Creating the test and training datasets

Now that we are finished with our transformations, we will create the training and test data frames. We will perform a 50/50 split between training and test:

# Take a sample of full vector
nrow(OnlineRetail) 
> [1] 536068 
pctx <- round(0.5 * nrow(OnlineRetail))
set.seed(1)

# randomize rows

df <- OnlineRetail[sample(nrow(OnlineRetail)), ]
rows <- nrow(df)
OnlineRetail <- df[1:pctx, ]  #training set
OnlineRetail.test <- df[(pctx + 1):rows, ]  #test set
rm(df)

# Display the number of rows in the training and test datasets.

nrow(OnlineRetail) 
> [1] 268034 
nrow(OnlineRetail.test) 
> [1] 268034 

Saving the results

It is a good idea to periodically save your data frames, so that you can pick up your analysis from various checkpoints.

In this example, I will first sort them both by InvoiceNo, and then save the test and train data sets to disk, where I can always load them back into memory as needed:

 

setwd("C:/PracticalPredictiveAnalytics...