Visual Analytics

Housekeeping

• Textbook
  

  It’s gonna be a drip fed mess.

• Assignments
  

  First one is quite early, like before week 4; 2-people groups.

  Second assignment is on multi-dimensional data

  This was converted from `md` to `org` using `pandoc -f gfm` at time: 2020-03-04T06-00-44

Visualisation

• Definition
  

  In a study it was defined as:

  “The use of computer supported, interactive, visual representations of data to amplify cognition”

  It’s all about making data quicker

Nomenclature

Using Zathura

Question 1

• Problem
  

  Visit the website: http://www.visualcomplexity.com/vc/. Have a close look at the available visualisation techniques. In your opinion, which techniques are among the most useful? Or which one is the most pretty visual display? Explain and justify your preference. Note: there is no right or wrong answer in this question.

• Working
  

  This Visual from the Google AI blog highlighting the important features detected by a neural network is a unique insight into the predictive modelling technique. It is not easy to understand the behaviour of a Neural Network and this visual offers a uniqe insight that could not easily be understood:

  

  This Visual however is arguably one of the most visually striking because of the vibrant colour choice:

  

Question 2

• Problem
  

  Explore the online demo: http://graphs.gapminder.org/world/. What have you discovered or found from the visualization of the “Wealth and Health of Nations data set”? E.g. is there any correlation between GDP and Life Expectancy? etc.

• Working
  

  There is a clear positive correlation between income and life expectancy, other features that are readily observable are:

  • Regions
    

    It can be observed that Europe is a wealthy region (Yellow) while Africa and Asia are poor regions (cyan, red))

    This could be further observed to be a function of distance from the equator, generally regions lose to the equator like Europe, UK, USA are quite wealthy where as regions nearer the equator are more likely to be impovrished.

    Exceptions to this are:

    • Australia due to the rich resources such as coal/uranium and the head start in education owing to the European Descent.
    • Russia is impovrished because the October Revolutions that followed the first

    World War greatly modified there economy relative to the rest of Europe, this decayed into authoritarianism and eventually extreme wealth inequality (with the rise of homelessness and oligarchs) following the collapse of the Soviet Union at the end of the cold-war.

    This could be a functoin of temperature, a higher temperature may lead to :

    • increased spread of bacterial disease
    • increased perishability of food
  • Population
    

    It isn’t possible to say much about the influence population has because the population is measured by region without taking account of size (e.g. China is quite large while Vietnam is relatively small) or habitability (e.g. Japan is very mountainous and hard to farm or ranch on).

    This is made more difficult because the income is measured per person but the population is not the relative space that person might enjoy (i.e. the population density), the GDP per country rather than capita might be more instructive.

  • Time Trends
    

    It appears that Europe (and Japan) have always enjoyed a higher GDP per capita while asia and Africa appear to lag in this respect.

    Other than the “reshuffling” caused by the Great Depression, WW I, WW II and the collapse of the soviet union Europe has always enjoyed a higher GDP hence a higher life expectancy.

    During the first two world wars (with the exception of Russia and Poland respectively, for obvious reasons) it appears that the influence of currency on life expectancy became even stronger, somewhat unfortunate for other regions given that these were European wars. The great depression also showed a similar effect on this correlation, going from a linear trend to an exponential.

  • China as an Exception
    

    China appears to not follow a correlation between GDP and Life Expectancy until the new milennia, life expectancy rises following the second world war despite no change in GDP per Capita.

    In the 70’s China begins to follow this trend moving in a diagonal fashion indicative of a correlation, this is presumably due to manufacturing exported to China perhaps due to the development of the micro-processor in the US.

    In the 2000’s China began to improve GDP per capita more significantly, perhaps due to free market policies, and followed a trend where GDP per capita would strongly correlated with life expectancy.

Question 3

• Problem
  

  Using a search engine, explore 3 applications/projects/tools that use visualisations. You are required to write a short summary about the applications/projects/tools. Why do you think they are significant? What are good and bad about these applications/projects/tools? Etc.

• Working
  
  • Gnome’s Baobab / WinDirStat / KDirStat
    

    Gnome’ Baobab, KDirStat and WinDirStat are disk usage analysers for Gnome, KDE and Windows respectively.

    

    

    • Significance
      

      All three tools use a descending list of directories with a bar chart indicating the proportion of disk space consumed by that directory, to the right is a graphic shosing this distribution.

    • Good Design
      

      All three tools have correctly implement an ordered list and bar chart, making it easy to understand and manage disk space on a system.

      Gnome’s Baobab uses a ring chart with a popup overlay to describe the corresponding directory.

      This choice of graph makes it easy to understand which directories are consuming the most space while still having an overview of the structure of the directories, the popup prevents the graph from becoming too busy and showing unnecessary information.

      The ring chart will also re-centre following a selection of a directory allowing deeply nested structures to be understood easily.

    • Bad Design
      

      Unfourtunately KDirstat and Windirstat only offer treemap visualisations, this choice of graph is vastly inferior to a ring chart because it can only clearly show a certain amount of information at an overview, it is difficult do understand deeply nested folder structures they won’t re-generate the plot without rescanning the drive.

  • GitHub Visualizer
    

    http://ghv.artzub.com/#repo=ranger-assets&climit=100&user=ranger

    The Github Visualiser is a way of visualising the proportionate activity of various repositiories of various projects.

    • Significance
      

      This provides a novel way to understand the:

      • relative popularity of repos
      • The language most composing a repo / project
      • How Active a project is generally and over time.
    • Good Design
      

      Using Bubbles to illustrate the is the overall share of a repo in a project provides a quick understanding at a glance.

      Using a Time Series Chart over time is an easy way to show trends.

    • Bad Design
      

      The visualisation is far too busy to understand what is going on, for instance the size of the bubbles are not made clear, are they popularity, size, frequency of commits or frequency of clones / pulls of the repo?

      For instance the visualisatoin of one of my favourite pieces of software Ranger is such that I can infer nothing from it other than the fact that the web page is written in HTML and the project is written in python, which is totally obvious and not particularly helpful to get a deeper understanding of the Ranger project as opposed to say the Midnight Commander Project, although the visualisation for Midnight Commander is significantly better and so this may be a scaling issue.

