Business Analytics Lab Worksheet 03 (bsad_lab03)
CME Group Foundation Business Analytics Lab
Joseph Cuellar
02/07/2019
About
The focus of this lab is on data outliers, data preparation, and data modeling. This lab requires the use of Microsoft Excel, R, and ERDplus.
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.
Important Note
For your assignment you may be using different data sets than what is included in this worksheet demo. Make sure to read carefully the instructions on Sakai.
Task 1: Data Outliers
First, we must calculate the mean, standard deviation, maximum, and minimum for the Age column using R.
In R, we must read in the file again, extract the column and find the values that are asked for.
#Read File
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#Name the extracted variable
age = mydata$Age #Calculate the average age below. Refer to Worksheet 2 for the correct command.
meanAge = mean(age)
meanAge## [1] 34.39765#Calculate standard deviation of age below. Refer to Worksheet 2 for the correct command.
spreadAge = sd(age)
spreadAge## [1] 11.04513#Calculate the maximum of age below. The command to find the maximum is max(variable) where variable is the extracted variable.
maxAge = max(age)
maxAge## [1] 73#Calculate the minimum of age below. The command to find the minimum is min(variable) where variable is the extracted variable.
minAge = min(age)
minAge## [1] 18Next, use the formula from class to detect any outliers. An outlier is value that “lies outside” most of the other values in a set of data. A common way to estimate the upper and lower threshold is to take the mean (+ or -) 3 * standard deviation. Try using this formula to find the upper and lower limit for age.
#Use the formula above to calculate the upper and lower threshold
upperOutlier = meanAge + 3*spreadAge
upperOutlier## [1] 67.53302lowerOutlier = meanAge - 3*spreadAge
lowerOutlier## [1] 1.262269Another similar method to find the upper and lower thresholds discussed in introductory statistics courses involves finding the interquartile range. Follow along below to see how we first calculate the interquartile range..
quantile(age) ## 0% 25% 50% 75% 100%
## 18 26 32 41 73lowerq = quantile(age)[2]
upperq = quantile(age)[4]
iqr = upperq - lowerq
iqr## 75%
## 15The formula below calculates the threshold. The threshold is the boundaries that determine if a value is an outlier. If the value falls above the upper threshold or below the lower threshold, it is an outlier.
Below is the upper threshold:
upperthreshold = (iqr * 1.5) + upperq
upperthreshold## 75%
## 63.5Below is the lower threshold:
lowerthreshold = lowerq - (iqr * 1.5)
lowerthreshold## 25%
## 3.5Are there any outliers? How many? It can also be useful to visualize the data using a box and whisker plot. The boxplot below supports the IQR we found of 15 and upper and lower threshold.
There are outliers. The upper threshold limit is 63.5 years of age. There are over 5 outliers. However, none of the outliers are below the lower threshold limit. All of the outliers are abolve the upper threshold limit of 63.5.
boxplot(age) 
Task 2: Data Preparation
Next, we must read the ‘creditriskorg.csv’ file into R. This is the original dataset and contains missing values.
newdata = read.csv(file="data/creditriskorg.csv")
head(newdata)## X X.1 X.2 X.3 X.4
## 1 Loan Purpose Checking Savings Months Customer Months Employed
## 2 Small Appliance $- $739.00 13 12
## 3 Furniture $- $1,230.00 25 0
## 4 New Car $- $389.00 19 119
## 5 Furniture $638.00 $347.00 13 14
## 6 Education $963.00 $4,754.00 40 45
## X.5 X.6 X.7 X.8 X.9 X.10 X.11
## 1 Gender Marital Status Age Housing Years Job Credit Risk
## 2 M Single 23 Own 3 Unskilled Low
## 3 M Divorced 32 Own 1 Skilled High
## 4 M Single 38 Own 4 Management High
## 5 M Single 36 Own 2 Unskilled High
## 6 M Single 31 Rent 3 Skilled LowWe observe that the column names are shifted down below because of the empty line. So, we must make sure to use the command skip and set the header to true.
