andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2246 knowledge-graph by maker-knowledge-mining
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Introduction: Stephen Jenkins wrote: I was thinking that you and your blog readers might be interested in “ An Economist’s Guide to Visualizing Data ” by Jonathan Schwabish, in the most recent Journal of Economic Perspectives (which is the American Economic Association’s main “outreach” journal in some ways). I replied: Ooh, I hate this so much! This seems to represent a horrible example of economists not recognizing that outsiders can help them. We do much much better in political science. To which Jenkins wrote: Ha! I guessed as much — hence sent it. And I’ll now admit I was surprised that JEP took the piece without getting Schwabisch to widen his reference points. To elaborate a bit: I agree with Schwabish’s general advice (“show the data,” “reduce the clutter,” and “integrate the text and the graph”). But then he illustrates with 8 before-and-after stories in which he shows an existing graph and then gives his improvements. My problem is that I don’t like most of his “afte
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1 This seems to represent a horrible example of economists not recognizing that outsiders can help them. [sent-3, score-0.335]
2 To elaborate a bit: I agree with Schwabish’s general advice (“show the data,” “reduce the clutter,” and “integrate the text and the graph”). [sent-8, score-0.247]
3 But then he illustrates with 8 before-and-after stories in which he shows an existing graph and then gives his improvements. [sent-9, score-0.313]
4 In just about every case, Swabish’s advice is reasonable and his graphs improve on the originals. [sent-11, score-0.23]
5 But I just don’t think his versions represent best practice. [sent-12, score-0.186]
6 And, in an influential journal, you’d like to demonstrate best practice. [sent-13, score-0.072]
7 Before: And after: The small scale and blurriness are my fault; something happened in my cut-and-paste, so please don’t blame Schwabish for that. [sent-15, score-0.188]
8 In any case, yes, the second display is better, but in addition I’d label the y-axes and, most obviously, I’d get rid of those heavy gray horizontal lines. [sent-16, score-0.403]
9 I’d also put tick marks on the x-axes, especially for the two graphs in the upper row, also he seems to have forgotten to put y-axes on the two graphs on the right. [sent-17, score-0.606]
10 As it is, the four subgraphs seem to merge into each other. [sent-18, score-0.118]
11 You really need some visual cues to separate them. [sent-19, score-0.102]
12 In each case, an existing graph is redrawn with only slight changes. [sent-21, score-0.449]
13 The excitement of visualization is not conveyed in this article at all . [sent-23, score-0.194]
14 Rather it all seems like a boring application of certain principles of graphics design. [sent-24, score-0.148]
15 But I agree with Jenkins: Schwabish should’ve taken a broader perspective. [sent-27, score-0.142]
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Introduction: Stephen Jenkins wrote: I was thinking that you and your blog readers might be interested in “ An Economist’s Guide to Visualizing Data ” by Jonathan Schwabish, in the most recent Journal of Economic Perspectives (which is the American Economic Association’s main “outreach” journal in some ways). I replied: Ooh, I hate this so much! This seems to represent a horrible example of economists not recognizing that outsiders can help them. We do much much better in political science. To which Jenkins wrote: Ha! I guessed as much — hence sent it. And I’ll now admit I was surprised that JEP took the piece without getting Schwabisch to widen his reference points. To elaborate a bit: I agree with Schwabish’s general advice (“show the data,” “reduce the clutter,” and “integrate the text and the graph”). But then he illustrates with 8 before-and-after stories in which he shows an existing graph and then gives his improvements. My problem is that I don’t like most of his “afte
Introduction: I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on this point with the infovis people. Maybe if I tell my own story, and then they tell their stories, this will point a way forward to a more constructive discussion. So here goes. I majored in physics in college and I worked in a couple of research labs during the summer. Physicists graph everything. I did most of my plotting on graph paper–this continued through my second year of grad school–and became expert at putting points at 1/5, 2/5, 3/5, and 4/5 between the x and y grid lines. In grad school in statistics, I continued my physics habits and graphed everything I could. I did notice, though, that the faculty and the other
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Introduction: To continue our discussion from last week , consider three positions regarding the display of information: (a) The traditional tabular approach. This is how most statisticians, econometricians, political scientists, sociologists, etc., seem to operate. They understand the appeal of a pretty graph, and they’re willing to plot some data as part of an exploratory data analysis, but they see their serious research as leading to numerical estimates, p-values, tables of numbers. These people might use a graph to illustrate their points but they don’t see them as necessary in their research. (b) Statistical graphics as performed by Howard Wainer, Bill Cleveland, Dianne Cook, etc. They–we–see graphics as central to the process of statistical modeling and data analysis and are interested in graphs (static and dynamic) that display every data point as transparently as possible. (c) Information visualization or infographics, as performed by graphics designers and statisticians who are
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Introduction: Dean Eckles writes: Some of my coworkers at Facebook and I have worked with Udacity to create an online course on exploratory data analysis, including using data visualizations in R as part of EDA. The course has now launched at https://www.udacity.com/course/ud651 so anyone can take it for free. And Kaiser Fung has reviewed it . So definitely feel free to promote it! Criticism is also welcome (we are still fine-tuning things and adding more notes throughout). I wrote some more comments about the course here , including highlighting the interviews with my great coworkers. I didn’t have a chance to look at the course so instead I responded with some generic comments about eda and visualization (in no particular order): - Think of a graph as a comparison. All graphs are comparison (indeed, all statistical analyses are comparisons). If you already have the graph in mind, think of what comparisons it’s enabling. Or if you haven’t settled on the graph yet, think of what
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Introduction: Sandeep Baliga writes : [In a recent study , Gilles Duranton and Matthew Turner write:] For interstate highways in metropolitan areas we [Duranton and Turner] find that VKT (vehicle kilometers traveled) increases one for one with interstate highways, confirming the fundamental law of highway congestion.’ Provision of public transit also simply leads to the people taking public transport being replaced by drivers on the road. Therefore: These findings suggest that both road capacity expansions and extensions to public transit are not appropriate policies with which to combat traffic congestion. This leaves congestion pricing as the main candidate tool to curb traffic congestion. To which I reply: Sure, if your goal is to curb traffic congestion . But what sort of goal is that? Thinking like a microeconomist, my policy goal is to increase people’s utility. Sure, traffic congestion is annoying, but there must be some advantages to driving on that crowded road or pe
Introduction: Jerzy Wieczorek has an interesting review of the book Graph Design for the Eye and Mind by psychology researcher Stephen Kosslyn. I recommend you read all of Wieczorek’s review (and maybe Kosslyn’s book, but that I haven’t seen), but here I’ll just focus on one point. Here’s Wieczorek summarizing Kosslyn: p. 18-19: the horizontal axis should be for the variable with the “most important part of the data.” See Kosslyn’s Figure 1.6 and 1.7 below. Figure 1.6 clearly shows that one of the sex-by-income groups reacts to age differently than the other three groups do. Figure 1.7 uses sex as the x-axis variable, making it much harder to see this same effect in the data. As a statistician exploring the data, I might make several plots using different groupings… but for communicating my results to an audience, I would choose the one plot that shows the findings most clearly. Those who know me well (or who have read the title of this post) will guess my reaction, whic
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