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DataCamp for Business 체험강의 설명
같은 돈으로 가장 가치 있는 집을 사려면 어디를 골라야 할까요? 먼저 지도를 만들어 볼 수 있지만, R에서의 공간 분석은 데이터가 담기는 객체 구조가 복잡해 다소 부담스러울 수 있습니다.
이 강의는 여러분이 이미 익숙한 데이터 프레임에서 시작해, R에서 공간 데이터를 분석하기 위해 사용하는 sp와 raster 패키지의 특수 객체를 소개하며 공간 데이터의 기본을 다집니다. 이러한 객체를 읽고, 탐색하고, 조작하는 방법을 배우고, 마지막에는 tmap 패키지를 활용해 지도를 만드는 방법까지 익히게 됩니다.
강의가 끝날 때쯤이면 작은 도시의 부동산 거래, 전 세계 국가의 인구, 미국 북동부 지역의 인구 분포, 뉴욕시 동네별 가구소득 중간값을 지도에 표현해 보게 됩니다.
선수 조건
Introduction to RIntroduction to Data Visualization with ggplot21
Basic mapping with ggplot2 and ggmap
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.
2
Point and polygon data
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.
3
Raster data and color
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.
4
Data import and projections
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.