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.
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.
Time Interval You can save an interval of time an an interval object in R with lubridate. This is quite useful for example, you want to understand the interval between two or more successive CTD casts
algoa = list.files("d:/semba/CTDs/algoa/processing/updown files/", pattern = "dst", full.names = TRUE) we notice that the files has an .cnv extenstion, which is oce–readable. We therefore load the oce package together the package in tidyverse.
In this post we will learn to work with date and time data in R. We will use the lubridate package developed by Garrett Grolemund and Hadley Wickham ~@lubridate. This package makes it easy to work with dates and time. Let’s us load the packages that we will use
require(lubridate) require(tidyverse) require(magrittr) require(oce) Data We will use the profiles data from Argo within the Indian Ocean. The data was downloaded from the Coriolis Global Data Assembly Center site (ftp://ftp.
Most data we collect has a time stamp. The time stamp indicate the date and time the data was collected. Dealing with dates is a complicated task becuase of different formats and the time zones. R has built in functions that make your life working with dates and times a bit easier. In addition, @lubridate developed a lubridate package that allows to work smoothly with dates and times.
