Announcement from PiCloud- (and this is apart from the 5 hours free that a beginner account gets)
Announcement from PiCloud- (and this is apart from the 5 hours free that a beginner account gets)
I am just listing down a set of basic R functions that allow you to start the task of business analytics, or analyzing a dataset(data.frame). I am doing this both as a reference for myself as well as anyone who wants to learn R- quickly.
I am not putting in data import functions, because data manipulation is a seperate baby altogether. Instead I assume you have a dataset ready for analysis and what are the top R commands you would need to analyze it.
For anyone who thought R was too hard to learn- here is ten functions to learning R
1) str(dataset) helps you with the structure of dataset
2) names(dataset) gives you the names of variables
3)mean(dataset) returns the mean of numeric variables
4)sd(dataset) returns the standard deviation of numeric variables
5)summary(variables) gives the summary quartile distributions and median of variables
That about gives me the basic stats I need for a dataset.
> names(faithful)  "eruptions" "waiting"
> str(faithful) 'data.frame': 272 obs. of 2 variables: $ eruptions: num 3.6 1.8 3.33 2.28 4.53 ... $ waiting : num 79 54 74 62 85 55 88 85 51 85 ...
> summary(faithful) eruptions waiting Min. :1.600 Min. :43.0 1st Qu.:2.163 1st Qu.:58.0 Median :4.000 Median :76.0 Mean :3.488 Mean :70.9 3rd Qu.:4.454 3rd Qu.:82.0 Max. :5.100 Max. :96.0 > mean(faithful) eruptions waiting 3.487783 70.897059 > sd(faithful) eruptions waiting 1.141371 13.594974
6) I can do a basic frequency analysis of a particular variable using the table command and $ operator (similar to dataset.variable name in other statistical languages)
> table(faithful$waiting) 43 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 62 63 64 65 66 67 68 69 70 1 3 5 4 3 5 5 6 5 7 9 6 4 3 4 7 6 4 3 4 3 2 1 1 2 4 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 96 5 1 7 6 8 9 12 15 10 8 13 12 14 10 6 6 2 6 3 6 1 1 2 1 1
or I can do frequency analysis of the whole dataset using
> table(faithful) waiting eruptions 43 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 62 63 64 65 66 67 1.6 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1.7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1.733 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
It helps plot the dataset
8) hist(dataset$variable) is better at looking at histograms
10) The tenth function for a beginner would be cor(dataset$var1,dataset$var2)
> cor(faithful) eruptions waiting eruptions 1.0000000 0.9008112 waiting 0.9008112 1.0000000
I am assuming that as a beginner you would use the list of GUI at http://rforanalytics.wordpress.com/graphical-user-interfaces-for-r/ to import and export Data. I would deal with ten steps to data manipulation in R another post.
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
 “AssetClass” “LargeStocksUS” “SmallStocksUS”
 “CorporateBondsUS” “TreasuryBondsUS” “RealEstateUS”
 “StocksCanada” “StocksUK” “StocksGermany”
 “StocksSwitzerland” “StocksEmergingMarkets”
> str(assetsCorr) #gives complete structure of dataset
‘data.frame’: 12 obs. of 11 variables:
$ AssetClass : Factor w/ 12 levels “CorporateBondsUS”,..: 4 5 2 6 1 12 3 7 11 9 …
$ LargeStocksUS : num 15.3 16.4 1 0 0 …
$ SmallStocksUS : num 13.49 16.64 0.66 1 0 …
$ CorporateBondsUS : num 9.26 6.74 0.38 0.46 1 0 0 0 0 0 …
$ TreasuryBondsUS : num 8.44 6.26 0.33 0.27 0.95 1 0 0 0 0 …
$ RealEstateUS : num 10.6 17.32 0.08 0.59 0.35 …
$ StocksCanada : num 10.25 19.78 0.56 0.53 -0.12 …
$ StocksUK : num 10.66 13.63 0.81 0.41 0.24 …
$ StocksGermany : num 12.1 20.32 0.76 0.39 0.15 …
$ StocksSwitzerland : num 15.01 20.8 0.64 0.43 0.55 …
$ StocksEmergingMarkets: num 16.5 36.92 0.3 0.6 0.12 …
> dim(assetsCorr) #gives dimensions observations and variable number
 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
describe(dataset) gives a detailed description on the variables
simple graphics can be given by
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.
