{"id":1286,"date":"2016-10-24T22:41:55","date_gmt":"2016-10-25T05:41:55","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f16\/?p=1286"},"modified":"2016-10-24T23:01:21","modified_gmt":"2016-10-25T06:01:21","slug":"dc-data-visualization","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/24\/dc-data-visualization\/","title":{"rendered":"DC Data Visualization"},"content":{"rendered":"
\"DC<\/a><\/div>\n
\n
\n

For this blog post, I analyzed DC Character data, a part of my group’s final project data set, to look at the relationship between the character’s sex (null, female, male, genderless, and transgender) and their alignment (good, bad, null, neutral, and reformed). I used Tableau to create a bar chart that parsed out the number of records we had. The various colored bars indicated the sex listed for each character: blue for null, orange for female, pink for genderless and turquoise for male.<\/p>\n

As we can see from the chart, there is a high proportion of males to other character sexes. When looking at my data visualization, I can see this more clearly than looking at the data. I can also see that there are more bad characters than good. There is less of a difference in the male-female ratio for null or neutral labeled characters but it may also indicate that there are just a fewer number of those characters. The visualization shows that there are few genderless characters and no transgender characters. All or no gender is considered in the data but not all are necessarily represented. That was an important decision that the archivist compiling the data made and decided to include.<\/p>\n

However, I cannot see the reformed characters data. It does not list the other genders as options (such as null, genderless or transgender) which means it was not recorded. It could also mean that I would have to clean and filter my data more precisely.<\/p>\n<\/div>\n

 <\/p>\n<\/div>\n

<\/div>\n","protected":false},"excerpt":{"rendered":"

For this blog post, I analyzed DC Character data, a part of my group’s final project data set, to look at the relationship between the character’s sex (null, female, male, … Continue reading “DC Data Visualization”<\/span><\/a><\/p>\n","protected":false},"author":35,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1286","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1286","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=1286"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1286\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=1286"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=1286"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=1286"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}