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've seen similar questions on Stack Overflow but virtually no conclusive answers, and certainly no answer that worked for me.
What is the easiest way to access and use objects (regression fits, data frames, other objects) that are located in the global R environment in the Markdown (Rstudio) script.
I find it surprising that there is no easy solution to this, given the tendency of the RStudio team to make things comfortable and effective.
Thanks in advance.
For better or worse, this omission is intentional. Relying on objects created outside the document makes your document less reproducible--that is, if your document needs data in the global environment, you can't just give someone (or yourself in two years) the document and data files and let them recreate it themselves.
For this reason, and in order to perform the render in the background, RStudio actually creates a separate R session to render the document. That background R session cannot see any of the environments in the interactive R session you see in RStudio.
The best way around this problem is to take the code you used to create the contents of your global environment and move it inside your document (you can use echo = FALSE if you don't want it to show up in the document). This makes your document self-contained and reproducible.
If you can't do that, there are a few approaches you can take to use the data in the global environment directly:
Instead of using the Knit HTML button, type rmarkdown::render("your_doc.Rmd") at the R console. This will knit in the current session instead of a background session. Alternatively:
Save your global environment to an .Rdata file prior to rendering (use R's save function), and load it in your document.
Well, in my case i found the following solution:
(1) Save your Global Environmental in a .Rdata file inside the same folder where you have your .Rmd file. (You just need click at disquet picture that is on "Global Environmental" panel)
(2) Write the following code in your script of Rmarkdown:
load(file = "filename.RData") # it load the file that you saved before
and stop suffering.
Going to RStudio´s 'Tools' and 'Global options' and visiting the 'R Markdown' tab, you can make a selection in 'Evaluate chunks in directory', there select the option 'Documents' and the R Markdown knitting engine will be accessing the global environment as plain R code does. Hope this helps those who search this info!
The thread is old but in case anyone's still looking for a solution (as I was):
You can pass an envir parameter to the render() (or knit() function) so that it can access objects from the environment it was called from.
rmarkdown::render(
input = input_rmd,
output_file = output_file,
envir = parent.frame()
)
I have the same problem myself. Some stuff is pretty time consuming to reproduce every time.
I think there could be another answer. What if you save your environment with the save.image() function to a different file than the standard .Rdata one. Then, bring it back with load().
To be sure you are using the same data, use the md5sum() from tools.
Cheers, Cord
I think I solved this problem by referring to the package explicitly in the code that is being knitted. Using the yarrr package, for example, I loaded the dataframe "pirates" using data(pirates). This worked fine at the console and within an Rstudio code chunk, but with knitr it failed following the pattern in the question above. If, however, I loaded the data into memory by creating an object using pirates <- yarrr::pirates, the document then knitted cleanly to HTML.
You can load the script in the desired environment as follows:
```{r, include=FALSE}
source("your-script.R", local = knitr::knit_global())
# or sys.source("your-script.R", envir = knitr::knit_global())
```
Next in the R Markdown document, you can use objects created in these scripts (e.g., data objects or functions).
https://bookdown.org/yihui/rmarkdown-cookbook/source-script.html
One option that I have not yet seen is the use of parameters.
This chapter goes through a simple example of how to do this.
Is there any way to compile knitr subfiles separately? What I have in mind is something like the package subfiles for latex just in combination with R/knitr/Sweave?
This would be great in case one has two exercises a first exercise with heavy computations and
don't want to compile the entire exercise always while working and testing the second one.
The patchDVI package does this for Sweave. I imagine it would be possible (maybe even easy) to modify it to do the same for knitr.
For example, in Sweave, you define variables in a chunk like so:
<<>>=
.TexRoot <- "main.tex"
.SweaveFiles <- c("subfile1.Rnw", "subfile2.Rnw")
#
and after Sweave is finished running that file, patchDVI will check whether the files subfile1.Rnw and subfile2.Rnw also need to be run, then will run LaTeX on the main.tex file once everything is up to date.
You don't need to do anything difficult, just use the cache options. Lots of details here, but it's probably as simple as specifying cache = T in the chunk options of your first exercise.
I am running R version 2.15.3 with RStudio version 0.97.312. I have one script that reads my data from various sources and creates several data.tables. I then have another r script which uses the data.tables created in the first script. I wanted to turn the second script into a R markdown script so that the results of analysis can be outputted as a report.
I do not know the purpose of read_chunk, as opposed to source. My read_chunk is not working, but source is working. With either instance I do not get to see the objects in my workspace panel of RStudio.
Please explain the difference between read_chunk and source? Why would I use one or the other? Why will my .Rmd script not work
Here is ridiculously simplified sample
It does not work. I get the following message
Error: object 'z' not found
Two simple files...
test of source to rmd.R
x <- 1:10
y <- 3:4
z <- x*y
testing source.Rmd
Can I run another script from Rmd
========================================================
Testing if I can run "test of source to rmd.R"
```{r first part}
require(knitr)
read_chunk("test of source to rmd.R")
a <- z-1000
a
```
The above worked only if I replaced "read_chunk" with "source". I
can use the vectors outside of the code chunk as in inline usage.
So here I will tell you that the first number is `r a[1]`. The most
interesting thing is that I cannot see the variables in RStudio
workspace but it must be there somewhere.
read_chunk() only reads the source code (for future references); it does not evaluate code like source(). The purpose of read_chunk() was explained in this page as well as the manual.
There isn't an option to run a chunk interactively from within knitr AFAIK. However, this can be done easily enough with something like:
#' Run a previously loaded chunk interactively
#'
#' Takes labeled code loaded with load_chunk and runs it in the /global/ envir (unless otherwise specified)
#'
#' #param chunkName The name of the chunk as a character string
#' #param envir The environment in which the chunk is to be evaluated
run_chunk <- function(chunkName,envir=.GlobalEnv) {
chunkName <- unlist(lapply(as.list(substitute(.(chunkName)))[-1], as.character))
eval(parse(text=knitr:::knit_code$get(chunkName)),envir=envir)
}
NULL
In case it helps anyone else, I've found using read_chunk() to read a script without evaluating can be useful in two ways. First, you might have a script with many chunks and want control over which ones run where (e.g., a plot or a table in a specific place). I use source when I want to run everything in a script (for example, at the start of a document to load a standard set of packages or custom functions). I've started using read_chunk early in the document to load scripts and then selectively run the chunks I want where I need them.
Second, if you are working with an R script directly or interactively, you might want a long preamble of code that loads packages, data, etc. Such a preamble, however, could be unnecessary and slow if, for example, prior code chunks in the main document already loaded data.