1. Automatic reports with knitr
How to organize recurring data analysis output
Ryan P. Mears
Harvard Medical School
January 24, 2013
Ryan P. Mears (Harvard Medical School) Automatic reports with knitr January 24, 2013 1/8

2. Background
Outline
1 Background
2 Automatic Reports
3 Other approaches with knitr
Ryan P. Mears (Harvard Medical School) Automatic reports with knitr January 24, 2013 2/8

3. Background
Rationale
Many data projects require much effort to improve signal/noise
Examples: image enhancement, optical character recognition,
MR imaging, electrophysiology, etc.
Unsupervised learning can assist efforts to remove artifacts.
Strategies: k-means, principal components, independent
components, etc.
Method: After classification select & remove likely noise sources
then compute criterion measures of data quality
Ryan P. Mears (Harvard Medical School) Automatic reports with knitr January 24, 2013 3/8

4. Background
Analysis Pipeline
Display Select Measure
1 Display Clustering result
2 Select features for further processing
3 Measure based on selection . . . repeat if under criterion
Ryan P. Mears (Harvard Medical School) Automatic reports with knitr January 24, 2013 4/8

5. Automatic Reports
Generate data table from existing graphics
Read & filter directory contents.
Create database of cases, graphics filenames and information.
Parse filenames and additional information to structure report.
Important: Initial flexibility of report structure
(inheritance/modularity etc.) will save work later.
BlockSubj Valid Clean BlinkA RangeA FinValid FinClean FinBlinkA FinRangeA
1 b1-109 262 112 87 135 262 191 0 71
2 b2-109 264 109 82 127 264 208 0 56
3 b3-109 262 80 152 110 262 203 0 59
4 b1-276 264 255 6 5 264 261 0 3
5 b2-276 264 241 19 16 264 263 0 1
6 b3-276 262 223 34 33 262 259 0 3
7 b1-297 264 166 83 91 264 230 0 34
8 b2-297 264 217 36 42 264 249 0 15
9 b3-297 263 190 64 71 263 245 0 18
10 b1-302 260 170 88 67 260 246 0 14
11 b2-302 263 182 72 53 263 253 0 10
12 b3-302 264 247 15 8 264 261 0 3
13 b1-333 263 152 103 53 263 255 0 8
Ryan P. Mears (Harvard Medical School) 189
14 b2-333 263 74 Automatic263
17 reports262 knitr0
with 1 January 24, 2013 5/8

6. Automatic Reports
A
LTEX templates: knitr parent & child documents
Slides are created from database cases.
Child document contains slide data & figure formatting.
A
Parent document contains LTEXdocument information.
Parent also contains code to iterate through database Page 1 of 1
ICselection_Parent.Rnw
cases.
Parent-child document structure separates content Printed For: ryan
Printed: 1/22/13 1:09:45 PM
from format.
<<run-all, results='hide', message=FALSE, echo=FALSE>>=
out = NULL
for (i in seq_len(75)) {
out = c(out, knit_child('ggchild-overlaypdf.Rnw', sprintf('template-%d.tex', i)))
}
<<write-results, message=FALSE,warning=FALSE,error=FALSE,echo=FALSE, results='asis'>>=
cat(out, sep = 'n')
@
end{document}
Ryan P. Mears (Harvard Medical School) Automatic reports with knitr January 24, 2013 6/8

7. Other approaches with knitr
Other approaches: knitr options, patterns & hooks
Customized chunk options
Brew patterns © LTEX
A
Markdown patterns © Pandoc © LTEX
A
Ryan P. Mears (Harvard Medical School) Automatic reports with knitr January 24, 2013 7/8

8. Acknowledgements
Neurodynamics Lab
Yihui Xie (Creator of the knitr package)
Jeromy Anglim’s Blog (Psychology and Statistics)
Markus Gesmann (Mages’ Blog)
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