Question

In: Computer Science

Background: This course is all about data visualization. However, we must first have some understanding about...

Background: This course is all about data visualization. However, we must first have some understanding about the dataset that we are using to create the visualizations.

Assignment:

Use RStudio to generate a word document with basic data analysis of the following dataset: dataset_price_personal_computers.csv

Questions/Requests:

Create a summary of stats for the dataset. (provide a screen shot)
Create a correlation of stats for the dataset. (provide a screen shot) (Hint: Transform may be needed)
What is the Min, Max, Median, and Mean of the Price? (provide a screen shot)
What is the correlation values between Price, Ram, and Ads? (provide a screen shot)
Create a subset of the dataset with only Price, CD, and Premium. (provide a screen shot)
Create a subset of the dataset with only Price, HD, and Ram where Price is greater than or equal to $1750. (provide a screen shot)
What percentage of Premium computers were sold? (provide a screen shot)(Hint: Categorical analysis)
How many Premium computers with CDs were sold? (provide a screen shot)(Hint: Contingency table analysis)
How many Premium computers with CDs priced over $2000 were sold? (provide a screen shot)(Hint: Conditional table analysis)

Your document should be an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format).

For : dataset_price_personal_computers.csv this is the link. With the help of this you can download that csv file

https://drive.google.com/file/d/1Op6XIzU5WuVF-w1OHqdcUJXMTUICaFy0/view

Sorry, i came to know that unable to download the file. Try this link and let me know

Please provide answers for this question including screenshots and code.

Thanks

Expert Solution

data<-read.csv("C:/Users/meet/Desktop/dataset_price_personal_computers .csv")[,-1]
#1

summary(data)

#2
str(data)
#creating a copy of data and saving it in data1
data1<-data
#converting column cd,multi & premium to integer type as they are factors
data1[,6:8]<-lapply(data1[,7:9],as.integer)
# now finding the correlation between the variables
cor(data1)

#output for 2nd ques

#3
summary(data$price)
#3 another way
min(data$price)
max(data$price)
mean(data$price)
median(data$price)

#4 the correlation values between Price, Ram, and Ads
cor(data[,c('price','ram','ads')])

#5 Create a subset of the dataset with only Price, CD, and Premium.
data_subset1=subset(data,select=c('price','cd','premium'))

#6 Create a subset of the dataset with only Price, HD, and Ram where Price is
#greater than or equal to $1750.
data_subset2<-subset(data,data$price>=1750,select=c('price','hd','ram'))

#7 percentage of Premium computers were sold

# answer is 90.22%
tab_premium<-table(data$premium)
prop.table(tab_premium)

#8 Premium computers with CDs were sold

#answer is 2824
table(data$premium,data$cd)

#9 Premium computers with CDs priced over $2000 were sold

#answer is2002
table(data$cd[which(data$price>2000)])

#output of 4th

#output of 5th

#output of 6th

#output 0f 7, 8 &9

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