  • Cancer Graphic
    

    http://web.archive.org/web/20140526084103/http://www.cancerresearchuk.org:80/cancer-info/cancerstats/causes/attributable-risk/visualisation/

    This Plot attached here shows the attributes most likely to cause cancer:

    cd /tmp
    #wget "http://web.archive.org/web/20140801035734/http://publications.cancerresearchuk.org/downloads/product/CS_POSTER_ATTRIB.pdf"
    command -v pdftoppm >/dev/null 2>&1 || { echo >&2 "command -v foo >/dev/null 2>&1 || { echo >&2 "I require pdftoppm but its not installed.  Aborting."; exit 1; }I require foo but it's not installed.  Aborting."; exit 1; }
    pdftoppm CS_POSTER_ATTRIB.pdf  CS_POSTER_ATTRIB -png
    mv CS_POSTER*png ~/Notes/Org/Attachments/Statistics/
    ls ~/Notes/Org/Attachments/Statistics/CS_POSTER_ATT*png
    

    

    • Significance
      

      This visualisation is significant because it effectively describes both the magnitude and interaction of various risk factors and behaviours on the probability of developing cancer.

      Statistically this can be a very difficult thing to describe and explain but this graphic very clearly shows what to look out or

    • Good Design
      

      Having a key in the centre of the graph makes it very easy to determine what the individual elements of the visualisation mean.

      The relative size of individual risk factors means that at a galnce it is very easy to determine risk factors for cancer.

    • Bad Design
      

      The graph unfourtunately is a little busy but this doesn’t appear to be in a way that is disproportionate to the amount of information conveyed.

    • Inferences
      

      An interesting component of this visualisation is that it clearly shows interactions of various items, for example the following are large to moderate risk factors for cancer generaly:

      • Overweight (BMI > 25 kg / m\(^2\), distninct from obesity which is $≈ > 30)
      • Inactivity

      While HRT is a very small risk factor for all types of cancer.

      This clearly illustrates that HRT may be medically appropriate in men despite the historical belive that such treamtent was correlated with prostate cancer in men ¹. Recent studies suggest that this might not be the case ² and HRT is (somewhat obviously) correlated with increased physical activity, weight loss ³ and general health metrics ⁴.

      This visualisation clearly shows that this treatment might generally lower the risk of cancer in men and provides a simple way to convey complex interactions between attributes and the complex statistics involved in this type of research in a way that clearly shows the important facts of the matter..

      Moreover this plot also only shows HRT as a risk factor in cancers that tend to overwhelmingly affect women, a distinction that is very important in that area of medical science.

Question 4

• Problem
  

  For the following visualisations, in your opinion, are they good or bad; Justify the answer:

• Working
  
  • Plot A
    

    

    This plot is overly busy but also not very descriptive, meaning at a glance it is not possible to determine what the differences between different regions actually are.

    Upon closer inspection it is not clear what the bar charts are trying to illustrate, clearly the venn diagram is trying to illustrate the proportion of communication and overlap, relative to thhe number of users. This is an interesting way to try and show the interactions between the different communication strategies but the choice of a Venn diagram was misguided. A venn diagram can only represent 3 sets with circles ¹ and this has seemingly artificially restricted the number of potential behavriou types that the plot has tried to convey to such an extent that they may as well be disregarded entirely because they are just not descriptive enough to understand.

    Interestingly Eastern countries use a combination of content/messges while western countries tend to use or the other, this could be related to the pictographical written laguage implemented by Eastern / Asian Countries and could lead to insights on human behaviour, unfourtunately the plot does not clearly describe the meaning of the colours and so no inferences can be easily made.

    Moreover the size of the circles are relative to the total number of users, this is somewhat misleading because the plot shows that 33% of the USA uses Social Media while only 15% of China uses social media, countries/regions are not uniformally distributed or constructed so choosing to use regions as a delimiter when using absolute size of users is potentially misleading. This is partially addressed however by the Global Social Network penetration bar chart below the plot.

  • Plot B
    

    

    • The amount of text overlayed on this graph makes it difficult to read.
    • The Growth rate of the publication should not be represented as a line because

    it indicates some type of continuous connection between the universities, it should be represented as either another column or ideally a seperate time series plot should be produced:

    • Such a plot should show the market share and allow the growth rate to be

    interpreted from the slope of the line in an organic fashion, that way the different universities could have both market share and growth rate compared between each other without trying to mentally visualise a cumullative summation.

Question 5

• Problem
  

  Optional: If you still have time, play around this website to see how visualization help to find patterns: http://rocs.hu-berlin.de/viz/sgb/ (Coronavirus Geographic and Network visualisations)

• Working
  

Last Lecture

Module Outline

Human Vision

Colour Vision   ATTACH

• Colour Blindness
  

Preattentive Processing

  sample(1:9, replace = TRUE, size = 30)

:  [1] *3* 9 8 9 1 1 9 6 2 1 2 2 4 9 7 5 *3* 8 6 5 5 6 3 5 2 1 1 8 7 7

• Tasks
  

  People can:

  • detect dargets
  • boundary detectoin
  • etc.

  Whereas machine learning algorithm’s to do that are ’cutting edge’.

• Animatoin
  

  Be careful with animation, it can be distracting

Visualisation Theory

Gestalt Principle

Visual Encoding

• Scales of Measurement
  

  Basically you just want to read this:

  • Lucy Park Visualisation

  And pay attention to things like this:

  

Evaluating a Visualisation

Potential Assignment resources

All Exercises

Question 1

• Perception
  

  Perception is identifying and understanding a sensory input, such as perceiving:

  • an angle as obtuse or acute
  • an object to be round or sharp by touch or sight
  • an accent to be British, German, Chinese etc. by sound.
  • an object to be falling off the table.

  Perception is inherently dependent on experience, for example a person without exposure may not be able to perceive differences in accent.

  It is unlikely that two different people will perceive things in the exact same way.

  Generally perception is not something done consciously.

• Cognition
  

  Cognition is processing information in an analytical way, working through a problem using prior training, it is done consciously.

  Examples of cognition include:

  • Determing the shape of a plot follows a certain model (e.g. polynomial, exponential/logarithmic, ARIMA, White Noise, etc.)
  • Recognising an object to be a 12 mm nut from brake caliper as opposed to a small metallic object.
  • Deciding that a person probably spent time in particular countries or regions given the languages they speak and accent they have.
  • Determining that an object that is falling can be modelled with calculus.

  Cognition depends on training, the majority of people can learn to work through a problem in a particular way.

Question 2

  library(tidyverse)
  x <- 1:20
  y <- x*3+2 + rnorm(length(x), 0, 15)
  data <- data.frame(x,y)


  ggplot(data, aes(x = x, y = y, col = y)) +
               geom_point() +
               guides(col = FALSE) +
               theme_classic() +
               stat_smooth(method = "lm", lty = 2, col = "purple") +
               labs(title = "Arbitrary time Series", x = "Time", y = "Response")

• Human Visual System and Automated Analysis
  

  The pros and cons of Human Perception and Automated Analysis are best highlighted by considering the difficulties of unsupervised learning in the realm of Machine Learning.

