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/scoring.csv") 

#Name the extracted variable
age = mydata$Age 
#Calculate the average age below. Refer to Worksheet 2 for the correct command.
agemean=mean(age)
mean(age)
## [1] 37.08412
#Calculate standard deviation of age below. Refer to Worksheet 2 for the correct command. 
agesd=sd(age)
sd(age)
## [1] 10.98637
#Calculate the maximum of age below. The command to find the maximum is max(variable) where variable is the extracted variable. 
agemax=max(age)
max(age)
## [1] 68
#Calculate the minimum of age below. The command to find the minimum is min(variable) where variable is the extracted variable. 
agemin=min(age)
min(age)
## [1] 18

Next, 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
upper=agemean+3*agesd
lower=agemean-3*agesd

Another 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   28   36   45   68
lowerq = quantile(age)[2]
upperq = quantile(age)[4]
iqr = upperq - lowerq

The 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% 
## 70.5

Below is the lower threshold:

lowerthreshold = lowerq - (iqr * 1.5)
lowerthreshold
## 25% 
## 2.5

Are 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.

boxplot(age)

When examing the file “scoring.csv”, the box plot above shows us that there are no outliers. If there were outliers present, there would be points above or below the horizontal lines. The horizontal lines represent the maximum and minimum values, i.e. our range. —————

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/scoring_original.csv")
head(newdata)
##   Status Seniority  Home Time Age Marital Records       Job Expenses
## 1   good         9  rent   60  30 married  no_rec freelance     $73K
## 2   good        17  rent   60  58   widow  no_rec     fixed     $48K
## 3    bad        10 owner   36  46 married yes_rec freelance     $90K
## 4   good         0  rent   60  24  single  no_rec     fixed     $63K
## 5   good         0  rent   36  26  single  no_rec     fixed     $46K
## 6   good         1 owner   60  36 married  no_rec     fixed     $75K
##   Income Assets Debt     Amount      Price   Finrat   Savings
## 1  $129K      0    0   $800.00    $846.00  94.56265  4.200000
## 2  $131K      0    0 $1,000.00  $1,658.00  60.31363  4.980000
## 3  $200K   3000    0 $2,000.00  $2,985.00  67.00168  1.980000
## 4  $182K   2500    0   $900.00  $1,325.00  67.92453  7.933333
## 5  $107K      0    0   $310.00    $910.00  34.06593  7.083871
## 6  $214K   3500    0   $650.00  $1,645.00  39.51368 12.830769

We 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/scoring_original.csv",header=TRUE,sep=",") 
head(newdata)
##   Status Seniority  Home Time Age Marital Records       Job Expenses
## 1   good         9  rent   60  30 married  no_rec freelance     $73K
## 2   good        17  rent   60  58   widow  no_rec     fixed     $48K
## 3    bad        10 owner   36  46 married yes_rec freelance     $90K
## 4   good         0  rent   60  24  single  no_rec     fixed     $63K
## 5   good         0  rent   36  26  single  no_rec     fixed     $46K
## 6   good         1 owner   60  36 married  no_rec     fixed     $75K
##   Income Assets Debt     Amount      Price   Finrat   Savings
## 1  $129K      0    0   $800.00    $846.00  94.56265  4.200000
## 2  $131K      0    0 $1,000.00  $1,658.00  60.31363  4.980000
## 3  $200K   3000    0 $2,000.00  $2,985.00  67.00168  1.980000
## 4  $182K   2500    0   $900.00  $1,325.00  67.92453  7.933333
## 5  $107K      0    0   $310.00    $910.00  34.06593  7.083871
## 6  $214K   3500    0   $650.00  $1,645.00  39.51368 12.830769

To 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?

When you attempt to try and use the function, we receive the result “argument is not numeric or logical: returning NA[1] NA”. In order to achieve the correct result, we must clean up the data as we were advised. You can clean it in Excel by following the instructions below.

Price = newdata$Price
mean(Price)
## Warning in mean.default(Price): argument is not numeric or logical:
## returning NA
## [1] NA

To 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)
Price = sub(",","",Price)
#substitute dollar sign with blank in all of checking 
# Example new = sub("\\$","",new)
Price = sub("\\$","",Price)
#Convert values to numeric to remove any NA
# Example new = as.numeric(new)
Price = as.numeric(Price)
## Warning: NAs introduced by coercion
#Calculate mean of checking with NA removed 
# Example mean(new,na.rm=TRUE)
mean(Price,na.rm=TRUE)
## [1] 1462.48

What are some other ways to clean this data? How about Excel? How does Excel treat the missing values and the “$” symbols? Other ways to clean up the data can be through direct interaction in Excel. You can change the formatting of each column and/or use a Find-Replace function to make changes to the data. By changing the formatting directly in Excel, you’re saving yourself from adding functions in RStudio to omit certain values or characters.

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.

Above is a screenshot of the File Size (37.8 MB). There are 24 columns and 100,000 rows in the CSV file “Taxi_Trips_Sample”

In the picture below, the unique entity is highlighted in yellow(the Trip IDs). There is only one unique identity because in order to be unique, the same value mustn’t be repeated. The Trip IDs are the unique identifiers because all the other values such as cab number, times, and destinations can be repeated. The fields in the data are the values within the spreadsheet throughout each column and row.

– Define a relational business logic integrity check for the column field ‘Trip Seconds’.

A relational business logic integrity check for the column field ‘Trip Seconds’ is to verify that values in the column don’t exceed the difference between ‘Trip Start Timestamp’ and ‘Trip End Timestamp’.

– Using https://erdplus.com/#/standalone draw a star like schema using at least the following tables:

Per the above directions, below is a star like schema using the above tables and more:

Every dimension contributes to the fact table. That is, the fact table would not work efficiently without supplementary dimensions. One dimension that I thought would be creative to include was that of “Cab Service”. With the ride sharing industry growing exponentially, cabs may attempt more creative strategies in order to remain in competition with companies like Lyft and Uber.