About

This worksheet includes two main tasks in data modeling (a key step to understand the data), basic steps to compute a simple signal-to-noise ratio.

Setup

Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.

Task 1

To begin the Lab, examine the content of the csv file ‘creditrisk.csv’ by opening the file in RStudio.

Create a simple star relational schema in ERDPlus standalone feature https://erdplus.com/#/standalone, take a screenshot of the image, and upload it below.

To add a picture, use the directions found in Lab00. Below are steps and an example to create a simple star relational schema in ERDPlus.

Steps to create an star relation schema using erdplus.

From the drop-down option select New Start Schema

Example of how to create an start schema using erdplus
Completed Star schema example

Finally export the diagram as an image.

Task 2

Next, read the csv file into R Studio. It can be useful to name your data to create a shortcut to it. Here we will label the data, ‘mydata’. To see the top head data in the console, one can ‘call’ it using the function ‘head’ and referring to it by its given shortcut name.

mydata = read.csv(file="data/creditrisk.csv")
head(mydata)
##      Loan.Purpose Checking Savings Months.Customer Months.Employed Gender
## 1 Small Appliance        0     739              13              12      M
## 2       Furniture        0    1230              25               0      M
## 3         New Car        0     389              19             119      M
## 4       Furniture      638     347              13              14      M
## 5       Education      963    4754              40              45      M
## 6       Furniture     2827       0              11              13      M
##   Marital.Status Age Housing Years        Job Credit.Risk
## 1         Single  23     Own     3  Unskilled         Low
## 2       Divorced  32     Own     1    Skilled        High
## 3         Single  38     Own     4 Management        High
## 4         Single  36     Own     2  Unskilled        High
## 5         Single  31    Rent     3    Skilled         Low
## 6        Married  25     Own     1    Skilled         Low

To capture, or extract, the checking and savings columns and perform some analytics on them, we must first be able to extract the columns from the data separately. Using the ‘$’ sign following the label for the data extracts a specific column. For convenience, we relabel the extracted data.

Below, we have extracted the checking column.

#Extracting the Checking Column
checking = mydata$Checking 

#Calling the Checking Column to display top head values
head(checking)
## [1]    0    0    0  638  963 2827

Now, fill in the code to extract and call the savings column.

#Extracting the Savings Column
Saving = mydata$Savings

#Calling the Savings Column
head(Saving)
## [1]  739 1230  389  347 4754    0

In order to calculate the mean, or the average by hand of the checkings columns, one can add each individual entry and divide by the total number or rows. This would take much time, but thankfully, R has a command for this.

We have done an example using the checkings column. Compute the same using the savings column.

#Using the 'mean' function on checking to calculate the checking average and naming the average 'meanChecking'
meanChecking = mean(checking)

#Calling the average
meanChecking
## [1] 1048.014
#Find the average of the savings column and name the average of the savings meanSavings
meanSaving = mean(Saving)

#Call meanSavings
meanSaving
## [1] 1812.562

Next, compute the standard deviation or spread of both the checkings and savings columns.

#Computing the standard deviation of standard deviation
spreadChecking = sd(checking)

#Find the standard deviation of savings 
spreadSaving = sd(Saving)

Now, to compute the SNR, the signal to noise ratio, a formula is created because there is no built in function.

SNR is the mean, or average, divided by the spread.

#Compute the snr of Checking and name it snr_Checking
snr_Checking = meanChecking/spreadChecking

#Call snr_Checking
snr_Checking
## [1] 0.3330006
#Find the snr of the savings and name it snr_Saving
snr_Saving = meanSaving/spreadSaving

#Call snr_Saving
snr_Saving
## [1] 0.5038695

Of the Checking and Savings, which has a higher SNR? Why do you think that is? Saving has a higher SNR because there are more customers with balances in their savings accounts than their checking accounts. ————