The document summarizes a presentation about analyzing large census databases using statistical software. It discusses the challenges of working with large census datasets containing over 20 million records and hundreds of variables. It describes using the R programming language to perform analyses more efficiently than other statistical packages like SPSS and Stata. Specifically, it discusses optimizing R code, using high-performance computing clusters, and improving hardware to handle big data analysis tasks that were previously too slow or memory-intensive. The presentation concludes by emphasizing the learning opportunities of the census data project and provides next steps like learning more R programming, linear algebra, and using advanced computing clusters.

1. Analyzing the census:
Large databases and statistical software challenges
Rogério Jerônimo Barbosa
PhD Candidate, Sociology – USP
Researcher at the Center for Metropolitan Studies (CEM)
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2. Presentation Structure
1. Objetives of this presentation
2. The Census Project
3. Statistical Softwares and Computer Processing
4. (Little) More Advanced Stuff...
5. Conclusions and a “to do list”
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3. 1. Objetives
• Share my personal experience with the Census
Databases.
• Give some hints on how to analyse big databases
• Show how R can be a good
environment/companion for “big data” analysis
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4. 2. The Census Project…
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5. 2. Census Project
• December 2011:
• Invited by Marta to become part of the project
• Jan/Apr 2012:
• Getting familiar with IBGE documentation and Census Databases
• We bought all PNADs and Census Data (Except for 1960 edition)
• May 2012:
• The team started working
• April 2013:
• End of (team) activities
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6. 2. Census Project
The team:
Rogério J Barbosa
PhD Candidate – Sociology/USP
Diogo Ferrari
PhD Candidate – Political Science/Michigan University
Ian Prates
PhD Candidate – Sociology/USP
Leonardo Barone
PhD Candidate – Public Administration/FGV-SP
Murillo Marschner Alves de Brito
PhD Candidate – Sociology/USP
Patrick Silva
Graduate Student (Master)– Political Science/USP
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7. 2. Census Project
• Challenges:
• Run (a lot!!) of descriptive analyses and statistical
models using the six huge Census databases (20 million
cases +) and sometimes other data too.
• Standardize variables and measures
• Do it all as fast as possible
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8. 2. Census Project
• Overview:
Census Edition
N Columns
N Cases
Size
1960
44 (100)
899.861
111,5 Mb
1970
54 (134)
24.793.359
2.997.910 Mb
1980
87 (168)
29.378.753
5.747.875 Mb
1991
144 (210)
17.045.710
5.520.452 Mb
2000
152 (226)
20.274.412
7.180.425 Mb
2010
169 (259)
20.798.610
8.493.590 Mb
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9. 3. Statistical Softwares and
Computer Processing...
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10. 3. Statistical Softwares and Computer Processing
HDD
RAM
Function • Storage
• Fast Access
• Processing
Size
• Terabytes
• Gigabytes
• Megabytes/Kilobytes
Speed
• Slow
• Fast
• Ultra-fast
CPU
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11. 3. Statistical Softwares and Computer Processing
HDD
RAM
CPU
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12. 3. Statistical Softwares and Computer Processing
Jaguar
• 112 GB de RAM
• 56 CPUs Intel Itanium 2
• 5 TB Storage
AdvancedLaboratoryfor
ScientificComputing
LCCA/CCE - USP
Puma
• 59 DELL PowerEdge 1950 servers,
• 2 Xeon 5430 (8 cores, 2,66 GHz) each
• 16 GB de RAM DDR2-FBDIMM 667 MHz
• Total: 994 MB RAM
• 300 GB HDD each
• Total: 17.7 TB
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13. 3. Statistical Softwares and Computer Processing
An example of a cluster structure:
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14. 3. Statistical Softwares and Computer Processing
• There is no such thing as a “Super Computer”
• Clusters do not have a “user friendly” interface: you
have to use command line (Linux Terminal)
•
•
•
•
You write command lines for statistical analysis and upload it
Then you write a “job” and submit it to the cluster queue
Wait for your turn...
Download a file with the results
• Clusters require parallel processing – otherwise, you are
not using their real power.
• Common Statistical softwares don’t do that!
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15. 3. Statistical Softwares and Computer Processing
• Parallel Computing
• “Who” to divide your processing tasks with?
