Data Visualisation - Introduction

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# Data Visualisation - Introduction
### Eugene
### February 03 2020

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# Course Contents

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1. System Configuration - installing software
2. Using RStudio
3. Introduction to R
4. Getting and Cleaning Data
5. Exploratory Analysis - making rough plots
6. Different Types of Plots
7. Playing with Aesthetics
8. Using Plotting Themes
9. Advanced Topics - Maps, Networks

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## Why We're Here

- Alternative to Excel
- Enables Reproducible Research
- Can Make Lots of Plots Quickly
    - Good for Exploratory Analysis
- Publication Ready Figures

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## And.... a gateway to so much more

- data capture
- statistical analysis
- machine learning
- artificial intelligence
- writing your thesis
- writing a blog

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## Not Why We're Here

- Won't discuss choices for data presentation
- Nor good practices in visualisations
    - but these are sort of in the background
- This isn't a machine learning course
    - but lots of the techniques we'll use are relevant
- So, this course it about skills development, how you use these is up to you.

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## We said we wouldn't discuss this....but

- Graphics are important, overlooked, and inconsistent
- Need to tell a story
- Can be misleading, almost always by accident
- Choice of colours - we'll spend some time on this
- Choice of fonts
- Keep it simple - reduce amount of ink
- Increasing number of options for showcasing your data

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# Let's Begin

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## Install R and RStudio

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### *R*
- Go to [*CRAN*](https://cran.r-project.org/)

### *RStudio*

- this is the IDE we will use (and pretty much everyone else uses)
- R is the engine, RStudio is the cockpit
- download from [*RStudio*](https://rstudio.com/products/rstudio/download/)

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## Using RStudio

- toolbar across the top
    - I don't use this very much
- set of quick links below that
    - top left (green plus sign) is about the only one I use
- 4 Panes
    - top left for files or looking at data
    - bottom left for the console
    - top right for *Environment* - tells what variables are stored
    - bottom right for plots and help

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- usual work flow is:
    - try commands out at the console (bottom left)
    - when that works, store them in a file (top left)
    - when sequence of commands works, put them into a document (also top left)

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## Extending R

- installing R just gives you *base* R
- beauty of this tool lies with *packages*
- we'll look at installing these from three sources:
    - CRAN
    - Bioconductor
    - github

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- [CRAN](https://cran.r-project.org/) 
    - example, on console type *install.packages("tidyverse")*
    - this installs the tidyverse package (or rather, family of packages)
    - over 20k packages on CRAN (see list [here](http://cran.nexr.com/web/packages/available_packages_by_name.html))
    - sometimes esoteric ([engsoccerdata](http://cran.nexr.com/web/packages/engsoccerdata/index.html))
    - sometimes cutting edge ([deep learning](http://cran.nexr.com/web/packages/keras/index.html))
    - each package heavily curated and maintained
    
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- [Bioconductor](www.bioconductor.org)
    - set of bioinformatics packages (lots of genomics)
    - start with *install.packages("BiocManager")*
    - then *install("some_genomics_package")* to use
    - list of packages [here](http://bioconductor.org/packages/release/BiocViews.html)
    - about 3,000 packages, including genome builds
    
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- github
    - packages in development
    - start with *install.packages("devtools")*
    - then *install_github("developer_name/package_name")*
    - almost 80k packages [here](http://rpkg.gepuro.net/)
    - the package *githubinstall* is useful to search these
   
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# Resources

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- books
 - *recommended text* **Data Visualization** by Kieran Healy (ISBN = 978-0691181622). ~€25. Also online at [https://socviz.co/index.html](https://socviz.co/index.html)
 - [Hadley's book, R for Data Science](https://r4ds.had.co.nz/)
 - [Data Visualization by Wilke](https://serialmentor.com/dataviz/), lots of his actual code is on github at [https://github.com/clauswilke/practical_ggplot2](https://github.com/clauswilke/practical_ggplot2)

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- websites
 - Karl Broman (https://www.biostat.wisc.edu/~kbroman/), and particularly [this presentation](https://www.biostat.wisc.edu/~kbroman/presentations/graphs_MDPhD2014.pdf)
 - course by Boemhke on github [github.com/uc-r/Intro-R](https://github.com/uc-r/Intro-R)
 - the good people at RStudio have lots of help at [resources.rstudio.com/](https://resources.rstudio.com/)
 - [Cedric](https://cedricscherer.netlify.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/). 
 
