TabulateAndGraph.R > report.txt
I generally break my projects into 4 pieces:
load.R
clean.R
func.R
do.R
load.R: Takes care of loading in all the data required. Typically this is a short file, reading in data from files, URLs and/or ODBC. Depending on the project at this point I'll either write out the workspace using save() or just keep things in memory for the next step.
clean.R: This is where all the ugly stuff lives - taking care of missing values, merging data frames, handling outliers.
func.R: Contains all of the functions needed to perform the actual analysis. source()'ing this file should have no side effects other than loading up the function definitions. This means that you can modify this file and reload it without having to go back an repeat steps 1 & 2 which can take a long time to run for large data sets.
do.R: Calls the functions defined in func.R to perform the analysis and produce charts and tables.
The main motivation for this set up is for working with large data whereby you don't want to have to reload the data each time you make a change to a subsequent step. Also, keeping my code compartmentalized like this means I can come back to a long forgotten project and quickly read load.R and work out what data I need to update, and then look at do.R to work out what analysis was performed.
If you'd like to see some examples, I have a few small (and not so small) data cleaning and analysis projects available online. In most, you'll find a script to download the data, one to clean it up, and a few to do exploration and analysis:
Baby names from the social security administration
30+ years of fuel economy data from the EPI
A big collection of data about the housing crisis
Movie ratings from the IMDB
House sale data in the Bay Area
Recently I have started numbering the scripts, so it's completely obvious in which order they should be run. (If I'm feeling really fancy I'll sometimes make it so that the exploration script will call the cleaning script which in turn calls the download script, each doing the minimal work necessary - usually by checking for the presence of output files with file.exists. However, most times this seems like overkill).
I use git for all my projects (a source code management system) so its easy to collaborate with others, see what is changing and easily roll back to previous versions.
If I do a formal report, I usually keep R and latex separate, but I always make sure that I can source my R code to produce all the code and output that I need for the report. For the sorts of reports that I do, I find this easier and cleaner than working with latex.
I agree with the other responders: Sweave is excellent for report writing with R. And rebuilding the report with updated results is as simple as re-calling the Sweave function. It's completely self-contained, including all the analysis, data, etc. And you can version control the whole file.
I use the StatET plugin for Eclipse for developing the reports, and Sweave is integrated (Eclipse recognizes latex formating, etc). On Windows, it's easy to use MikTEX.
I would also add, that you can create beautiful reports with Beamer. Creating a normal report is just as simple. I included an example below that pulls data from Yahoo! and creates a chart and a table (using quantmod). You can build this report like so:
Sweave(file = "test.Rnw")
Here's the Beamer document itself:
%
\documentclass[compress]{beamer}
\usepackage{Sweave}
\usetheme{PaloAlto}
\begin{document}
\title{test report}
\author{john doe}
\date{September 3, 2009}
\maketitle
\begin{frame}[fragile]\frametitle{Page 1: chart}
<<echo=FALSE,fig=TRUE,height=4, width=7>>=
library(quantmod)
getSymbols("PFE", from="2009-06-01")
chartSeries(PFE)
#
\end{frame}
\begin{frame}[fragile]\frametitle{Page 2: table}
<<echo=FALSE,results=tex>>=
library(xtable)
xtable(PFE[1:10,1:4], caption = "PFE")
#
\end{frame}
\end{document}
I just wanted to add, in case anyone missed it, that there's a great post on the learnr blog about creating repetitive reports with Jeffrey Horner's brew package. Matt and Kevin both mentioned brew above. I haven't actually used it much myself.
The entries follows a nice workflow, so it's well worth a read:
Prepare the data.
Prepare the report template.
Produce the report.
Actually producing the report once the first two steps are complete is very simple:
library(tools)
library(brew)
brew("population.brew", "population.tex")
texi2dvi("population.tex", pdf = TRUE)
For creating custom reports, I've found it useful to incorporate many of the existing tips suggested here.
Generating reports:
A good strategy for generating reports involves the combination of Sweave, make, and R.
Editor:
Good editors for preparing Sweave documents include:
StatET and Eclipse
Emacs and ESS
Vim and Vim-R
R Studio
Code organisation:
In terms of code organisation, I find two strategies useful:
Read up about analysis workflow (e.g., ProjectTemplate,
Josh Reich's ideas, my own presentation on R workflow
Slides
and Video )
Study example reports and discern the workflow
Hadley Wickham's examples
My examples on github
Examples of reproducible research listed on Cross Validated
I use Sweave for the report-producing side of this, but I've also been hearing about the brew package - though I haven't yet looked into it.
