Generate the audit dataset.

Description

Rattle uses an artificial dataset for demonstration purposes. This function retrieves the source data https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data and then transforms the data in a variety of ways.

Usage

acquireAuditData(write.to.file=FALSE)

Arguments

Details

See the function definition for details of the processing done on the data downloaded from the UCI repository.

Value

By default the function returns a data frame containing the audit dataset. If write.to.file is TRUE then the data frame is returned invisibly.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

audit, rattle.

List the rules corresponding to the rpart decision tree

Description

Display a list of rules for an rpart decision tree.

Usage

asRules(model, compact=FALSE, ...)

Arguments

Details

Traverse a decision tree to generate the equivalent set of rules, one rule for each path from the root node to a leaf node.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Not run: asRules.rpart(my.rpart)

List the rules corresponding to the rpart decision tree

Description

Display a list of rules for an rpart decision tree.

Usage

## S3 method for class 'rpart'
asRules(model, compact=FALSE, classes=NULL, ...)

Arguments

Details

Traverse a decision tree to generate the equivalent set of rules, one rule for each path from the root node to a leaf node.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Not run: asRules.rpart(my.rpart)

Sample dataset to illustrate Rattle functionality.

Description

The audit dataset is an artificially constructed dataset that has some of the characteristics of a true financial audit dataset for modelling productive and non-productive audits of a person's financial statement. A productive audit is one which identifies errors or inaccuracies in the information provided by a client. A non-productive audit is usually an audit which found all supplied information to be in order.

The audit dataset is used to illustrate binary classification. The target variable is identified as TARGET\_Adjusted.

The dataset is quite small, consisting of just 2000 entities. Its primary purpose is to illustrate modelling in Rattle, so a minimally sized dataset is suitable.

The dataset itself is derived from publicly available data (which has nothing to do with audits).

Format

A data frame. In line with data mining terminology we refer to the rows of the data frame (or the observations) as entities. The columns are refered to as variables. The entities represent people in this case. We describe the variables here:

ID

This is a unique identifier for each person.

Age

The age.

Employment

The type of employment.

Education

The highest level of education.

Marital

Current marital status.

Occupation

The type of occupation.

Income

The amount of income declared.

Gender

The persons gender.

Deductions

Total amount of expenses that a person claims in their financial statement.

Hours

The average hours worked on a weekly basis.

IGNORE_Accounts

The main country in which the person has most of their money banked. Note that the variable name is prefixed with IGNORE. This is recognised by Rattle as the default role for this variable.

RISK_Adjustment

This variable records the monetary amount of any adjustment to the person's financial claims as a result of a productive audit. This variable, which should not be treated as an input variable, is thus a measure of the size of the risk associated with the person.

TARGET_Adjusted

The target variable for modelling (generally for classification modelling). This is a numeric field of class integer, but limited to 0 and 1, indicating non-productive and productive audits, respectively. Productive audits are those that result in an adjustment being made to a client's financial statement.

Perform binning over numeric data

Description

Perform binning.

Usage

binning(x, bins=4, method=c("quantile", "wtd.quantile", "kmeans"),
                     labels=NULL, ordered=TRUE, weights=NULL)

Arguments

Details

Bin the provided nmeric data into the specified number of bins using one of the supported methods. The bins will have the names specified by labels, if supplied. The result can optionally be an ordered factor.

Value

A factor is returned.

Author(s)

Daniele Medri and Graham Williams

References

Package home page: https://rattle.togaware.com

Generate a frequency count of the initial digits

Description

In the context of Benford's Law calculate the distribution of the frequencies of the first digit of the numbers supplied as the argument.

Usage

calcInitialDigitDistr(l, digit=1, len=1,
 sp=c("none", "positive", "negative"))

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Determine area under a curve (e.g. a risk or recall curve) of a risk chart

Description

Given the evaluation returned by evaluateRisk, for example, calculate the area under the risk or recall curves, to use as a metric to compare the performance of a model.

Usage

calculateAUC(x, y)

Arguments

Details

The area is returned.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

evaluateRisk.

