Descriptive Statistics using R

Consider the following data-set showing the customer usage of the treadmill. The requirement is to do Descriptive Analytics on this data to create a customer profile for each Treadmill.

Product	Age	Gender	Education	Marital Status	Usage	Fitness	Income	Miles
TM195	18	Male	14	Single	3	4	29562	112
TM195	19	Male	15	Single	2	3	31836	75
TM195	19	Female	14	Partnered	4	3	30699	66
TM195	19	Male	12	Single	3	3	32973	85
TM195	20	Male	13	Partnered	4	2	35247	47
TM195	20	Female	14	Partnered	3	3	32973	66
TM195	21	Female	14	Partnered	3	3	35247	75
TM195	21	Male	13	Single	3	3	32973	85
TM195	21	Male	15	Single	5	4	35247	141
TM195	21	Female	15	Partnered	2	3	37521	85
TM498	19	Male	14	Single	3	3	31836	64
TM498	20	Male	14	Single	2	3	32973	53
TM498	20	Female	14	Partnered	3	3	34110	106
TM498	20	Male	14	Single	3	3	38658	95
TM498	21	Female	14	Partnered	5	4	34110	212
TM498	21	Male	16	Partnered	2	2	34110	42
TM498	21	Male	12	Partnered	2	2	32973	53
TM498	23	Male	14	Partnered	3	3	36384	95
TM498	23	Male	14	Partnered	3	3	38658	85
TM498	23	Female	16	Single	3	3	45480	95
TM798	31	Male	16	Partnered	6	5	89641	260
TM798	33	Female	18	Partnered	4	5	95866	200
TM798	34	Male	16	Single	5	5	92131	150
TM798	35	Male	16	Partnered	4	5	92131	360
TM798	38	Male	18	Partnered	5	5	104581	150
TM798	40	Male	21	Single	6	5	83416	200
TM798	42	Male	18	Single	5	4	89641	200
TM798	45	Male	16	Single	5	5	90886	160
TM798	47	Male	18	Partnered	4	5	104581	120
TM798	48	Male	18	Partnered	4	5	95508	180

The following code assumes the data-set to be available in .csv file to be read into the R environment
#Open the File dialog to select the .csv file containing the data-set
myFile <- file.choose()

#Read the .csv content into R and store in myData variable
myData  <- read.csv(myFile, header = TRUE)
class(myData)


#Create variables with .csv file column header values
attach(myData)

#Show summary for the entire file
summary(myData)

 
#Histogram of Age
hist(Age, col = heat.colors(14), main = "Histogram of Age", xlab = "Age")

#BoxPlot of Age
boxplot(Age, horizontal = TRUE, col = "RED", main = "BoxPlot of Age")

 
#BoxPlot of Age as a factor of Gender
boxplot(Age~Gender, horizontal = TRUE, col = c("RED", "LIGHTBLUE"), main = "BoxPlot of Age by Gender")
 

#BoxPlot of Age as a factor of Product
boxplot(Age~Product, horizontal = TRUE, col = c("RED", "LIGHTBLUE", "GREEN"), main = "BoxPlot of Age by Product")


#Show summary grouped by Product
by(myData, INDICES = Product, FUN = summary)
 
 
 

Some inference of this summary is as follows:

• Product TM798 is mostly used by Males
• Frequency of usage of TM798 is more when compared with other Products
• TM798 is mostly used by customers with higher income and education compared to other products
• Customers who bought TM798 are satisfied with the fitness achieved with use of this product

REFERENCES
https://www.greatlearning.in/great-lakes-pgpba/