  Unsupervised Machine Learning algorithms such as clustering and PCA (in 2 and 3 dimensions obviously) are very easily performed by humans, but implementing such algorithms automatically and analytically can be quite complex and in some cases resource intensive.

  This is very similar to the proximity Gestalt law discussed in question 8 below at No description for this link.

  An advantage to the Human visual system is that the analysis can be done rapidly and requires no prior programming and maybe to a degree a human can be influenced quite rapidly in how the data is analysed or perceived whereas programming a machine learning algorithm or even a deductive algorithm can be complex and time consuming.

  The advantage to using Automated analysis and algorithms is that an algorithm:

  • is unbiased to the origins or nature of the data
  • can be extended to disjoint, large data that might have patterns outside what humans would be expected to recognise
  • can be extended to multiple dimensions
    • For example using KNN analysis might involve computing distances in higher dimensions
    • Or interpreting data with many variables, PCA can `flatten` out variables that don’t significantly contribute to the variance of the response.
  • Summary
    
    • Humans can make rapid and accurate relative judgements but these judements are

    susceptible to bias and influence.

    • Machines can make unbiased consistent judgements based on an algorithm (or

    atleast a machine learning algorithm) but cannot out of the box make a relative value judgement without human guidance.

Question 3

Question 4

• (1) Number of Colours
  

  Humans can only identify about 8 colours on a plot, so try to use \(7\pm2\) and less than 10.

• (2) Data Type
  

  Data may be ordered and may be continuous or discrete:

  	Ordered	Unordered
  Continuous	Colours should use a smooth divergent/sequential pallet	NA
  Discrete	Colours should use a small number of sequential colours	Colour

  pallet should be a small number of distinct colours |

  If data is continuous (and / or ordinal) then colours that follow a pattern should be used in order to illustrate that pattern, for example:

  library(RColorBrewer)
  library(tidyverse)
  # colorRampPalette(brewer.pal(9, "Blues"))(100) %>% plot()
  # my_cols <- brewer.pal(7,"Greens")
  # par(pty = "s", mai = c(0.1, 0.1, 0.4, 0.1))
  # display.brewer.pal(3,"Accent")
   display.brewer.pal(9,"Blues")
  

  

  If data is discrete then discrete and distinct colours should be used, if that data is also non-ordinal then those colours should avoid having a pattern, for example:

  library(RColorBrewer)
  library(tidyverse)
  # colorRampPalette(brewer.pal(9, "Blues"))(100) %>% plot()
  # my_cols <- brewer.pal(7,"Greens")
  # par(pty = "s", mai = c(0.1, 0.1, 0.4, 0.1))
  display.brewer.pal(3,"Accent")
  # display.brewer.pal(9,"Blues")
  

  

• (3) Viewing Medium
  

  A visualisation may have different levels of perceived brightness depending on how it’s viewed, for example a visualisation made on a pc with a black background (i.e. dark mode) may lead to a plot looking overly saturated, if that plot was printed the distinction between various factors may not be clear in graph.

• (4) Contrast
  

  If a plot is converted to greyscale in order to print, there may be insufficient contrast to perceive differences, this can be avoided if attention is paid to ensure good ’seperation’ between elements regardless of contrast.

• (5) edge enhancement
  

  using colours to highlight edges may make a plot harder or easier to read, for example consider the following histograms:

  library(tidyverse)
  library(gridExtra)
  a <- rnorm(1000, 3, 300)
  b <- rbinom(length(a), size = 1, prob = 0.3)
  birthwt <- data.frame("A" = a, "B" = b )
  birthwt_pretty <- birthwt
  birthwt_pretty$B <- ifelse(birthwt$B, "A", "B")
  
  hist <- ggplot(birthwt_pretty, aes(x = A, fill = B,  y = ..density..)) +
           theme_classic() +
           labs(x = "Birth Weight", y = "Density") +
           guides(fill = guide_legend("Factor"))
  
  plots <- list()
  
  plots[[1]] <- hist + geom_histogram(position = "dodge2", col = "blue", binwidth = 300, lwd = 1)
  plots[[2]] <- hist + geom_histogram(position = "dodge2", col = "red", binwidth = 300, lwd = 1)
  plots[[3]] <- hist + geom_histogram(position = "dodge2", col = "black", binwidth = 300, lwd = 1)
  plots[[4]] <- hist + geom_histogram(position = "dodge2", binwidth = 300, lwd = 1)
  
  
  layout <- matrix(c(1:4), byrow = TRUE, nrow = 2)
  grid.arrange(grobs = plots, layout_matrix = layout)
  

  

  This effect can be made more pronounced when the plots have different scales an are arranged relative to each other:

  a <- rnorm(1000, 3, 300)
  b <- rbinom(length(a), size = 1, prob = 0.3)
  birthwt <- data.frame("A" = a, "B" = b )
  birthwt_pretty <- birthwt
  birthwt_pretty$B <- ifelse(birthwt$B, "A", "B")
  
  hist <- ggplot(birthwt_pretty, aes(x = A, fill = B,  y = ..density..)) +
           theme_classic() +
           labs(x = "Birth Weight", y = "Density") +
           guides(fill = guide_legend("Factor"))
  
  # hist + geom_histogram(position = "dodge2", col = "blue", binwidth = 300)
  
  plots <- list()
  
  # Dodge
  plots[[1]] <- hist + geom_histogram(position = "dodge2", col = "blue", binwidth = 300, lwd = 2)
  
  # Overlay
  plots[[2]] <-  hist + geom_histogram(binwidth = 300, col = "black", lwd = 2)
  
  # Single Histogram
  plots[[3]] <- hist + geom_histogram(binwidth = 300, col = "green", aes(group = 1), fill = "lightblue", lwd = 2)
  
  # Facet Grid
   plots[[4]] <- hist + geom_histogram(binwidth = 300, col = "purple", lwd = 2) +
           facet_grid(. ~ B) +
           guides(fill = FALSE)
  
  layout <- matrix(c(1, 1, 2, 3, 4, 4), byrow = TRUE, nrow = 3)
  # arrangeGrob(grobs = plots, layout_matrix = layout)
  grid.arrange(grobs = plots, layout_matrix = layout)
  

  

Question 5

• Pokemon dashboard
  

  The Pokemon type dashboard uses colours effectively because it uses discrete and distinct colours to represent discrete variables.

  The colour choices are an intelligent choice of sufficiently distinct colours that are also differently related to the information to make the plot easy to interpret.

