Finding data in new places

• Since your first forays into doing data science, you’ve used data from two sources:
  • Built-in datasets from RStudio.
  • Campaign data from the Campaign Manager.
• Data can be found in many other places though, especially online.
• In this lab, we’ll read an observational study dataset from a website.
  • We’ll use this data to then explore what factors are associated with a person’s lung capacity.

Importing our data

• Rather than export-ing the data and then upload-ing and importing-ing it, we’ll pull the data straight from the webpage into R.

• You can find the data online here:

  • (Right-click and select Open in New Window) https://raw.githubusercontent.com/IDSUCLA/dataset/main/fev.csv

• Click on the Import Dataset button under the Environment tab.

  • Then click on the From Text (readr) option.
  • Type or copy/paste the URL into the box.
  • Click Update.

• Before importing, change the following Import Options:

  • Name: lungs
  • Uncheck the First Row as Names
    
  • Change Delimiter to Whitespace

Our new data

• Variables that were measured include:
  • Age in years.
  • Lung capacity, measured in liters.
  • The youth’s heights, in inches
  • Genders; "1" for males, "0" for females.
    
  • Whether the participant was a smoker, "1", or non-smoker "0".

About the data

• The data come from the Forced Expiratory Volume (FEV) study that took place in the late 1970’s.
• The observations come from a sample of 654 youths, aged 3 to 19, in/around East Boston.
• Researchers were interested in answering the research question:

What is the effect of childhood smoking on lung health?

Cleaning your data

• Now that we’ve got the data loaded, we need to clean it to get it ready for use (look at lab 1F for help). Specifically:
  • We want to name the variables: "age", "lung_cap", "height", "gender", "smoker", in that order.
  • Change the type of variable for gender and smoker from numeric to character.
• After changing the variable types for gender and smoker:
  • For gender, use recode to change "1" to "Male" and "0" to "Female".
  • For smoker, use recode to change "1" to "Yes" and "0" to "No".

Analyzing our data

• Our lungs data is from an observational study.
• Write down a reason the researchers couldn’t use an experiment to test the effects of smoking on children’s lungs.
• Observational studies are often helpful for analyzing how variables are related:
  • Do you think that a person’s age affects their lung capacity? Make a sketch of what you think a scatterplot of the two variables would look like and explain.
• Use the lungs data to create an xyplot of age and lung_cap.
  • Interpret the plot and describe why the relationship between the two variables makes sense.

Smoking and lung capacity

• Make a plot that can be used to answer the statistical investigative question:

Do people who smoke tend to have lower lung capacity than those who do not smoke?

• Use your plot to answer the question.
  • Were you surprised by the answer? Why?
  • Can you suggest a possible confounding factor that might be affecting the result?

Let’s compare

• Create three subsets of the data:
  • One that includes only 13-year-olds …
  • One that includes only 15-year-olds …
  • and one that includes only 17-year-olds.
• Make a plot that compares the lung capacity of smokers and non-smokers for each subset.
• How does the relationship between smoking and lung capacity change as we increase the age from 13 to 15 to 17?

Sum it up!

• Does smoking affect lung capacity? If so, how?
  • Support your answers with appropriate plots.
  • Explain why you included the variables you used in your plots.