This is a fairly long post and is a basic collection of material for a book/paper. It is on interfaces to use R. If you feel I need to add more on a particular R interface, or if there is an error in this- please feel to contact me on twitter @decisionstats or mail ohri2007 on google mail.
|Faster learning for new programmers||Can create junk analysis by clicking menus in GUI|
|Easier creation of advanced models or graphics||Cannot create custom functions unless you use command line|
|Repeatability of analysis is better||Advanced techniques and custom flexibility of data handling R can be done in command line|
|Syntax is auto-generated||Can limit scope and exposure in learning R syntax|
A brief list of the notable Graphical User Interfaces is below-
1) R Commander- Basic statistics
2) Rattle- Data Mining
3) Deducer- Graphics (including GGPlot Integration) and also uses JGR (a Jave based GUI)
4) RKward- Comprehensive R GUI for customizable graphs
5) Red-R – Dataflow programming interface using widgets
1) R Commander- R Commander was primarily created by Professor John Fox of McMaster University to cover the content of a basic statistics course. However it is extensible and many other packages can be added in menu form to it- in the form R Commander Plugins. Quite noticeably it is one of the most widely used R GUI and it also has a script window so you can write R code in combination with the menus.
As you point and click a particular menu item, the corresponding R code is automatically generated in the log window and executed.
It can be found on CRAN at http://cran.r-project.org/web/packages/Rcmdr/index.html
Advantages of Using R Commander-
1) Useful for beginner in R language to do basic graphs and analysis and building models.
2) Has script window, output window and log window (called messages) in same screen which helps user as code is auto-generated on clicking on menus, and can be customized easily. For example in changing labels and options in Graphs. Graphical output is shown in seperate window from output window.
3) Extensible for other R packages like qcc (for quality control), Teaching Demos (for training), survival analysis and Design of Experiments (DoE)
4) Easy to understand interface even for first time user.
5) Menu items which are not relevant are automatically greyed out- if there are only two variables, and you try to build a 3D scatterplot graph, that menu would simply not be available and is greyed out.
Comparative Disadvantages of using R Commander-
1) It is basically aimed at a statistical audience( originally students in statistics) and thus the terms as well as menus are accordingly labeled. Hence it is more of a statistical GUI rather than an analytics GUI.
2) Has limited ability to evaluate models from a business analysts perspective (ROC curve is not given as an option) even though it has extensive statistical tests for model evaluation in model sub menu. Indeed creating a Model is treated as a subsection of statistics rather than a separate menu item.
3) It is not suited for projects that do not involve advanced statistical testing and for users not proficient in statistics (particularly hypothesis testing), and for data miners.
Menu items in the R Commander window:
File Menu – For loading script files and saving Script files, Output and Workspace
It is also needed for changing the present working directory and for exiting R.
Edit Menu – For editing scripts and code in the script window.
Data Menu – For creating new dataset, inputting or importing data and manipulating data through variables. Data Import can be from text,comma separated values,clipboard, datasets from SPSS, Stata,Minitab, Excel ,dbase, Access files or from url.
Data manipulation included deleting rows of data as well as manipulating variables.
Also this menu has the option for merging two datasets by row or columns.
Statistics Menu-This menu has options for descriptive statistics, hypothesis tests, factor analysis and clustering and also for creating models. Note there is a separate menu for evaluating the model so created.
Graphs Menu-It has options for creating various kinds of graphs including box-plot, histogram, line, pie charts and x-y plots.
The first option is color palette- it can be used for customizing the colors. It is recommended you adjust colors based on your need for publication or presentation.
A notable option is 3 D graphs for evaluating 3 variables at a time- this is really good and impressive feature and exposes the user to advanced graphs in R all at few clicks. You may want to dazzle a presentation using this graph.
Also consider scatterplot matrix graphs for graphical display of variables.