Examples

## this is usually used in the context of the evaluateRisk function
## Not run: ev <- evaluateRisk(predicted, actual, risk)

## imitate this output here
ev <- data.frame(Caseload=c(1.0, 0.8, 0.6, 0.4, 0.2, 0),
                 Precision=c(0.15, 0.18, 0.21, 0.25, 0.28, 0.30),
                 Recall=c(1.0, 0.95, 0.80, 0.75, 0.5, 0.0),
                 Risk=c(1.0, 0.98, 0.90, 0.77, 0.30, 0.0))

## Calculate the areas unde the Risk and the Recall curves.
calculateAUC(ev$Caseload, ev$Risk)
calculateAUC(ev$Caseload, ev$Recall)

List Cluster Centers for a Hierarchical Cluster

Description

Generate a matrix of centers from a hierarchical cluster.

Usage

centers.hclust(x, object, nclust=10, use.median=FALSE)

Arguments

Details

For the specified number of clusters, cut the hierarchical cluster appropriately to that number of clusters, and return the mean (or median) of each resulting cluster.

Author(s)

Daniele Medri and Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Echo data in a human readable form.

Description

Format data in the most appropriate human readable form.

Usage

comcat(x, ...)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

  comcat(dim(iris))

Draw nodes of a decision tree

Description

Draw the nodes of a decision tree

Usage

drawTreeNodes(tree, cex = par("cex"), pch = par("pch"),
                           size = 4 * cex, col = NULL, nodeinfo = FALSE,
                           units = "", cases = "obs", 
                           digits = getOption("digits"),
                           decimals = 2,
                           print.levels = TRUE, new = TRUE) 

Arguments

Details

A variation of draw.tree() from the maptree package.

Author(s)

Graham.Williams@togaware.com, Denis White

References

Package home page: https://rattle.togaware.com

Examples

## this is usually used in the context of the plotRisk function
## Not run: drawTreeNodes(rpart(Species ~ ., iris))

Draw trees from an Ada model

Description

Using the Rattle drawTreeNodes, draw a selection of Ada trees.

Usage

drawTreesAda(model, trees=0, title="")

Arguments

Details

Using Rattle's drawTreeNodes underneath, a plot for each of the specified trees from an Ada model will be displayed.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Not run: drawTreesAda(ds.ada)

Generate an error matrix from actua and predicted data.

Description

An error matrix reports the true/false potisitve/negative rates.

Usage

errorMatrix(actual,
                        predicted,
                        percentage=TRUE,
                        digits=ifelse(percentage,1,3),
                        count=FALSE)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

  ## Not run: errorMatrix(model)

Summarise the performance of a data mining model

Description

By taking predicted values, actual values, and measures of the risk associated with each case, generate a summary that groups the distinct predicted values, calculating the accumulative percentage Caseload, Recall, Risk, Precision, and Measure.

Usage

evaluateRisk(predicted, actual, risks)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

plotRisk.

Examples

## simulate the data that is typical in data mining

## we often have only a small number of positive known case
cases <- 1000
actual <- as.integer(rnorm(cases) > 1)
adjusted <- sum(actual)
nfa <- cases - adjusted

## risks might be dollar values associated adjusted cases
risks <- rep(0, cases)
risks[actual==1] <- round(abs(rnorm(adjusted, 10000, 5000)), 2)

## our models will generated a probability of a case being a 1
predicted <- rep(0.1, cases) 
predicted[actual==1] <- predicted[actual==1] + rnorm(adjusted, 0.3, 0.1)
predicted[actual==0] <- predicted[actual==0] + rnorm(nfa, 0.1, 0.08)
predicted <- signif(predicted)

## call upon evaluateRisk to generate performance summary
ev <- evaluateRisk(predicted, actual, risks)

## have a look at the first few and last few
head(ev)
tail(ev)

## the performance is usually presented as a Risk Chart
## under the CRAN MS/Windows this causes a problem, so don't run for now
## Not run: plotRisk(ev$Caseload, ev$Precision, ev$Recall, ev$Risk)

A wrapper for plotting rpart trees using prp

Description

Plots a fancy RPart decision tree using the pretty rpart plotter.