 
 
 
<img src="https://github.com/yihui/xaringan/releases/download/v0.0.2/karl-moustache.jpg" height="80px" width="100px" align="right"/> 
 
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- Blogs and Podcasts
 - [www.simplystatistics.org](www.simplystatistics.org)
 - [varianceexplained.org](http://varianceexplained.org/)
 - [Not So Standard Deviations](http://nssdeviations.com/)

<img src="images/hillary.jpeg" height="100px" width="100px" align="right"/>
 
 
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- Online Courses
 - Coursera: [Data Science from Johns Hopkins](https://www.coursera.org/specializations/jhu-data-science). The course notes are on [github](http://datasciencespecialization.github.io/)
 - edx.org [course from Irizarry](https://www.edx.org/course/data-science-visualization)
 - [datacamp](www.datacamp.com)

<img src="images/rafael.jpg" height="100px" width="100px" align="right"/>
 
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- Miscellaneous
    - [Dublin R MeetUp](https://www.meetup.com/DublinR/) 
    - [RWeekly.org](rweekly.org), round up of events in the world of R
    - [#TidyTuesday](https://twitter.com/search?q=%23TidyTuesday&src=typeahead_click) on twitter
    - [R Cheatsheets](https://rstudio.com/resources/cheatsheets/)
    - if you get stuck, google is your friend. Often sends you to stackoverflow.com or stackexchange.com
    
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- Some stuff about graphics in general
    - [again, from Irizarry](http://genomicsclass.github.io/book/pages/plots_to_avoid.html)
    - [hit parade of graphs in R](https://www.r-graph-gallery.com/index.html)
    - [Cedric Scherer again](https://cedricscherer.netlify.com/)
    - some stuff from [Christian Burkhard](https://ggplot2tor.com/make_any_plot_look_better/make_any_plot_look_better/)
    - and from [Laura Ellis](https://www.littlemissdata.com/)
    - and from [Peter Aldhous](http://paldhous.github.io/ucb/2016/dataviz/)
    - [colours in R](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf)
    - cool book on good graphics from [Stephen Few](https://nces.ed.gov/programs/slds/pdf/08_F_06.pdf)
    - [The Glamour of Graphics](https://www.williamrchase.com/slides/assets/player/KeynoteDHTMLPlayer.html#0) talk from last months RStudio Conference
   
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## What You Have to Do

- Do a datacamp course every week
- Produce four data graphics
    - the first three of these will be specified
    - last one will your chance to express yourself
    - do one #TidyTuesday challenge
    
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## Course Communications

- moodle, of course
- twitter via #data_vis_2020
    - I'll be doing lots of retweets here
    - but also for general communications
- email at eugene.hickey@tudublin.ie
- you'll need to create an account at datacamp.com and send me the email you'll use for this account
   - I can check your progress on datacamp courses

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# Some Example Images

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```
## OGR data source with driver: ESRI Shapefile 
## Source: "/home/eugene/Desktop/Coursera/Maps/Africa", layer: "Africa"
## with 762 features
## It has 3 fields
```

```
## Response [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918692/bin/NIHMS958804-supplement-Supplementary_Table.xlsx]
## Date: 2020-02-02 20:27
## Status: 200
## Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
## Size: 1.27 MB
## <ON DISK> /tmp/RtmpNG1kEk/file4a3e3d5e3481.xlsx
```

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