Graphical display of R surpasses any other statistical software in appeal as well as ease of creation- using GUI to create graphs can further help the user to get the most of data insights using R at a very minimum effort.
Models Menu-This is somewhat of a labeling peculiarity of R Commander as this menu is only for evaluating models which have been created using the statistics menu-model sub menu.
It includes options for graphical interpretation of model results,residuals,leverage and confidence intervals and adding back residuals to the data set.
Distributions Menu- is for cumulative probabilities, probability density, graphs of distributions, quantiles and features for standard distributions and can be used in lieu of standard statistical tables for the distributions. It has 13 standard statistical continuous distributions and 5 discrete distributions.
Tools Menu- allows you to load other packages and also load R Commander plugins (which are then added to the Interface Menu after the R Commander GUI is restarted). It also contains options sub menu for fine tuning (like opting to send output to R Menu)
Help Menu- Standard documentation and help menu. Essential reading is the short 25 page manual in it called Getting “Started With the R Commander”.
R Commander Plugins- There are twenty extensions to R Commander that greatly enhance it’s appeal -these include basic time series forecasting, survival analysis, qcc and more.
see a complete list at
- DoE – http://cran.r-project.org/web/packages/RcmdrPlugin.DoE/RcmdrPlugin.DoE.pdf
- epack- http://cran.r-project.org/web/packages/RcmdrPlugin.epack/RcmdrPlugin.epack.pdf
- Export- http://cran.r-project.org/web/packages/RcmdrPlugin.Export/RcmdrPlugin.Export.pdf
- MAc- http://cran.r-project.org/web/packages/RcmdrPlugin.MAc/RcmdrPlugin.MAc.pdf
- qcc- http://cran.r-project.org/web/packages/RcmdrPlugin.qcc/RcmdrPlugin.qcc.pdf and http://cran.r-project.org/web/packages/qcc/qcc.pdf
- Teaching Demos
Note the naming convention for above e plugins is always with a Prefix of “RCmdrPlugin.” followed by the names above
Also on loading a Plugin, it must be already installed locally to be visible in R Commander’s list of load-plugin, and R Commander loads the e-plugin after restarting.Hence it is advisable to load all R Commander plugins in the beginning of the analysis session.
However the notable E Plugins are
1) DoE for Design of Experiments-
Full factorial designs, orthogonal main effects designs, regular and non-regular 2-level fractional
factorial designs, central composite and Box-Behnken designs, latin hypercube samples, and simple D-optimal designs can currently be generated from the GUI. Extensions to cover further latin hypercube designs as well as more advanced D-optimal designs (with blocking) are planned for the future.
2) Survival- This package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
3) qcc -GUI for Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts
4) epack- an Rcmdr “plug-in” based on the time series functions. Depends also on packages like , tseries, abind,MASS,xts,forecast. It covers Log-Exceptions garch
and following Models -Arima, garch, HoltWinters
5)Export- The package helps users to graphically export Rcmdr output to LaTeX or HTML code,
via xtable() or Hmisc::latex(). The plug-in was originally intended to facilitate exporting Rcmdr
output to formats other than ASCII text and to provide R novices with an easy-to-use,
easy-to-access reference on exporting R objects to formats suited for printed output. The
package documentation contains several pointers on creating reports, either by using
conventional word processors or LaTeX/LyX.
6) MAc- This is an R-Commander plug-in for the MAc package (Meta-Analysis with
Correlations). This package enables the user to conduct a meta-analysis in a menu-driven,
graphical user interface environment (e.g., SPSS), while having the full statistical capabilities of
R and the MAc package. The MAc package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of the unique features of the MAc
package is in its integration of user-friendly functions to complete the majority of statistical steps
involved in a meta-analysis with correlations.
You can read more on R Commander Plugins at http://wp.me/p9q8Y-1Is
Rattle- R Analytical Tool To Learn Easily (download from http://rattle.togaware.com/)
Rattle is more advanced user Interface than R Commander though not as popular in academia. It has been designed explicitly for data mining and it also has a commercial version for sale by Togaware. Rattle has a Tab and radio button/check box rather than Menu- drop down approach towards the graphical design. Also the Execute button needs to be clicked after checking certain options, just the same as submit button is clicked after writing code. This is different from clicking on a drop down menu.