newdata = read.csv(file="data/creditriskorg.csv",skip=1,header=TRUE,sep=",")
head(newdata)## Loan.Purpose Checking Savings Months.Customer Months.Employed
## 1 Small Appliance $- $739.00 13 12
## 2 Furniture $- $1,230.00 25 0
## 3 New Car $- $389.00 19 119
## 4 Furniture $638.00 $347.00 13 14
## 5 Education $963.00 $4,754.00 40 45
## 6 Furniture $2,827.00 $- 11 13
## Gender Marital.Status Age Housing Years Job Credit.Risk
## 1 M Single 23 Own 3 Unskilled Low
## 2 M Divorced 32 Own 1 Skilled High
## 3 M Single 38 Own 4 Management High
## 4 M Single 36 Own 2 Unskilled High
## 5 M Single 31 Rent 3 Skilled Low
## 6 M Married 25 Own 1 Skilled LowTo calculate the mean for Checking in R, follow Worksheet 2. Extract the Checking column first and then find the average using the function built in R. What happens when we try to use the function?
checking = newdata$Checking
MeanChecking = mean(checking)## Warning in mean.default(checking): argument is not numeric or logical:
## returning NAMeanChecking## [1] NATo resolve the error, we must understand where it is coming from and correct for. There are missing values in the csv file, which is quite common as most datasets are not perfect. Additionally, there are commas within the excel spreadsheet, and R does not recognize that ‘1,234’ is equivalent to ‘1234’. Lastly, there are ‘$’ symbols throughout the file which is not a numerical symbol either.
The sub function replaces these symbols with something else. So, in order to remove the comma in the number “1,234”, we must substitute it with just an empty space.
As shown on the worksheet, type and copy the exact commands to find the mean with the NA values removed.
#substitute comma with blank in all of checking. Below are examples using a hypothetical variable name 'new'.
# Example new = sub(",","",new)
newchecking = sub(",","",checking)
#substitute dollar sign with blank in all of checking
# Example new = sub("\\$","",new)
newchecking = sub("\\$","",newchecking)
#Convert values to numeric to remove any NA
# Example new = as.numeric(new)
newchecking = as.numeric(newchecking)## Warning: NAs introduced by coercion#Calculate mean of checking with NA removed
# Example mean(new,na.rm=TRUE)
meanChecking = mean(newchecking,na.rm=TRUE)
meanChecking## [1] 2559.805What are some other ways to clean this data? How about Excel? How does Excel treat the missing values and the “$” symbols?
Excel will clean the data by highlighting values that are not numeric. It will allow the user to identify values that are skewing the data or even preventing chunks from running. Excel can be very useful when utilized correctly.
Task 3: Data Modeling
Now, we will look at Chicago taxi data. Go and explore the interactive dashboard and read the description of the data.
Chicago Taxi Dashboard: https://data.cityofchicago.org/Transportation/Taxi-Trips-Dashboard/spcw-brbq
Chicago Taxi Data Description: http://digital.cityofchicago.org/index.php/chicago-taxi-data-released/
– Open in RStudio or Excel the taxi trips sample csv file located in the data folder. Note the size of the file, the number of columns and of rows here. Identify the unique entities, and fields in the data.
Size:38, 727 kb Number of Columns: 24 Number of Rows: 100,000 Field Names: Taxi ID, Trip ID, Trip Start Time, Trip End Time, Trip Seconds, Trip Miles, Pickup Census Tract, Pickup Community Area, Dropoff Community Area, Fare, Tips, Tolls, Extras, Trip Total, Payment Type, Company, Pickup Centroid Longitude, Pickup Centriod Location, Dropoff Centroid Latitude, Dropoff Centroid Longitude, Dropoff Centroid Location, Community Areas. Entities: Taxi, Neighbor, Total Money Collected, Distance, Trips.
– Define a relational business logic integrity check for the column field ‘Trip Seconds’.