  The colours choses are also saturated enough to allow transperancy to be mapped to frequency and for the treemap to be sufficiently distinct.

  

• Nuclear Waste Sites
  

  The Nuclear Waste Dashboard uses colour effectively because it uses a different colour pallette for the two types of plots which represent the number of sites in each category and the geographical location of any such site in that category.

  Tying a colour to a variable is important to visualise data without confusing the intended audience.

  

• Neighbourhood Diversity
  

  The Neighbourhood Diversity visualisation uses colour effectively because the colour choses is a continous sequential pallete increasing in saturation, this makes interpreting continuous data easier because the pallete has a one-to-one correspondence with the density of the observation.

  

• Using Colours
  

  The following graphic is an exemplar graphic from DataQuest but it shows how it is often desirable to restructure a visualisation based on the data as opposed to overusing colours which can be difficult to interpret in high amounts.

  

Question 6

Question 7

Question 8

• Grouping
  
  • Objects close together will be perceived as groups
    • This is an example of humans performing something simple that is quite complex for machines.
• Like Elements
  
  • If elements are similar in features such as shape, colour or size they will people are more ready to consider the data as grouped together
    • This means it can be important to use techniques such as blurring and desaturating visualisations in order to remove or enhance such distinctions as appropriate.
• Connected Elements
  
  • Data That is connected in some way (by a line, curve or border) will give the effect of creating clusters or groups in data

Question 9

• Quantitative Value
  

  If data has a quantitative value, the attributes that should be considered are

  1. Position
  2. Length
  3. Angle
  4. Area
  5. Volume
• Nominal Value
  

  If data has a nominal value, the five most important attributes to consider are:

  1. Position of the Data
  2. Hue
  3. Texture
  4. Connection
  5. Containment

Question 10

Connection Approach

Enclosure Approach

Connection+Enclosure Approach

Tree Graphs

• Tree Maps
  

  Voronoi Treemaps are very nice

Drawing Graphics Technologies

PlantUML

• Examples
  

  There are many examples located here

Mermaid

• Supported properties
  

  file - Output file. It should be either svg, png or pdf.

  width - Width of the page. Optional.

  height - Height of the page. Optional.

  theme - Theme of the chart, could be default, forest, dark or neutral. Optional.

  background-color - Background color. Example: transparent, red, ’#F0F0F0’. Optional.

  mermaid-config-file - JSON configuration file for mermaid. Optional.

  css-file - CSS file for the page. Optional.

  pupeteer-config-file - JSON configuration file for puppeteer. Optional.

FlowchartJS

Question 1

• Possible Improvements
  
  1. minimizing the intersectoin of edges
  2. Using Colours to show the distinction between nodes

  Possible tools that can be used are:

• Generated DOT Graph
  

  The easiest way to generate graph is to use the DOT library with PlantUML:

  Expand Code Click Here

  # #+begin_src plantuml :output both :file ./Attachments/VisualAnalytics/DOTGraphwk4q1.png :exports both :eval never-export :eval never-export

  @startdot
  strict digraph graphName {
  concentrate=true
  fillcolor=green
  color=blue
  style="filled, rounded"
   A [shape=box, fillcolor="#a31621", style="rounded, filled"]
  
   edge [
      arrowhead="none"
    ];
  
   node[
      fontname="Fira Code",
      shape="square",
      fixedsize=false,
      style=rounded
    ];
  
  
  # A -> B [dir="both"]
  A -> B
  A -> C
  A -> G
  B [shape=box, fillcolor="#bfdbf7", style="rounded, filled"]
  B -> F
  B -> C
  B -> H
  B -> H
  C [shape=box, fillcolor="#053c5e", style="rounded, filled"]
  C -> A
  C -> B
  C -> D
  D [shape=box, fillcolor="#1f7a8c", style="rounded, filled"]
  D -> G
  D -> C
  E [shape=box, fillcolor="#eaf4d3", style="rounded, filled"]
  E -> H
  E -> A
  F [shape=box, fillcolor="#0f5257", style="rounded, filled"]
  F -> A
  F -> B
  G [shape=box, fillcolor="#0b3142", style="rounded, filled"]
  G -> A
  G -> D
  H [shape=box, fillcolor="#9c92a3", style="rounded, filled", arrowType="dot"]
  H -> E [arrowType="dot"]
  H -> B
  }
  @enddot
  

  

  Figure 25: DOT graph created using PlantUML

• Sequence Diagram
  

  Plant UML can also create a sequence diagram which is a different type of visualisation but is still an interesting way to visualise the relationships.

  #+begin_src javascript :exports code
  @startuml
      A-->B
      A-->C
      A-->G
      B-->F
      B-->C
      B-->H
      B-->H
      C-->A
      C-->B
      C-->D
      D-->G
      D-->C
      E-->H
      E-->A
      F-->A
      F-->B
      G-->A
      G-->D
      H-->E
      H-->B
  @enduml
  

  

  Figure 26: DOT graph created using PlantUML

Question 2

• Good Examples
  
  • Solar Winds
    

    The proceeding image shown in figure 27 is an exemplar graph from a commercial piece of software called Solar Winds², it is an effective plot because:

    • The nodes are colour coded
    • The nodes use symbols as a way to express an independent categorical variable
    • The edges do not overlap
    • there is adequate spacing throughout the graph

    

    Figure 27: Exemplar plot for Solar Winds product

  • Standord Paper Visualisation
    

    The Proceeding Plot shown in figure 28 is a graph showing feature construction in a paper written at Standford University ⁵ presumably produced in TikZ, it is an effective graph because:

    • the colours to denote the two main discrete variables are distinct
    • The edges don’t overlap
    • The Nodes tesalate in a way that makes the graph easy to read
    • Where a quasi-node is used to branch an edge to facilitate node tesallation it is denoted by a slight bulge making it clear to delineate the edges

    

    Figure 28: Correlation Network Diagram of mtcars data

  • Unix Development
    

    The proceeding plot shown in figure 1 is a graph taken from the graphviz documentation created using the DOT language. It is a effective graph because:

    • the nodes are sufficiently spaced
    • the edges do not overlap unless necessary
      • If the edges do overlap it is to minimise the cost of spacing
• Bad Examples
  
  • igraph Library Example
    

    The following plot shown in figure 29 from the R graph gallery³ is bad example of a graph because the edges are hard to discern the labels are not clearly delineated, the edges cross over unnecessarily and the thickness of the edges is too small

    

    Figure 29: Correlation Network Diagram of mtcars data

  • Research chronology
    

    The proceeding graph is an exemplar plot of a service known as Research Chronology⁴.

