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)

## No, you cannot find the total cumulative sales from histogram. Histogram shows the frequency with which sales occurred in a determined range. In other words, we can see from this histogram how many times a sale within 14000-16000 occurred e.g., 4. But we cannot see the exact amount of such sales therefore it is not possible to determine te total 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
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 histogram is better, because in such chart the analyst can see the number of occurrences in the pie chart the analyst could make an estimation based on the size of the slices, if there is any, if one is bigger than the other but nothing accurate.

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

scatter.smooth(tv,sales)

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

scatter.smooth(Paper,sales)

#Sales vs. Pos
Pos= mydata$pos
plot(Pos,sales)

scatter.smooth(Pos,sales)

There is a strong relationship between sales and radio, it is a positive relationship that is if one increases the other will increase too. The second strong -positive- relationship seems between sales and tv. Meanwhile, the relationship between sales and paper seems to be a negative one that means the higher the values of one variable the lower the values of the other. However, the relationshipo between Sales and Pos seems to be a sporadic trend maybe resulting in a low correlation coefficient.

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 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 the one between sales and radio. The closer the coefficient is to +1 the stronger the relationship between two variables that means they will move in the same direction and with the same magnitude. In this case, that occurs with sales and radio (0.977) and it could be observed in the 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?

The age bin with the highest age 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?

The age bin with the highest divorce 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 your observations

The male gender seems to be more representative in this population. There is no correlation between months employed and age, and we can see a highly accumulation of data (or people employed) in the range of 20-40 ages employed for a period of 0-40 months.

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.

We can observe that, as stated previously, male gender represents a significant part of the entire population. It is also important to remark that female gender is only present, with 135 records, in the “divorced” category. WHile the others is totally composed by male the married status with 36 records, single with 233 and divorced 21 records.
———————————————————————-