This exercise builds upon previous exercise so you don’t have to import any libraries or datasets. We have made an object murders_expanded which contains the murders dataset with the extra rate- and rank-columns from previous exercise. Don’t use murders!

4. The dplyr function filter is used to choose specific rows of the data frame to keep. Unlike select which is for columns, filter is for rows. For example, you can show just the New York row like this:

filter(murders_expanded, state == "New York")

You can use other logical vectors to filter rows.

Use filter to show the top 5 entries with the highest murder rates. After we add murder rate and rank (We have already done this step for you). Remember that you can filter based on the rank column. Store your result in deadly_cities

5. We can remove rows using the != operator. For example, to remove Florida, we would do this:

no_florida <- filter(murders_expanded, state != "Florida")

Create a new data frame called no_south that removes states from the South region. How many states are in this category? You can use the function nrow for this. Store the number in nr_no_south.

6. We can also use %in% to filter with dplyr. You can therefore see the data from New York and Texas like this:

filter(murders_expanded, state %in% c("New York", "Texas"))

Create a new data frame called murders_ne_w with only the states from the Northeast and the West. How many states are in this category? Store the number in nr_murders_ne_w.