Advantages of Using Rattle
1) Useful for beginner in R language to do building models,cluster and data mining.
2) Has separate tabs for data entry,summary, visualization,model building,clustering, association and evaluation. The design is intuitive and easy to understand even for non statistical background as the help is conveniently explained as each tab, button is clicked. Also the tabs are placed in a very sequential and logical order.
3) Uses a lot of other R packages to build a complete analytical platform. Very good for correlation graph,clustering as well decision trees.
4) Easy to understand interface even for first time user.
5) Log for R code is auto generated and time stamp is placed.
6) Complete solution for model building from partitioning datasets randomly for testing,validation to building model, evaluating lift and ROC curve, and exporting PMML output of model for scoring.
7) Has a well documented online help as well as in-software documentation. The help helps explain terms even to non statistical users and is highly useful for business users.
Example Documentation for Hypothesis Testing in Test Tab in Rattle is ”
Distribution of the Data
* Kolomogorov-Smirnov Non-parametric Are the distributions the same?
* Wilcoxon Signed Rank Non-parametric Do paired samples have the same distribution?
Location of the Average
* T-test Parametric Are the means the same?
* Wilcoxon Rank-Sum Non-parametric Are the medians the same?
Variation in the Data
* F-test Parametric Are the variances the same?
* Correlation Pearsons Are the values from the paired samples correlated?”
Comparative Disadvantages of using Rattle-
1) It is basically aimed at a data miner. Hence it is more of a data mining GUI rather than an analytics GUI.
2) Has limited ability to create different types of graphs from a business analysts perspective Numeric variables can be made into Box-Plot, Histogram, Cumulative as well Benford Graphs. While interactivity using GGobi and Lattiticist is involved- the number of graphical options is still lesser than other GUI.
3) It is not suited for projects that involve multiple graphical analysis and which do not have model building or data mining.For example Data Plot is given in clustering tab but not in general Explore tab.
4) Despite the fact that it is meant for data miners, no support to biglm packages, as well as parallel programming is enabled in GUI for bigger datasets, though these can be done by R command line in conjunction with the Rattle GUI. Data m7ining is typically done on bigger datsets.
5) May have some problems installing it as it is dependent on GTK and has a lot of packages as dependencies.
This has the Execute Button (shown as two gears) and which has keyboard shortcut F2. It is used to execute the options in Tabs-and is equivalent of submit code button.
Other buttons include new Projects,Save and Load projects which are files with extension to .rattle an which store all related information from Rattle.
It also has a button for exporting information in the current Tab as an open office document, and buttons for interrupting current process as well as exiting Rattle.
It has the following options.
● Data Type- These are radio buttons between Spreadsheet (and Comma Separated Values), ARFF files (Weka), ODBC (for Database Connections),Library (for Datasets from Packages),R Dataset or R datafile, Corpus (for Text Mining) and Script for generating the data by code.
● The second row-in Data Tab in Rattle is Detail on Data Type- and its apperance shifts as per the radio button selection of data type in previous step. For Spreadsheet, it will show Path of File, Delimiters, Header Row while for ODBC it will show DSN, Tables, Rows and for Library it will show you a dropdown of all datasets in all R packages installed locally.
● The third row is a Partition field for splitting dataset in training,testing,validation and it shows ratio. It also specifies a Random seed which can be customized for random partitions which can be replicated. This is very useful as model building requires model to be built and tested on random sub sets of full dataset.
● The fourth row is used to specify the variable type of inputted data. The variable types are
○ Input: Used for modeling as independent variables
○ Target: Output for modeling or the dependent variable. Target is a categoric variable for classification, numeric for regression and for survival analysis both Time and Status need to be defined
○ Risk: A variable used in the Risk Chart
○ Ident: An identifier for unique observations in the data set like AccountId or Customer Id
○ Ignore: Variables that are to be ignored.