    It is a poor graph because:

    • There are too many overlaps between edges
    • There are too many colours to uniquely identify the discrete variables
    • The plot needs to be further spaced

    

    Figure 30: Graph Depiction of research chronology

  • Mapping the BlogoSphere
    

    The proceeding plot shown in figure 30 is an attempt to map the relationships between blogs, it is a poor graph simply because it is too dense, the graph needs to be expanded and trimmed significantly before it could be effective.

    

Question 3

• File System
  

  The most natural type of tree structure is the directory structure of a a computer, for example the tree structure of the provided DOI_Tree can be visualised

  cd ~/Dropbox/Studies/2020Autumn/Visual_Analytics/03_Material/Lecture3Materials4Students/DOI_Tree
  tree
  

  .
  ├── Data
  │   ├── 2811.xml
  │   ├── 3137_filesystem.xml
  │   ├── 6814_filesystem.xml
  │   ├── CategoryAustralia_6254.txt
  │   ├── CategoryAustralia_6254.xml
  │   ├── CategoryAustralia.xlsx
  │   ├── CategoryUSA_20289.xml
  │   ├── CategoryUSA.txt
  │   ├── CategoryUSA.xlsx
  │   ├── Safety events ontology New.txt
  │   ├── Safety events ontology New.xml
  │   └── treeml.dtd
  ├── DOITreeVis.jar
  ├── META-INF
  │   └── MANIFEST.MF
  └── ReadMe.txt
  
  2 directories, 15 files
  

• Tree Machine Learning Technique
  

  A more nuanced form of a tree structure is the machine learning technique whereby input variables are seperated to correspond to binned output variables in an effort to model the behaviour of a system without using regression. This technique can be used for continuous and discrete data, so for example the following code returns the plot shown in figure 1:

    library(tree)
    library(tidyverse)
  
    if(require('pacman')){
      library('pacman')
    }else{
      install.packages('pacman')
      library('pacman')
    }
  
    pacman::p_load(caret, scales, ggplot2, rmarkdown, shiny, ISLR, class, BiocManager, corrplot, plotly, tidyverse, latex2exp, stringr, reshape2, cowplot, ggpubr, rstudioapi, wesanderson, RColorBrewer, colorspace, gridExtra, grid, car, boot, colourpicker, tree, ggtree, mise, rpart, rpart.plot, knitr, MASS, magrittr)
  
    CSeat.tb <- as_tibble(Carseats)
  
  
    thresh <- (mean(CSeat.tb$Sales)+0.5*sd(CSeat.tb$Sales)) %>% round()
    CSeat.tb$CatSales <- ifelse(CSeat.tb$Sales > thresh, "High", "Low")
    CSeat.tb$CatSales <- factor(x = CSeat.tb$CatSales, levels = c("Low", "High"), ordered = TRUE)
    CSeat.tb <- CSeat.tb[,c(12, 2:11)]
  CatSalesModTree.rpart <- rpart(formula = CatSales ~ ., data = CSeat.tb)
  rpart.plot(CatSalesModTree.rpart, box.palette="OrGy", shadow.col="gray", nn=TRUE)
  

  

  Figure 32: Example of Tree Plot using Built in Data

• Genetic Lineage
  

  Genetic Lineage is another example of a tree structure graph, for example a the tree of life would look something to the effect of:

  

  Figure 33: Example of Tree of life

Question 4

• Techniques using connections or node links are:
  
  1. Generalized Pythagoras Trees (2014) by Fabian Beck
     • https://doi.org/10.5220/0004654500170028
  2. StackTree Layout
  3. Career Tree
  4. Ordered Tree Drawing
  5. GeoReferenced Tree Layout
  6. Rhizome Navigation
  7. Quadratic Programming Layout
  8. Clustergram
  9. Perspective Mapping
  10. Planar Upward Drawing
• Techniques using Enclosure Techniques include
  
  1. Mixed Projection Treemap
  2. Bubble Treemap
  3. Stable Treemap
  4. Hybrid Treemap
  5. Columnar Treemap
  6. Heirarchy map
  7. Word Treemap
  8. 3D-Treemap
  9. Circular Treemap
  10. Eichhorn Tree

Question 5

• Treemap
  

  file:///home/ryan/Dropbox/Studies/2020Autumn/Visual_Analytics/03_Material/Lecture3Materials4Students/Treemap-4.0.5/Monitor.jar

  The UI is very mouse heavy which makes it difficult to manipulate, the UI is also a little busy.

  Overall though being able to double click through the enclosed regions to move through the bins and being able to change the partitioning method from strip to slice and dice makes it easy to manipulate.

• DOI_Tree
  

  file:///home/ryan/Dropbox/Studies/2020Autumn/Visual_Analytics/03_Material/Lecture3Materials4Students/DOI_Tree/DOITreeVis.jar

  Unfourtanetly I was unable to load the built in data sets I had created without having a crash, this was the output regardless:

   prefuse.data.io.DataIOException: org.xml.sax.SAXParseException; lineNumber: 1; columnNumber: 1; Content is not allowed in prolog.
  	at prefuse.data.io.TreeMLReader.readGraph(TreeMLReader.java:46)
  	at prefuse.data.io.AbstractGraphReader.readGraph(AbstractGraphReader.java:33)
  	at visualization.Visualizer.setVisualization(Visualizer.java:423)
  	at visualization.Visualizer.openDataFileAction(Visualizer.java:407)
  	at visualization.Visualizer.actionPerformed(Visualizer.java:103)
  	at java.desktop/javax.swing.AbstractButton.fireActionPerformed(AbstractButton.java:1967)
  	at java.desktop/javax.swing.AbstractButton$Handler.actionPerformed(AbstractButton.java:2308)
  	at java.desktop/javax.swing.DefaultButtonModel.fireActionPerformed(DefaultButtonModel.java:405)
  	at java.desktop/javax.swing.DefaultButtonModel.setPressed(DefaultButtonModel.java:262)
  

  So overall I would say that a definite downside to this tool is that it is a little unstable (or very possibly I’m using the wrong java, I’m just using whatever my distro shipped with)

• D&C Treemap
  

  file:///home/ryan/Dropbox/Studies/2020Autumn/Visual_Analytics/03_Material/Lecture3Materials4Students/D&CTreemap/DCTreemap.jar

  The aestheticis very pleasant as shown in figure 34 but having to mouse over to determine the clusters is very inconvenient (and the font renders incorrectly on my system)

  

