Admins and users with the Has SpotIQ privilege can run an R script using the Custom Analysis feature of SpotIQ. This means you can run an R script from any point in ThoughtSpot where you find the Custom Analysis menu option.

The R language is an open source programming language used for statistical computing. As such, knowledgable users can use R to perform sophisticated analysis in a ThoughtSpot environment.

This section explains the feature and how to use it. It is not meant as an R primer. To learn more about R and how to use it, visit the (R Project for Statistical Computing)[https://www.r-project.org/].

Understand R script requirements in ThoughtSpot

ThoughtSpot provides R running as a service within a ThoughtSpot cluster. Permissions are restricted. This means the R script does not have permission to system commands.

The ThoughtSpot cluster has pre-installed the basic R packages. If your script requires a specific package, you may request your Thoughtspot cluster admin to install the package on your behalf.

ThoughtSpot internally transforms and binds an R script prior to sending it to the cluster’s R service. The system expects each script have a well-defined structure which is the following:

####R SCRIPT####
<Fill script body>
####COLUMN BINDINGS (ONE PER LINE)####
<Fill column bindings here>

The scripts contains the column bindings with the answer results appearing as parameters in the R script. ThoughtSpot expects for each .paramn in R your script must provide a corresponding binding. The following pseudo code illustrates an R script in a form suitable for ThoughtSpot:

####R SCRIPT####
df <- data.frame(.param0,.param1, ...);
...
write.csv(..., file=#output_csv#, ...);

Notice that .param0 refers to first column in column binding and .param1 refers to the second. Should you need a third binding, you would use .param2 and so forth.

The output of the script is either PNG or CSV. This example script uses #output_csv# to emit data in a CSV (tabular) format. Use #output_png# to emit data in PNG format.

Presently, error reporting is limited for R scripts in SpotIQ. You should validate your R script independent of your ThoughtSpot environment. Once you are sure they are free of syntax or other errors, then try the script in ThoughtSpot.

Try a Custom Analysis with R

The following illustrates how to run an R analysis on data that has a sales column and a zip code column.

  1. Log into ThoughtSpot and go to the Search bar.
  2. Use Choose Sources to locate a source with sales and zip code data. This example uses Phone Sales data.

  3. Enter sales zip code in the search bar.

    If your source contains the proper data, you should see something similar to the following:

  4. Choose Actions > Custom Analyze.

    ThoughtSpot opens the Customize Analysis dialog.

  5. Choose the Customize algorithms tab.

  6. In the Select Algorithms section, click the Custom R Script box.

    Selecting this option unsets all the other options on this tab and displays the Refine Parameters field.

  7. Enter this sample script in the field.

     ####R SCRIPT####
 library(ggplot2)
 set.seed(20)
 df <- data.frame(.param0, .param1)
 cluster <- kmeans(df[1:2], 3, nstart = 20)
 cluster$cluster <- as.factor(cluster$cluster)
 png(file=#output_png#,width=400,height=350,res=72)
 print(ggplot(df, aes(.param0, .param1, color = cluster$cluster)) + geom_point())
 ####COLUMN BINDINGS (ONE PER LINE)####
 Sales
 Zip Code

    This script binds .param0 to Sales and .param1 to the Zip Code column. You can see from the script that the output should be PNG (#output_png#).

  8. Check your work.

  9. Click Trigger Analysis.

    SpotIQ runs your analysis in the background.

  10. Go to the SpotIQ page and click on the results of your newly triggered analysis.

    You should see the results in PNG format similar to the following:

You can run another R script directly on this result to get CSV results. Try this on your own. Here is the script to give you CSV output:

####R SCRIPT####
set.seed(20);
df <- data.frame(.param0,.param1);
cluster <- kmeans(df[1:2], 3, nstart = 20);
df$Cluster <- as.factor(cluster$cluster);
colnames(df)[1] <- 'Sales';
colnames(df)[2] <- 'Zip Code';
write.csv(df, file=#output_csv#, row.names=FALSE);
####COLUMN BINDINGS (ONE PER LINE)####
Sales
Zip Code