visualization_lecture

Course banner

Introduction

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

Talks

Slides

Installation

Install Python and the required packages

python -m venv venv_viz
source venv_viz/bin/activate
pip install -e requirements.txt

Course content

Declaration

Generative AI tools were used in creating these resources. All content was independently verified by the author.

Notes

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

https://github.com/neelsoumya/visualization_lecture/blob/main/mathematics_data_science.pdf

https://osf.io/mnh8d/

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

Code

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

https://github.com/neelsoumya/anova_linear_mixed_effects_examples

Rmarkdown package using Tufte’s principles

https://github.com/rstudio/tufte

Resources

R graph gallery
R shiny applications
Thinking outside the 10 dimensional box

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

https://youtu.be/zwAD6dRSVyI

University of Cambridge data visualization resources

https://cambiotraining.github.io/visual-data-communication/materials/functions-and-objects.html

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/

Visualization alternatives

https://dominicroye.github.io/blog/2025-12-14-broken-charts/ <!–

Video lectures

https://www.youtube.com/watch?v=ZDSj-jEoaoc –>

Reproducibility

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:

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

clone it

git clone https://github.com/neelsoumya/teaching_reproducible_science_R

cd teaching_reproducible_science_R

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

setwd('~/teaching_reproducible_science_R')

In R studio, run the markdown

rmarkdown.rmd

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

Template code

rmarkdown.rmd

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

Running this will create a report like the following:

https://github.com/neelsoumya/teaching_reproducible_science_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/teaching_reproducible_science_R/blob/main/simple_project.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/teaching_reproducible_science_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)

Example tSNE

Example heatmaps

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

A screenshot of how to create R packages in R Studio

A screenshot of how to create R markdowns in R Studio

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/teaching_reproducible_science_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/teaching_reproducible_science_R/blob/main/tst.md

Command is
```
  pandoc tst.md -o test.ipynb
```

Code examples

A very simple R markdown will look like the following

Schematic of R markdown

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)

Basic elements of markdown

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/teaching_reproducible_science_R/blob/main/rmarkdown.rmd

Running this will create a report like the following:

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

Screenshot of completed report

A typical header of a R markdown file will look like

---

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


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

This site is open source. Improve this page.