Making-visualizations

List each item you identify using the following format. The easiest way is to copy and paste the template. For now don't bother grouping them, we'll collect a bunch first and then see what the appropriate categories are based on what we find.

Google Doc instead? Yes

Pattern

1. Another pithy idea. Why it's important. How to accomplish it. [Where It's From, page number/URL. curator initials]

Example

1. Choose color combinations with good contrast. This makes it easier for people to separate the principle components. Identify a set to use and then ask your colleagues for feedback, use a web-based color choosing tool. [Charlie's Book of Viz, page 33. cfp]
2. Avoid visual distortion in data graphics. This will allow the viewer to perceive reality more accurately. Table - best way to show numbers (20 numbers or less>prefer table to a graph). Representation of numbers should be directly proportional to the numerical quantities represented. Clear, detailed labeling to defeat graphical distortion and ambiguity. Show data variation, not design variation. (The Visual Display of Quantitative Information, Chapter 2, p.56, p.61)
3. Don't tell lies in your graph by planning graphical representation accurately. That avoid inaccurate reflection of reality. If plotting government spending and dept over the years, take population and inflation into account. In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units(The Visual Display of Quantitative Information, Chapter 2, p.68)
4. A use of 2 or 3 varying dimensions to show one-dimensional data is a weak and inefficient technique. The number of information should not exceed the number of dimensions in the data (The Visual Display of Quantitative Information, Chapter 2, p.71)
5. Context is essential for graphical integrity. Graphics must not quote data out of context. In the quantitative thinking data graphics should answer “Compared to What?” question. (The Visual Display of Quantitative Information, Chapter 2, p.74)
6. Blending quantitative multiples, narrative text and images is useful for monitoring data-rich processes. Multiples help to monitor and analyse typical to finance multi-variable processes, combining overview with detail (Visual Explanations, p.110-111)
7. Consider sparklines for data-rich representation: datawords – data-intense, data-simple, word-sized graphics. Tracks and compares changes over time, by showing overall trend along with local detail; shows recent change in relation to many pat changes; sparklines gives a context for nuanced analysis and better decisions; shows intensity/frequency of occurrence. Should often be embedded in text and tables : possibility of writing with data graphics; Variation in slopes are best detected when slopes are 45 average; moderately greater in length than in height; changing the relative weight of the data-lines and also muting the contrast between the data and background to reduce optical noise; avoiding strong frames around that create unintentional optical clutter; printing and viewing data density of 500 spraklines on A3 size paper (Beautiful Evidence, p.46-63)

Start-Up

• "Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency. Graphical displays should:
  • show the data
  • induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
  • avoid distorting what the data have to say
  • present many numbers in a small space
  • make large data sets coherent
  • encourage the eye to compare different p[pieces of data
  • reveal the data at several levels of detail, from a broad overview to the fine structure
  • serve a reasonably clear purpose: description, exploration, tabulation, or decoration
  • be closely integrated with the statistical and verbal descriptions of a data set"
  • - "The Visual Display of Quantitative Information", pg. 13
• "Graphics should be reserved for the richer, more complex, more difficult statistical material." - "The Visual Display of Quantitative Information", pg. 30

• Scientific and engineering - Mobeen