Showing the Right Numbers and Visuals Library Paper

According to the author, ‘ggplot is an implementation of the grammar of graphics’ which is a set of rules for producing visualizations of data. In this first plot, we will track the trajectory of life expectancy over time for each country in the data.

1. map year to x and lifeExp to y.
2. use geom_line to show how lifeExp changes over time. (did you notice a mistaken assignment to the y parameter in the book?)
3. use grouping to make each line refer to a specific country in the dataset
4. facet the data on continent
5. try moving the facets around on the page – 5 across and 5 down
6. add a smoother, change the y scale to a log scale and add the dollar sign
7. add the labels as described in the book
8. try using facet_grid
9. be sure that you know what categorical variables, ordered and unordered, are. compare continuous variables.
10. use the glimpse function on the gss_sm data set. Try this gss_sm %>% glimpse(). What do you think the pipe operator (%>%) does?
11. make a smoothed scatterplot of the relationship between age of the respondent and the number of children. What did you learn?
12. facet the result with the sex and race of the respondent (person who respond to the survey)
13. experiment with the alpha attribute
14. experiment with combining attributes in the facet wrapper
15. geom_bar uses count by default
16. describe how the geom_bar used the count function to determine how much water had been used.
17. Use the prop function and group by ‘1’ to show by region
18. show just the religion column in a table
19. create a bar chart showing the frequency of religious distribution in the data
20. use fill to highlight the different religions
21. create a stacked bar chart to show the frequency of religions by region
22. use the position = dodge attribute to create individual religious frequency bars
23. facet the religious frequency chart by region
24. create a histogram showing midwest regions by size
25. experiment with the number of bins in the previous histogram
26. Where does the count variable come from?
27. use subset to show the data in a histogram just from Ohio and Wisconsin
28. create a kernel density plot of the area of the midwest states

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