Exploring the IPEDS Data II

Most analyses require data wrangling. Luckily, there are many functions in the tidyverse that facilitate data frame cleaning. For example, the drop_na() function will remove observations with missing values. By default, drop_na() will check all columns for missing values and will remove all observations with one or more missing values.

miss_ex <- tibble(
             animal = c("dog", "cat", "rat", NA),
             name   = c("Woodruf", "Stryker", NA, "Morris"),
             age    = c(1:4))
miss_ex

miss_ex %>% 
     drop_na() %>% 
     arrange(desc(age))

# A tibble: 2 x 3
  animal    name   age
   <chr>   <chr> <dbl>
1    cat Stryker     2
2    dog Woodruf     1

Deze oefening maakt deel uit van de cursus

Interactive Maps with leaflet in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Remove colleges with missing sector information
ipeds <- 
    ipeds_missing %>% 
    ___()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Interactive Maps with leaflet in R

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Begin de cursus gratis

Chapter 1 will introduce students to the htmlwidgets package and the leaflet package. Following this introduction, students will build their first interactive web map using leaflet. Through the process of creating this first map students will be introduced to many of the core features of the leaflet package, including adding different map tiles, setting the center point and zoom level, plotting single points based on latitude and longitude coordinates, and storing leaflet maps as objects. Chapter 1 will conclude with students geocoding DataCamp’s headquarters, and creating a leaflet map that plots the headquarters and displays a popup describing the location.

Exercise 1: Introduction to leaflet Exercise 2: Loading the leaflet Library Exercise 3: Creating an Interactive Web Map Exercise 4: Map Tiles Exercise 5: Provider Tiles Exercise 6: Adding a Custom Map Tile Exercise 7: Setting the Default Map View Exercise 8: A Map with a View I Exercise 9: A Map with a Narrower View Exercise 10: Plotting DataCamp HQ Exercise 11: Mark it Exercise 12: Adding Popups and Storing your Map

In chapter 2 students will build on the leaflet map they created in chapter 1 to create an interactive web map of every four year college in California. After plotting hundreds of points on an interactive leaflet map, students will learn to customize the markers on their leaflet map. This chapter will also how to color code markers based on a factor variable.

Exercise 1: Introduction to IPEDS Data Exercise 2: Cleaning up the Base Map Exercise 3: Exploring the IPEDS Data Exercise 4: Exploring the IPEDS Data II

Huidige oefening

Exercise 5: Mapping California Colleges Exercise 6: California Colleges Exercise 7: The City of Colleges Exercise 8: Circle Markers Exercise 9: Labels and Pop-ups Exercise 10: Making our Map Pop Exercise 11: What is California's Northernmost College?Exercise 12: Building a Better Pop-up Exercise 13: Swapping Popups for Labels Exercise 14: Color Coding Colleges Exercise 15: Creating a Color Palette using colorFactor Exercise 16: A Legendary Map

In chapter 3 students will expand on their map of all four year colleges in California to create a map of all American colleges. First, in section 3.1 students will review and build on the material from Chapter 2 to create a map of all American colleges. Then students will re-plot the colleges on their leaflet map by sector (public, private, or for-profit) using groups to enable users to toggle the colleges that are displayed on the map. In section 3.3 students will learn to add multiple base maps so that users can toggle between multiple map tiles.

Exercise 1: The Leaflet Extras Package Exercise 2: Middle America Exercise 3: Building a Base Exercise 4: Down South Exercise 5: Overlay Groups Exercise 6: Mapping Public Colleges Exercise 7: Mapping Public and Private Colleges Exercise 8: Mapping All Colleges Exercise 9: Base Groups Exercise 10: Change up the Base Exercise 11: Putting it all Together Exercise 12: Pieces of Flair Exercise 13: Adding a Piece of Flair Exercise 14: A Cluster Approach

In Chapter 4 students will learn to map polygons, which can be used to define geographic regions (e.g., zip codes, states, countries, etc.). Chapter 4 will start by plotting the zip codes in North Carolina that fall in the top quartile of mean family incomes. Students will learn to customize the polygons with color palettes and labels. Chapter 4 will conclude with adding a new layer to the map of every college in America that displays every zip code with a mean income of $200,000 or more during the 2015 tax year. Through the process of mapping zip codes students will learn about spatial data generally, geoJSON data, the @ symbol, and the addPolygons() function. Furthermore, students will have an opportunity to practice applying many of the options that they learned about in the previous chapters, such as popups and labels, as well as new ways to customize their maps, such as the highlight option in addPolygons().

Exercise 1: Spatial Data Exercise 2: Introduction to Spatial Data Exercise 3: Exploring Spatial Data Exercise 4: Joining Spatial Data Exercise 5: Mapping Polygons Exercise 6: addPolygons() Function Exercise 7: NC High Income Zips Exercise 8: addPolygon() Options Exercise 9: Let's do some Logging Exercise 10: Putting it All Together Exercise 11: Wealthiest Zip Codes in America Exercise 12: Final Map Exercise 13: Thank you!