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.

PART I: 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)

TASK 1. 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)

TASK 2A. Create a new histogram plot for Sales.

sales=mydata$sales
hist(sales)

###TASK 2B. Can you find the total cummulative sales from the histogram? Explain your answer ANSWER: You can estimate close by adding the bars. However, there is a range of 2,000 within every box so getting the exact number of cumulative sales is not possible. 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)

TASK 3A. Create a pie chart for variable state_table (which contains the count for each state)

x = c(2,3,4,5)
pie(state_table)

TASK 3B. Compare the pie chart to the earlier bar chart. Which one you think is a better comparative representation of the data and why?

ANSWER: The bar chart. The pie chart does a good job comparing variables to one another however, the bar chart gives numerical data for every state. ———-

PART II: 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)

TASK 4A. 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)

TASK 5A. 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

TASK 5B. Which pair has the highest correlation? How do these results reconcile with the scatter plots observations?

ANSWER: The sales vs tv graph has the highest correlation. It is consistent with the scatter plot as the points have a strong upward trend.

PART III: 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

Click on Sheet 1 (bottom left). – 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.
###TASK 6A. Go to new worksheet. Double-click on the ‘Age’ variable in Measures and select the ‘Histogram’ view. Capture a screen shot and include here.
###TASK 6B. Which age bin has the highest age count and what is the count? ANSWER: The bing with the highest age count is between 20-25 with a count of 97.
###TASK 7A. Drag-drop the variable ’Marital Status’found under Dimensions into the Marks Color box (make sure to drop in the Marks Color box until you see the small plus mark). Capture a screen shot and include here.
###TASK 7B. Which age bin has the highest divorce count and what is the count? ANSWER: The age bin 20-25 has the largest divorce count at 46.
###TASK 8A. 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 (until you see the small plus mark). Capture a screen shot and include here.
###TASK 8B. Share your observations ANSWER: As people grow older they typically have more months employed. Male workers tend to stick around a bit longer. This plot is pretty scattered however, it does look as though there is some sort of upward trend.
###TASK 9A. 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. More male than female.
###TASK 9B. Share your observations. ANSWER: There are noticeably more males than females. It seems that all males are either single or married. Females however, seem to have more divorces than males. They have 135 accounts of divorce and males only have 21. ###TASK 10A. Create another new sheet. Double-click ‘Job’ and then double-click ‘Number of Records’. Add another variable to the graph by drag-drop the dimension variable ‘Gender’ into the Marks Color box (until you see the small plus mark). Click on staked bars. Capture a screen shot and include here.
###TASK 10B. Share your observations. ANSWER: Again, there are obviously more males than females in this. However, a higher number of females are unemployed than males. Males hold almost 4 times as many people in management as females do. While when it comes to unskilled work males have a count of 61 and females 28.