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)

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?

In my opinion the bar chart is a much better representation of the data because it allows you to viasualize and understand the data much better than the pie chart because it gives you the numbers on the side. It is also a lot easier to see which states are performing better, and which ones are not. On the other hand the pie chart is a lot harder to read and understand because it doesn’t really show any numbers.

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

scatter.smooth(tv,sales)

#Sales vs Paper
sales = mydata$sales
paper = mydata$paper
plot(paper,sales)

scatter.smooth(paper,sales)

#Sales vs Pos
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 with sales v. radio with a correlation of .9771381. This result confirms what we are able to see in the scatter plot because the scatter plot of sales v. radio is the one that is the most organized, and least spread out. We can see that sales v. paper is also the one with the least correlation because of the -0.2830683 result, but also because of how spread out the dots are, and how far from the line they are.

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?
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 25 has the biggest divorce count with a number of 47.

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

By looking at this scatter plot you can tell that most females who are employed are between the ages of 20-30. They are also employed for an average of 20-25 months.There are only a few women who have stayed at the company for over 60 months, and although there are some young employees, most of them are over the age of 40. You can also tell that there are more male hires, who stay there for a long period of time, also younger males have worked at the company for a longer period of time, when compared to females.

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.

By looking at this graph you can tell that all the females that work in this company are divorced. Most males are single with over 200 of them making up this marital status, and there’s a small number of men who are either married (which is around 38 of them), or divorced which there are 20 men who are divorced.