1.2. Trend for both data sets

       

-Drag Data Year to Columns

-All Opoids Deaths & All Opoids Rx to Rows

-Since the y-axis are in difference scale,we need to reduce the scale by clicking down arror to each Rows data and select “QuickTable calculation” – “Percentage Difference”

-Notice that the unit will be the same andthere are reduction of number of deaths for the year 2016 in both data sets

1.3. Segmentation

-Open new Sheet and name it Segmentation

-Drag Alll Opoids Death & All Opoids RX toColumns

-Drag County name to Rows

-Drag County name to Colors

-Select the sort for All OpoidDeaths

1.4. Baseline

- Download Global_T_SST_MSL.csv and open it

- Data\New Data source to open a new Data set

1.6. Heatmap

-Open a new Sheet

-Drag Date to Columns (Year)

-Drag Date to Rows (Change to Month)

-Drag Median to Color

-Change color scheme

-Change view to “Fit Width”

-Change color bar limit from -1 to +1

-Change sheet name to Heatmap of globaltemperature

1.7. Plot in 2 axes

-Open new Sheet

-Drag Date to Columns

-Drag Median to Rows

-Drag MSL to the right axis

-Drag Date to Detail, change to Month

-Notice the change

-Change sheet name: Global Temp and MSL

2. Configure R to operate in Tableau

Download R:

- For Window OS: download here: https://cran.r-project.org/bin/windows/base/

- For Mac OS: download here: https://cran.r-project.org/bin/macosx/

Download RStudio: https://rstudio.com/products/rstudio/download/

Install Rserve package:

- Open R

- Tools\Install Packages: type in Rserve

Load Rserve package in R:

    > library(Rserve)

    > Rserve()

Open Tableau:

- Help\Settings and Performance\Manage External Service Connection\

- External Service: Rserve

- Serve: localhost

- Port 6311

- Click on Test Connection

- If connection successful, press OK

- You are ready to run R in Tableau

 3. Data mining in Tableau using R

3.1. Load input data and working with Script()

- Download the `mtcars.csv` from the link below

- Open Tableau and load the text file `mtcars.csv`

- This is a sample data sets with different specifications of car's type, fuel consumption, made, weight, etc. In our data mining example, we gonna simulate the "mile per gallon: mpg" variable based on different input files

- Now let's create a simple script.

- Go to Sheet

- Analysis\Create Calculate Fields...

- A new window appears. Enter the name for the Field: "SimpleR"

- Select SCRIPT_REAL from the right window and start typing the input

        SCRIPT_REAL('output <- .arg1 + .arg2+.arg3',AVG([Mpg]),AVG([Cyl]),AVG([Disp]))

- Press Ok to go back to Sheet

- Drag SimpleR to Rows

- Drag Mpg to Columns 

- Drag Car to Color

 - Observe the change

3.2. Linear Modeling with R and Tableau

We will create the Linear Modeling model in R and visualize that in Tableau.

Open RStudio

- Tools\Install Packages, type in caret to install

- Type in the following script into R script:

        library(caret)

        data(mtcars)

        set.seed(123)

        indT <- createDataPartition(y=mtcars$mpg,p=0.6,list=FALSE)

        training <- mtcars[indT,]

        modLM <- train(mpg ~ cyl + wt + hp, data = training,method="lm")

        save(modLM,file="c:/CLEMSON/Workshop/LMmodel.rda") 

- The LMmodel.rda has been saved to your local computer, next we will load this model into Tableau

Open Tableau

- Open a new sheet

- Analysis\Create Calculate Fields...

- A new window appears. Enter the name for the Field: "LinearMod"

- Select SCRIPT_REAL from the right window and start typing the input

    

        SCRIPT_REAL('

        mydata <- data.frame(mpg=.arg1, cyl=.arg2, wt=.arg3,hp=.arg4)

        load("c:/CLEMSON/Workshop/LMmodel.rda")

        output <- predict(modLM, newdata = mydata)

        ',

        AVG([Mpg]),

        AVG([Cyl]),

        AVG([Wt]),

        AVG([Hp]))

- Press Ok to go back to Sheet

- Drag LinearMod to Rows

- Drag Mpg to Columns 

- Drag TrainTest to Color & Shape

- Go to Analytics tab, Drag Trend line to the plot (over to Linear)

- Observe the change in correlation for training and testing sets

- Save the sheet with name: "Linear Modeling"

3.3. Random Forest with R and Tableau

We will create the Random Forest model in R and visualize that in Tableau.