Question 1

  # #+begin_src javascript :exports code
  # #+begin_src javascript :exports code
  @startdot
  strict digraph graphName {
          concentrate=false # This merges edges/lines
          fillcolor=green
          color=blue
          A [shape=box, fillcolor="#a31621", style="rounded, filled"]

          edge [
          arrowhead="none"
          ];

          node[
          fontname="Fira Code",
          shape="square",
          fixedsize=false,
          style=rounded
          ];


          # A -> B [dir="both"]
          A -> B
          A -> C -> B -> A
          A -> C
          B [shape=box, fillcolor="#bfdbf7", style="rounded, filled"]
          B -> F
          B -> H
          C [shape=box, fillcolor="#053c5e", style="rounded, filled"]
          D [shape=box, fillcolor="#1f7a8c", style="rounded, filled"]
          E [shape=box, fillcolor="#eaf4d3", style="rounded, filled"]
          F [shape=box, fillcolor="#0f5257", style="rounded, filled"]
          F -> B
          G [shape=box, fillcolor="#0b3142", style="rounded, filled"]
          G -> A
          G -> D
          G -> G
          A -> A
          H [shape=box, fillcolor="#9c92a3", style="rounded, filled", arrowType="dot"]
          H -> E [arrowType="dot"]
          H -> B
  }
  @enddot

  # #+begin_src javascript :exports code
  # #+begin_src javascript :exports code
  @startdot
  strict digraph graphName {
          concentrate=false # This merges edges/lines
          fillcolor=green
          color=blue
          A [shape=box, fillcolor="#a31621", style="rounded, filled"]

          edge [
          arrowhead="none"
          ];

          node[
          fontname="Fira Code",
          shape="square",
          fixedsize=false,
          style=rounded
          ];


          # A -> B [dir="both"]
          A -> B
          A -> C
          C -> B
          B [shape=box, fillcolor="#bfdbf7", style="rounded, filled"]
          B -> F
          C [shape=box, fillcolor="#053c5e", style="rounded, filled"]
          D [shape=box, fillcolor="#1f7a8c", style="rounded, filled"]
          E [shape=box, fillcolor="#eaf4d3", style="rounded, filled"]
          F [shape=box, fillcolor="#0f5257", style="rounded, filled"]
          G [shape=box, fillcolor="#0b3142", style="rounded, filled"]
          G -> A
          G -> D
          H [shape=box, fillcolor="#9c92a3", style="rounded, filled", arrowType="dot"]
          H -> E [arrowType="dot"]
          H -> B
  }
  @enddot

Question 2

Question 3

• Using Force Directed Methods on Large Graphs
  

  For very large graphs force directed methods can become unwieldly, slow to compute and generally the output can be to a degree unpredictable.

  So generally no, these methods are not effective for large graphs, however in saying that, they might still give an interesting overview of the data set.

Question 4

• Structural Clustering
  
  • Multilevel Approaches
    

    One Method is to implement a form of dimension-reduction, this is an alorithm that essentially groups multiple nodes into a single cluster and then instead represents those clusters.

    This can be visualised as levels of height, similar to a pyramid, each level increasing in height corresponds to a reduction in the number of nodes until eventually there would only be one node at the top, then the question simply becomes choosing the level that corresponds to the most effective Visualisation (this is somewhat similar to a dendrogram).

  • Collapsible Nodes
    

    Collapsible Nodes leverages the interactive capacity of computing devices to folder multiple nodes into a common root, this is similar to the directory structure of a computer, to visualise this compare the terminal output of the following:

     tree -L 1
     tree -L 2
     tree -L 3
    

• Abstraction / Aggregation
  

  Abstraction and aggregation involves merging nodes into common nodes by a similar trait, for example instead of listing every single car manufacturer as seperate nodes, maybe it makes sense to seperate the nodes by the vehicle fuel type or the country of origin (so BMW, Mercedes, VW would become ’European Car’ and Dodge, Chrysler, Ford would become ’US Car’).

  This (probably) requires human intervention to a degree in order to identify which aggregate terms are the most reasonable to the topic that the visualisation is being used for.

Question 5

Question 6

  # #+begin_src javascript :exports code
  # #+begin_src javascript :exports code
  @startdot
  strict digraph graphName {
          concentrate=true # This merges edges/lines
          fillcolor=green
          color=blue
          A [shape=box, fillcolor="#a31621", style="rounded, filled"]

          edge [
          arrowhead="none"
          ];

          node[
          fontname="Fira Code",
          shape="square",
          fixedsize=false,
          style=rounded
          ];


          # A -> B [dir="both"]
          A -> B
          A -> C
          A -> H
          B [shape=box, fillcolor="#bfdbf7", style="rounded, filled"]
          B -> A
          B -> F
          B -> H
          C [shape=box, fillcolor="#053c5e", style="rounded, filled"]
          C -> A
          C -> H
          C -> E
          C -> G
          D [shape=box, fillcolor="#1f7a8c", style="rounded, filled"]
          D -> B
          D -> F
          D -> E
          E [shape=box, fillcolor="#eaf4d3", style="rounded, filled"]
          E -> C
          E -> D
          E -> G
          F [shape=box, fillcolor="#0f5257", style="rounded, filled"]
          F -> B
          F -> D
          F -> G
          G [shape=box, fillcolor="#0b3142", style="rounded, filled"]
          G -> C
          G -> E
          G -> F
          H [shape=box, fillcolor="#9c92a3", style="rounded, filled", arrowType="dot"]
          H -> A
          H -> B
          H -> C
  }
  @enddot

• Visualise the Adjacency Matrix
  

  The Adjacency Matrix can be turned into vectors by using the following UltiSnips snippet in vim:

  snippet vec
  ${1:var} <- c("${0:${VISUAL:/ /","/g}}")
  endsnippet
  
  snippet numvec
  ${1:var} <- c(${0:${VISUAL:/ /, /g}})
  endsnippet
  

  Hence putting this together in R in code listing 4 the following plot of the adjacency matrix shown in table 1 may be produced as shown in figure 1:

  Expand Code Click Here

  Listing 4: Using GGplot2 to produce an adjacency Matrix

  library(tidyverse)
  
  names <- c("A", "B", "C", "D", "E", "F", "G", "H")
  A <- c(0, 1, 1, 0, 0, 0, 0, 1)
  B <- c(1, 0, 0, 1, 0, 1, 0, 1)
  C <- c(1, 0, 0, 0, 1, 0, 1, 1)
  D <- c(0, 1, 0, 0, 1, 1, 0, 0)
  E <- c(0, 0, 1, 1, 0, 0, 1, 0)
  Fv <- c(0, 1, 0, 1, 0, 0, 1, 0)
  G <- c(0, 0, 1, 0, 1, 1, 0, 0)
  H <- c(1, 1, 1, 0, 0, 0, 0, 0)
  
  data <- matrix(data = c(A, B, C, D, E, Fv, G, H), nrow = 8, ncol = 8, byrow = TRUE)
  rownames(data) <- names
  colnames(data) <- names
  
