Steps to Follow:

Exploratory Data Analysis:

Load the airquality dataset.

Summarize the dataset using functions like summary$(),$ str$(),$ and head$().$

Accuracy:

Identify any potential inaccuracies by cross$-$referencing with known values $($if available$).$

Check for outliers using boxplots $($boxplot$())$ and histograms $($hist$()).$

Completeness:

Check for missing values using is$.$na$()$ and sum$($is$.$na$()).$

Discuss the implications of missing data and strategies to handle it$.$

Consistency:

Verify that all columns have consistent data types.

Ensuring that temperature $($Temp$),$ wind speed $($Wind$),$ and other values are within reasonable ranges.

Validity:

Validate that all data falls within expected ranges $($e$.$g$.,$ Month should be between $5$ and $9..$

Use assertthat or similar packages to enforce validation rules.

Uniqueness:

Check for duplicate rows using duplicated$()$ and unique$().$

Discuss the impact of duplicates and methods to resolve them.

Generating a Report:

Write a detailed report summarizing your findings for each data quality attribute.

Provide recommendations for improving data quality.

Sample R Code Snippets $($Guidelines$)$:

Loading and Summarizing Data:

# Load the airquality dataset

data$("$airquality$")$

# Summary and structure

summary$($airquality$)$

str$($airquality$)$

head$($airquality$)$

Checking for Missing Values:

# Check for missing values

sum$($is$.$na$($airquality$))$

Identifying Duplicates:

# Check for duplicate rows

duplicated$\_$rows $<-$ airquality$[$duplicated$($airquality$),]$

print$($duplicated$\_$rows$)$

Outlier Detection:

# Boxplot for detecting outliers in Ozone

boxplot$($airquality$Ozone, main $=$ "Boxplot of Ozone", ylab $=$ "Ozone $($ppb$)",$ col$=$"lightblue"$)$

# You can do it for other variables in dataset

Submission:

Submit an R script with your analysis.

Provide a written report $($PDF or Word$)$ summarizing your findings and recommendations to improve data quality based on your findings.