Usage

fancyRpartPlot(model, main="", sub, caption, palettes, type=2, ...)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Use rpart to build a decision tree.

## Not run: library(rpart)

## Set up the data for modelling.

set.seed(42)
ds     <- weather
target <- "RainTomorrow"
risk   <- "RISK_MM"
ignore <- c("Date", "Location", risk)
vars   <- setdiff(names(ds), ignore)
nobs   <- nrow(ds)
form   <- formula(paste(target, "~ ."))
train  <- sample(nobs, 0.7*nobs)
test   <- setdiff(seq_len(nobs), train)
actual <- ds[test, target]
risks  <- ds[test, risk]

# Fit the model.

fit <- rpart(form, data=ds[train, vars])

## Plot the model.

fancyRpartPlot(fit)

## Choose different colours.

fancyRpartPlot(fit, palettes=c("Greys", "Oranges"))

## Add a main title to the plot.

fancyRpartPlot(fit, main=target) 


## End(Not run)

Generate a string to add a title to a plot

Description

Generate a string that is intended to be eval'd that will add a title and sub-title to a plot. The string is a call to title, supplying the given arguments, pasted together, as the main title, and generating a sub-title that begins with ‘Rattle’ and continues with the current date and time, and finishes with the current user's username. This is used internally in Rattle to adorn a plot with relevant information, but may be useful outside of Rattle.

Usage

genPlotTitleCmd(..., vector=FALSE)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

eval, title, plotRisk.

Examples

# generate some random plot
plot(rnorm(100))

# generate the string representing the command to add titles
tl <- genPlotTitleCmd("Sample Plot of", "No Particular Importance")

# cause the string to be executed as an R command
eval(parse(text=tl))

Model.

Description

Model.

Usage

ggVarImp(model, ...)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Not run: ggVarImp(model)

List the variables used by an adaboost model

Description

Returns a list of the variables used and their frequencies.

Usage

listAdaVarsUsed(model)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

List trees from an Ada model

Description

Display the textual representation of a selection of Ada trees.

Usage

listTreesAda(model, trees=0)

Arguments

Details

Using rpart's print method display each of the specified trees from an Ada model.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Not run: listTreesAda(ds.ada)

Versions of Installed Packages

Description

Generate a list of packages installed and their version number.

Usage

listVersions(file="", ...)

Arguments

Details

This function is useful in reporting problems or bugs, to ensure there is a clear match of R package versions between the system exhibiting the issue and the test system replicating the issue.

By default the information is written to the console in a comma separated form, that is ideally designed to be written to a CSV file for emailing.

Author(s)

Graham.Williams@togaware.com

See Also

write.csv

Calculate the mode of a vector, array or list.

Description

The mode is the most common or modal value of a list.

Usage

modalvalue(x, na.rm=FALSE)

Arguments

Details

This function calculates the mode of a vector, array or list (lists are flattened). This code originated from an anonymous post on the R Wiki.

Plot three lines on a risk chart, one vertical and two horizontal

Description

Plots a a vertical line at x up to max of y1 and y2, then horizontal from this line at y1 and y2. Intended for plotting on a plotRisk.

Usage

plotOptimalLine(x, y1, y2, pr = NULL, colour = "plum", label = NULL)

Arguments

Details

Intended to plot an optimal line on a Risk Chart as plotted by plotRisk.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

plotRisk.

Examples

## this is usually used in the context of the plotRisk function
## Not run: ev <- evaluateRisk(predicted, actual, risk)

## imitate this output here
ev <- NULL
ev$Caseload  <- c(1.0, 0.8, 0.6, 0.4, 0.2, 0)
ev$Precision <- c(0.15, 0.18, 0.21, 0.25, 0.28, 0.30)
ev$Recall    <- c(1.0, 0.95, 0.80, 0.75, 0.5, 0.0)
ev$Risk      <- c(1.0, 0.98, 0.90, 0.77, 0.30, 0.0)

## plot the Risk Chart
plotRisk(ev$Caseload, ev$Precision, ev$Recall, ev$Risk,
         chosen=60, chosen.label="Pr=0.45")

## plot the optimal point
plotOptimalLine(40, 77, 75, colour="maroon")

Plot a risk chart

Description

Plots a Rattle Risk Chart. Such a chart has been developed in a practical context to present the performance of data mining models to clients, plotting a caseload against performance, allowing a client to see the tradeoff between coverage and performance.

