The next exercise is designed to use an interactive visualization tool. The file LaptopSales.csv is a comma-separated file with nearly 300,000 rows. It has been provided by ENBIS (the European Network for Business and Industrial Statistics) as part of a contest organized in the fall of 2009.

At what price are the laptops actually selling?

Does price change with time?

(Hint:

Make sure that the date column is recognized as such. The software should then enable different temporal aggregation choices, e.g., plotting the data by weekly or monthly aggregates, or even by day of the week.)

Are prices consistent across retail outlets?

How does the price change with configuration?

dataset- https://docs.google.com/spreadsheets/d/e/2PACX-1vTlvVXEEr_zmH9mkoTzbjbBjQBFMbESDj7IANpEIxa-5HMM195xxFSGDD7gTgm2zTicQF6NsY1reSd2/pubhtml?widget=true&headers=false%22%3E%3C/iframe%3E

So far my codes are

summary(LaptopSales$'Retail Price')

#Min. 1st Qu.MedianMean 3rd Qu.Max.NA's

#168.0440.0500.0508.1575.0890.013443

boxplot(LaptopSales$Retail.Price,main="The actual laptop prices",

ylab= "Retail Price", xlab="Laptops",

col=

"bisque"

,medcol=

"red"

,boxlty=

0

,border=

"black"

, whisklty=

1

,staplelwd=

4

,outpch=

13

,outcex=

1

,outcol=

"dark blue"

)

But I got error messages- Error in plot.window(xlim = xlim, ylim = ylim, log = log, yaxs = pars$yaxs) :

need finite 'ylim' values

In addition: Warning messages:

1: Unknown or uninitialised column: `Retail.Price`.

2: In min(x) : no non-missing arguments to min; returning Inf

3: In max(x) : no non-missing arguments to max; returning -Inf

Please help. Thank you

Imagine that you are a new analyst for a company called Acell (a company selling laptops). You have been provided with data about products and sales. You need to help the company with their business goal of planning a product strategy and pricing policies that will maximize Acell's projected revenues in 2009.

With this dataset and the use of R, your job is to:

Use an interactive visualization tool and answer the following questions.

At what price are the laptops actually selling?

Does price change with time?

(Hint: Make sure that the date column is recognized as such. The software should then enable different temporal aggregation choices, e.g., plotting the data by weekly or monthly aggregates, or even by day of the week.)

Are prices consistent across retail outlets?

How does price change with configuration?

dataset- https://docs.google.com/spreadsheets/d/1md4AZTDaId9WEGvl3Z9YwfXIgae-C6QnnH4f7do4-MU/edit?usp=sharing