● In addition the weight calculator can be used to perform mathematical operations on certain variables and identify certain variables as more important than others.
Summary Sub-Tab has Summary for brief summary of variables, Describe for detailed summary and Kurtosis and Skewness for comparing them across numeric variables.
Distributions Sub-Tab allows plotting of histograms, box plots, and cumulative plots for numeric variables and for categorical variables Bar Plot and Dot Plot.
It also has Benford Plot for Benford’s Law on probability of distribution of digits.
Correlation Sub-Tab– This displays corelation between variables as a table and also as a very nice plot.
Principal Components Sub-Tab– This is for use with Principal Components Analysis including the SVD (singular value decomposition) and Eigen methods.
Interactive Sub-Tab- Allows interactive data exploration using GGobi and Lattice software. It is a powerful visual tool.
Test Tab-This has options for hypothesis testing of data for two sample tests.
Transform Tab-This has options for rescaling data, missing values treatment, and deleting invalid or missing values.
Cluster Tab-It gives an option to KMeans, Hierarchical and Bi-Cluster clustering methods with automated graphs,plots (including dendogram, discriminant plot and data plot) and cluster results available. It is highly recommended for clustering projects especially for people who are proficient in clustering but not in R.
Associate Tab-It helps in building association rules between categorical variables, which are in the form of “if then”statements. Example. If day is Thursday, and someone buys Milk, there is 80% chance they will buy Diapers. These probabilities are generated from observed frequencies.
Model Tab-The Model tab makes Rattle one of the most advanced data mining tools, as it incorporates decision trees(including boosted models and forest method), linear and logistic regression, SVM,neural net,survival models.
Evaluate Tab-It as functionality for evaluating models including lift,ROC,confusion matrix,cost curve,risk chart,precision, specificity, sensitivity as well as scoring datasets with built model or models. Example – A ROC curve generated by Rattle for Survived Passengers in Titanic (as function of age,class,sex) This shows comparison of various models built.
Log Tab- R Code is automatically generated by Rattle as the respective operation is executed. Also timestamp is done so it helps in reviewing error as well as evaluating speed for code optimization.
JGR- Deducer- (see http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual
JGR is a Java Based GUI. Deducer is recommended for use with JGR.
Deducer has basically been made to implement GGPLOT in a GUI- an advanced graphics package based on Grammer of Graphics and was part of Google Summer of Code project.
It first asks you to either open existing dataset or load a new dataset with just two icons. It has two initial views in Data Viewer- a Data view and Variable view which is quite similar to Base SPSS. The other Deducer options are loaded within the JGR console.
Advantages of Using Deducer
1. It has an option for factor as well as reliability analysis which is missing in other graphical user interfaces like R Commander and Rattle.
2. The plot builder option gives very good graphics -perhaps the best in other GUIs. This includes a color by option which allows you to shade the colors based on variable value. An addition innovation is the form of templates which enables even a user not familiar with data visualization to choose among various graphs and click and drag them to plot builder area.
3. You can set the Java Gui for R (JGR) menu to automatically load some packages by default using an easy checkbox list.
4. Even though Deducer is a very young package, it offers a way for building other R GUIs using Java Widgets.
5. Overall feel is of SPSS (Base GUI) to it’s drop down menu, and selecting variables in the sub menu dialogue by clicking to transfer to other side.SPSS users should be more comfortable at using this.
6. A surprising thing is it rearranges the help documentation of all R in a very presentable and organized manner
7. Very convenient to move between two or more datasets using dropdown.
8. The most convenient GUI for merging two datasets using common variable.
Dis Advantages of Using Deducer
1. Not able to save plots as images (only options are .pdf and .eps), you can however copy as image.
2. Basically a data viualization GUI – it does offer support for regression, descriptive statistics in the menu item Extras- however the menu suggests it is a work in progress.
3. Website for help is outdated, and help documentation specific to Deducer lacks detail.
Components of Deducer-
Data Menu-Gives options for data manipulation including recoding variables,transform variables (binning, mathematical operation), sort dataset, transpose dataset ,merge two datasets.