Usage

plotRisk(cl, pr, re, ri = NULL, title = NULL,
    show.legend = TRUE, xleg = 60, yleg = 55,
    optimal = NULL, optimal.label = "", chosen = NULL, chosen.label = "",
    include.baseline = TRUE, dev = "", filename = "", show.knots = NULL,
    show.lift=TRUE, show.precision=TRUE,
    risk.name = "Risk", recall.name = "Recall",
    precision.name = "Precision")

Arguments

Details

Caseload is the percentage of the entities in the dataset covered by the model at a particular probability cutoff, so that with a cutoff of 0, all (100%) of the entities are covered by the model. With a cutoff of 1 (0%) no entities are covered by the model. A diagonal line is drawn to represent a baseline random performance. Then the percentage of positive cases (the recall) covered for a particular caseload is plotted, and optionally a measure of the percentage of the total risk that is also covered for a particular caseload may be plotted. Such a chart allows a user to select an appropriate tradeoff between caseload and performance. The charts are similar to ROC curves. The precision (i.e., strike rate) is also plotted.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

evaluateRisk, genPlotTitleCmd.

Examples

## this is usually used in the context of the evaluateRisk function
## Not run: ev <- evaluateRisk(predicted, actual, risk)

## imitate this output here
ev <- NULL
ev$Caseload  <- c(1.0, 0.8, 0.6, 0.4, 0.2, 0)
ev$Precision <- c(0.15, 0.18, 0.21, 0.25, 0.28, 0.30)
ev$Recall    <- c(1.0, 0.95, 0.80, 0.75, 0.5, 0.0)
ev$Risk      <- c(1.0, 0.98, 0.90, 0.77, 0.30, 0.0)

## plot the Risk Chart
plotRisk(ev$Caseload, ev$Precision, ev$Recall, ev$Risk,
         chosen=60, chosen.label="Pr=0.45")

## Add a title
eval(parse(text=genPlotTitleCmd("Sample Risk Chart")))

Print a representation of the Random Forest models to the console

Description

A randomForest model, by default, consists of 500 decision trees. This function walks through each tree and generates a set of rules which are printed to the console. This takes a considerable amount of time and is provided for users to access the actual model, but it is not yet used within the Rattle GUI. It may be used to display the output of the RF (but it takes longer to generate than the model itself!). Or it might only be used on export to PMML or SQL.

Usage

printRandomForests(model, models=NULL, include.class=NULL, format="")

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Display a ruleset for a specific model amongst the 500.
## Not run: printRandomForests(rfmodel, 5)

## Display a ruleset for specific models amongst the 500.
## Not run: printRandomForests(rfmodel, c(5,10,15))

## Display a ruleset for each of the 500 models.
## Not run: printRandomForests(rfmodel)

Generate accessible data structure of a randomForest model

Description

A randomForest model, by default, consists of 500 decision trees. This function walks through each tree and generates a set of rules. This takes a considerable amount of time and is provided for users to access the actual model, but it is not yet used within the Rattle GUI. It may be used to display the output of the RF (but it takes longer to generate than the model itself!). Or it might only be used on export to PMML or SQL.

Usage

randomForest2Rules(model, models=NULL)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Generate a ruleset for a specific model amongst the 500.
## Not run: randomForest2Rules(rfmodel, 5)

## Generate a ruleset for specific models amongst the 500.
## Not run: randomForest2Rules(rfmodel, c(5,10,15))

## Generate a ruleset for each of the 500 models.
## Not run: randomForest2Rules(rfmodel)

Display the Rattle User Interface

Description

The Rattle user interface uses the RGtk2 package to present an intuitive point and click interface for data mining, extensively building on the excellent collection of R packages by very many authors for data manipulation, exploration, analysis, and evaluation.

