OpenStreetMap is a collaborative project to create a free editable geographic database of the world. Open street Map (OSM) is name refer is a global open access mapping project. The data from OSM can be used in various ways including production of paper maps and electronic maps, geocoding of address and place names, and route planning.
To easy access of OSM spatial data, Mark Padgham and Robin Lovelace developed osmdata, which is an R package for downloading data from OSM.
Data is the most important asset. It validates a research story and a conclusions; it provides a platform of confidence for other researchers who might continue your work; and it is a resource that can be used by researchers in other fields to undertake new work, perhaps completely unrelated to your own research interests. Well-organised data that is accessible to the research community can continue to provide extended benefit and value long after your projects have been completed.
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.
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.
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.
