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 = 'Number of case numbers per state' )

How to create a histogram

# Extract the TV column from the data and create a histogram by running the command hist(variable) 

tv=mydata$tv
hist(tv, xlab = 'amount of $(thousands) spent on advertising with TVs', ylab = '# case studies on TVS')

# where variable corresponds to the extracted sales column variable
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)

#you can estimate the total cumulative sales by taking the x-axis values and multiplying them by the corresponding y-axis values. For example, taking the value average betwee 10000 and 12000 (11000) and multiplying by 1, because it occurred once. Or taking the value average between 16000 and 18000 (17000) and multiplying by 5, because it occurred 5 times. Do this for each bar, then add up the totals, and you can get a rough estimate of the cumulative sales. However, you cannot get an exact value of total cumulative 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
state = mydata$State
s_table=table(state)
pie(s_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?

##I think that the bar chart is a better representation because the values of the numbers are close together, and it is more difficult to tell the difference in values on the pie chart compared to the bar chart.

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)

paper=mydata$paper
plot(paper,sales)

scatter.smooth(paper,sales)

pos=mydata$pos
plot(pos,sales)

scatter.smooth(pos,sales)

The trend of the tv/sales table is upward sloping, meaning the more money spent on advertising through TVs, the more they sell. Paper/sales seems to be the opposite, and they should probably stop spending so much money on advertising on papers. It is not worth spending more money on advertising papers. Finally, the point of sales relationship seems to be a bit interesting. They should only spend about 1,000 on the point of sale advertisements, because if they spend more, it decreases sales, unless they spend more than $2,500 on point of sales advertisements.

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 following 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 of variables is between sales and radio, because it is the closest number to 1, meaning the slope of the smoothed scatterplot line is close to 1. The correlation between sales and tv is also very highly correlated. The graph that shows that the relation between tv and sales is pretty straight upward sloping.

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?
The age bin from 22 to 26 has the highest age count, and the count is 97.

The age bin from 22 to 26 has the highest age count, and the count is 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?
The age bin of 22-26 has the highest divorce count at 46 divorced couples.

The age bin of 22-26 has the highest divorce count at 46 divorced couples.

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
It seems that in general, women tend to be employed for shorter periods of time compared to men. And for both men and women, the younger they are, the less months employed.

It seems that in general, women tend to be employed for shorter periods of time compared to men. And for both men and women, the younger they are, the less months employed.

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.
All of females that receive a loan are divorced, whereas a majority of males who are single get a loan.

All of females that receive a loan are divorced, whereas a majority of males who are single get a loan.