Essentially, I have a number of surveys for which I produce summary statistics. Same surveys, same reports every time. I built a Sweave template for the reports (which takes a bit of work). But once the work is done, I have a separate R script that lets me point out the new data. I press "Go", Sweave dumps out a few score .tex files, and I run a little Python script to pdflatex them all. My predecessor spent ~6 weeks each year on these reports; I spend about 3 days (mostly on cleaning data; escape characters are hazardous).
It's very possible that there are better approaches now, but if you do decide to go this route, let me know - I've been meaning to put up some of my Sweave hacks, and that would be a good kick in the pants to do so.
I'm going to suggest something in a different sort of direction from the other submitters, based on the fact that you asked specifically about project workflow, rather than tools. Assuming you're relatively happy with your document-production model, it sounds like your challenges really may be centered more around issues of version tracking, asset management, and review/publishing process.
If that sounds correct, I would suggest looking into an integrated ticketing/source management/documentation tool like Redmine. Keeping related project artifacts such as pending tasks, discussion threads, and versioned data/code files together can be a great help even for projects well outside the traditional "programming" bailiwick.
Agreed that Sweave is the way to go, with xtable for generating LaTeX tables. Although I haven't spent too much time working with them, the recently released tikzDevice package looks really promising, particularly when coupled with pgfSweave (which, as far as I know is only available on rforge.net at this time -- there is a link to r-forge from there, but it's not responding for me at the moment).
Between the two, you'll get consistent formatting between text and figures (fonts, etc.). With brew, these might constitute the holy grail of report generation.
At a more "meta" level, you might be interested in the CRISP-DM process model.
"make" is great because (1) you can use it for all your work in any language (unlike, say, Sweave and Brew), (2) it is very powerful (enough to build all the software on your machine), and (3) it avoids repeating work. This last point is important to me because a lot of the work is slow; when I latex a file, I like to see the result in a few seconds, not the hour it would take to recreate the figures.
I use project templates along with R studio, currently mine contains the following folders:
info : pdfs, powerpoints, docs... which won't be used by any script
data input : data that will be used by my scripts but not generated by them
data output : data generated by my scripts for further use but not as a proper report.
reports : Only files that will actually be shown to someone else
R : All R scripts
SAS : Because I sometimes have to :'(
I wrote custom functions so I can call smart_save(x,y) or smart_load(x) to save or load RDS files to and from the data output folder (files named with variable names) so I'm not bothered by paths during my analysis.
A custom function new_project creates a numbered project folder, copies all the files from the template, renames the RProj file and edits the setwd calls, and set working directory to new project.
All R scripts are in the R folder, structured as follow :
00_main.R
setwd
calls scripts 1 to 5
00_functions.R
All functions and only functions go there, if there's too many I'll separate it into several, all named like 00_functions_something.R, in particular if I plan to make a package out of some of them I'll put them apart
00_explore.R
a bunch of script chunks where i'm testing things or exploring my data
It's the only file where i'm allowed to be messy.
01_initialize.R
Prefilled with a call to a more general initialize_general.R script from my template folder which loads the packages and data I always use and don't mind having in my workspace
loads 00_functions.R (prefilled)
loads additional libraries
set global variables
02_load data.R
loads csv/txt xlsx RDS, there's a prefilled commented line for every type of file
displays which files hava been created in the workspace
03_pull data from DB.R
Uses dbplyr to fetch filtered and grouped tables from the DB
some prefilled commented lines to set up connections and fetch.
Keep client side operations to bare minimum
No server side operations outside of this script
Displays which files have been created in the workspace
Saves these variables so they can be reloaded faster
Once it's been done once I switch off a query_db boolean and the data will reloaded from RDS next time.
It can happen that I have to refeed data to DBs, If so I'll create additional steps.
04_Build.R
Data wrangling, all the fun dplyr / tidyr stuff goes there
displays which files have been created in the workspace
save these variables
Once it's been done once I switch off a build boolean and the data will reloaded from RDS next time.
05_Analyse.R
Summarize, model...
report excel and csv files
95_build ppt.R
template for powerpoint report using officer
96_prepare markdown.R
setwd
load data
set markdown parameters if needed
render
97_prepare shiny.R
setwd
load data
set shiny parameters if needed
runApp
98_Markdown report.Rmd
A report template
99_Shiny report.Rmd
An app template
For writing a quick preliminary report or email to a colleague, I find that it can be very efficient to copy-and-paste plots into MS Word or an email or wiki page -- often best is a bitmapped screenshot (e.g. on mac, Apple-Shift-(Ctrl)-4). I think this is an underrated technique.
For a more final report, writing R functions to easily regenerate all the plots (as files) is very important. It does take more time to code this up.
On the larger workflow issues, I like Hadley's answer on enumerating the code/data files for the cleaning and analysis flow. All of my data analysis projects have a similar structure.
