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
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
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 😉 😉
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
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)
Please use the following code to get a 15% discount on the 2 Day Conference Pass: AJAY11.
Predictive Analytics World announces new full-day workshops coming to San Francisco March 13-19, amounting to seven consecutive days of content.
These workshops deliver top-notch analytical and business expertise across the hottest topics.
Register now for one or more workshops, offered just before and after the full two-day Predictive Analytics World conference program (March 14-15). Early Bird registration ends on January 31st – take advantage of reduced pricing before then.
Make savings now with the early bird rate. Receive $200 off your registration rate for Predictive Analytics World – San Francisco (March 14-15), plus $100 off each workshop for which you register.
Carole-Ann’s 2011 Predictions for Decision Management
For Ajay Ohri on DecisionStats.com
What were the top 5 events in 2010 in your field?
Maturity: the Decision Management space was made up of technology vendors, big and small, that typically focused on one or two aspects of this discipline. Over the past few years, we have seen a lot of consolidation in the industry – first with Business Intelligence (BI) then Business Process Management (BPM) and lately in Business Rules Management (BRM) and Advanced Analytics. As a result the giant Platform vendors have helped create visibility for this discipline. Lots of tiny clues finally bubbled up in 2010 to attest of the increasing activity around Decision Management. For example, more products than ever were named Decision Manager; companies advertised for Decision Managers as a job title in their job section; most people understand what I do when I am introduced in a social setting!
Boredom: unfortunately, as the industry matures, inevitably innovation slows down… At the main BRMS shows we heard here and there complaints that the technology was stalling. We heard it from vendors like Red Hat (Drools) and we heard it from bored end-users hoping for some excitement at Business Rules Forum’s vendor panel. They sadly did not get it
Scrum: I am not thinking about the methodology there! If you have ever seen a rugby game, you can probably understand why this is the term that comes to mind when I look at the messy & confusing technology landscape. Feet blindly try to kick the ball out while superhuman forces are moving randomly the whole pack – or so it felt when I played! Business Users in search of Business Solutions are facing more and more technology choices that feel like comparing apples to oranges. There is value in all of them and each one addresses a specific aspect of Decision Management but I regret that the industry did not simplify the picture in 2010. On the contrary! Many buzzwords were created or at least made popular last year, creating even more confusion on a muddy field. A few examples: Social CRM, Collaborative Decision Making, Adaptive Case Management, etc. Don’t take me wrong, I *do* like the technologies. I sympathize with the decision maker that is trying to pick the right solution though.
Information: Analytics have been used for years of course but the volume of data surrounding us has been growing to unparalleled levels. We can blame or thank (depending on our perspective) Social Media for that. Sites like Facebook and LinkedIn have made it possible and easy to publish relevant (as well as fluffy) information in real-time. As we all started to get the hang of it and potentially over-publish, technology evolved to enable the storage, correlation and analysis of humongous volumes of data that we could not dream of before. 25 billion tweets were posted in 2010. Every month, over 30 billion pieces of data are shared on Facebook alone. This is not just about vanity and marketing though. This data can be leveraged for the greater good. Carlos pointed to some fascinating facts about catastrophic event response team getting organized thanks to crowd-sourced information. We are also seeing, in the Decision management world, more and more applicability for those very technology that have been developed for the needs of Big Data – I’ll name for example Hadoop that Carlos (yet again) discussed in his talks at Rules Fest end of 2009 and 2010.
Self-Organization: it may be a side effect of the Social Media movement but I must admit that I was impressed by the success of self-organizing initiatives. Granted, this last trend has nothing to do with Decision Management per se but I think it is a great evolution worth noting. Let me point to a couple of examples. I usually attend traditional conferences and tradeshows in which the content can be good but is sometimes terrible. I was pleasantly surprised by the professionalism and attendance at *un-conferences* such as P-Camp (P stands for Product – an event for Product Managers). When you think about it, it is already difficult to get a show together when people are dedicated to the tasks. How crazy is it to have volunteers set one up with no budget and no agenda? Well, people simply show up to do their part and everyone has fun voting on-site for what seems the most appealing content at the time. Crowdsourcing applied to shows: it works! Similar experience with meetups or tweetups. I also enjoyed attending some impromptu Twitter jam sessions on a given topic. Social Media is certainly helping people reach out and get together in person or virtually and that is wonderful!
Image via Wikipedia
What are the top three trends you see in 2011?
Performance: I might be cheating here. I was very bullish about predicting much progress for 2010 in the area of Performance Management in your Decision Management initiatives. I believe that progress was made but Carlos did not give me full credit for the right prediction… Okay, I am a little optimistic on timeline… I admit it… If it did not fully happen in 2010, can I predict it again in 2011? I think that companies want to better track their business performance in order to correct the trajectory of course but also to improve their projections. I see that it is turning into reality already here and there. I expect it to become a trend in 2011!
Insight: Big Data being available all around us with new technologies and algorithms will continue to propagate in 2011 leading to more widely spread Analytics capabilities. The buzz at Analytics shows on Social Network Analysis (SNA) is a sign that there is interest in those kinds of things. There is tremendous information that can be leveraged for smart decision-making. I think there will be more of that in 2011 as initiatives launches in 2010 will mature into material results.
