Business Analytics Lab Worksheet 01
CME Group Foundation Business Analytics Lab
Your Name Here
Summer 2017
About
R-Studio is a free and open-source integrated development environment (IDE) for R, a programming language for statistical computing and graphics. The Credit Risk Data displays the credit risk of an individual based on the loan they have taken out and other features of the individual.
Capabilities
R studio is able to compute various statistical and graphical techniques, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, time series plots, maps, etc.
Setup
After downloading the bdad_lab01 zip folder, make sure to open the folder in the downloads, right click it, and select ‘extract’. This will give you a new unzipped folder. Next, we must set this folder as the working directory. The way to do this is to open R Studio, go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Now, follow the worksheet directions to complete the lab.
Task 1
To begin the Lab, follow Task 1 as outlined in the worksheet. Examine the content of the csv file in Excel, create a simple star relational schema in erdplus, take a screenshot of the image, and upload it below.
To add a picture, use the directions found in Lab 0. Below is an example of what the simple star relational schema should look like.
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 data in the console, one can ‘call’ it by referring to it by its given 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 LowTo 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
checking ## [1] 0 0 0 638 963 2827 0 0 6509 966 0
## [12] 0 322 0 396 0 652 708 207 287 0 101
## [23] 0 0 0 141 0 2484 237 0 335 3565 0
## [34] 16647 0 0 0 940 0 0 218 0 16935 664
## [45] 150 0 216 0 0 0 265 4256 870 162 0
## [56] 0 0 461 0 0 0 580 0 0 0 0
## [67] 758 399 513 0 0 565 0 0 0 166 9783
## [78] 674 0 15328 0 713 0 0 0 0 0 303
## [89] 900 0 1257 0 273 522 0 0 0 0 514
## [100] 457 5133 0 644 305 9621 0 0 0 0 0
## [111] 6851 13496 509 0 19155 0 0 374 0 828 0
## [122] 829 0 0 939 0 889 876 893 12760 0 0
## [133] 959 0 0 0 0 698 0 0 0 12974 0
## [144] 317 0 0 0 192 0 0 0 0 0 942
## [155] 0 3329 0 0 0 0 0 0 339 0 0
## [166] 0 105 0 216 113 109 0 0 8176 0 468
## [177] 7885 0 0 0 0 0 0 0 0 0 734
## [188] 0 0 172 644 0 617 0 586 0 0 0
## [199] 0 0 522 585 5588 0 352 0 2715 560 895
## [210] 305 0 0 0 8948 0 0 0 0 0 483
## [221] 0 0 0 663 624 0 0 152 0 0 498
## [232] 0 156 1336 0 0 0 2641 0 0 0 0
## [243] 0 887 0 0 0 0 18408 497 0 946 986
## [254] 8122 0 778 645 0 682 19812 0 0 859 0
## [265] 0 0 0 0 0 795 0 0 0 0 852
## [276] 0 0 425 0 0 0 11072 0 219 8060 0
## [287] 0 0 0 1613 757 0 0 977 197 0 0
## [298] 0 0 0 256 296 0 0 0 298 0 8636
## [309] 0 0 19766 0 0 0 0 4089 0 271 949
## [320] 0 911 0 0 0 0 271 0 0 0 0
## [331] 4802 177 0 0 996 705 0 0 5960 0 759
## [342] 0 651 257 955 0 8249 0 956 382 0 842
## [353] 3111 0 0 2846 231 0 17366 0 332 242 0
## [364] 929 0 0 0 0 0 0 0 646 538 0
## [375] 0 0 0 135 2472 0 10417 211 16630 0 642
## [386] 0 296 898 478 315 122 0 0 0 670 444
## [397] 3880 819 0 0 0 0 0 0 0 0 0
## [408] 161 0 0 789 765 0 0 983 0 0 798
## [419] 0 193 497 0 0 0 0Now, fill in the code to extract and call the savings column.
#Extracting the Savings Column
#Calling the Savings ColumnIn 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
#Call mean savingsNext, 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 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
#Call snr_SavingOf the Checking and Savings, which has a higher SNR? Why do you think that is?
Task 3
After using Watson Analytics to find patterns in the data, save your work and upload a screenshot here. Refer to Task 1 on how to upload a photo.