Category: Analytics

GrapheR

GNU General Public License
Image via Wikipedia

GrapherR

GrapheR is a Graphical User Interface created for simple graphs.

Depends: R (>= 2.10.0), tcltk, mgcv
Description: GrapheR is a multiplatform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graphs are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
License: GPL-2
LazyLoad: yes
Packaged: 2011-01-24 17:47:17 UTC; Maxime
Repository: CRAN
Date/Publication: 2011-01-24 18:41:47

More information about GrapheR at CRAN
Path: /cran/new | permanent link

Advantages of using GrapheR

  • It is bi-lingual (English and French) and can import in text and csv files
  • The intention is for even non users of R, to make the simple types of Graphs.
  • The user interface is quite cleanly designed. It is thus aimed as a data visualization GUI, but for a more basic level than Deducer.
  • Easy to rename axis ,graph titles as well use sliders for changing line thickness and color

Disadvantages of using GrapheR

  • Lack of documentation or help. Especially tips on mouseover of some options should be done.
  • Some of the terms like absicca or ordinate axis may not be easily understood by a business user.
  • Default values of color are quite plain (black font on white background).
  • Can flood terminal with lots of repetitive warnings (although use of warnings() function limits it to top 50)
  • Some of axis names can be auto suggested based on which variable s being chosen for that axis.
  • Package name GrapheR refers to a graphical calculator in Mac OS – this can hinder search engine results

Using GrapheR

  • Data Input -Data Input can be customized for CSV and Text files.
  • GrapheR gives information on loaded variables (numeric versus Factors)
  • It asks you to choose the type of Graph 
  • It then asks for usual Graph Inputs (see below). Note colors can be customized (partial window). Also number of graphs per Window can be easily customized 
  • Graph is ready for publication



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Data Visualization: Central Banks

Iron Ore Company of Canada
Image via Wikipedia

Trying to compare the transparency of central banks via the data visualization of two very different central banks.

One is Reserve Bank of India and the other is Federal Reserve Bank of New York

Here are some points-

1) The federal bank gives you a huge clutter of charts to choose from and sometimes gives you very difficult to understand charts.

see http://www.newyorkfed.org/research/global_economy/usecon_charts.html

and http://www.newyorkfed.org/research/directors_charts/us18chart.pdf

us18chart

2) The Reserve bank of India choose Business Objects and gives you a proper drilldown kind  of  graph and tables. ( thats a lot of heavy metal and iron ore China needs from India 😉 😉

Foreign Trade – Export      Time-line: ALL

TIME LINE COUNTRY COMMODITY AMOUNT (US $ MILLION) EXPORT QUANTITY
2010:07 (JUL) – P China IRON ORE (Units: TON) 205.06 1878456
2010:06 (JUN) – P China IRON ORE (Units: TON) 427.68 6808528
2010:05 (MAY) – P China IRON ORE (Units: TON) 550.67 5290450
2010:04 (APR) – P China IRON ORE (Units: TON) 922.46 9931500
2010:03 (MAR) – P China IRON ORE (Units: TON) 829.75 13177672
2010:02 (FEB) – P China IRON ORE (Units: TON) 706.04 10141259
2010:01 (JAN) – P China IRON ORE (Units: TON) 577.13 8498784
2009:12 (DEC) – P China IRON ORE (Units: TON) 545.68 9264544
2009:11 (NOV) – P China IRON ORE (Units: TON) 508.17 9509213
2009:10 (OCT) – P China IRON ORE (Units: TON) 422.6 7691652
2009:09 (SEP) – P China IRON ORE (Units: TON) 278.04 4577943
2009:08 (AUG) – P China IRON ORE (Units: TON) 276.96 4371847
2009:07 (JUL) China IRON ORE (Units: TON) 266.11 4642237
2009:06 (JUN) China IRON ORE (Units: TON) 241.08 4584354

Source : DGCI & S, Ministry of Commerce & Industry, GoI

 

You can see the screenshots of the various visualization tools of the New York Fed Reserve Bank and Indian Reserve Bank- if the US Fed is serious about cutting the debt maybe it should start publishing better visuals

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Chapman/Hall announces new series on R

Rice University, Houston, Texas, USA - Cohen H...
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R Authors get more choice and variety now-
http://www.mail-archive.com/r-help@r-project.org/msg122965.html
We are pleased to announce the launch of a new series of books on R. 