Image by Intersection Consulting via Flickr
Collaboration: Social Media for the Enterprise is a discipline in the making. Social Media was initially seen for the most part as a Marketing channel. Over the years, companies have started experimenting with external communities and ideation capabilities with moderate success. The few strategic initiatives started in 2010 by “old fashion” companies seem to be an indication that we are past the early adopters. This discipline may very well materialize in 2011 as a core capability, well, or at least a new trend. I believe that capabilities such Chatter, offered by Salesforce, will transform (slowly) how people interact in the workplace and leverage the volumes of social data captured in LinkedIn and other Social Media sites. Collaboration is of course a topic of interest for me personally. I even signed up for Kare Anderson’s collaboration collaboration site – yes, twice the word “collaboration”: it is really about collaborating on collaboration techniques. Even though collaboration does not require Social Media, this medium offers perspectives not available until now.
Brief Bio-
Carole-Ann is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry. Her claim to fame is the strategy and direction of Blaze Advisor, the then-leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience. She speaks often at Industry conferences and has conducted university classes in France and Washington DC.
Leveraging her Masters degree in Applied Mathematics / Computer Science from a “Grande Ecole” in France, she started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication – as well as conducting strategic consulting gigs around change management.
She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs mostly around change management.
While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).
Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.
She also became addicted to Twitter in the process. She is active on all kinds of social media, always looking for new digital experience!
Outside of work, Carole-Ann loves spending time with her two boys. They grow fruits in their Northern California home and cook all together in the French tradition.
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.
Analyzing data can have many challenges associated with it. In the case of business analytics data, these challenges or constraints can have a marked effect on the quality and timeliness of the analysis as well as the expected versus actual payoff from the analytical results.
Challenges of Analytical Data Processing-
1) Data Formats- Reading in complete data, without losing any part (or meta data), or adding in superfluous details (that increase the scope). Technical constraints of data formats are relatively easy to navigate thanks to ODBC and well documented and easily search-able syntax and language.
The costs of additional data augmentation (should we pay for additional credit bureau data to be appended) , time of storing and processing the data (every column needed for analysis can add in as many rows as whole dataset, which can be a time enhancing problem if you are considering an extra 100 variables with a few million rows), but above all that of business relevance and quality guidelines will ensure basic data input and massaging are considerable parts of whole analytical project timeline.
2) Data Quality-Perfect data exists in a perfect world. The price of perfect information is one business will mostly never budget or wait for. To deliver inferences and results based on summaries of data which has missing, invalid, outlier data embedded within it makes the role of an analyst just as important as which ever tool is chosen to remove outliers, replace missing values, or treat invalid data.
3) Project Scope-
How much data? How much Analytical detail versus High Level Summary? Timelines for delivery as well as refresh of data analysis? Checks (statistical as well as business)?
How easy is it to load and implement the new analysis in existing Information Technology Infrastructure? These are some of the outer parameters that can limit both your analytical project scope, your analytical tool choice, and your processing methodology.
4) Output Results vis a vis stakeholder expectation management-
Stakeholders like to see results, not constraints, hypothesis ,assumptions , p-value, or chi -square value. Output results need to be streamlined to a decision management process to justify the investment of human time and effort in an analytical project, choice,training and navigating analytical tool complexities and constraints are subset of it. Optimum use of graphical display is a part of aligning results to a more palatable form to stakeholders, provided graphics are done nicely.
Eg Marketing wants to get more sales so they need a clear campaign, to target certain customers via specific channels with specified collateral. In order to base their business judgement, business analytics needs to validate , cross validate and sometimes invalidate this business decision making with clear transparent methods and processes.
Given a dataset- the basic analytical steps that an analyst will do with R are as follows. This is meant as a note for analysts at a beginner level with R.
Package -specific syntax
update.packages() #This updates all packages
install.packages(package1) #This installs a package locally, a one time event
library(package1) #This loads a specified package in the current R session, which needs to be done every R session
CRAN________LOCAL HARD DISK_________R SESSION is the top to bottom hierarchy of package storage and invocation.
ls() #This lists all objects or datasets currently active in the R session
> names(assetsCorr) #This gives the names of variables within a dataframe
[1] “AssetClass” “LargeStocksUS” “SmallStocksUS”
[4] “CorporateBondsUS” “TreasuryBondsUS” “RealEstateUS”
[7] “StocksCanada” “StocksUK” “StocksGermany”
[10] “StocksSwitzerland” “StocksEmergingMarkets”
> dim(assetsCorr) #gives dimensions observations and variable number
[1] 12 11
str(Dataset) – This gives the structure of the dataset (note structure gives both the names of variables within dataset as well as dimensions of the dataset)
head(dataset,n1) gives the first n1 rows of dataset while
tail(dataset,n2) gives the last n2 rows of a dataset where n1,n2 are numbers and dataset is the name of the object (here a data frame that is being considered)
summary(dataset) gives you a brief summary of all variables while
library(Hmisc)
describe(dataset) gives a detailed description on the variables
simple graphics can be given by
hist(Dataset1)
and
plot(Dataset1)
As you can see in above cases, there are multiple ways to get even basic analysis about data in R- however most of the syntax commands are intutively understood (like hist for histogram, t.test for t test, plot for plot).
For detailed analysis throughout the scope of analysis, for a business analytics user it is recommended to using multiple GUI, and multiple packages. Even for highly specific and specialized analytical tasks it is recommended to check for a GUI that incorporates the required package.
Follow me on Twitter
DECISION STATS 