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.
One of the prime purposes of using a computer is to automate a task that would be very tedious to perform by hand. The usual implication is that some task is to be performed over and over again in some systematic way. This chapter will be concerned with the programming concept of a control flow, a feature that is at the heart of nearly every computer algorithm. The two important control flows statements are* count-controlled* loops like for loops and conditional statements such as if-else construct.
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.
One of the key task in data preparation is to organize thee dataset in a way that makes analysis and plottng easier. In practice, the data is often not stored like that and the data comes to us with repeated observations included on a single row. This is often done as a memory saving technique or because there is some structure in the data that makes the ‘wide’ format attractive.