Chapman & Hall/CRC: The R Series

Aims and Scope
This book series reflects the recent rapid growth in the development and 
application of R, the programming language and software environment for 
statistical computing and graphics. R is now widely used in academic research, 
education, and industry. It is constantly growing, with new versions of the 
core software released regularly and more than 2,600 packages available. It is 
difficult for the documentation to keep pace with the expansion of the 
software, and this vital book series provides a forum for the publication of 
books covering many aspects of the development and application of R.

The scope of the series is wide, covering three main threads:
• Applications of R to specific disciplines such as biology, epidemiology, 
genetics, engineering, finance, and the social sciences.
• Using R for the study of topics of statistical methodology, such as linear 
and mixed modeling, time series, Bayesian methods, and missing data.
• The development of R, including programming, building packages, and graphics.

The books will appeal to programmers and developers of R software, as well as 
applied statisticians and data analysts in many fields. The books will feature 
detailed worked examples and R code fully integrated into the text, ensuring 
their usefulness to researchers, practitioners and students.

Series Editors
John M. Chambers (Department of Statistics, Stanford University, USA; 
j...@stat.stanford.edu)
Torsten Hothorn (Institut für Statistik, Ludwig-Maximilians-Universität, 
München, Germany; torsten.hoth...@stat.uni-muenchen.de)
Duncan Temple Lang (Department of Statistics, University of California, Davis, 
USA; dun...@wald.ucdavis.edu)
Hadley Wickham (Department of Statistics, Rice University, Houston, Texas, USA; 
had...@rice.edu)

Call for Proposals
We are interested in books covering all aspects of the development and 
application of R software. If you have an idea for a book, please contact one 
of the series editors above or one of the Chapman & Hall/CRC statistics 
acquisitions editors below. Please provide brief details of topic, audience, 
aims and scope, and include an outline if possible.

We look forward to hearing from you.

Best regards,Rob Calver (rob.cal...@informa.com)
David Grubbs (david.gru...@taylorandfrancis.com)
John Kimmel (john.kim...@taylorandfrancis.com)

 

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Handling time and date in R

John Harrison's famous chronometer
Image via Wikipedia

One of the most frustrating things I had to do while working as financial business analysts was working with Data Time Formats in Base SAS. The syntax was simple enough and SAS was quite good with handing queries to the Oracle data base that the client was using, but remembering the different types of formats in SAS language was a challenge (there was a date9. and date6 and mmddyy etc )

Data and Time variables are particularly important variables in financial industry as almost everything is derived variable from the time (which varies) while other inputs are mostly constants. This includes interest as well as late fees and finance fees.

In R, date and time are handled quite simply-

Use the strptime( dataset, format) function to convert the character into string

For example if the variable dob is “01/04/1977) then following will convert into a date object

z=strptime(dob,”%d/%m/%Y”)

and if the same date is 01Apr1977

z=strptime(dob,"%d%b%Y")

 

does the same

For troubleshooting help with date and time, remember to enclose the formats

%d,%b,%m and % Y in the same exact order as the original string- and if there are any delimiters like ” -” or “/” then these delimiters are entered in exactly the same order in the format statement of the strptime

Sys.time() gives you the current date-time while the function difftime(time1,time2) gives you the time intervals( say if you have two columns as date-time variables)

 

What are the various formats for inputs in date time?

%a
Abbreviated weekday name in the current locale. (Also matches full name on input.)
%A
Full weekday name in the current locale. (Also matches abbreviated name on input.)
%b
Abbreviated month name in the current locale. (Also matches full name on input.)
%B
Full month name in the current locale. (Also matches abbreviated name on input.)
%c
Date and time. Locale-specific on output, "%a %b %e %H:%M:%S %Y" on input.
%d
Day of the month as decimal number (01–31).
%H
Hours as decimal number (00–23).
%I
Hours as decimal number (01–12).
%j
Day of year as decimal number (001–366).
%m
Month as decimal number (01–12).
%M
Minute as decimal number (00–59).
%p
AM/PM indicator in the locale. Used in conjunction with %I and not with %H. An empty string in some locales.
%S
Second as decimal number (00–61), allowing for up to two leap-seconds (but POSIX-compliant implementations will ignore leap seconds).
%U
Week of the year as decimal number (00–53) using Sunday as the first day 1 of the week (and typically with the first Sunday of the year as day 1 of week 1). The US convention.
%w
Weekday as decimal number (0–6, Sunday is 0).
%W
Week of the year as decimal number (00–53) using Monday as the first day of week (and typically with the first Monday of the year as day 1 of week 1). The UK convention.
%x
Date. Locale-specific on output, "%y/%m/%d" on input.
%X
Time. Locale-specific on output, "%H:%M:%S" on input.
%y
Year without century (00–99). Values 00 to 68 are prefixed by 20 and 69 to 99 by 19 – that is the behaviour specified by the 2004 POSIX standard, but it does also say ‘it is expected that in a future version the default century inferred from a 2-digit year will change’.
%Y
Year with century.
%z
Signed offset in hours and minutes from UTC, so -0800 is 8 hours behind UTC.
%Z
(output only.) Time zone as a character string (empty if not available).

