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)

# 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 = 'Frequency')

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)

sum(sales)
## [1] 334344

You can find the cumulative value of sales from the histogram in 2 ways. First, you can take each value on the x-axes, and multiply it by its corresponding value on the y-axis, then sum those values together. Although, this would only give you a range as to what the sales value would be, since the sales values lay within a certain range as displayed on the histogram. So, the minimum value would be $312,000 and the maximum value would be $352,000. In order to find the true value, you would take the shortcut route and use the equation ‘sum(sales)’. The value received from this equation is displayed above ($334,344), which lays between the histogram boundaries.

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
x = c(3, 3, 2, 3, 3, 4, 2)
labels = c("AZ", "CA", "CO", "FL", "IL", "MI", "MN")
pie(x, labels)

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

COMPARISON

Examining both data visualizations, the bar chart provides a better comparative representation of the data. This is because the bar chart better displays the distribution of the values across the different states. It’s to hard to differentiate between the values in the pie chart because they are close in size.

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.
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)

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 actually between sales and radio. The ‘cor’ function with respect to radio produces the highest value. Not only is it displayed in the correlation equation, but also on the scatter plot. Looking at the (radio, sales) scatter plot, there is a steady increase in sales as radio ads increase. Also, the points plotted on the graph are very snug against the line of best fit, thus carrying the smallest deviations from the line. This emphasizes an even stronger relationship.

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?
Histogram of Age

Histogram of Age

The above image is of a histogram displaying age distribution. The bin with the highest age count is age bin 22, with an age count of 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?
Marital Status vs. Age

Marital Status vs. Age

Above is an image displaying the relationship between age and marital status. The age bin with the highest divorce count is 22, with a divorce count of 46.

3C) Create another new sheet. Double-click ‘Months Employed’ and then double-click ‘Age’. Make sure Age appears in the columns field as shown in the image below. From the Sum(Age) drop down menu select Dimension. Repeat for Sum(Months Employed). Add another variable to the scatter plot by drag-drop the dimension variable ‘Gender’ into the Marks Color box. Capture a screen shot and include here. Share your observations
Months Employed vs. Age vs. Gender

Months Employed vs. Age vs. Gender

The above image displays the relationship between an individual’s age, gender, and the number of months they’ve been employed. One can observe that the majority of females are clustered into a group that has worked below 65 months and younger than 45. This would infer that the majority of females are working less as they grow older unlike men, whose relationship with age and months employed is positively and directly related.

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.
Gender vs. Number of Records vs. Marital Status

Gender vs. Number of Records vs. Marital Status

Above is a bubble chart displaying the relationship between Gender, Number of Records, and Marital Status. The observation one can make is that the majority of records on hand are attributed to single males, followed by divorced females. This would insinuate that the majority of loans being distributed are to single males, followed by divorced females. With this inforamtion, a bank manager could target this demographic. They could also examine the loan purposes for these borrowers then form targeted marketing where things people are taking loans are for are located.