Wat is een rasterobject?

Net als sp-klassen hebben de raster-klassen methodes om objecten eenvoudig te bekijken en te bewerken, zoals print() en summary(). En je kunt altijd dieper in hun structuur duiken met str().

Laten we erin duiken en kijken naar een raster dat we voor je hebben geladen: pop. Let op een paar dingen:

Kun je zien waar de coördinateninformatie wordt bewaard?
Kun je aan de summary() zien hoe groot het raster is?
Wat denk je dat er in dit raster wordt opgeslagen?

Deze oefening maakt deel uit van de cursus

Georuimtelijke data visualiseren in R

Cursus bekijken

Oefeninstructies

Print pop naar de console.
Roep str() aan op pop met max.level = 2.
Roep summary() aan op pop.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

library(raster)

# Print pop


# Call str() on pop, with max.level = 2


# Call summary on pop

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Georuimtelijke data visualiseren in R

SkillTag.level.intermediateSkillTag.label

4.6+

Begin de cursus gratis

We'll dive in by displaying some spatial data -- property sales in a small US town -- using ggplot2 and we'll introduce you to the ggmap package as a quick way to add spatial context to your plots. We'll talk about what makes spatial data special and introduce you to the common types of spatial data we'll be working with throughout the course.

Exercise 1: Introduction to spatial data Exercise 2: Grabbing a background map Exercise 3: Putting it all together Exercise 4: Insight through aesthetics Exercise 5: Useful get_map() and ggmap() options Exercise 6: Different maps Exercise 7: Leveraging ggplot2's strengths Exercise 8: A quick alternative Exercise 9: Common types of spatial data Exercise 10: Drawing polygons Exercise 11: Choropleth map Exercise 12: Raster data as a heatmap

You can get a long way with spatial data stored in data frames, but it makes life easier if they are stored in special spatial objects. In this chapter we'll introduce you to the spatial object classes provided by the sp package, particularly for point and polygon data. You'll learn how to explore and subset these objects by exploring a world map. The reward for learning about these object classes: we'll show you the package tmap which requires spatial objects as input, but makes creating maps really easy! You'll finish up by making a map of the world's population.

Exercise 1: Introducing sp objects Exercise 2: Let's take a look at a spatial object Exercise 3: What's inside a spatial object?Exercise 4: A more complicated spatial object Exercise 5: sp and S4 Exercise 6: Walking the hierarchy Exercise 7: Further down the rabbit hole Exercise 8: More sp classes and methods Exercise 9: Subsetting by index Exercise 10: Accessing data in sp objects Exercise 11: Subsetting based on data attributes Exercise 12: tmap, a package that works with sp objects Exercise 13: Introduction to tmap Exercise 14: Building a plot in layers Exercise 15: Why is Greenland so big?Exercise 16: Saving a tmap plot

While the sp package provides some classes for raster data, the raster package provides more useful classes. You'll be introduced to these classes and their advantages and then learn to display them. The examples continue with the theme of population from Chapter 2, but you'll look at some much finer detail datasets, both spatially and demographically. In the second half of the chapter you'll learn about color -- an essential part of any visual display, but especially important for maps.

Exercise 1: Het raster-pakket Exercise 2: Wat is een rasterobject?

Huidige oefening

Exercise 3: Een paar handige methoden Exercise 4: Een ingewikkelder object Exercise 5: Een pakket dat Raster-objecten gebruikt Exercise 6: Kleurschalen Exercise 7: Kies het juiste palet Exercise 8: Een aangepaste doorlopende kleurenpalet toevoegen aan ggplot2-plots Exercise 9: Aangepaste palet in tmap Exercise 10: Meer over kleurenschaal Exercise 11: Een voorbeeld van een intervalschaal Exercise 12: Een voorbeeld van een divergerende schaal Exercise 13: Een kwalitatief voorbeeld

In this chapter you'll follow the creation of a visualization from raw spatial data files to adding a credit to a map. Along the way, you'll learn how to read spatial data into R, more about projections and coordinate reference systems, how to add additional data to a spatial object, and some tips for polishing your maps.

Exercise 1: Reading in spatial data Exercise 2: Reading in a shapefile Exercise 3: Reading in a raster file Exercise 4: Getting data using a package Exercise 5: Coordinate reference systems Exercise 6: Merging data from different CRS/projections Exercise 7: Converting from one CRS/projection to another Exercise 8: Adding data to spatial objects Exercise 9: The wrong way Exercise 10: Checking data will match Exercise 11: Merging data attributes Exercise 12: A first plot Exercise 13: Polishing a map Exercise 14: Subsetting the neighborhoods Exercise 15: Adding neighborhood labels Exercise 16: Tidying up the legend and some final tweaks Exercise 17: Wrap up