Analysis Menu-Gives options for frequency tables, descriptive statistics,cross tabs, one sample tests (with plots) ,two sample tests (with plots),k sample tests, correlation,linear and logistic models,generalized linear models.
Plot Builder Menu- This allows plots of various kinds to be made in an interactive manner.
Correlation using Deducer.
Red-R – A dataflow user interface for R (see http://red-r.org/
Red R uses dataflow concepts as a user interface rather than menus and tabs. Thus it is more similar to Enterprise Miner or Rapid Miner in design. For repeatable analysis dataflow programming is preferred by some analysts. Red-R is written in Python.
Advantages of using Red-R
1) Dataflow style makes it very convenient to use. It is the only dataflow GUI for R.
2) You can save the data as well as analysis in the same file.
3) User Interface makes it easy to read R code generated, and commit code.
4) For repeatable analysis-like reports or creating models it is very useful as you can replace just one widget and other widget/operations remain the same.
5) Very easy to zoom into data points by double clicking on graphs. Also to change colors and other options in graphs.
6) One minor feature- It asks you to set CRAN location just once and stores it even for next session.
7) Automated bug report submission.
Disadvantages of using Red-R
1) Current version is 1.8 and it needs a lot of improvement for building more modeling types as well as debugging errors.
2) Limited features presently.
RKWard (see http://rkward.sourceforge.net/)
It is primarily a KDE GUI for R, so it can be used on Ubuntu Linux. The windows version is available but has some bugs.
Advantages of using RKWard
1) It is the only R GUI for time series at present.
In addition it seems like the only R GUI explicitly for Item Response Theory (which includes credit response models,logistic models) and plots contains Pareto Charts.
2) It offers a lot of detail in analysis especially in plots(13 types of plots), analysis and distribution analysis ( 8 Tests of normality,14 continuous and 6 discrete distributions). This detail makes it more suitable for advanced statisticians rather than business analytics users.
3) Output can be easily copied to Office documents.
Disadvantages of using RKWard
1) It does not have stable Windows GUI. Since a graphical user interface is aimed at making interaction easier for users- this is major disadvantage.
2) It has a lot of dependencies so may have some issues in installing.
3) The design categorization of analysis,plots and distributions seems a bit unbalanced considering other tabs are File, Edit, View, Workspace,Run,Settings, Windows,Help.
Some of the other tabs can be collapsed, while the three main tabs of analysis,plots,distributions can be better categorized (especially into modeling and non-modeling analysis).
4) Not many options for data manipulation (like subset or transpose) by the GUI.
5) Lack of detail in documentation as it is still on version 0.5.3 only.
Analysis, Plots and Distributions are the main components and they are very very extensive, covering perhaps the biggest range of plots,analysis or distribution analysis that can be done.
Thus RKWard is best combined with some other GUI, when doing advanced statistical analysis.
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.
Packaged: 2011-01-24 17:47:17 UTC; Maxime
Date/Publication: 2011-01-24 18:41:47
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
- 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
- How would a graph of negative and positive acceleration differ(wiki.answers.com)
- Contest to build an R package recommendation engine(dataists.com)
- Dracula Graph Library(graphdracula.net)
- Why does a cumulative frequency graph never go down(wiki.answers.com)
- Heatmap tables (r-bloggers.com)
- Graph.tk – Online Graphing Utility (freetech4teachers.com)
- Graph Generator(topcoder.com)
Summary of R GUIs
Using R from other software- Please note that interfaces to R exist from other software as well. These include software from SAS Institute, IBM SPSS, Rapid Miner,Knime and Oracle.
