Business Analytics Lab Worksheet 05 (bsad

About

R can be used to make basic visual analytics, which can be helpful in understanding the data holistically. Additionally, R can help find correlations between variables and create scatter plots.

Tableau is a tool more tailored for visual analytics, while R is a powerful tool for statistics and other advanced topics in data analytics. In this lab we will explore both capabilities using two earlier sets of data credistrisk and marketing.

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.

Note

For your assignment you may be using different data sets than what is included here. Always read carefully the instructions on Sakai. Starting with this worksheet, tasks/questions to be completed/answered are highlighted in larger bolded fonts and numbered according to the particular task section.

Task 1: Basic Visual Analytics in R

Read the file marketing.csv and make sure all the columns are captured by checking the first couple rows of the dataset “head(mydata)”

mydata = read.csv("data/marketing.csv")
head(mydata)

##   case_number State sales radio paper  tv pos
## 1           1    IL 11125    65    89 250 1.3
## 2           2    IL 16121    73    55 260 1.6
## 3           3    AZ 16440    74    58 270 1.7
## 4           4    AZ 16876    75    82 270 1.3
## 5           5    IL 13965    69    75 255 1.5
## 6           6    MI 14999    70    71 255 2.1

How to create a bar chart using categorical variable

# Extract the State column from mydata
state = mydata$State
# Create a frequency table to extract the count for each state
state_table = table(state)
state_table

## state
## AZ CA CO FL IL MI MN 
##  3  3  2  3  3  4  2

# Execute the  command 
barplot(state_table)

1A) Repeat the above bar chart by adding proper labels to X and Y axis. See example below.

# Add labels to plot by replacing the ?? with a proper title
barplot(state_table, xlab= 'State', ylab = 'Case Number' )

How to create a histogram

# Extract the TV column from the data and create a histogram by running the command hist(variable) 
# where variable corresponds to the extracted sales column variable
tv=mydata$tv
hist(tv)

1B) Create a new histogram plot for Sales. Can you find the total cummulative sales from the histogram? Explain your answer

sales=mydata$sales
hist(sales)

Total cumulative sales cannot be found from the histogram since it does not provide specific sales values, but rather a range of sales. The histogram tells you how many sales fall into each particular sales range. For example, 16,121 would fall in the $16,000-18,000 range, but the specific value of 16,121, is not found in the histogram.

How to create a pie chart

# The command to create a pie chart is pie(variable) where  variable is in reference to the particular column extracted from the file. In this example we define a variable called x. 
x = c(2,3,4,5)
pie(x)

1C) Create a pie chart for variable state. The pie chart should show the count of each state similar to the barplot above

pie(state_table)

1D) Compare the pie chart to the earlier bar chart. Which one you think is a better comparative representation of the data and why?

The bar chart is a better comparative representation of data since it it provides numerics and is more visually appealing. The pie chart does not have any numeric values, and it is hard to tell the variation between sections since they are relatively similar in size. The bar chart has the case number on the vertical access and state name on the horizontal, making the range of values easy to compare and understand.

Task 2 Scatter Plots & Correlation

The previous task focused on visualizing one variable. A good way to visualize two variables and also very common is a scatter plot.

How to create a scatter plot

# Plot Sales vs. Radio
# Radio will be on the x-axis
# Sales will be on the y-axis

sales = mydata$sales
radio = mydata$radio
plot(radio,sales)

# It is easier to see the trend and possible relationship by including a line that fit through the points.
# This is done with the command 
scatter.smooth(radio,sales)

2A) Create three other separate scatter plots for Sales vs TV, Sales vs Paper, and Sales vs Pos. Include the best fitting line in each plot. Pay attention to what goes on the x-axis and the y-axis. Put Sales always on the Y-axis

sales=mydata$sales
tv=mydata$tv
plot(tv,sales)

scatter.smooth(tv,sales)

sales=mydata$sales
paper=mydata$paper
plot(paper,sales)

scatter.smooth(paper,sales)

sales=mydata$sales
pos=mydata$pos
plot(pos,sales)

scatter.smooth(pos,sales)

2B) Share your observations on trends and potential relationships

In terms of marketing data, it is clear that the relationship between sales and tv marketing and sales and radio marketing is stronger than newspaper or pos, indicated by positive correlation and line of best fit. This could mean that tv and radio marketing are bigger contributors to raising sales. When the relationship between sales and paper marketing is looked at, there is a negative correlation exhibited by the downward sloping line of best fit and scattered variables. As for sales and pos, the relationship is unclear/variable due to the lack of correlation on the line of best fit with the distance of the variables; the line also experiences a large dip.

To quantify the strength of any relationships in the data, we need to look at the correlation between two variables.

How to compute correlation

cor(sales,radio)

## [1] 0.9771381

2C) Repeat the correlation calculation for the followinig each pair of variables (sales,tv), (sales,paper), and (sales,pos)

cor(sales,tv)

## [1] 0.9579703

cor(sales,paper)

## [1] -0.2830683

cor(sales,pos)

## [1] 0.0126486

2D) Which pair has the highest correlation? How do these results reconcile with the scatter plots observations?

