Principal Component Analysis (PCA) Principal Component Analysis (PCA) is widely used to explore data. This technique allows you visualize and understand how variables in the dataset varies. Therefore, PCA is particularly helpful where the dataset contain many variables.This is a method of unsupervised learning that allows you to better understand the variability in the data set and how different variables are related.
The Components in PCA are the underlying structure in the data.
What is Raster Data? Raster or “gridded” data are data that are stored in pixels. In the spatial world, each pixel represents an area on the Earth’s surface. In this post will focus raster package and its key function for importing and manipulating raster objects. I expect that toward the end of the post, you will have a glimpse of this package and you will be able to:
Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim.
WorldClim is a set of global climate layers (gridded climate data in GeoTiff format) that can be used for mapping and spatial modeling. WordlClim version 2 contains average monthly climatic gridded data for the period 1970-2000 with different spatial resolutions, from 30 seconds (~1 km2) to 10 minutes (~340 km2).
Introduction Network Common Data Form (NetCDF) is a widely used format for storing array–based data as variables. NetCDF are developed and maintained by Unidata was originally developed for storing and distributiing climate data , such as those generatd by climate simulation or reanalysis models. It has also been adopted in other fields, particularly in oceanography, where large mutidimensional arrays of data are generatted from satellite observation systems. The NetCDF format is a platform-independent because can be transeerred among servers and coputers that are running different operating systems, without a need to convert the file that fit a particular sytem.
We begin with answering the questions. And the possible reason to reach the goal is to define questions like;
what is a raster dataset? What tools/functions are used to import raster in R? How to I work with and plot raster data in R How missing or bad data in R are handled with R Objectives
Describe the fundamental attributes of a raster dataset Explore raster attributtes and metadata Import raster dataset into R workspace visualize raster object Distinguish single versus multi-bands rasters Introduction to Raster data This this section introduce you to the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R.