I'll add my voice to sweave. For complicated, multi-step analysis you can use a makefile to specify the different parts. Can prevent having to repeat the whole analysis if just one part has changed.
I also do what Josh Reich does, only I do that creating my personal R-packages, as it helps me structure my code and data, and it is also quite easy to share those with others.
create my package
load
clean
functions
do
creating my package: devtools::create('package_name')
load and clean: I create scripts in the data-raw/ subfolder of my package for loading, cleaning, and storing the resulting data objects in the package using devtools::use_data(object_name). Then I compile the package.
From now on, calling library(package_name) makes these data available (and they are not loaded until necessary).
functions: I put the functions for my analyses into the R/ subfolder of my package, and export only those that need to be called from outside (and not the helper functions, which can remain invisible).
do: I create a script that uses the data and functions stored in my package.
(If the analyses only need to be done once, I can put this script as well into the data-raw/ subfolder, run it, and store the results in the package to make it easily accessible.)
A very simple question:
I am writing and running my R scripts using a text editor to make them reproducible, as has been suggested by several members of SO.
This approach is working very well for me, but I sometimes have to perform expensive operations (e.g. read.csv or reshape on 2M-row databases) that I'd better cache in the R environment rather than re-run every time I run the script (which is usually many times as I progress and test the new lines of code).
Is there a way to cache what a script does up to a certain point so every time I am only running the incremental lines of code (just as I would do by running R interactively)?
Thanks.
## load the file from disk only if it
## hasn't already been read into a variable
if(!(exists("mytable")){
mytable=read.csv(...)
}
Edit: fixed typo - thanks Dirk.
Some simple ways are doable with some combinations of
exists("foo") to test if a variable exists, else re-load or re-compute
file.info("foo.Rd")$ctime which you can compare to Sys.time() and see if it is newer than a given amount of time you can load, else recompute.
There are also caching packages on CRAN that may be useful.
After you do something you discover to be costly, save the results of that costly step in an R data file.
For example, if you loaded a csv into a data frame called myVeryLargeDataFrame and then created summary stats from that data frame into a df called VLDFSummary then you could do this:
save(c(myVeryLargeDataFrame, VLDFSummary),
file="~/myProject/cachedData/VLDF.RData",
compress="bzip2")
The compress option there is optional and to be used if you want to compress the file being written to disk. See ?save for more details.
After you save the RData file you can comment out the slow data loading and summary steps as well as the save step and simply load the data like this:
load("~/myProject/cachedData/VLDF.RData")
This answer is not editor dependent. It works the same for Emacs, TextMate, etc. You can save to any location on your computer. I recommend keeping the slow code in your R script file, however, so you can always know where your RData file came from and be able to recreate it from the source data if needed.
(Belated answer, but I began using SO a year after this question was posted.)
This is the basic idea behind memoization (or memoisation). I've got a long list of suggestions, especially the memoise and R.cache packages, in this query.
You could also take advantage of checkpointing, which is also addressed as part of that same list.
I think your use case mirrors my second: "memoization of monstrous calculations". :)
Another trick I use is to do a lot of memory mapped files, which I use a lot of, to store data. The nice thing about this is that multiple R instances can access shared data, so I can have a lot of instances cracking at the same problem.
I want to do this too when I'm using Sweave. I'd suggest putting all of your expensive functions (loading and reshaping data) at the beginning of your code. Run that code, then save the workspace. Then, comment out the expensive functions, and load the workspace file with load(). This is, of course, riskier if you make unwanted changes to the workspace file, but in that event, you still have the code in comments if you want to start over from scratch.
Without going into too much detail, I usually follow one of three approaches:
Use assign to assign a unique name for each important object throughout my execution. Then include an if(exists(...)) get(...) at the top of each function to get the value or else recompute it. (same as Dirk's suggestion)
Use cacheSweave with my Sweave documents. This does all the work for you of caching computations and retrieves them automatically. It's really trivial to use: just use the cacheSweave driver and add this flag to each block: <<..., cache=true>>=
Use save and load to save the environment at crucial moments, again making sure that all names are unique.
The 'mustashe' package is great for this kind of problem. In addition to caching the results, it also can include links to dependencies so that the code is re-run if the dependencies change.
Disclosure: I wrote this tool ('mustashe'), though I do not make any financial gains from others using it. I made it for this exact purpose for my own work and want to share it with others.
Below is a simple example. The foo variable is created and "stashed" for later. If the same code is re-run, the foo variable is loaded from disk and added to the global environment.
library(mustashe)
stash("foo", {
foo <- some_long_running_opperation(1e3)
}
#> Stashing object.
The documentation has additional examples of more complex use-cases and a detailed explanation of how it works under the hood.