Introduction Often in environmental studies we are interested in assessing the presence or absence of a long term trend. A widely applied is a parametric test for trend, which involves fitting a linear model that includes some measure of time as one of the predictor variables, and possibly allowing for serially correlated errors in the model. Instead of fitting the data to time series parametric test, Stephen Millard bundles several functions in EnvStats package that are non–parametric and agnostic in dealing with trend (Millard 2013).
Forecast time series introduction Time series analysis comprises methods for predicting the future based on the historical in order to extract meaningful statistics and other characteristics of the data. In other words, time series forecasting is the use of a model to predict future values based on previously observed values. Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post.
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.
One of the key task of scientist is communicate your analysis and result to the different group of people. The typical data analysis workflow looks like this: you go out and collect data and you organize it in a file or spreadsheet or database. Then interact with R using scripts to run some analyses, perhaps saving some intermediate results along the way or maybe always working on the raw data.
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.
