Kernel density estimation is a popular tool for visualizing the distribution of data. In this post, we are going to look on how to create smoothed map of random points. We will use a shapefile dataset that contains potential fishing zones derived from sea surface temperature recorded between January and June 2020 in Pemba channel. You can simply download the file from this link.
Once you have downloaded the file, unzip and browse in the uncompressed file you find the shapefile pfz.
R is a flexible language that allows to work with different kind of data format [@bradley]. This inluced integer, numeric, character, complex, dates and logical. The default data type or class in R is double precision—numeric. In a nutshell, R treats all kind of data into five categories but we deal with only four in this book. Before proceeding, we need to clear the workspace by typing rm(list = ls()) after the prompt in the in a console.
In the previous post, you installed and got a glimpse of R and Rstudio program. In this chapter you begin to do something with them. We will begin with some simple calculations and then move on to variables. We then move to functions and later to packages. We will learn the basic data types that are widely used in R and how to construct them
This chapter provides example of foundational programming concepts in R.
Introduction An increase in complexity and scale of environmental data both from satellite and insitu observations. This means scientists are increasingly required to develop data skills needed to design reproducible workflows for the simulation, collection, organization, pocessing, analysis and presentation of the results. However, to gain that skills requires some knowledge of coding using one or more computer languages. Coding also known as scripting, makes your work explicitly described, and transparent and completely reproducible.