  # Rotate matrix 90-degrees clockwise.
  rotate <- function(x) {
      t(apply(x, 2, rev))
  }
  
  rotate_ACW <- function(x) {
      t(apply(x, 1, rev))
  }
  
  
  
    # Create Column Names
  make_colnames <- function(matrL) {
                       factor(paste("col", 1:ncol(matrL)),
                              levels = paste("col", 1:ncol(matrL)),
                              ordered = TRUE)
  }
  
   # Create Row Names
  make_rownames <- function(matrL) {
                       factor(paste("row", 1:nrow(matrL)),
                              levels = paste("row", 1:nrow(matrL)),
                              ordered = TRUE)
  }
  
  # Create Row Names with reversed Order
  make_rownames <- function(matrL) {
                       factor(paste("row", nrow(matrL):1),
                              levels = paste("row", nrow(matrL):1),
                              ordered = TRUE)
  }
  
  
  expand.grid_matrix <- function(mat) {
    data <- expand.grid("x" = LETTERS[1:8], "y" = rev(LETTERS[1:8]))
    data$z <- as.vector(rotate(mat))
    data$z <- as.logical(data$z)
  
    return(data)
  }
  
  
  data <- expand.grid_matrix(data)
  
  
  
  
  
  
  
  ggplot(data, aes(x = x, y = y, fill =z)) +
    geom_tile() +
    labs(x = "", y = "", title = "Adjacency Matrix", fill = "Pair")
  

  

Question 7   ggnet

# Load Packages
library(plotly)
library(igraph)
library(igraphdata)

# Load the Data
data("UKfaculty")

# Set the Layout Type
G <- upgrade_graph(UKfaculty)
L <- layout.circle(G)

# Create Vertices and Edges
vs <- igraph::V(G)
es <- as.data.frame(get.edgelist(G))

Nv <- length(vs)
Ne <- length(es[1]$V1)

# Crete Nodes
Xn <- L[,1]
Yn <- L[,2]

network <- plot_ly(x = ~Xn, y = ~Yn, mode = "markers", text = vs$label, hoverinfo = "text")

# Create Edges
edge_shapes <- list()
for(i in 1:Ne) {
  v0 <- es[i,]$V1
  v1 <- es[i,]$V2

  edge_shape = list(
    type = "line",
    line = list(color = "#030303", width = 0.3),
    x0 = Xn[v0],
    y0 = Yn[v0],
    x1 = Xn[v1],
    y1 = Yn[v1]
  )

  edge_shapes[[i]] <- edge_shape
}


# Create Network
axis <- list(title = "", showgrid = FALSE, showticklabels = FALSE, zeroline = FALSE)

fig <- layout(
  network,
  title = 'UK Faculty',
  shapes = edge_shapes,
  xaxis = axis,
  yaxis = axis
)

fig

Question 1   ATTACH

Question 2

• Geometric Zooming
  

  Geometric zooming involves enlarging an area of the content of avisualisation without any modification of the presentation of the information.

• Semantic Zooming
  

  Semantic Zooming enlarges the focus section but changes the way in which the visualisation is presented, for example, taking a subtree of a treemap into it’s own visualisation would be an example of semantic zooming.

Question 3

Question 4

Question 5

Question 6

• Git Time Machine for Atom
  

  The Git Time Machine package for the Atom Text editor integrates on overview of all the past changes to a file with a timeline on the bottom illustraiting those changes by way of a bubble chart, this is shown in figure 44.

  This is an example of an overview+detail approach because the diff report shows the detail of the plot while the lower portion provides the context in which the changes occured.

  

  Figure 44: Screenshot of the Git Time Machine Package for Atom

• Flight Radar 24   ATTACH
  

  The Flight Radar 24 Visualisation provides an image with a slider that allows a comparison between two scatterplots of aeroplane locations, as shown in figure 45.

  

  Figure 45: Visualisation of Airline Travel

• Pandemic International   ATTACH
  

  The pandemic international COVID-19 visualisation, shown in figure 46 is an example of an overview+detail visualisation because detail regarding the specific case numbers is provided next to an overview of the total number of cases over time.

  

  Figure 46: Daily Reported Cases of COVID-19

• Evernote Visualiser   ATTACH
  

  This EverNote Visualiser provides an overview+content visualisation of a users notes in evernote by providing a snapshot of the content of a note and an overview of the interconnections of that note, this is shown in figure 47

  

  Figure 47: Evernote Visualiser providing Overview+Content of notes.

• Git Visualizer   ATTACH
  

  The GitUp git visualiser provides a visualisation of the canges in a git repository as shown in figure 48 and is an example of an overview+content visualiser because it provides an detailed explanation of the changes of a commit next to a visual overlay of the overall changes.

  

  Figure 48: GitUp Git Visualiser

Question 7   ATTACH

Question 8   ATTACH

Question 1

Question 2

Question 3

• Splunk
  
  • Description
    

    Splunk is a visualisation platform used for interpreting big data through a web-style interface, designed to allow access to visualisations across a variety of devices.

  • Pros
    
    • 1. Platform Agnostic
      

      Splunk implements web technologies to make there software work on a variety of devices, the advantage to this is that you can use a mobile OS to browse visualisations, it can be far more immersive to browse visualisations on an iPad than a Desktop for instance.

    • 2. Modern Interface
      

      The interface to use splunk is a modern web platform which makes for a good user experience.

    • 3. Accessible
      

      Splunk is designed to allow people with out experience in programming to draw insights from visualisations, meaning that anybody can interpret data regardless of there background.

  • Cons
    
    • Slow Interface
      

      The problem with using a web interface is that it will be slower and less responsive than a native program, this takes away from the user experience.

    • Complex Architecture
      

      Splunk has a lot of features which means that Implementing it can be quite complex, potentially requiring on-going staff to keep it effective.

    • Learning Curve
      

      Splunk has a high learning curve, difficult to get started with.

• Tableau
  
  • Description
    

    Tableau is a commercial visualisation software which can turn data into visualisations through a GUI.

  • Pros
    
    • High Performance
      

      The unser interface is robust and stable and is renowned to operate fast even on big data.

    • Large User Base
      

      There is a large user base for Tableau which can make finding help easier.

    • Ease of Use
      

      Tableau has an easier learning curve than a programming language owing to it’s GUI interface, this does however have the trade off that there is less room to grow.

