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)

I cannot calculate the total cummulative sales from a histogram because histograms depict the number of occurrences (or frequency) of a variable. In this case, the histogram can tell me there are 5 occurances of sales that are between 16000 and 18000, but I do not know the totals of each of those occurances. Therefore, a cummulative total is not possible to calculate based on a histogram alone.

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
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 representation of the data because it reveals the number of occurances of each state. The pie chart is more visual, but does not give you any specific 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.
# Plot Sales vs. TV
# TV will be on the x-axis
# Sales will be on the y-axis

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

# Best fitting line
scatter.smooth(tv,sales)

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

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

# Best fitting line
scatter.smooth(paper,sales)

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

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

# Best fitting line
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?

Sales and radio both have the highest correlation, which is shown in the scatter plot since they had the strongest positive trend. Tv and sales is a close second, which is also revealed in the positive trend of their scatter plot. Sales and Pos have the lowest correlation, as revealed by the sporadic data points in their scatter plot. Finally, Sales and Paper have a negative correlation, which is also seen in the negative trend of their scatter plot.

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?
img1_path <- "imgs/Screenshot.png"
knitr::include_graphics(img1_path)

The highest age bin is 22 with 97 counts.

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?

img1_path <- "imgs/Screenshot2.png"
knitr::include_graphics(img1_path)

The age bin with the highest divorce count is 22 with 46 counts.

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

img1_path <- "imgs/Screenshot3.png"
knitr::include_graphics(img1_path)

The correlation between age and months employed seems to be very low since the data points are scattered sporadically. However, there is a large amount of them in the lower left corner, which means there are many between age 20-50 who have been employed 0 and 50 months. There is also a higher male population than female.

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.
img1_path <- "imgs/Screenshot4.png"
knitr::include_graphics(img1_path)

There are 135 records of divorced females, but no records of married or single females. As for the males, there are records of males who are married and divorced, but the highest record is of single males at a count of 233.