visualization_lecture

Introduction

Material, notes, code and data for teaching visualization, dimensionality reduction and mathematics of machine learning.

Talks

• Talk on visualization

https://www.youtube.com/watch?v=Bhv46UYAkg0

• Talk on unsupervised learning

https://youtu.be/VMrSHp-sCHo?si=P_FOEZ6pJHy9xJf6

Slides

• Slides on visualization are here:

https://github.com/neelsoumya/visualizationlecture/blob/main/visualizationlecture.pdf

Short version

https://github.com/neelsoumya/visualizationlecture/blob/main/visualizationlecture_SHORT.pdf

• Slides on unsupervised learning are here:

https://github.com/neelsoumya/visualizationlecture/blob/main/Presentationintro.pptx

Notes

Some notes, mathematical proofs, concepts and activities are here:

https://github.com/neelsoumya/visualizationlecture/blob/main/mathematicsdata_science.pdf

https://osf.io/mnh8d/

https://www.researchgate.net/publication/375186575EverythingyouwantedtoknowaboutthemathematicsofdimensionalityreductionandvisualizationbutwereafraidtoaskTeachingresourcesandactivities

Code

Code for visualization in R, model diagnostics and linear mixed effects models

https://github.com/neelsoumya/anovalinearmixedeffectsexamples

Rmarkdown package using Tufte's principles

https://github.com/rstudio/tufte

Resources

• Thinking outside the 10 dimensional box

https://www.popularmechanics.com/science/math/a27737/visualize-higher-dimensions/

https://youtu.be/zwAD6dRSVyI

• Different kinds of cartographic projections

https://whereexactlymaps.com/blogs/articles/an-introduction-to-map-projections

https://www.viewsoftheworld.net/?p=752

• Great talks on visualization and how to do it right

https://www.youtube.com/watch?v=iipVlV4I_Vg&list=PLB2SCq-tZtVnXalwtfVPcjwy0xJbu-btN&index=4

• Minard drawing (The best statistical graphic ever drawn)

https://www.edwardtufte.com/tufte/minard

code for analysing this data

https://github.com/joannecheng/napoleon_analysis

https://thoughtbot.com/blog/analyzing-minards-visualization-of-napoleons-1812-march

• Tufte's principles in R

https://jtr13.github.io/cc19/tuftes-principles-of-data-ink.html

• Data visualization excellence

https://classes.engr.oregonstate.edu/eecs/winter2018/cs519-400/modules/4-data-visualization/1-excellence-integrity/

• Visualization projects

https://nagix.github.io/

• Good and bad visualizations

https://policyviz.com/2023/02/07/10-ways-to-mislead-with-data-visualization/

https://wpdatatables.com/data-visualization-examples/

https://wpdatatables.com/misleading-data-visualization-examples/

https://www.qlik.com/us/data-visualization/data-visualization-examples

https://policyviz.com/2020/08/10/on-diverging-color-palettes-in-maps/

https://www.oldstreetsolutions.com/good-and-bad-data-visualization

https://viz.wtf/

https://r-graph-gallery.com/

Installation

• Install R

  https://www.r-project.org/

• and R Studio

https://www.rstudio.com/products/rstudio/download/preview/

• Install the following packages in R:

```r install.packages('rmarkdown') install.packages('knitr') install.packages('tinytex') install.packages('sqldf') install.packages('ggplot2') install.packages('gplots') install.packages('lme4') install.packages('lmerTest') install.packages('pROC') install.packages('precrec') install.packages('PRROC') install.packages('boot') install.packages('mlbench') install.packages('caret') install.packages('rpart') install.packages('partykit') install.packages('rlib') devtools::install_github('neelsoumya/rlib') install.packages('multcomp') install.packages('lsmeans') install.packages('moments')

