Problem: follow the following steps and answer the questions accordingly. Make sure save the output of each step including the diagrams/charts you generate. Make sure to submit the .R files and the document that has all the plots and answers to the question. ( two files : Midterm.R , and Midterm.docx)
Problem: follow the following steps and answer the questions accordingly. Make sure save the output of each
step including the diagrams/charts you generate. Make sure to submit the .R files and the document that has
all the plots and answers to the question. ( two files : Midterm.R , and Midterm.docx)
Step 1: Load the data
We will use the air quality dataset that you should already have as part of your R installation.
Step 2: Clean the data
After you load the data, there will be some NAs in the data. You need to figure out what to do about those
nasty NAs.
Step 3: Understand the data distribution
Create the following visualizations:
• Histograms for each of the variables
• Boxplot for Ozone, and boxplots for different wind values (round the wind to get a good number of
“buckets”)
Step 3: Explore how the data changes over time
First, create appropriate dates (this data was from 1973). Then create line charts for ozone, temp, wind and
solar.R (one line chart for each, and then one chart with 4 lines, each having a different color).
Note that for the chart with 4 lines, you need to think about how to effectively use the y-axis.
Step 4: Look at all the data via a heatmap
Create a heatmap, with each day (using dates) along the x-axis and ozone, temp, wind and solar.r along the yaxis.
Note that you need to figure out how to show the relative change equally across all the variables.
Step 5: Look at all the data via a scatter chart
Create a scatter chart, with the x-axis representing the wind, the y-axis representing the temperature, the size
of each dot representing the ozone and the color representing solar.R.
Step 6: Final analysis
• Do you see any patterns after exploring the data?
• What was the most useful visualization?
Comments from Customer
air <-airquality #copy the built-in data to new data frame
View(air)
this should be a built-in dataset when as part of R studio installation
https://rpubs.com/shailesh/air-quality-exploration
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