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.)
I'm writing my first R package and have made a successful build with documentation using roxygen2 and added data sets.
However, I would also like ship an example script with how I use the functions in the r package. But I don't know where to put it.
Let's say I have created MyPackage. I have put my function scripts in the /R folder. Let's say I have:
foo1.R
foo2.R
foo3.R
Somewhere I'd also like to put a script with my workflow. Let's say I have a file, MyWorkflow.R:
library(MyPackage)
load(file='inData.R') # Loads indata variables A, B and C
X=foo1(A)
Y=foo2(X,B)
Z=foo3(Y,C)
Can I do this? If so, where do I put it? Is it an OK procedure - or generally frowned upon?
Any help or thoughts are appreciated.
Thanks.
Carl
Edit:
I looked at the link on demo/ and exec/, but didn't understand the exec/ folder thing. Grateful if you could clarify/exemplify/point to good uses of...
If I understand correctly, I'm not looking for an example or demo/, since the script won't necessarily be executable without tweaking by the user (e.g. to provide input data or paths). I "just" want to add an example script showing how I work with these functions.
I realise I should probably dive into the world of vignettes, but have difficulty in finding the time/oomph/energy to do so.
I also saw that there's the inst/ folder. Could you shed some light on the different uses of these options or hint at good examples of where they've been used (I often find examples more informative than reading an explanatory text that's above my level - I often get the feeling of being like a dog looking at a ceiling fan ;)
Will add info to the GitHub README. Thx for good suggestion!
Created inst/Workflow_Example/workflow.R. Upon build & reload, a Workflow_Example folder was created in the library with workflow.R script in it.
In combination with an explanatory remark in the README, this looks like what I was after. Problem solved or am I not seeing something obvious? Am I e.g. violating conventions/conduct/good practice?
You could either put it in demo/ or exec/ depending on the format of the script. See here for more details. I would mention the workflow and where it lives in the README regardless, and if you host your code on Github, you could create a wiki to describe the workflow and place the script there. This would be similar to what nrussell has mentioned in a comment above.
Is is possible to change the "Control + R" shortcut for sending scripts from the R text editor in the Windows GUI to the R console? I'd like to change it to "Control + Enter" to be more like the shortcut on my Mac. I do all my normal work on a Mac but have to use R on a PC to interface with some PC-only computational software.
Additional tidbits:
I'd rather not run an IDE on the PC if I don't have to, though perhaps this is the solution.
I use Rstudio on my Mac, but Rstudio does not get along with the PC software I'm running
The short answer is:
"No, there are no [built-in] ways to alter the menu shortcuts in the R Console"
I'm however gathering here -community wiki style- some of suggestions posted as remarks to this questions.
One approach may be to download the R source, hack it (see circa line 625 of src/gnuwin32/editor.c: ), and build the R binary anew (see the R for Windows FAQ for the tools you need to build from source). This seems to be a rather radical approach for the mere convenience of using an alternate keystroke sequence...
A similar approach may be to create an automatic patcher program which would patch the R executable, by locating the byte patterns surrounding the compiled logic of editor.c mentioned above and replacing it with a byte sequence for the desired keystroke. This solution may be sensitive to changes in the binaries, but also avoids the build process altogether...
An easier way to achieve this is probably by using an external text editor. Most modern editors have macros or configs that can be used, for example, to execute a source command in R for the selected text.
Customizing keyboard shortcuts is made available in Rstudio 0.99.644.
See https://support.rstudio.com/hc/en-us/articles/206382178-Customizing-Keyboard-Shortcuts for more information.
I have two versions of SPSS at work. SPSS 11 running on Windows XP and SPSS 20 running on Linux. Both copies of SPSS work fine. Files created with either version of SPSS open without incident on the other version of SPSS. I.E. - I can create a .sav file with SPSS 20 on Linux and open it on SPSS 11 on Windows without incident.
But, if I create a .sav file with SPSS 20 and import the data into either R or PSPP (on Linux), I get a bunch of warnings. The data appears to import correctly, but I am concerned by the warnings. I do not see any warning when importing a .sav from SPSS 11 or other .sav files I have been sent. Many of the analysts at my company use SPSS so I've gotten SPSS files from different versions of SPSS and I have never before seen this warning. The warning messages are nearly identical between PSPP and R which makes sense. AFAIK, they use the same underlying libs to import the data. This is the R error:
Warning messages:
1: In read.spss("test.sav") :
test.sav: File-indicated value is different from internal value for at least one of the three system values. SYSMIS: indicated -1.79769e+308, expected -1.79769e+308; HIGHEST: 1.79769e+308, 1.79769e+308; LOWEST: -1.79769e+308, -1.79769e+308
2: In read.spss("test.sav") :
test.sav: Unrecognized record type 7, subtype 18 encountered in system file
The .sav file is really simple. It has two columns, dumb and dumber. Both are integers. The first two contains two values of 1.0. The second row contains two values of 2.0. I can provide the file on request (I don't see any way to upload it to SO). If anyone would like to see the actual file, PM me and I'll send it to you.
dumb dumber
1.0 1.0
2.0 2.0
Thoughts? Anyone know the best way to file a bug against R without getting roasted alive on the mailing list? :-)
EDIT: I used the term "Error" in the title line. I'll leave it, but I should not have used this word. The comments below are correct in pointing out that the messages I am seeing are warnings, not errors. I do however feel that this is made clear in the body of the question above. Clearly, the SPSS data format has changed over time and SPSS/IBM have failed to document these changes which is the root of the problem.
It's not an error message. It is only a warning. SPSS refuses to document their file formats so people have not been motivated to track down by reverse engineering the structure of new "subtypes". There is no way to file a bug report without getting roasted because there is no bug .... other than a closed format and that bug complaint should be filed with the owners of SPSS!
EDIT: The R-Core is a volunteer group and takes it responsibilities very seriously. It exerts major efforts to track down anything that affects the stability of systems or produces erroneous calculations. If you were willing to be a bit more respectful of the authors of R and suggest the possibility of collaboration on the R-devel mailing list to identify solutions to this problem without using the term "bug", you would arouse much less hostility. There might be someone who would be willing to see if a simple .sav file such as the one you constructed could be examined under a hexadecimal microscope to identify whatever infinite negative value is being mistaken for another infinite negative value. Most of the R-Core is not in possession of working copies of SPSS.
You could offer this link as an example of the product of others who have attempted the reverse engineering of SPSS .sav formats:
http://svn.opendatafoundation.org/ddidext/org.opendatafoundation.data/references/pspp_source/sfm-read.c
Edit: 4/2015; I have seen a recent addition to the ?read.spss help file that refers one to pkg:memisc: "A different interface also based on the PSPP codebase is available in package memisc: see its help for spss.system.file." I have used that package's function successfully (once) on files created by more recent versions of SPSS.
The SPSS file format is not publicly documented and can change, but IBM SPSS does provide free libraries that can read and write the SAV file format. These mask any changes to the format. You can get them from the SPSS Community website (along with many other free goodies including the SPSS integration with R). Go to www.ibm.com/developerworks/spssdevcentral and look around. BTW, there have been substantial additions/changes to the sav file since year 2000, although the core data can still be read by old versions.
HTH,
Jon Peck