Open RStudio

- Type in the following script into R script:

        library(caret)

        data(mtcars)

        set.seed(123)

        indT <- createDataPartition(y=mtcars$mpg,p=0.6,list=FALSE)

        training <- mtcars[indT,]

        modRF <- train(mpg ~ cyl + wt + hp, data = training,method="rf")

        save(modRF,file="c:/CLEMSON/Workshop/RFmodel.rda")

- The RFmodel.rda has been saved to your local computer, next we will load this model into Tableau

Open Tableau

- Open a new sheet

- Analysis\Create Calculate Fields...

- A new window appears. Enter the name for the Field: "RandomForest"

- Select SCRIPT_REAL from the right window and start typing the input

    

        SCRIPT_REAL('

        mydata <- data.frame(mpg=.arg1, cyl=.arg2, wt=.arg3,hp=.arg4)

        load("c:/CLEMSON/Workshop/RFmodel.rda")

        output <- predict(modRF, newdata = mydata)

        ',

        AVG([Mpg]),

        AVG([Cyl]),

        AVG([Wt]),

        AVG([Hp]))

- Press Ok to go back to Sheet

- Drag RandomForest to Rows

- Drag Mpg to Columns 

- Drag TrainTest to Color & Shape

- Go to Analytics tab, Drag Trend line to the plot (over to Linear)

- Observe the change in correlation for training and testing sets

- Save the sheet with name: "Random Forest"

3.4. Principal Component Analysis (PCA) with R and Tableau

We will create the PCA model in R and visualize that in Tableau.

Open RStudio

- Type in the following script into R script:

        data(mtcars)

        #Ignore vs & am (PCA works good with numeric data )

        datain <- mtcars[,c(1:7,10:11)]

        mtcars.pca <- prcomp(datain,center=TRUE,scale=TRUE)

        save(mtcars.pca,file="c:/CLEMSON/Workshop/PCAmodel.rda")

- The PCAmodel.rda has been saved to your local computer, next we will load this model into Tableau

Open Tableau

- Open a new sheet

- Analysis\Create Calculate Fields...

- A new window appears. Enter the name for the Field: "PCA1"

- Select SCRIPT_REAL from the right window and start typing the input

    

        SCRIPT_REAL('

        load("c:/CLEMSON/Workshop/PCAmodel.rda")

        PCA1 <- mtcars.pca$x[,1]

        ',ATTR([Car]))

- Similarly, create a new field name: "PCA2"

        SCRIPT_REAL('

        load("c:/CLEMSON/Workshop/PCAmodel.rda")

        PCA2 <- mtcars.pca$x[,2]

        ' ,ATTR([Car]))

- Press Ok to go back to Sheet

- Drag PCA1 to Columns

- Drag PCA2 to Rows 

- Drag Wt to Color

- Drag Car to Tooltip, click on Tooltip, change to Label

- Change the colorbar 

- Observe the change in correlation for training and testing sets

- Save the sheet with name: "PCA"

3.5. Kmeans clustering with R and Tableau

Open Tableau

- Open a new sheet

- Analysis\Create Calculate Fields...

- A new window appears. Enter the name for the Field: "kmeans"

- Select SCRIPT_REAL from the right window and start typing the input

    

        SCRIPT_REAL(' 

        mydata <- data.frame(mpg=.arg1, cyl=.arg2, wt=.arg3,hp=.arg4)

        set.seed(123)

        km <- kmeans(mydata,3) #Split into 3 clusters

        km$cluster

        ',

        AVG([Mpg]),

        AVG([Cyl]),

        AVG([Wt]),

        AVG([Hp]))

- Press Ok to go back to Sheet

- Drag Wt to Columns

- Drag Mpg to Rows 

- Drag kmeans to Color & Shape

- Drag Car to Tooltip, click on Tooltip, change to Label

- Change the colorbar 

- Observe the change in correlation for training and testing sets

- Save the sheet with name: "k-means"

3.6. Fuzzy C-means clustering with R and Tableau

Open Tableau

- Open a new sheet

- Analysis\Create Calculate Fields...

- A new window appears. Enter the name for the Field: "Fuzzy C-Means"

- Select SCRIPT_REAL from the right window and start typing the input

    

        SCRIPT_REAL('

        library(ppclust) # You will need to install this package first using R

        mydata <- data.frame(mpg=.arg1, cyl=.arg2, wt=.arg3,hp=.arg4)

        set.seed(123)

        res.fcm <- fcm(mydata, centers=3)

        res.fcm$cluster

        ',

        AVG([Mpg]),

        AVG([Cyl]),

        AVG([Wt]),

        AVG([Hp]))

- Press Ok to go back to Sheet

- Drag Wt to Columns

- Drag Mpg to Rows 

- Drag Fuzzy C-Means to Color & Shape

- Drag Car to Tooltip, click on Tooltip, change to Label

- Change the colorbar 

- Observe the change in correlation for training and testing sets

- Save the sheet with name: "Fuzzy C-means"

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You can find the Tableau file for R attached