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Generate synthetic data using R

Sidharth Macherla
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If you are building data science applications and need some data to demonstrate the prototype to a potential client, you will most likely need synthetic data. In this article, we discuss the steps to generating synthetic data using the R package ‘conjurer’. 

Steps to build synthetic data

1. Installation

Install conjurer package by using the following code. Since the package uses base R functions, it does not have any dependencies. 
 install.packages("conjurer")

2. Build customers

A customer is identified by a unique customer identifier(ID). A customer ID is alphanumeric with prefix “cust” followed by a numeric. This numeric ranges from 1 and extend to the number of customers provided as the argument within the function. For example, if there are 100 customers, then the customer ID will range from cust001 to cust100. This ensures that the customer ID is always of the same length. Let us build a group of customer IDs using the following code. For simplicity, let us assume that there are 100 customers. customer ID is built using the function buildCust. This function takes one argument “numOfCust” that specifies the number of customer IDs to be built.
library(conjurer)
customers <- buildCust(numOfCust =  100)
print(head(customers))
#[1] "cust001" "cust002" "cust003" "cust004" "cust005" "cust006"

3. Build products

The next step is building some products. A product is identified by a product ID. Similar to a customer ID, a product ID is also an alphanumeric with prefix “sku” which signifies a stock keeping unit. This prefix is followed by a numeric ranging from 1 and extending to the number of products provided as the argument within the function. For example, if there are 10 products, then the product ID will range from sku01 to sku10. This ensures that the product ID is always of the same length. Besides product ID, the product price range must be specified. Let us build a group of products using the following code. For simplicity, let us assume that there are 10 products and the price range for them is from 5 dollars to 50 dollars. Products are built using the function buildProd. This function takes 3 arguments as given below.
library(conjurer)
products <- buildProd(numOfProd = 10, minPrice = 5, maxPrice = 50)
print(head(products))
#     SKU Price
# 1 sku01 43.60
# 2 sku02 48.56
# 3 sku03 36.16
# 4 sku04 19.02
# 5 sku05 17.19
# 6 sku06 25.35

4. Build transactions

Now that a group of customer IDs and Products are built, the next step is to build transactions. Transactions are built using the function genTrans. This function takes 5 arguments. The details of them are as follows.
Let us build transactions using the following code 
transactions <- genTrans(cycles = "y", spike = 12, outliers = 1, transactions = 10000)
Visualize generated transactions by using 
TxnAggregated <- aggregate(transactions$transactionID, by = list(transactions$dayNum), length)
plot(TxnAggregated, type = "l", ann = FALSE)

5. Build final data

Bringing customers, products and transactions together is the final step of generating synthetic data. This process entails 3 steps as given below.

5.1 Allocate customers to transactions

The allocation of transactions is achieved with the help of buildPareto function. This function takes 3 arguments as detailed below.
Let us now allocate transactions to customers first by using the following code.
customer2transaction <- buildPareto(customers, transactions$transactionID, pareto = c(80,20))
Assign readable names to the output by using the following code. 
names(customer2transaction) <- c('transactionID', 'customer')

#inspect the output
print(head(customer2transaction))
#   transactionID customer
# 1     txn-91-11  cust072
# 2    txn-343-25  cust089
# 3    txn-264-08  cust076
# 4    txn-342-07  cust030
# 5      txn-2-19  cust091
# 6    txn-275-06  cust062

5.2 Allocate products to transactions

Now, using similar step as mentioned above, allocate transactions to products using following code. 
product2transaction <- buildPareto(products$SKU,transactions$transactionID,pareto = c(70,30))
names(product2transaction) <- c('transactionID', 'SKU')

#inspect the output
print(head(product2transaction))
#   transactionID   SKU
# 1    txn-182-30 sku10
# 2    txn-179-21 sku01
# 3    txn-179-10 sku10
# 4    txn-360-08 sku01
# 5     txn-23-09 sku01
# 6    txn-264-20 sku10

5.3 Final data

Now, using a similar step as mentioned above, allocate transactions to products using the following code. 
df1 <- merge(x = customer2transaction, y = product2transaction, by = "transactionID")

dfFinal <- merge(x = df1, y = transactions, by = "transactionID", all.x = TRUE)

#inspect the output
print(head(dfFinal))
#   transactionID customer   SKU dayNum mthNum
# 1      txn-1-01  cust076 sku03      1      1
# 2      txn-1-02  cust062 sku04      1      1
# 3      txn-1-03  cust087 sku07      1      1
# 4      txn-1-04  cust010 sku04      1      1
# 5      txn-1-05  cust039 sku01      1      1
# 6      txn-1-06  cust010 sku01      1      1
Thus, we have the final data set with transactions, customers and products. Interpret the results The column names of the final data frame can be interpreted as follows.

Summary & concluding remarks

In this article, we started by building customers, products and transactions. Later on, we also understood how to bring them all together in to a final data set. At the time of writing this article, the package is predominantly focused on building the basic data set and there is room for improvement. If you are interested in contributing to this package, please find the details at contributions.
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