Usage

rattle(csvname=NULL, dataset=NULL, useGtkBuilder=TRUE)

Arguments

Details

Refer to the Rattle home page in the URL below for a growing reference manual for using Rattle.

Whilst the underlying functionality of Rattle is built upon a vast collection of other R packages, Rattle itself provides a collection of utility functions used within Rattle. These are made available through loading the rattle package into your R library. The See Also section lists these utility functions that may be useful outside of Rattle.

Rattle can initialise some options using a .Rattle file if the folder in which Rattle is started. The currently supported options are .RATTLE.DATA, .RATTLE.SCORE.IN, and .RATTLE.SCORE.OUT.

If the environment variable RATTLE\_DATA is defined then that is set as the default CSV file name to load. Otherwise, if .RATTLE.DATA is defined then that will be used as the CSV file to load. Otherwise, if csvname is provided then that will be used.

Two environments are exported by Rattle, capturing the current rattle state (crs) and the current rattle variables (crv).

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

evaluateRisk, genPlotTitleCmd, plotRisk.

Examples

# You can start rattle with a path to a csv file to pre-specify the
# dataset. You then need to click Execute to load the data.

## Not run: rattle(system.file("csv", "weather.csv", package = "rattle"))

Print information about a multinomial model

Description

Displays a textual reveiw of the performance of a multinom model.

Usage

rattle.print.summary.multinom(x, digits = x$digits, ...)

Arguments

Details

Print a summary of a multinom model. This is sipmly a modification of the print.summary.multinom function to add the number of entities!

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Extract Rattle and related package information.

Description

Display system information, including versions of Rattle and R, operating system, and versions of other packages used by Rattle. Useful for reporting bugs but also invisibly returns a list of packages that have updates available and can be passed to install.packages().

Usage

rattleInfo(all.dependencies=FALSE,
           include.not.installed=FALSE,
           include.not.available=FALSE,
           include.libpath=FALSE)

Arguments

Details

This is a support function to list useful information to provide the developers with information about the system environment when running Rattle. It is intended to provide the information that is useful in reporting bugs.

It also lists the currently installed version of a number of packages that Rattle makes use of as well as checking for any updates available for those packages.

If updates are found then a command is generated and printed so that a user can simply copy and paste the command to update the relevant packages. The function also invisibly returns the list of packages that can be updated, so that we can do something like: install.packages(rattleInfo()).

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

rattle.

Interal Rattle user interface callbacks.

Description

These are exported from the package so that the GUI can access the callbacks. They should otherwise be ignored.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Transform a numeric vector by grouping it according to the values of the supplied factor and then rescaling within the groups.

Description

The numeric vector is remapped to integers from 0 to max-1, with any missing values mapped to the midpoint. Original idea from Tony Nolan. This will eventually be generalised to do the remapping using any of the rescaling functions.

Usage

rescale.by.group(x, by=NULL, type = "irank", itop = 100)

Arguments

Details

This Rattle support function, which is also useful by itself, provides a simple mechanism to rescale a numeric variable. Several rescalings are possible. The rescaling is done by first grouping the observations according to the by argument.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

rattle.

Plot a risk chart

Description

Plots a Rattle Risk Chart for binary classification models using ggplot2. Such a chart has been developed in a practical context to present the performance of data mining models to clients, plotting a caseload against performance, allowing a client to see the tradeoff between coverage and performance.

Usage

riskchart(pr,
          ac,
          ri               = NULL,
          title            = "Risk Chart",
          title.size       = 10,
          subtitle         = NULL,
          caption          = TRUE,
          show.legend      = TRUE,
          optimal          = NULL,
          optimal.label    = "",
          chosen           = NULL,
          chosen.label     = "",
          include.baseline = TRUE,
          dev              = "",
          filename         = "",
          show.knots       = NULL,
          show.lift        = TRUE,
          show.precision   = TRUE,
          show.maximal     = TRUE,
          risk.name        = "Risk",
          recall.name      = "Recall",
          precision.name   = "Precision",
          thresholds       = NULL,
          legend.horiz     = TRUE)

Arguments

Details

Caseload is the percentage of the entities in the dataset covered by the model at a particular probability cutoff, so that with a cutoff of 0, all (100%) of the entities are covered by the model. With a cutoff of 1 (0%) no entities are covered by the model. A diagonal line is drawn to represent a baseline random performance. Then the percentage of positive cases (the recall) covered for a particular caseload is plotted, and optionally a measure of the percentage of the total risk that is also covered for a particular caseload may be plotted. Such a chart allows a user to select an appropriate tradeoff between caseload and performance. The charts are similar to ROC curves. The precision (i.e., strike rate) is also plotted.

