Maps

Lab 3F

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red in your journal.

Space, Click, Right Arrow or swipe left to move to the next slide.

Informative and Fun!

Maps are some of the most interesting plots to make because the info represents:
- Where we live.
- Where we go.
- Places that interest us.
Maps are also helpful to display geographic information.
- John Snow (the physician, not the character from Game of Thrones…) once famously used a map to discover how cholera was transmitted.
In this lab, we’ll use R to create an interactive map of the mtns data we scraped in Lab 3E.

Getting ready to map

The map we’ll be creating will end up in RStudio’s Viewer pane.
- Which means you’ll need to alternate between building the map and loading the lab.
You’ll find it very helpful, for this lab, to write all of the commands, including the load_lab(23) command, as an R Script.
- This way you can edit the code that builds the map and quickly reload the lab.

Load your data!

In Lab 3E you created a dataset. Load it into RStudio now by filling in the blank with the file name of the data.

load("___.Rda")

Didn’t finish the lab or save the data file? Ask a friend to share it!

Build a Basic Map

Let’s start by building a basic map!
(1) Write and run code using the leaflet() function and the mtns data to create the leaf that we can use for mapping.

mtns_leaf <- leaflet(____)

(2) Then, write and run code inserting mtns_leaf into the addTiles() function and assign the output the name mtns_map.
Run mtns_map in the console to look at your basic map with no data displayed.
- Be sure to try clicking on the map to pan and zoom.

Including our data

Now we can add markers for the locations of the mountains using the addMarkers() function.
(3) Fill in the blanks below with the basic map we’ve created and the values for latitude and longitude.

addMarkers(mtns_map = ____, lng = ~____, lat = ~____)

(4) Write and run code supplying the peak variable, in a similar way as we supplied the lat and long variables, to the popup argument and include it in the code above.
(5) Click on a marker within a state of your choice and write down the name of the mountain you clicked on.

Colorize

Our current map looks pretty good, but what if we wanted to add some colors to our plot?
(6) Fill in the blanks below to create a new variable that assigns a color to each mountain based on the state it’s located in.

mtns <- mutate(____, state_colors = colorize(____))

Now that we’ve added a new variable, we need to re-build mtns_leaf and mtns_map to use it.
- (7) Write and run code creating mtns_leaf and mtns_map as you did before.
- Then change addMarkers to addCircleMarkers and keep all of the arguments the same.

Showing off our colors

To add the colors to our plot, use the addCircleMarkers like before but this time include color = ~state_colors as an argument.
It’s hard to know just what the different colors mean so let’s add a legend.
- First, assign the map with the circle markers as mtns_map.
- (8) Then, fill in the blanks below to place a legend in the top-right hand corner.

addLegend(____, colors = ~unique(____), labels = ~unique(____))