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. For clarity, tasks/questions to be completed/answered are highlighted in red color and numbered according to their particular placement in the task section. Quite often you will need to add your own code chunk.

Execute all code chunks, preview, publish, and submit link on Sakai.

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 a 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) Use the code chunk below to repeat the above bar chart by adding proper labels to X and Y axis.

# Add title and labels to plot by replacing the ?? with the proper wordings
barplot(state_table, main = 'Sales per State', xlab= 'State', ylab = 'Case Number' )

A more elegant representation of the bar plot would be to order the bars by increasing value. This is shown in the code chunk below.

# Order and execute
barplot(state_table[order(state_table)])

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. Explain what the x-axis and y-axis represent. Can one derive the total cummulative sales from the histogram? Explain your answer. 

# Extract the sales column from the data and create a histogram by running the command hist(variable) 
# where variable corresponds to the extracted sales column variable
sales=mydata$sales
hist(sales)

The x - axis represents the total sales of paper, tv and radio and the y - axis represents how frequently they were sold. It is possible to get the total sales in this histogram, you simply take the sum of each range.

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 new pie chart for state count. Refer to variable state_table to capture the frequency count. 

pie(state_table)

##### 1D) What does each slice of the pie represent? Compare the pie chart to earlier bar charts. Which type of charts is a better representation of the data and why so?

Each slice represnets the sales in each state. When comparing it to the earlier bar charts, I think the histrograms/bar charts better reoresent this data. the frequency is clear and easier to ditect where as the pie chart does not give a sales range or frequency, and it is more difficult to obtain this information.

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. A scatter plot is a good way to study relationships and trends.

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 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 variable 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)

# 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)

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

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

##### 2B) Share your observations on trends and relationships. How do your observations reconcile with your findings from lab04?

From the scatter plots above, it is evident that TV is the only varibale that really has a positvie impact on sales. Similar to lab04 where this was also the case. Essenitally, in both lab instanaces, TV increased sales. When it comes to the variable, paper it seems to have a negative impact in sales, as sales show to be decreasing. Lastly, POS demonstrated no correlation or relationship with sales.

As part of any data anlytics it is important to consider both qualitative and quantitative analysis. Scatter plots provide us with qualitative insights on possible trends and relationships. 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 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 your scatter plots observations?

The pair that has the highest correlation is “(sales,tv).” This is also evident by the scatter plot as it shows a positive, increasing correlation. The dots are close together and are in an upward direction where as the other two pairs do not show any sort of realtionship.

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. Make sure to download the academic version and not the free limited trial version.

– 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?

From the histogram, it is evident the age bin with the highest ocunt is 22 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?

From the histogram, it is evident that the age bin with the highest divorce count is 22 and the count is 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 a story on what the data is telling us

This scatter plot demonstrates the realtionship between age, gender and months employeed. From this particular realionship it is clear that both genders start wokring at age 20, as the age increases the number of females in jobs decreases, most likely due to having children and taking care of families.

##### 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 a story on what the data is telling us.

I generated a pie chart for the last graph to get a variety of ways to show the data. While the pie chart does not show exact numbers, I thought it demonstrated the fact that the population seems to be dominated by men. Unforutnetly most women seem to be the ones that end up getting a divorce