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

See Also

evaluateRisk, genPlotTitleCmd.

Examples

## Not run: 

## Use rpart to build a decision tree.

library(rpart)

## Set up the data for modelling.

set.seed(42)
ds     <- weather
target <- "RainTomorrow"
risk   <- "RISK_MM"
ignore <- c("Date", "Location", risk)
vars   <- setdiff(names(ds), ignore)
nobs   <- nrow(ds)
form   <- formula(paste(target, "~ ."))
train  <- sample(nobs, 0.7*nobs)
test   <- setdiff(seq_len(nobs), train)
actual <- ds[test, target]
risks  <- ds[test, risk]

# Build the model.

model <- rpart(form, data=ds[train, vars])

## Obtain predictions.

predicted <- predict(model, ds[test, vars], type="prob")[,2]

## Plot the Risk Chart.

riskchart(predicted, actual, risks)

## End(Not run)

Save a plot in some way

Description

For the current device, or for the device identified, save the plot displayed there in some way. This is either saved to file, copied to the clipboard for pasting into other applications, or sent to the printer for saving a hard copy.

Usage

savePlotToFile(file.name, dev.num=dev.cur())
copyPlotToClipboard(dev.num=dev.cur())
printPlot(dev.num=dev.cur()) 

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Given specific contents of env add other dataset related variables.

Description

This rattle support function is used for encapsulating data mining objects. The supplied environment is augmented with other data derived from the supplied data, such as a sample trianing dataset, list of numeric variables, and a formula for modelling.

Usage

setupDataset(env, seed=NULL)

Arguments

Details

The supplied object (an environment) is assumed to also contain the variables data (a data frame), target (a character string naming the target variable), risk (a character string naming the risk variable), and inputs (a character vector naming all the input variables). This function then adds in the variables vars (the variables used for modelling), numerics (the numeric vars within inputs), nobs (the number of observations), form (the formula for building models), train (a 70% training dataset).

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Generate a representation of a tree in a Random Forest

Description

Often we want to view the actual trees built by a random forest. Although reviewing all 500 trees might be a bit much, this function allows us to at least list them.

Usage

treeset.randomForest(model, n=1, root=1, format="R")

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

Examples

## Display a treeset for a specific model amongst the 500.
## Not run: treeset.randomForests(rfmodel, 5)

Sample dataset of daily weather observations from Canberra airport in Australia.

Description

One year of daily weather observations collected from the Canberra airport in Australia was obtained from the Australian Commonwealth Bureau of Meteorology and processed to create this sample dataset for illustrating data mining using R and Rattle.

The data has been processed to provide a target variable RainTomorrow (whether there is rain on the following day - No/Yes) and a risk variable RISK_MM (how much rain recorded in millimetres). Various transformations were performed on the source data. The dataset is quite small and is useful only for repeatable demonstration of various data science operations.

The source dataset is Copyright by the Australian Commonwealth Bureau of Meteorology and is provided as part of the rattle package with permission.

Usage

weather

Format

The weather dataset is a data frame containing one year of daily observations from a single weather station (Canberra).

Date

The date of observation (a Date object).

Location

The common name of the location of the weather station.

MinTemp

The minimum temperature in degrees celsius.

MaxTemp

The maximum temperature in degrees celsius.

Rainfall

The amount of rainfall recorded for the day in mm.

Evaporation

The so-called Class A pan evaporation (mm) in the 24 hours to 9am.

Sunshine

The number of hours of bright sunshine in the day.

WindGustDir

The direction of the strongest wind gust in the 24 hours to midnight.

