We have already discussed in class, that we should not randomly select your idioms. Also, while doing my last assignment I spent lot of time in “design dilemma”. While figuring out which graph to use, I happened to read an article by Stephen Few in which he mentions about best means to encode quantitative data in graphs. He states that, there is a procedure to follow while creating your visualization.<\/p>\n
Step 1: Understand the relationship\/message you are trying to present
\nStep 2: Select the best suitable graph
\nStep 3: Format your chart<\/p>\n
He mentions that almost all typical business information can be addressed by either one or combination of the below mentioned 7 quantitative message types (off course there are exceptions to this) and he has suggested suitable encoding methods which can be a quick cheat guide during our design dilemmas.<\/p>\n
Disclaimer: There can be other choices as well, this is just one of the few. 2. Ranking:<\/strong> When you have to communicate the order i.e. either highest to lowest or vice versa 3. Time Series:<\/strong> When you want to convey how things have changed over time. 4. Part-to-whole:<\/strong> When you want to represent some values as ratios or part of the whole 5. Correlation:<\/strong> When you want to compare 2 values and see if there is any relationship between them. 6. Deviation:<\/strong> To show difference between 2 sets of value 7. Distribution<\/strong> \u2013 If you want to measure counts of values per interval along a quantitative scale Reference:\u00a0https:\/\/www.perceptualedge.com\/articles\/Whitepapers\/Communicating_Numbers.pdf<\/p>\n","protected":false},"excerpt":{"rendered":" We have already discussed in class, that we should not randomly select your idioms. Also, while doing my last assignment I spent lot of time in “design dilemma”. While figuring out which graph to use, I happened to read an article by Stephen Few in which he mentions about best means to encode quantitative data … Continue reading Do not apply Eenie, Meenie, Minie, Moe technique to graph selection<\/span><\/a><\/p>\n","protected":false},"author":1884,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"qubely_global_settings":"","qubely_interactions":"","kk_blocks_editor_width":"","_kiokenblocks_attr":"","_kiokenblocks_dimensions":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-704","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"gutentor_comment":0,"qubely_featured_image_url":null,"qubely_author":{"display_name":"akalra","author_link":"https:\/\/blogs.scu.edu\/dataviz\/author\/akalra\/"},"qubely_comment":0,"qubely_category":"Uncategorized<\/a>","qubely_excerpt":"We have already discussed in class, that we should not randomly select your idioms. Also, while doing my last assignment I spent lot of time in “design dilemma”. While figuring out which graph to use, I happened to read an article by Stephen Few in which he mentions about best means to encode quantitative data…","post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/704","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/users\/1884"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/comments?post=704"}],"version-history":[{"count":2,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/704\/revisions"}],"predecessor-version":[{"id":706,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/704\/revisions\/706"}],"wp:attachment":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/media?parent=704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/categories?post=704"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/tags?post=704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n<\/em>1. Nominal Comparison<\/strong>: When you have to compare between one or more measures in any order.
\nSuitable Graph<\/strong>: The best encoding method is using either a horizontal or a vertical bar chart, but for large data sets it is better to use simple data points.<\/p>\n
\nSuitable Graph:<\/strong> Again, bar charts are most suitable for this.
\nExtra tip: For highlighting highest values sort in descending order and for lowest values, sort in ascending.<\/p>\n
\nSuitable Graph<\/strong>: Line Chart: When you want to stress on the trend and shape of data
\nBar Chart: When you want to stress on comparison between individual values
\nPoints + Line chart: To show individual values and simultaneously highlighting shape of the data.<\/p>\n
\nSuitable Graph:<\/strong> Bar charts are suitable to represent this relation.
\nCaution: Do not use pie chart for this, it is difficult to compare size of slices of a pie.
\nUse stacked bar chart when you want to display both the parts and the whole.<\/p>\n
\nSuitable Graph:<\/strong> Trend line and points (scatter plot) are suitable for this type of relationship.<\/p>\n
\nSuitable Graph:<\/strong> Only when displaying time series and deviation together
\nLine Chart \u2013 To stress on shape of data
\nPoints + Line chart \u2013 To stress on both on individual values and simultaneously highlighting shape of data<\/p>\n
\nSuitable Graph:<\/strong> Histograms are a good fit to emphasize individual values
\nUse lines to emphasize on shape of data<\/p>\n