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https://github.com/dcs-training/datavisualisationwithr

Data Visualisation with R Workshop (delivered by the Centre in December 2020). This workshop is focusing on visualising your data. Go to the readme file
https://github.com/dcs-training/datavisualisationwithr

data-analysis data-visualisation data-wrangling r

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Data Visualisation with R Workshop (delivered by the Centre in December 2020). This workshop is focusing on visualising your data. Go to the readme file

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README 

        
# DataVisualisationWithR



Hello Everyone! This is the Readme File for the **Data Visualisation with R** Workshop (delivered by the Centre in December 2020). 



This workshop is focusing on **visualising your data** . 



There is much more you can do with R and R Studio and there are a lot of video tutorials you can watch and posts you can read: 



- If you want to learn more on **Data Wrangling** with R: [https://www.linkedin.com/learning/data-wrangling-in-r/welcome?u=50251009 ](https://www.linkedin.com/learning/data-wrangling-in-r/welcome?u=50251009)[https://datacarpentry.org/R-ecology-lesson/03-dplyr.html ](https://datacarpentry.org/R-ecology-lesson/03-dplyr.html)

- If you want to keep learn **small bits over a long period** of time this is a very interesting approach[ https://www.linkedin.com/learning/r-for-data-science- lunchbreak-lessons/exercise-files?u=50251009 ](https://www.linkedin.com/learning/r-for-data-science-lunchbreak-lessons/exercise-files?u=50251009)

- **Machine Learning** with R[ https://machinelearningmastery.com/machine- learning-in-r-step-by-step/ ](https://machinelearningmastery.com/machine-learning-in-r-step-by-step/)[https://www.datacamp.com/community/tutorials/machine-learning-in-r ](https://www.datacamp.com/community/tutorials/machine-learning-in-r)

- If you want to learn more on using **R and GIS together**  

[http://research.shca.ed.ac.uk/past-by-numbers/ ](http://research.shca.ed.ac.uk/past-by-numbers/)[https://www.jessesadler.com/post/gis-with-r-intro/ ](https://www.jessesadler.com/post/gis-with-r-intro/)

 



More generally, the good thing of R being an Open Source software means you can find  a lot of help online. If at any point of your research you get stuck on something just google the issue and 99% of times someone else posted about it already! 



The best sites on where to **find info and help** are: 



- [https://stackoverflow.com/ ](https://stackoverflow.com/)

- [https://www.r-bloggers.com/ ](https://www.r-bloggers.com/)



Finally if you want to **learn more about what R can do** you can find more info in here: 



- [https://www.r-project.org/about.html ](https://www.r-project.org/about.html)

- [https://blog.revolutionanalytics.com/2012/07/a-big-list-of-the-things-r-can-do.html ](https://blog.revolutionanalytics.com/2012/07/a-big-list-of-the-things-r-can-do.html)

- [https://simplystatistics.org/2019/03/13/10-things-r-can-do-that-might-surprise- you/ ](https://simplystatistics.org/2019/03/13/10-things-r-can-do-that-might-surprise-you/)



What you are going to find in this repo



- In  the  ***installation  instructions***  you  can  find  the  installation instructions.  

- In the ***DataVisCode***  you can find all the R Script. 

- In the ***datasets*** you are going to find all information concerning the datasets used. 

- In the ***RVisualisation.pptx*** you are going to find the ppt presentation used during the workshop 



How to set an R project  



- We are going to cover the subject during the first class but you can find more info on how to set a project in here[ https://support.rstudio.com/hc/en- us/articles/200526207-Using-Projects ](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects)

- Or you can watch this video:[ https://www.youtube.com/watch?v=pyJMWlDptYw ](https://www.youtube.com/watch?v=pyJMWlDptYw)

- For this class you would need 3 subfolders : 

- Data 

- Code  

- Graphs



## Software Installation



Below are the steps to do so and get set. 



## On Noteable



1. Go to https://noteable.edina.ac.uk/login

2. Login with your EASE credentials

3. Select RStudio as a personal notebook server and press start

4. Go to File >New Project>Version Control>Git

5. Copy and Paste this repository URL https://github.com/DCS-training/PCA-2023 as the Repository URL

6. The Project directory name will filled in automatically but you can change it if you want your folder in Notable to have a different name

7. Decide where to locate the folder. By default, it will locate it in your home directory 

8. Press Create Project



Congratulations you have now pulled the content of the repository on your Notable server space the last thing you need to do is to install the packages not already installed in Noteable.