The highest correlation is between sales and radio with .9771381. These results reconcile with the scatter plot observations because the data points for radio were close to the line of best fit and exhibited positive correlation.

Task 3 - Basic Visual Analytics in Tableau

Follow the directions on the worksheet, download tableau academic on your personal computer or use one of the labs computers.

– Download Tableau academic here: https://www.tableau.com/academic/students

– Refer to file ‘creditrisk.csv’ in the data folder

– Start Tableau and enter your LUC email if prompted.

– Import the file into Tableau. Choose the Text File option when importing

– Under the dimensions tab located on the left side of the screen DOUBLE click on the ‘Loan Purpose’, then DOUBLE click on ‘Number of Records’ variable located under Measures on the bottom left of the screen.

– From the upper right corner of the screen select the horizontal bars view and note the new chart. Familiarize yourself with the tool by trying other views. Only valid views will be highlighted in the menu.

– Create a new sheet by clicking on the icon in the bottom next to your current sheet.

3A) Double-click on the ‘Age’ variable in Measures and select the ‘Histogram’ view. Capture a screen shot and include here. Which age bin has the highest age count and what is the count?

Age bin 22 with count of age 97.

3B) Drag-drop the variable ’Marital Status’found under Dimensions into the Marks Color box. Capture a screen shot and include here. Which age bin has the highest divorce count and what is the count?

Age bin 22 with a count of 46.

Business Analytics Lab Worksheet 05 (bsad_lab05)

CME Group Foundation Business Analytics Lab

Sara Bozovic

10/17/17

About

Setup

Note

Task 1: Basic Visual Analytics in R

1A) Repeat the above bar chart by adding proper labels to X and Y axis. See example below.

1B) Create a new histogram plot for Sales. Can you find the total cummulative sales from the histogram? Explain your answer

1C) Create a pie chart for variable state. The pie chart should show the count of each state similar to the barplot above

1D) Compare the pie chart to the earlier bar chart. Which one you think is a better comparative representation of the data and why?

Task 2 Scatter Plots & Correlation

2A) Create three other separate scatter plots for Sales vs TV, Sales vs Paper, and Sales vs Pos. Include the best fitting line in each plot. Pay attention to what goes on the x-axis and the y-axis. Put Sales always on the Y-axis

2C) Repeat the correlation calculation for the followinig each pair of variables (sales,tv), (sales,paper), and (sales,pos)

2D) Which pair has the highest correlation? How do these results reconcile with the scatter plots observations?

The highest correlation is between sales and radio with .9771381. These results reconcile with the scatter plot observations because the data points for radio were close to the line of best fit and exhibited positive correlation.

Task 3 - Basic Visual Analytics in Tableau

3A) Double-click on the ‘Age’ variable in Measures and select the ‘Histogram’ view. Capture a screen shot and include here. Which age bin has the highest age count and what is the count?

3B) Drag-drop the variable ’Marital Status’found under Dimensions into the Marks Color box. Capture a screen shot and include here. Which age bin has the highest divorce count and what is the count?

Business Analytics Lab Worksheet 05 (bsad_lab05)

CME Group Foundation Business Analytics Lab

Sara Bozovic

10/17/17

About

Setup

Note

Task 1: Basic Visual Analytics in R

1A) Repeat the above bar chart by adding proper labels to X and Y axis. See example below.

1B) Create a new histogram plot for Sales. Can you find the total cummulative sales from the histogram? Explain your answer

1C) Create a pie chart for variable state. The pie chart should show the count of each state similar to the barplot above

1D) Compare the pie chart to the earlier bar chart. Which one you think is a better comparative representation of the data and why?

Task 2 Scatter Plots & Correlation

2A) Create three other separate scatter plots for Sales vs TV, Sales vs Paper, and Sales vs Pos. Include the best fitting line in each plot. Pay attention to what goes on the x-axis and the y-axis. Put Sales always on the Y-axis

2B) Share your observations on trends and potential relationships

2C) Repeat the correlation calculation for the followinig each pair of variables (sales,tv), (sales,paper), and (sales,pos)

2D) Which pair has the highest correlation? How do these results reconcile with the scatter plots observations?

The highest correlation is between sales and radio with .9771381. These results reconcile with the scatter plot observations because the data points for radio were close to the line of best fit and exhibited positive correlation.

Task 3 - Basic Visual Analytics in Tableau

3A) Double-click on the ‘Age’ variable in Measures and select the ‘Histogram’ view. Capture a screen shot and include here. Which age bin has the highest age count and what is the count?

3B) Drag-drop the variable ’Marital Status’found under Dimensions into the Marks Color box. Capture a screen shot and include here. Which age bin has the highest divorce count and what is the count?

3D) In a new sheet generate a view of Gender, Number of Records, and Marital Status. Choose the best fitting view of your choice for the intended scope. Capture a screen shot and include here. Share your observations.