Also to read the helpful documentation (especially for time zone level, and leap year seconds and differences)
http://stat.ethz.ch/R-manual/R-patched/library/base/html/difftime.html
http://stat.ethz.ch/R-manual/R-patched/library/base/html/strptime.html
http://stat.ethz.ch/R-manual/R-patched/library/base/html/Ops.Date.html
http://stat.ethz.ch/R-manual/R-patched/library/base/html/Dates.html

 

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Mapping Health Statistics at CDC.gov

Astronaut Buzz Aldrin during the first human l...
Image via Wikipedia

CDC.gov has a great tool for showing United States statistics on death and injury, drillable by various details.

The tool is hosted at http://wisqars.cdc.gov:8080/cdcMapFramework/

As a test I decided to map out injuries due to fire arms , and compare firearm deaths of white people versus the whole population.(see firearm deaths file)

See white people are more likely than black people to own guns (also read http://www.ncbi.nlm.nih.gov/pubmed/9572612 ), but it seems statistically they are less likely to be injured by firearms- so it could affect support for gun control laws on a racial ground- that was my null hypothesis. No politics, just plain statistics. I dont know- why dont you look at the data and decide-

 

 

 

 

 

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Common Analytical Tasks

WorldWarII-DeathsByCountry-Barchart
Image via Wikipedia

 

Some common analytical tasks from the diary of the glamorous life of a business analyst-

1) removing duplicates from a dataset based on certain key values/variables
2) merging two datasets based on a common key/variable/s
3) creating a subset based on a conditional value of a variable
4) creating a subset based on a conditional value of a time-date variable
5) changing format from one date time variable to another
6) doing a means grouped or classified at a level of aggregation
7) creating a new variable based on if then condition
8) creating a macro to run same program with different parameters
9) creating a logistic regression model, scoring dataset,
10) transforming variables
11) checking roc curves of model
12) splitting a dataset for a random sample (repeatable with random seed)
13) creating a cross tab of all variables in a dataset with one response variable
14) creating bins or ranks from a certain variable value
15) graphically examine cross tabs
16) histograms
17) plot(density())
18)creating a pie chart
19) creating a line graph, creating a bar graph
20) creating a bubbles chart
21) running a goal seek kind of simulation/optimization
22) creating a tabular report for multiple metrics grouped for one time/variable
23) creating a basic time series forecast

and some case studies I could think of-

 

As the Director, Analytics you have to examine current marketing efficiency as well as help optimize sales force efficiency across various channels. In addition you have to examine multiple sales channels including inbound telephone, outgoing direct mail, internet email campaigns. The datawarehouse is an RDBMS but it has multiple data quality issues to be checked for. In addition you need to submit your budget estimates for next year’s annual marketing budget to maximize sales return on investment.

As the Director, Risk you have to examine the overdue mortgages book that your predecessor left you. You need to optimize collections and minimize fraud and write-offs, and your efforts would be measured in maximizing profits from your department.

As a social media consultant you have been asked to maximize social media analytics and social media exposure to your client. You need to create a mechanism to report particular brand keywords, as well as automated triggers between unusual web activity, and statistical analysis of the website analytics metrics. Above all it needs to be set up in an automated reporting dashboard .

As a consultant to a telecommunication company you are asked to monitor churn and review the existing churn models. Also you need to maximize advertising spend on various channels. The problem is there are a large number of promotions always going on, some of the data is either incorrectly coded or there are interaction effects between the various promotions.

As a modeller you need to do the following-
1) Check ROC and H-L curves for existing model
2) Divide dataset in random splits of 40:60
3) Create multiple aggregated variables from the basic variables

4) run regression again and again
5) evaluate statistical robustness and fit of model
6) display results graphically
All these steps can be broken down in little little pieces of code- something which i am putting down a list of.
Are there any common data analysis tasks that you think I am missing out- any common case studies ? let me know.

 

 

 

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