In the post titled Access, Download, Process and VIsualize sea surface height and geostrophic current from AVISO in R posted in my blog on Monday, Apr 15, 2019, I explained how we can download the satellite data like sea surface height from AVISO in R. I illustrate in detail getting the data using xtractomatic package (Mendelssohn 2018). Though xtractomatic package provide functions that allows us to get access to the ERDDAP server and get the data, but one big challenge is that the data comes is array and need an expensive computation process, especially if you deal with gridded data for a long term time series.

In this post we demonstrate advanced two-dimensional visualization techniques in the form of graphical displays of the types of data typically encountered in oceanography, using R programming. The first example displays graphically a temperature and chlorophyll-a concentration time series for the last 20 years from acquired with MODIS satellite sensors. We need some packages for this task loaded in the session. These packages can be loaded either using library or require functions.

Overview By far the most popular software for processing data is the Microsoft Excel spreadsheet software (http://office.microsoft.com/excel). Following its first introduction for Macs in the mid-80s, and later for Microsoft Windows, the software has seen multiple modifications and improvements before being included in the Microsoft Office suite, which also contains Microsoft Word and Microsoft PowerPoint (http://office.microsoft.com). A free open-source alternative to Excel is Calc included in the cross-platform OpenOffice suite that also contains Writer and Impress (http://openoffice.

There are many possible reasons why a website does not work as expected. For example, you may have misconfigured certain options in the website configuration file (such as config.yaml), or marked a post as draft but forgetten to unmark it before publishing the site. It is easy to make these mistakes, and often hard to diagnose them. Fortunately, the function blogdown::check_site() can run a series of checks in your website project to help you identify common problems.

In the previous post I illustrated a simple way to do Principal Component Analysis in R. I simply used the output results from prcomp() function of R base. But, I constantly find hard to the untidy output that prcomp generates and wished to get a tidy result. In this post I will illustrate the approaches that I was inspired by Claus Wilke in the post PCA tidyverse style.