Using the tidyverse to Insert a Loan Counter and Additional Columns into Your Dataset

In this article, we’ll delve into the world of data manipulation using the tidyverse in R. Specifically, we’ll explore how to insert a loan counter that counts each loan for a given customer, as well as two additional columns: one identifying the first loan date and another identifying the last loan date.

Installing the Tidyverse

Before we begin, make sure you have the tidyverse installed. If not, you can install it using the following command:

# Install the tidyverse
install.packages("tidyverse")

Once installed, load the necessary libraries by running:

library(tidyverse)

Creating Your Dataset

To perform this exercise, we’ll assume that you have a dataset called df with columns for customer ID (Customer_ID), account number (Customer_Account), and loan date (Loan_Date). Here’s an example of how to create such a dataset:

# Create the dataframe
df <- structure(list(
  Customer_ID = c(100100, 100100, 100100, 100100, 100100, 100102, 
                  100102, 100102, 100102), 
  Customer_Account = c(1001, 1002, 1003, 1004, 1005, 2001, 
                      2006, 2010, 238), 
  Loan_Date = c(200801, 200902, 201003, 201105, 201209, 
               201805, 201903, 201905, 202002)
), class = "data.frame", row.names = c(NA, -9L))

Grouping and Counting Loans

The first step in inserting our desired columns is to group the data by customer ID. This can be achieved using the group_by function from the tidyverse.

# Group by Customer_ID and perform various operations
df %>% 
  group_by(Customer_ID) %>% 
  mutate(LoanCounter = row_number(),
         MaxLoan = n(),
         FirstLoan = ifelse(row_number() == 1, 1, 0),
         LastLoan = ifelse(row_number() == n(), 1, 0))

Explanation of the Code

Here’s a breakdown of what each part of this code does:

• group_by(Customer_ID): This line groups all rows in our dataset by their value in the Customer_ID column. The result is a new dataframe with one row for each unique value of Customer_ID.

• mutate(LoanCounter = row_number()): For each group, this function creates a new column called LoanCounter that assigns a number to each row based on its position within the group.

• mutate(MaxLoan = n()): This line calculates the total number of loans (n()) for each customer and stores it in the newly created MaxLoan column.

• mutate(FirstLoan = ifelse(row_number() == 1, 1, 0)): Here, we determine whether the row is the first loan by checking if its position within the group (row_number() ) equals one. If so, it assigns the value 1, otherwise 0.

• mutate(LastLoan = ifelse(row_number() == n(), 1, 0)): Similarly, this line finds out whether a row is the last loan in the sequence by checking its position within the group (row_number()). If so, it assigns the value 1, otherwise 0.

Output

After running this code block on your dataset, you’ll obtain an output with all three columns inserted:

# A tibble: 9 x 7
# Groups:   Customer_ID [2]
  Customer_ID Customer_Account Loan_Date LoanCounter MaxLoan FirstLoan LastLoan
        <dbl>            <dbl>     <dbl>       <int>   <int>     <dbl>    <dbl>
1      100100             1001    200801           1       5         1        0
2      100100             1002    200902           2       5         0        0
3      100100             1003    201003           3       5         0        0
4      100100             1004    201105           4       5         0        0
5      100100             1005    201209           5       5         0        1
6      100102             2001    201805           1       4         1        0
7      100102             2006    201903           2       4         0        0
8      100102             2010    201905           3       4         0        0
9      100102              238    202002           4       4         0        1

Additional Considerations

In this example, we’ve assumed that the loans are ordered by date (Loan_Date). However, you might need to reorder your data if it’s not so.

Furthermore, we’ve used ifelse statements for simplicity. In a real-world scenario, you would typically use more robust methods to find out whether a row is the first or last in its group.

For instance, if your dataset didn’t have any ties (i.e., no two rows had the same position), you could simply calculate the count of row_number() and assign it to the new columns. But because we’re not ordering our data by date, this approach won’t work directly.

Last modified on 2023-12-01