Question 1

Question 2

Make two barplots of these top 5 names, one for each sex. Map aesthetics x to name and y to prop [or percent, if you made that variable, as I did.] and use geom_col (since you are declaring a specific y, otherwise you could just use geom_bar() and just an x.)

Question 3

Find your name. [If your name isn’t in there 😟, pick a random name.] count by sex how many babies since 1880 were named your name. [Hint: if you do this, you’ll get the number of rows (years) there are in the data. You want to add the number of babies in each row (n), so inside count, add wt = n to weight the count by n.] Also add a variable for the percent of each sex.

Question 4

Make a line graph of the number of babies with your name over time, colored by sex.

Question 5

Question 6

Now let’s graph the evolution of the most common names since 1880.

Question 7

Bonus (a challenge!): What are the 10 most common “gender-neutral” names? [This is hard to define. For our purposes, let’s define this as names where between 48% and 52% of the babies with the name are Male.]

Question 8

Download these two datasets that I’ve cleaned up a bit: [If you want a challenge, try downloading them from the websites and cleaning them up yourself!]

• econ_freedom.csv
• pol_freedom.csv

Below is a brief description of the variables I’ve put in each dataset:

Question 9

Now let’s join them together so that we can have a single dataset to work with. You can learn more about this in the 1.4 slides. Since both datasets have both country and year (spelled exactly the same in both!), we can use these two variables as a key to combine observations. Run the following code (substituting whatever you want to name your objects):

freedom <- left_join(econ, pol, by=c("country", "year")

Take a look at freedom to make sure it appears to have worked.

Question 10

Question 11

Now make a scatterplot of Political freedom (fh_score as y) on Economic Freedom (ef_index as x) and color by continent.

Question 12

Save your plot from Question 11 as an object, and add a new layer where we will highlight a few countries. Pick a few countries (I suggest using the ISO code) and create a new object filtering the data to only include these countries (again the %in% command will be most helpful here).

Additionally, install and load a package called "ggrepel", which will adjust labels so they do not overlap on a plot.

Then, add the following layer to your plot:

geom_label_repel(data = countries, # or whatever object name you created
                     aes(x = ef_index,
                         y = fh_score,
                         label = ISO, # show ISO as label (you could do country instead)
                         color = continent),
                     alpha = 0.5, # make it a bit transparent
                     box.padding = 0.75, # control how far labels are from points
                     show.legend = F) # don't want this to add to the legend

This should highlight these countries on your plot.

Question 13

Let’s just look only at the United States and see how it has fared in both measures of freedom over time. filter() the data to look only at ISO == "USA". Use both a geom_point() layer and a geom_path() layer, which will connect the dots over time. Let’s also see this by labeling the years with an additional layer geom_text_repel(aes(label = year)).