Download R For Mac
Welcome to Practical Time Series Analysis! Many of us are 'accidental' data analysts.
R Studio
We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a 'cookbook' approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site.
You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language.
Download R For Mac For Free
It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are 'mission critical' in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
Why R for Natural Resource Stewardship Science? R is an open-source implementation of the S language for statistical computing. For almost 30 years, applied statisticians have been submitting implementations of their new techniques to StatLib. When those implementations were written as libraries for the commercial S-plus implementation, statisticians were providing software for free, but users (including those same statisticians) had to pay a third party to be able to run the software. A very small group of statisticians took it upon themselves to write a complete open-source implementation of S that would run under most operating systems, which they called R. Since then, the vast majority of implementations of new statistical techniques have been made available as R packages, which include the code as a library of functions and at least some documentation.
Because R is very useful for 'computing with data,' experts in many fields use it for their work. Because R is open source, many of those experts make their field-specific code and functions freely available as packages. For example, EPA folks use R, and make their tools for generating spatial survey samples (and for analyzing the resulting data!) available as package spsurvey.
Both Helsel's and Millard's tools for water quality are available as well-documented R packages. Climate researchers use R with netCDF files, so there are packages for reading and writing netCDF files (netCDF, ncdf4) as well generating standard climate diagrams, imputing missing weather data, downscaling from coarse data, etc. (climtol, clim.pact, seas, anm, zyp). Phenology researchers provide packages bise and pheno. Jari Oksanen (with help from others) provides package vegan for vegetation analysis (ordination, classification, analysis of similarity).
Ecologists working on habitat analysis and spatial prediction provide adehabitat, grasp, BIOMOD, ModelMap. Wildlife ecologists and statisticians from FWS, USGS, and elsewhere provide packages for estimating abundances and occupancy, including unmarked, mra, Rcapture, secr, PresenceAbsence, trip, and tripEstimation.
Social scientists provide a set of packages for using the 2000 census data. Bioconductor is a project with many packages for analyzing microarray data, DNA and protein sequence data, and other molecular biology bioinformatics. Researchers on visual display of information, reproducible research, and accessibility also build and share tools in R.
How To Download R For Mac
ROpenSci is a consortium of scientists building R packages to make science more powerful, reproducible, and transparent, through open data and open software. When this page was last updated, the main CRAN repository included over 13000 such packages, Bioconductor had 1560, and many others were available on r-forge or GitHub.
By learning how to use R, we can leverage all of those efforts and expertise, and not reinvent those wheels. If we improve a wheel or write one for a different need, we can in turn make our improved wheel available to others as a new package, or work with the authors of the original package and let them incorporate our additions and improvements.