WindGustSpeed

The speed (km/h) of the strongest wind gust in the 24 hours to midnight.

Temp9am

Temperature (degrees C) at 9am.

RelHumid9am

Relative humidity (percent) at 9am.

Cloud9am

Fraction of sky obscured by cloud at 9am. This is measured in "oktas", which are a unit of eigths. It records how many eigths of the sky are obscured by cloud. A 0 measure indicates completely clear sky whilst an 8 indicates that it is completely overcast.

WindSpeed9am

Wind speed (km/hr) averaged over 10 minutes prior to 9am.

Pressure9am

Atmospheric pressure (hpa) reduced to mean sea level at 9am.

Temp3pm

Temperature (degrees C) at 3pm.

RelHumid3pm

Relative humidity (percent) at 3pm.

Cloud3pm

Fraction of sky obscured by cloud (in "oktas": eighths) at 3pm. See Cload9am for a description of the values.

WindSpeed3pm

Wind speed (km/hr) averaged over 10 minutes prior to 3pm.

Pressure3pm

Atmospheric pressure (hpa) reduced to mean sea level at 3pm.

ChangeTemp

Change in temperature.

ChangeTempDir

Direction of change in temperature.

ChangeTempMag

Magnitude of change in temperature.

ChangeWindDirect

Direction of wind change.

MaxWindPeriod

Period of maximum wind.

RainToday

Integer: 1 if precipitation (mm) in the 24 hours to 9am exceeds 1mm, otherwise 0.

TempRange

Difference between minimum and maximum temperatures (degrees C) in the 24 hours to 9am.

PressureChange

Change in pressure.

RISK_MM

The amount of rain. A kind of measure of the "risk".

RainTomorrow

The target variable. Did it rain tomorrow?

Author(s)

Graham.Williams@togaware.com

Source

The daily observations are available from https://www.bom.gov.au/climate/data. Copyright Commonwealth of Australia 2010, Bureau of Meteorology.

Definitions adapted from https://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml

References

Package home page: https://rattle.togaware.com. Data source: https://www.bom.gov.au/climate/dwo/ and https://www.bom.gov.au/climate/data.

See Also

weatherAUS, audit.

Daily weather observations from multiple Australian weather stations.

Description

Daily weather observations from multiple locations around Australia, obtained from the Australian Commonwealth Bureau of Meteorology and processed to create this realtively large sample dataset for illustrating analytics, data mining, and data science using R and Rattle.

The data has been processed to provide a target variable RainTomorrow (whether there is rain on the following day - No/Yes) and a risk variable RISK_MM (how much rain recorded in millimeters). Various transformations are performed on the data.

The weatherAUS dataset is regularly updated an updates of this package usually correspond to updates to this dataset. The data is updated from the Bureau of Meteorology web site.

The locationsAUS dataset records the location of each weather station.

The source dataset comes from the Australian Commonwealth Bureau of Meteorology. The Bureau provided permission to use the data with the Bureau of Meteorology acknowledged as the source of the data, as per email from Cathy Toby (C.Toby@bom.gov.au) of the Climate Information Services of the National CLimate Centre, 17 Dec 2008.

A CSV version of this dataset is available as https://rattle.togaware.com/weatherAUS.csv.

Usage

weatherAUS

Format

The weatherAUS dataset is a data frame containing over 140,000 daily observations from over 45 Australian weather stations.

Date

The date of observation (a Date object).

Location

The common name of the location of the weather station.

MinTemp

The minimum temperature in degrees celsius.

MaxTemp

The maximum temperature in degrees celsius.

Rainfall

The amount of rainfall recorded for the day in mm.

Evaporation

The so-called Class A pan evaporation (mm) in the 24 hours to 9am.

Sunshine

The number of hours of bright sunshine in the day.

WindGustDir

The direction of the strongest wind gust in the 24 hours to midnight.

WindGustSpeed

The speed (km/h) of the strongest wind gust in the 24 hours to midnight.

Temp9am

Temperature (degrees C) at 9am.

RelHumid9am

Relative humidity (percent) at 9am.