1. Open the 'Install.R' file and run the code within it 

2. Now you can open the 'PCA.R' file and you can follow along



## On your own machine



### R and RStudio



* R and RStudio are separate downloads and installations. R is the

underlying statistical computing environment, but using R alone is no

fun. RStudio is a graphical integrated development environment (IDE) that makes

using R much easier and more interactive. You need to install R before you

install RStudio. After installing both programs, you will need to install 

some specific R packages within RStudio. Follow the instructions below for

your operating system, and then follow the instructions to install

**`tidyverse`** and **`RSQLite`**.



#### Windows



> ## If you already have R and RStudio installed

>

> * Open RStudio, and click on "Help" > "Check for updates". If a new version is

> available, quit RStudio, and download the latest version for RStudio.

> * To check which version of R you are using, start RStudio and the first thing

>  that appears in the console indicates the version of R you are

>  running. Alternatively, you can type `sessionInfo()`, which will also display

>  which version of R you are running. Go on

>  the [CRAN website](https://cran.r-project.org/bin/windows/base/) and check

> whether a more recent version is available. If so, please download and install

> it. You can [check here](https://cran.r-project.org/bin/windows/base/rw-FAQ.html#How-do-I-UNinstall-R_003f) for

> more information on how to remove old versions from your system if you wish to do so.

{: .solution}



> ## If you don't have R and RStudio installed

>

> * Download R from

>  the [CRAN website](https://cran.r-project.org/bin/windows/base/release.htm).

> * Run the `.exe` file that was just downloaded

> * Go to the [RStudio download page](https://www.rstudio.com/products/rstudio/download/#download)

> * Under *Installers* select **RStudio x.yy.zzz - Windows Vista/7/8/10** (where x, y, and z represent version numbers)

> * Double click the file to install it

> * Once it's installed, open RStudio to make sure it works and you don't get any

> error messages.

{: .solution}



#### macOS



> ## If you already have R and RStudio installed

>

> * Open RStudio, and click on "Help" > "Check for updates". If a new version is

>	available, quit RStudio, and download the latest version for RStudio.

>	* To check the version of R you are using, start RStudio and the first thing

>	  that appears on the terminal indicates the version of R you are running. Alternatively, you can type `sessionInfo()`, which will 

>	also display which version of R you are running. Go on

>	  the [CRAN website](https://cran.r-project.org/bin/macosx/) and check

>	  whether a more recent version is available. If so, please download and install

>	  it.

{: .solution}



> ## If you don't have R and RStudio installed

>

> * Download R from

>   the [CRAN website](https://cran.r-project.org/bin/macosx/).

> * Select the `.pkg` file for the latest R version

> * Double click on the downloaded file to install R

> * It is also a good idea to install [XQuartz](https://www.xquartz.org/) (needed

>   by some packages)

> * Go to the [RStudio download page](https://www.rstudio.com/products/rstudio/download/#download)

> * Under *Installers* select **RStudio x.yy.zzz - Mac OS X 10.6+ (64-bit)**

>   (where x, y, and z represent version numbers)

> * Double click the file to install RStudio

> * Once it's installed, open RStudio to make sure it works and you don't get any

>   error messages.

{: .solution}



#### Linux



* Follow the instructions for your distribution

 from [CRAN](https://cloud.r-project.org/bin/linux), they provide information

 to get the most recent version of R for common distributions. For most

 distributions, you could use your package manager (e.g., for Debian/Ubuntu run

 `sudo apt-get install r-base`, and for Fedora `sudo yum install R`), but we

 don't recommend this approach as the versions provided by this are

 usually out of date. In any case, make sure you have at least R 3.5.1.

* Go to the [RStudio download

  page](https://www.rstudio.com/products/rstudio/download/#download)

* Under *Installers* select the version that matches your distribution, and

   install it with your preferred method (e.g., with Debian/Ubuntu `sudo dpkg -i

   rstudio-x.yy.zzz-amd64.deb` at the terminal).

* Once it's installed, open RStudio to make sure it works and you don't get any

   error messages.



### Organizing your working directory



Using a consistent folder structure across your projects will help keep things

organized, and will help you to find/file things in the future. This

can be especially helpful when you have multiple projects. In general, you may

create directories (folders) for **scripts**, **data**, and **documents**. 

If you want to learn more about how to get set have a look (https://datacarpentry.org/R-ecology-lesson/00-before-we-start.html)[https://datacarpentry.org/R-ecology-lesson/00-before-we-start.html]



All material here collected is free to use but it is covered by a [![License: CC BY-NC 4.0](https://licensebuttons.net/l/by-nc/4.0/80x15.png)](https://creativecommons.org/licenses/by-nc/4.0/) license