```

• Download a zip file of this repository and unzip it

or

clone it

```r git clone https://github.com/neelsoumya/teachingreproduciblescience_R

cd teachingreproduciblescience_R ```

• Go to this new directory and set working directory to this directory in R.

r setwd('~/teaching_reproducible_science_R')

• In R studio, run the markdown

rmarkdown.rmd

https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/rmarkdown.rmd

Template code

rmarkdown.rmd

https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/rmarkdown.rmd

Running this will create a report like the following:

https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/rmarkdown.pdf

Exercises

• Create an Rmarkdown for the Boston housing dataset. See the tutorial below and just load and plot the data.

https://medium.com/analytics-vidhya/a-simple-ml-project-in-r-using-the-boston-dataset-e1143146ffb0

• A simple script to help you get started is here simple_script.R

https://github.com/neelsoumya/teachingreproduciblescienceR/blob/main/simpleproject.R

Resources and further reading

https://rmarkdown.rstudio.com/lesson-1.html

https://bookdown.org/yihui/rmarkdown-cookbook/

https://ropensci.org/

https://bookdown.org/home/

https://github.com/neelsoumya/dsSurvival_bookdown

https://www.coursera.org/learn/r-programming

https://swcarpentry.github.io/r-novice-gapminder/guide/index.html

https://github.com/aaronpeikert/repro-workshop

Lecture notes outline

https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/slides.pdf

• Your data, your model decisions, parameters and your data filtering decisions will keep on changing. How do you know 6 months later what has changed? Document your code and your output and your design decisions all in one place.

• Reproducible pipeline

• Know exactly what changed and when

• Know how to rerun the analysis and get the (same) results

• This is like your research notebook

• Some experiences/case studies of using Rmarkdown notebooks and helping biologists use them to analyze their own data (30 minute talk)

• When you are deep in your work, it can be difficult to make code pretty, comment it and make it reproducible. But you will regret not doing this when you park the work and 6 months later your colloaborators/reviewers ask for additional analysis or changing some assumption, etc. Your code should then be ready (you should be able to click a button and reproduce the figures for your paper).

• Example of IL10 project (tSNE and heatmaps from a bioinformatics project)

• You can easily create R packages or Rmarkdown documents in R Studio (see screenshots in this folder/repository and below)

• You can also easily create graphical user interfaces

  Here is a demo:

  https://sb2333medschl.shinyapps.io/shinyapp/

  and code app.R:

  https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/shinyapp/app.R

• The concepts are the same in any programming language (R/Python)

• Bottomline: we are all busy and we would all rather publish papers, but in the long term these best practices will make us more productive

• This is like protocols (used in experimental biology) for computer scientists. Also like a lab notebook but for computational people.

• You can also write an entire paper in R markdown, where each figure is generated from the code

• A short demonstration in R studio of how to create an R markdown document

• A bridge into R and Python: issues with each (Ahmad and Soumya). Pandocs solution by Neil.

  See tst.md file.

  https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/tst.md

  Command is

  r pandoc tst.md -o test.ipynb

Code examples

• A very simple R markdown will look like the following

• Here are some very simple commands that you use to generate your document (you write your code in R and it gets compiled into a document like PDF)

See the link below for more details

https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

• Now head over to the file named rmarkdown.rmd

https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/rmarkdown.rmd

Running this will create a report like the following:

https://github.com/neelsoumya/teachingreproduciblescience_R/blob/main/rmarkdown.pdf

• A typical header of a R markdown file will look like

```r

---

title: "Analysis and Writeup"

header-includes:

• \usepackage{placeins}

• \usepackage{float}

• \floatplacement{figure}{H}

output:

pdf_document:

fig_caption: yes

keep_tex: yes

latex_engine: xelatex

number_sections: yes

word_document: default

html_document:

df_print: paged

bibliography: Periphery_project.bib

urlcolor: blue

---

```{r include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

knitr::opts_chunk$set(cache = TRUE)

knitr::opts_chunk$set(warning = FALSE)

knitr::opts_chunk$set(out.extra = '')

knitr::opts_chunk$set(fig.pos = 'H')

```

\begin{centering}

\vspace{3 cm}

\Large

\normalsize

Soumya Banerjee, r format(Sys.time(), "%b %d %Y")

\vspace{3 cm}

\end{centering}

\setcounter{tocdepth}{2}

\tableofcontents

\newpage

```{r,include=FALSE}

library(knitr)

library(gridExtra)

library(rmarkdown)

EQUATIONS in rmarkdown

$$ eGFR = eGFR{0} + b{before}*t_{before} $$

```

Italics in rmarkdown using metafor

Code can be rendered or shown in rmarkdown using

```

dsBaseClient::ds.summary(x='surv_object')

```

```{r, include=FALSE}

Load packages and settings

library(sqldf)

library(ggplot2)

library(knitr)

library(rmarkdown)

library(gplots)

library(RColorBrewer)

library(png)

library(grid)

library(gridExtra)

library(lme4)

library(lmerTest)

library(rpart)

```

```{r, echo=FALSE}

code here

```

Acknowledgements

Neil Lawrence, Carl Henrik Ek, Joyeeta Ghose

Contact

Soumya Banerjee

sb2333@cam.ac.uk