A brief list is shown below-
1) SAS/IML Interface to R- You can read about the SAS Institute’s SAS/ IML Studio interface to R at http://www.sas.com/technologies/analytics/statistics/iml/index.html
2) Rapid Miner Extension to R-You can view integration with Rapid Miner’s extension to R here at http://www.youtube.com/watch?v=utKJzXc1Cow
3) IBM SPSS plugin for R-SPSS software has R integration in the form of a plugin. This was one of the earliest third party software offering interaction with R and you can read more at http://www.spss.com/software/statistics/developer/
4) Knime- Konstanz Information Miner also has R integration. You can view this on
5) Oracle Data Miner- Oracle has a data mining offering to it’s very popular database software which is integrated with the R language. The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html
6) JMP- JMP version 9 is the latest to offer interface to R. You can read example scripts here at http://blogs.sas.com/jmp/index.php?/archives/298-JMP-Into-R!.html
R Excel- Using R from Microsoft Excel
Microsoft Excel is the most widely used spreadsheet program for data manipulation, entry and graphics. Yet as dataset sizes have increased, Excel’s statistical capabilities have lagged though it’s design has moved ahead in various product versions.
R Excel basically works at adding a .xla plugin to
Excel just like other Plugins. It does so by connecting to R through Rpackages.
Basically it offers the functionality of R
functions and capabilities to the most widely distributed spreadsheet program. Alldata summaries, reports and analysis end up in a spreadsheet-
R Excel enables R to be very useful for people not
knowing R. In addition it adds (by option) the menus of R Commander as menus inExcel spreadsheet.
Enables R and Excel to communicate thus tieing an advanced statistical tool to the most widely used business analytics tool.
No major disadvatage at all to a business user. For a data statistical user, Microsoft Excel is limited to 100,000 rows, so R data needs to be summarized or reduced.
Graphical capabilities of R are very useful, but to a new user, interactive graphics in Excel may be easier than say using Ggplot ot Ggobi.
You can read more on this at http://rcom.univie.ac.at/ or the complete Springer Book http://www.springer.com/statistics/computanional+statistics/book/978-1-4419-0051-7
The combination of cloud computing and internet offers a new kind of interaction possible for scientists as well analysts.
Here is a way to use R on an Amazon EC2 machine, thus renting by hour hardware and computing resources which are scaleable to massive levels , whereas the software is free.
Here is how you can connect to Amazon EC2 and run R.
Running R for Cloud Computing.
1) Logging onto Amazon Console http://aws.amazon.com/ec2/
Note you need your Amazon Id (even the same id which you use for buying books).Note we are into Amazon EC2 as shown by the upper tab. Click upper tab to get into the Amazon EC2
2) Choosing the right AMI-On the left margin, you can click AMI -Images. Now you can search for the image-I chose Ubuntu images (linux images are cheaper) and latest Ubuntu Lucid in the search .You can choose whether you want 32 bit or 64 bit image. 64 bit images will lead to faster processing of data.Click on launch instance in the upper tab ( near the search feature). A pop up comes up, which shows the 5 step process to launch your computing.
3) Choose the right compute instance- – there are various compute instances and they all are at different multiples of prices or compute units. They differ in terms of RAM memory and number of processors.After choosing the compute instance of your choice (extra large is highlighted)- click on continue-
4) Instance Details-Do not choose cloudburst monitoring if you are on a budget as it has a extra charge. For critical production it would be advisable to choose cloudburst monitoring once you have become comfortable with handling cloud computing..
5) Add Tag Details- If you are running a lot of instances you need to create your own tags to help you manage them. It is advisable if you are going to run many instances.
6) Create a key pair- A key pair is an added layer of encryption. Click on create new pair and name it (note the name will be handy in coming steps)
7) After clicking and downloading the key pair- you come into security groups. Security groups is just a set of instructions to help keep your data transfer secure. You want to enable access to your cloud instance to certain IP addresses (if you are going to connect from fixed IP address and to certain ports in your computer. It is necessary in security group to enable SSH using Port 22.
Last step- Review Details and Click Launch
8) On the Left margin click on instances ( you were in Images.>AMI earlier)
It will take some 3-5 minutes to launch an instance. You can see status as pending till then.
9) Pending instance as shown by yellow light-
10) Once the instance is running -it is shown by a green light.
Click on the check box, and on upper tab go to instance actions. Click on connect-
You see a popup with instructions like these-
· Open the SSH client of your choice (e.g., PuTTY, terminal).
· Locate your private key, nameofkeypair.pem
· Use chmod to make sure your key file isn’t publicly viewable, ssh won’t work otherwise:
chmod 400 decisionstats.pem
· Connect to your instance using instance’s public DNS [ec2-75-101-182-203.compute-1.amazonaws.com].