  • Cons
    
    • High Cost and Training
      

      Tableau is commercial software and so it comes attached with a high cost, this means that if it is adopted any visualisations are tethered to the software and that training people to use it will also tether people to that software.

      This also means it can be difficult to find people with the training to produce high-quality Tableau Visualisations because individuals may not be willing to undertake training, as of there own volition, for a propriatery piece of software as opposed to something like GGPlot, Plotly or D3 which could be used anywhere or any place of emplyment.

    • Security Issues
      

      Tableau is closed-source, this means that the community has had no opportunity to inspect the codebase for vulnerabilities.

    • Integration Issues
      

      It can be difficult to easily embed Tableau into a business’s products because of it’s proprietary nature, compare this to something like Shiny whcih in contrast can be simpler to implement.

• Pentaho
  
  • Description
    
  • Pros
    
    • Many Features
      

      Pentaho has a vast array of features that users can implement.

    • Open Source
      

      Components of Pentaho are open source, this means that there is a higher capacity for software integration and for community review of software security.

    • Compatability
      

      Pentaho is compatable with many database sources including MySQL as opposed to for example Tableau which requires driver selectors.

  • Cons
    
    • Cryptic Error Messages
      

      Bugs encountered when using this software are very difficult to fix and the error messages are usually not descriptive of a pathway to rectify the issue.

    • Inconsistent
      

      Products in the Pentaho suite are implemented in an inconsistent fashion, making manipulating the software difficult and tiring.

    • Poor Documentation
      

      This platform assumes that users are familiar with scripting and the documentation assumes that, so this software can be very challenging to users not experienced with computer programming.

Question 4

• GGPlot
  

  GGPlot is a graphing library that implements the grammar of graphics.

  • Pros
    
    • Layers
      

      The benefit of using ggplot2 (as opposed to for example base-r graphing libraries) is that plots can be built from the bottom-up by adding layers on top of each other.

    • Quality
      

      GGPlot Produces some of the nicest static plots of any Data Viz platform.

      • By merging many plots into a gif by using a for loop and imagemagic some degree of dynamic behaviour can be somewhat implemented.
  • Cons
    

    There are non really other than the learning curve. There is a steep learning curve, you have to learn how to use R and then how to coerce data into a tidy data format.

    The necessity to coerce data into a tidy data format isn’t really an impediment though because it can actually make analysis easier generally.

• Plotly
  

  Plotly is an implementation of D3 that is available in Python and R.

  • Pros
    
    • Interactive
      

      Plotly allows for fully interactive plots to be created and embedded into HTML files via JavaScript (without actually needing to write any and just writing in Python and R).

    • Complements ggplot2
      

      ggplot2 objects can be passed to plotly using the plotly::ggplotly() wrapper.

  • Cons
    
    • Documentation
      

      Documentation treats R users like second class citizens but it’s still fairly useful.

    • Performance
      

      For large Data sets (like > 1000) the performance of this library is an issue. I ran into this problem when trying to analyse the Marvel dataset in my assignment, ideally it should be used on datasets of around 200 observations (this could also be an issue with the R memory management)

• Shiny
  

  Shiny is a platform to produce dynamic interactive HTML pages from inside R.

  • Pros
    

    This package produces very nice pages and dashboards without needing to leave R the convenience of this is that analysis and publishing can be done all in one place.

    Combined with a package to produce static sites from within R like bookdown (or even the python package MkDocs in conjuction with knitr) this means that documentation and publication of statistical analysis can all be done in one place.

  • Cons
    

    The syntax is a little different from ordinary R because you need to specify variables in odd ways for shiny components, this can be difficult for a data scientist not experienced in web development or programming.

Question 5

Question 6

• R
  

  R is an open source

  • Pros
    

    The statistical packages implemented in R are often more rigorously implemented and this in part is one of the reasons that it is larger in academia than Python.

  • Cons
    
    • System
      

      R is inferior to python for system level scripting, this makes it less versatile and for this reason it is not used in spheres like Astronomy or even in industry generally.

    • Neural Networks
      

      Google writes all there machine learning in Python, so, if you want to use Neural Networks you’ll have to use python anyway.

    • Syntax
      

      R is very lispy and nowhere nearly as readable as Python (although I like {bracket[s]…})

• Apache Mahout
  

  Apache Mahout is linear alebra framework to allow data scientists and mathematicians to rapidly implement algorithms.

  • Pros
    

    Mahout is efficient in that it optimizes operations given to it into a mathematically equivalent although computationally easier methodology.

  • Cons
    

    Mahout isn’t well documented and because of this many people implement MapReduce jobs until the size of the data set has been reduced enough for working in R.

• Oracle Data Miner
  

  The oracle Data Miner allows people to directly interact with data inside a database in an interactive fashion.

  • Pros
    

    This Allows an easy representation for data that must be trapped in a database for performance concerns to be analysed in a relatively painless way.

    As an example performing text mining in the context of say a GitHub wiki is very simple, clone the wiki and use grep or recoll, but trying to analyse Wikipedia is quite difficult because mediawiki uses a MySQL database. This tool allows an easy methodology to take advantage of the structured database to draw insites.

  • Cons
    
    • Requires an Oracle Dev Account to Download
    • Only Provides an rpm package for linux
      • This is a nuisance because deb is very common and an AppImage or snap would be cross platform, this means that users of debian have to either use alien to get the right format or install various C libraries in order to compile from source.

Resources

DONE Decide on Data to Analyse

• Potential Ideas
  
  • Model the Corona virus is a bit obvious
  • Modelling interlinked files in org-mode could be fun but also a bit old hat
  • She really really like baobab but I don’t think I can use that
  • Analysing Wikipedia could be really fun
  • Planetary systems would be really nice
  • Could I do something with NMR?
• DataSets
  

  Instead of a dendrogram or treemap lets do social data,

  • Marvel Universe Looks Like a Social Network
    • Dataset

  That looks promising, let’s do that.

  Also you could just make a dendrogram or even simply use your folder structure and make a treemap.

  • Pulsars
  • Meteorite
  • Cheat Sheets
  • Asteroids (27 Features !)
  • ExoPlants (10k X 50)
  • Org Parser
    

    An Org parser lke Orga (JS) or OrgParse.py Could give interesting Info on links maybe?

• Program
  
  • D3
    • Tidy Tree
    • SunBurst
      • I really like this
        • Could I do a hack to create a file structure out of nodes
          • Then you could use ranger (and tmsu) and Baobab
    • General Heirarchical
  • Gephi
  • iGraph inside R
  • Look Here Generally

DONE Submit through Vuws