• Between Computers (clusters)
• Between “cores” of the same computer (this is feasible using
personal computers!)
• How to do that?
• Implicitly: specialized statistical softwares (expensive)
• Explicitly: you write your parallel codes yourself! (hard)
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16. 3. Statistical Softwares and Computer Processing
• Parallel Computing: not everything is (easily) parallelizable
Minimizing the squared residuals...
Specialized softwares use (very complicated ) approximations...
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17. 3. Statistical Softwares and Computer Processing
• Parallel Computing: not everything is (easily) parallelizable
Iterative methods for getting maximum likelihood estimators...
(Fisher Scoring Algorithm:
the actual step depends on the results of the previous one)
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Specialized softwares use (very complicated ) approximations...

18. 3. Statistical Softwares and Computer Processing
• Summary of the problems:
• Clusters are hard to use
(We didn’t become friends of Jaguar and Puma...)
• We didn’t have resources to buy parallel versions of
the standard softwares
• The fast softwares were not able to open the data
• We didn’t know advanced algebra for explicitly write
our parallel codes in R for modelling
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19. 3. Statistical Softwares and Computer Processing
• So we discovered...
RAM
HDD
CPU
Very fast
access
XDF Files
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20. 3. Statistical Softwares and Computer Processing
• Diogo’s bechmark:
CrossTab
Plot a graph
OLS
Percentiles TOTAL
R Revolution
(4 Census)
< 1 min
< 25 s
< 3min
< 30 s
1min40
SPSS
(1 census)
2min18s
4min20s
2min20s
2min20s
+15min
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21. 3. Statistical Softwares and Computer Processing
My trial:
• OLS Regression
• 75 dummy variables for age
• Dummy for gender
• Interactions (age*gender)
Plotting the
results
4 seconds
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22. 3. Statistical Softwares and Computer Processing
• Summary of the solutions:
• Some used (including me) SPSS for recoding and descriptive
statistics
• Revolution R for modelling
• Stata and (conventional) R for other stuff that used less amount
of data
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23. 4. (Little) More Advanced Stuff…
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24. (Little) More Advanced Stuff...
• My Purpose: use R* for every analysis
* Or similars, like Python, Julia etc...
• How to do that (once conventional R is
limited)?
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25. 4. (Little) More Advanced Stuff...
1 – The “bigger” the better: better hardware
makes it faster
• Better processor (multicore)
• More RAM
• Solid State Disks
2 – Update R Algebra libraries
• Optimized Linear Algebra Subsystem (BLAS)
• Taylored to your processor!!
• Little bit difficult to do: compile BLAS + recompile R
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26. 4. (Little) More Advanced Stuff...
3 – Use 64-bit system and softwares
4 – Use “professional” database management
• SQL for managing Data
• ODBC connections for exporting it to R
• Import just the pieces you need at the moment
5 – Minimize copies of data stored in RAM
• R objects make redundant copies
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27. 4. (Little) More Advanced Stuff...
6 – Optimize your code
• Do not do a bunch of loops: vetorize!
• Use “lower level” funtions:
• lm.fit instead of lm
• If possible, use C++
My multilevel regression:
1 hour -> 9 seconds
• Use “lower level” objects:
• Matrices instead of data.frames
• Use “integer” instead of “double”:
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28. 4. (Little) More Advanced Stuff...
6 – Optimize your code
Example: 7 million cases, 3 variables + survey weights
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29. 4. (Little) More Advanced Stuff...
7 – Use bigdata packages
• ff/ffbase
• bigalgebra / bigmemory etc
• biglm / speedglm
8 – Use the “garbage can” to free memory
• gc()
9 – Do not sort data!
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30. 5. Summing up and a
“to do list”
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31. Conclusions:
1 – Large database are challenging...
(and if you are crazy enough you can even have fun with it!)
2 – The Census project was a great opportunity
for trying and learning new stuff!
Do do list:
1 – Learn more R, SQL and programing
2 – Learn more math (mainly Linear Algebra)
3 – Become friends with Puma and Jaguar
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32. Thanks!
Visit:
CEM Website:
http://www.fflch.usp.br/centrodametropole/
Sociais & Métodos (Our Blog):
http://sociaisemetodos.wordpress.com/
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