As an Oceanography, one key parameter that need to get right is the bathymetry. Bathymetry is the science of determining the topography of the seafloor. Bathymetry data is used to generate navigational charts, seafloor profile, biological oceanography, beach erosion, sea-level rise, etc. There prenty of bathymetry data and one of the them is the GEBCO Gridded Bathymetry Data.
The General bathymetric Chart of the Oceans (GEBCO) consists of an international group of experts in ocean mapping.
Elevation data is used for a wide array of applications, including, for example, visualization, hydrology, and ecological modelling. There are several sources for digital elevation models such as the Shuttle Radar Topography Mission (SRTM), the USGS National Elevation Dataset (NED), Global DEM (GDEM), and others. Each of these DEMs has pros and cons for their use. Prior to its closure in January of 2018, Mapzen combined several of these sources to create a synthesis elevation product that utilizes the best available elevation data for a given region at given zoom level.
Introduction This post offers some technique on how to analyse data from a surveys and questionnaires in R, provides tips on visualizing survey data, and exemplifies how survey and questionnaire data can be analyzed.
Questionnaires and surveys are widely used in research and thus one of the most common research designs. Questionnaires elicit three types of data:
Factual Behavioral Attitudinal While factual and behavioral questions are about what the respondent is and does, attitudinal questions tap into what the respondent thinks or feels.
tidymodels tidymodels is a suite of packages that make machine learning with R a breeze. R has many packages for machine learning, each with their own syntax and function arguments. tidymodels aims to provide an unified interface, which allows data scientists to focus on the problem they’re trying to solve, instead of wasting time with learning package syntax.
The tidymodels has a modular approach meaning that specific, smaller packages designed to work hand in hand.
Heatmaps are powerful data visualization tools broadly widely used with meteorologic and oceanographic data. Heatmaps are excellent at tracking signals that move, like ocean current. These diagrams can be used for many more types of atmospheric features. The concept is to represent a matrix of values as colors where usually is organized by a gradient. This post explains how to create a heatmap of ocean current in R using the geom_tile(), geom_contour_filled from ggplot2 (Wickham 2016) and geom_contour_fill from metR package (Campitelli 2019).
