Section $1$: write a R script to complete the following tasks on data cleaning and descriptive statistics:

Set the R working directory on your computer.

Import "airquality" data from any of the format: Rdata, csv$,$ or txt and name it as raw.

Change variable name "Solar.R$"$ to "SolarRadiation"

Get mean ozone level for the following sample or sub$-$samples.

the overall mean Ozone level for the entire samplemean ozone level when Temp is no higher than $70$ langmean ozone level when Temp is higher than $70$ but below $80$ langmean ozone level when Temp is above $80$ lang but wind is no higher than $10$ miles per hour

Create a histogram for Ozone level.

Calculate the variance and the standard deviation of Ozone

Get a subsample of data with no missing value for Ozone variable using na$.$omit$()$ function and name the new data as raw$\_$no$\_$NA$.$ Feel free to copy, paste, and run the following code to your script: raw$\_$no$\_$NA $<-$ na$.$omit$($insert the name of your data$).$ You can read for details about the$$omit functionLinks to an external site..

Calculate correlation coefficients for the following three pairs:

Ozone with WindOzone with TempOzone with SolarRadiation

Calculate a complex mean Ozone level for each month and make a bar plot.

Tabulate variable MonthUse function tapply$()$ to generate mean ozone level for each monthGenerate a bar plot for monthly mean ozone levels.Assign the title of the bar plot as $$Ozone level by Month$$Label x axis as $$Month$$Label y axis as $$Mean Ozone Level$$Use rainbow color or heat colors for the barsAdd the value of May, June, July, August, September to the x axis.

Generate an xy plot for Ozone and Solar Radiation.