Enter the following command line:
ssh -i decisionstats2.pem email@example.com
Note- If you are using Ubuntu Linux on your desktop/laptop you will need to change the above line to ubuntu@… from root@..
ssh -i yourkeypairname.pem -X firstname.lastname@example.org
(Note X11 package should be installed for Linux users- Windows Users will use Remote Desktop)
12) Install R Commander on the remote machine (which is running Ubuntu Linux) using the command
From the press release, here comes Oracle Open World. They really have an excellent rock concert in that as well.
Oracle Develop will again feature a .NET track for Oracle developers. Oracle Develop is suited for all levels of .NET developers, from beginner to advanced. It covers introductory Oracle .NET material, new features, deep dive application tuning, and includes three hours of hands-on labs apply what you learned from the sessions.
To register, go to Oracle Develop registration site.
Oracle OpenWorld will include several sessions on using the Oracle Database on Windows and .NET.
Session schedules and locations for Windows and .NET sessions at Oracle Develop and OpenWorld are now available.
With ODAC 220.127.116.11.2, developers can connect to Oracle Database versions 9.2 and higher from Visual Studio 2010 and .NET Framework 4. ODAC components support the full framework, as well as the new .NET Framework Client Profile.
- Download ODAC 18.104.22.168.2
- Oracle’s integration with Visual Studio 2010 video demo
- ODAC 22.214.171.124.2 Data Sheet
- New Features List
Learn about Oracle’s beta and production plans to support Microsoft Entity Framework with Oracle Database.
Data Mining Using the RDOM Package
By Casimir Saternos
Open R and enter the following command.
This command loads the RODM library and as well the dependent RODBC package. The next step is to make a database connection.
> DB <- RODM_open_dbms_connection(dsn="orcl", uid="dm", pwd="dm")
Subsequent commands use the DB object (an instance of the RODBC class) to connect to the database. The DNS specified in the command is the name you used earlier for the Data Source Name during the ODBC connection configuration. You can view the actual R code being executed by the command by simply typing the function name (without parentheses).
And say making a Model in Oracle and R-
> numrows <- length(orange_data[,1])
> orange_data.rows <- length(orange_data[,1])
> orange_data.id <- matrix(seq(1, orange_data.rows), nrow=orange_data.rows, ncol=1, dimnames= list(NULL, c(“CASE_ID”)))
> orange_data <- cbind(orange_data.id, orange_data)
This adjustment to the data frame then needs to be propagated to the database. You can confirm the change using the sqlColumns function, as listed earlier.
> RODM_create_dbms_table(DB, "orange_data") > sqlColumns(DB, 'orange_data')$COLUMN_NAME
> glm <- RODM_create_glm_model(
database = DB,
data_table_name = “orange_data”,
case_id_column_name = “CASE_ID”,
target_column_name = “circumference”,
model_name = “GLM_MODEL”,
mining_function = “regression”)
Information about this model can then be obtained by analyzing value returned from the model and stored in the variable named glm.
> glm$model.model_settings > glm$glm.globals > $glm.coefficients
Once you have a model, you can apply the model to a new set of data. To begin, create or retrieve sample data in the same format as the training data.
> query<-('select 999 case_id, 1 tree, 120 age, 32 circumference from dual') > orange_test<-sqlQuery(DB, query) > RODM_create_dbms_table(DB, "orange_test") and Finally, the model can be applied to the new data set and the results analyzed.
results <- RODM_apply_model(database = DB, data_table_name = "orange_test", model_name = "GLM_MODEL", supplemental_cols = "circumference")
When your session is complete, you can clean up objects that were created (if you like) and you should close the database connection:
> RODM_drop_model(database=DB,'GLM_MODEL') > RODM_drop_dbms_table(DB, "orange_test") > RODM_drop_dbms_table(DB, "orange_data") > RODM_close_dbms_connection(DB)
See the full article at http://www.oracle.com/technetwork/articles/datawarehouse/saternos-r-161569.html