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Getting started with R

Ever wanted to try R, but wasn't sure where to start? Or perhaps you are unsure of what R is and what it can do?

New for 2021, I have completely rewritten, revised and greatly expanded my introduction to R guide. Additionally, I have added additional guides for importing data into R, and to give an overview of R graphics and creating plots (see below).

The introduction guide is now much more comprehensive, and includes everything you need to get started with R! Check them out:

Part 1: Introduction to R An introduction to R - what is R, where to get R, how to get started using R, a look at objects and functions, an overview of data structures, and getting started with manipulating data.
Part 2: Importing data into R This guide shows you how to import your existing data into R from .csv or Excel files.
Part 3: An overview of graphics in R This guide gives you an overview of some of the graphical capabilities of base R to produce high quality plots.


Getting started with R: Importing data into R

In the second part of this expanded guide series introducing you to R, this guide shows you how to import data into R from csv files and Excel spreadsheets.

Of course, R can import many other data types, but these two are very common and will get you started!


Getting started with R: An overview of graphics in R

In the final part of this expanded guide series introducing you to R, this guide gives you an overview of the graphics systems in R, and gets you started creating plots using base graphics.


Updates

Hello! you may have noticed a lack of updates to the blog in the last two years. This was in part due to me buying a house and spending a long time renovating it (and the garden), which took up most of my spare time. Since that is now *mostly* complete (it is never complete), I am able to spend some time updating the blog again.

Since i first started the blog, I have become more experienced in using R and use it in many more different ways compared to how I first started. I also now try and write more efficient R code, usually in the form of functions, than what i did previously. Looking back at some of my guides, much of the code is "inefficient", but it is accessible to beginners. As part of updates, I intend to revisit several of my guides and update them with new code and new explanations. However, the goal of the blog is to always make the code accessible to everyone, and that will not change.

I've also made a few changes to the site to tweak the design slightly. These changes are quite minor so you may not notice them, but they should "tidy up" some of the design. If you notice any errors, please let me know!

Additionally, the guides on the blog now have a new "information" panel. Here's an example:

Title My uber guide
Author Benjamin Bell
Published Today
Last updated
R version 4.1.1
Packages base

This will replace the information boxes that previously showed on each guide, and keep all the useful details in one place.

There are definitely some new guides coming soon (really this time), and to start, why don't you check out the completely rewritten and updated guide to using different line types in R?


Bathymetric maps in R: Getting and plotting data

Bathymetric maps are topographic maps of water bodies (oceans, seas, lakes, rivers), equivalent to Digital Elevation Models (DEMs), except for submerged terrain.

Useful in their own right, bathymetric maps can also improve the look of maps created using DEMs, giving depth to water features.

This 2-part guide shows you how to create bathymetric maps in R using freely available data from online sources, how to import and plot this in R. Part 2 explores using different colour schemes/palettes for really effective maps. Read on for more!




Bathymetric maps in R: Colour palettes and break points

Part 2 of the guide for making bathymetric maps in R. This part focuses on creating effective colour schemes using break points to control the colour.




DEMs and where to find them

Digital Elevation Models (DEMs) are a 3D representation of the terrain, and can form the basis for creating maps, or carrying out spatial analysis.

DEMs come in many shapes and sizes, from commercial DEMs with 1 m spatial resolutions, to freely available DEMs with 30 m to 90 m resolutions.

In this guide, I'll show you some of the freely available DEMs, where to obtain the data, and how to use them in R. Read to find out how!


Creating simple location maps in R

R has some great GIS capabilities thanks to an extensive range of packages for handling GIS data. I have previously shown some of this capability for creating climate data maps, although I have not explored mapping basics, until now.

This guide will show you some basic techniques for creating simple, yet effective location maps in R, using freely available data. Stop using screenshots of Google Earth for your location maps, and start making your own! No GIS skills are needed. Read on to see how the create the map below!


How to add error bars in R

R has no error bar function? Surely not!

Well, yes and no. Error bars can be added to plots using the arrows() function and changing the arrow head. You can add vertical and horizontal error bars to any plot type. Simply provide the x and y coordinates, and whatever you are using for your error (e.g. standard deviation, standard error).

Read on to see how this is done with examples.


Quick guide to opening files from within R

R has loads of useful, but little known functions that can make life a little bit easier. As part of the "quick guide" series, this quick guide takes a look at opening files from within R - no more opening up file explorer and hunting around for that file!