– Using https://erdplus.com/#/standalone draw a star like schema using at least the following tables:
- A Fact table for Trip
- A Dimension table for Taxi Company
- A Dimension table for Payment Type
- A Dimension table for Total Fare
mydata = read.csv(file="data/Taxi_Trips_Sample.csv")
head(mydata)## Trip.ID
## 1 3e7d6d8ccf1425ae1dcd584f5c3ca303cf6362ed
## 2 3e7d6e5c4e87f01a475c8200b33777e85497da89
## 3 3e7d6e69c1d6755d9e7484a453cd93a3ee9fed4c
## 4 3e7d6efe43222b0ebc698583916674c648dd4520
## 5 3e7d6f001e9bcda8478a489cb53293d26328ac85
## 6 3e7d6f2a03527d63dc01b95e829fdfdd706102da
## Taxi.ID
## 1 b47c583b142d75b42882975eaab19c6cb98d82686016576cce6e305b1b99eb16aacfb9a21ff61c84873a6c3dde282756c162c538c8b69554fd8f811f3a8f60a2
## 2 bc1c0381e3bca623e6c04f3410f7b67201a9fc85c6b66d0f420a88099d38448f9b9874e246da49cf2ef32ea3d027eec9c5b484fe77dbfc033c389b5576ac66bd
## 3 f529487ccf3a5d538cd246342379d54314e90dc6c573f94a72f5c2238189c5131e12c1e493c71ccdaed6751d13f53fa1d8b51a1591c48891dc6beb3f9df3a18f
## 4 0f831bff43d83f396f2e4950126c6137dcdb60fb4c8580ffe860203747a83a789b22f2f9e4fbdd0dd8ed8c310366d8935228ddbcadf708fb9691ca5dd1b6c802
## 5 e5274d6c103515af3ce705182d0bbbea7ca077a6f23b1736254f2de8ba3e1687dd77f5fb541b7f00b1ebc24cfde54caf5a9562f046a0559acbfe1e7159e17c1a
## 6 329d9f0b72ce0fea6c2cc7ea3347924c11d98702e5cc39eacf6252038304bb019457c905f38066a2f9aa4b8732ac30ea22d8740e177a314ef7b0327ad5766cee
## Trip.Start.Timestamp Trip.End.Timestamp Trip.Seconds Trip.Miles
## 1 08/19/2014 02:45:00 PM 08/19/2014 02:45:00 PM 480 0.15
## 2 09/23/2013 05:15:00 PM 09/23/2013 05:15:00 PM 420 0.00
## 3 02/16/2014 10:15:00 PM 02/16/2014 10:30:00 PM 420 1.70
## 4 05/10/2013 08:00:00 PM 05/10/2013 08:45:00 PM 2340 13.80
## 5 02/21/2016 07:15:00 PM 02/21/2016 07:15:00 PM 300 0.70
## 6 12/10/2015 05:30:00 PM 12/10/2015 06:00:00 PM 1020 1.40
## Pickup.Census.Tract Dropoff.Census.Tract Pickup.Community.Area
## 1 17031280100 17031839100 28
## 2 17031081800 17031281900 8
## 3 NA NA 6
## 4 17031980000 17031060400 76
## 5 17031081500 17031081500 8
## 6 NA NA NA
## Dropoff.Community.Area Fare Tips Tolls Extras Trip.Total Payment.Type
## 1 32 $7.05 $0.00 $0.00 $1.50 $8.55 Cash
## 2 28 $6.05 $0.00 $0.00 $0.00 $6.05 Cash
## 3 4 $7.05 $0.00 $0.00 $0.00 $7.05 Cash
## 4 6 $31.25 $0.00 $0.00 $3.00 $34.25 Cash
## 5 8 $5.50 $0.00 $0.00 $0.00 $5.50 Cash
## 6 NA $9.25 $0.00 $0.00 $1.00 $10.25 Cash
## Company Pickup.Centroid.Latitude
## 1 41.88530
## 2 Taxi Affiliation Services 41.89322
## 3 Taxi Affiliation Services 41.94423
## 4 41.97907
## 5 41.89251
## 6 NA
## Pickup.Centroid.Longitude Pickup.Centroid.Location
## 1 -87.64281 POINT (-87.642808 41.8853)
## 2 -87.63784 POINT (-87.637844 41.893216)
## 3 -87.65600 POINT (-87.655998 41.944227)
## 4 -87.90304 POINT (-87.90304 41.979071)
## 5 -87.62621 POINT (-87.626215 41.892508)
## 6 NA
## Dropoff.Centroid.Latitude Dropoff.Centroid.Longitude
## 1 41.88099 -87.63275
## 2 41.87926 -87.64265
## 3 41.97517 -87.68752
## 4 41.95067 -87.66654
## 5 41.89251 -87.62621
## 6 NA NA
## Dropoff.Centroid..Location Community.Areas
## 1 POINT (-87.632746 41.880994) 29
## 2 POINT (-87.642649 41.879255) 37
## 3 POINT (-87.687516 41.975171) 57
## 4 POINT (-87.666536 41.950673) 75
## 5 POINT (-87.626215 41.892508) 37
## 6 NA