Module 6: Data Visualization as a Tool for Analysis

We introduce ggplot package and learn how they can explore and analyze data through more complex visualization.

Data Visualization as a Tool for Analysis

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Lab 6

In this lab, you will work with midwest.dta.

General Guidelines:

You will encounter a few functions we did not cover in the lecture video. This will give you some practice on how to use a new function for the first time. You can try following steps:

1. Start by typing ?new_function in your Console to open up the help page
2. Read the help page of this new_function. The description might be too technical for now. That’s OK. Pay attention to the Usage and Arguments, especially the argument x or x,y (when two arguments are required)
3. At the bottom of the help page, there are a few examples. Run the first few lines to see how it works
4. Apply it in your lab questions

It is highly likely that you will encounter error messages while doing this lab Here are a few steps that might help get you through it.

1. Locate which line is causing this error first
2. Check if you may have a typo in the code. Sometimes another person can spot a typo faster than you.
3. If you enter the code without any typo, try googling the error message
4. Scroll through the top few links see if any of them helps
5. Try working on the next few questions while waiting for answers by TAs

Questions

Recall ggplot works by mapping data to aesthetics and then telling ggplot how to visualize the aesthetic with geoms. Like so:

midwest %>%
  ggplot(aes(x = percollege, 
             y = percbelowpoverty,
             color = state,
             size = poptotal,
             alpha = percpovertyknown)) + 
  geom_point() + facet_wrap(vars(state))

1. Which is more highly correlated with poverty at the county level, college completion rates or high school completion rates? Is it consistent across states? Change one line of code in the above graph.

geoms

For the following, write code to reproduce each plot using midwest

4. Notice here inmetro is numeric, but I want it to behave like a discrete variable so I use x = as.character(inmetro). Use labs(title = "Asian population by metro status") to create the title.

5. Use geom_boxplot() instead of geom_point() for “Asian population by metro status”

6. Use geom_jitter() instead of geom_point() for “Asian population by metro status”

7. Use geom_jitter() and geom_boxplot() at the same time for “Asian population by metro status”. Does order matter?

8. Histograms are used to visualize distributions. What happens when you change the bins argument? What happens if you leave the bins argument off?

midwest %>%
  ggplot(aes(x = perchsd)) +
  geom_histogram(bins = 100) +
  labs(title = "distribution of county-level hs completion rate")

9. Remake “distribution of county-level hs completion rate” with geom_density() instead of geom_histogram().

10. Add a vertical line at the median perchsd using geom_vline. You can calculate the median directly in the ggplot code.

Aesthetics

For the following, write code to reproduce each plot using midwest

1. Use x, y, color and size

2. Use x, y, color and size

4. When making bar graphs, color only changes the outline of the bar. Change the aestethic name to fill to get the desired result

midwest %>% 
  count(state) %>% 
  ggplot(aes(x = state,
             y = n, 
             color = state)) + 
  geom_col()

5. There’s a geom called geom_bar that takes a dataset and calculates the count. Read the following code and compare it to the geom_col code above. Describe how geom_bar() is different than geom_col

midwest %>% 
  ggplot(aes(x = state,
             color = state)) +
  geom_bar()

Well done! You’ve learned how to work with R to create some awesome looking visuals!

Want to improve this tutorial? Report any suggestions/bugs/improvements on here! We’re interested in learning from you how we can make this tutorial better.