Cloud9am

Fraction of sky obscured by cloud at 9am. This is measured in "oktas", which are a unit of eigths. It records how many eigths of the sky are obscured by cloud. A 0 measure indicates completely clear sky whilst an 8 indicates that it is completely overcast.

WindSpeed9am

Wind speed (km/hr) averaged over 10 minutes prior to 9am.

Pressure9am

Atmospheric pressure (hpa) reduced to mean sea level at 9am.

Temp3pm

Temperature (degrees C) at 3pm.

RelHumid3pm

Relative humidity (percent) at 3pm.

Cloud3pm

Fraction of sky obscured by cloud (in "oktas": eighths) at 3pm. See Cload9am for a description of the values.

WindSpeed3pm

Wind speed (km/hr) averaged over 10 minutes prior to 3pm.

Pressure3pm

Atmospheric pressure (hpa) reduced to mean sea level at 3pm.

ChangeTemp

Change in temperature.

ChangeTempDir

Direction of change in temperature.

ChangeTempMag

Magnitude of change in temperature.

ChangeWindDirect

Direction of wind change.

MaxWindPeriod

Period of maximum wind.

RainToday

Integer: 1 if precipitation (mm) in the 24 hours to 9am exceeds 1mm, otherwise 0.

TempRange

Difference between minimum and maximum temperatures (degrees C) in the 24 hours to 9am.

PressureChange

Change in pressure.

RISK_MM

The amount of rain. A kind of measure of the "risk".

RainTomorrow

The target variable. Did it rain tomorrow?

Author(s)

Graham.Williams@togaware.com

Source

Observations were drawn from numerous weather stations. The daily observations are available from https://www.bom.gov.au/climate/data. Copyright Commonwealth of Australia 2010, Bureau of Meteorology.

Definitions adapted from https://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml

References

Package home page: https://rattle.togaware.com. Data source: https://www.bom.gov.au/climate/dwo/ and https://www.bom.gov.au/climate/data.

See Also

weather, audit.

Returns a list of the names of the numeric variables in a data frame.

Description

A rattle support function.

Usage

whichNumerics(data)

Arguments

Author(s)

Graham.Williams@togaware.com

References

Package home page: https://rattle.togaware.com

The wine dataset from the UCI Machine Learning Repository.

Description

The wine dataset contains the results of a chemical analysis of wines grown in a specific area of Italy. Three types of wine are represented in the 178 samples, with the results of 13 chemical analyses recorded for each sample. The Type variable has been transformed into a categoric variable.

The data contains no missing values and consits of only numeric data, with a three class target variable (Type) for classification.

Usage

wine

Format

A data frame containing 178 observations of 13 variables.

Type

The type of wine, into one of three classes, 1 (59 obs), 2(71 obs), and 3 (48 obs).

Alcohol

Alcohol

Malic

Malic acid

Ash

Ash

Alcalinity

Alcalinity of ash

Magnesium

Magnesium

Phenols

Total phenols

Flavanoids

Flavanoids

Nonflavanoids

Nonflavanoid phenols

Proanthocyanins

Proanthocyanins

Color

Color intensity.

Hue

Hue

Dilution

D280/OD315 of diluted wines.

Proline

Proline

Source

The data was downloaded from the UCI Machine Learning Repository.

It was read as a CSV file with no header using read.csv. The columns were then given the appropriate names using colnames and the Type was transformed into a factor using as.factor. The compressed R data file was saved using save:

  UCI <- "https://archive.ics.uci.edu/ml"
  REPOS <- "machine-learning-databases"
  wine.url <- sprintf("
  wine <- read.csv(wine.url, header=FALSE) 
  colnames(wine) <- c('Type', 'Alcohol', 'Malic', 'Ash', 
                      'Alcalinity', 'Magnesium', 'Phenols', 
                      'Flavanoids', 'Nonflavanoids',
                      'Proanthocyanins', 'Color', 'Hue', 
                      'Dilution', 'Proline')
  wine$Type <- as.factor(wine$Type)
  save(wine, file="wine.Rdata", compress=TRUE)
  

References

Asuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository [https://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science.