Since the x-axis represents the number of games within season and y-axis tracks cumulative points relative to the league average, lines with a positive slope represent above average performance and a negative slope represents below average performance.
This design makes it easy to query a good (or bad) team for a particular year (via direct manipulation) to see who the team is as well as how they’ve compared to the competition in other years. https://blog.cpsievert.me/2018/03/30/visualizing-geo-spatial-data-with-sf-and-plotly/. Any dendrogram object can be provided to plot_dendro(), but this particular example visualizes the similarity of US states in terms of their arrest statistics via a hierarchical clustering model on the USArrests dataset. It also uses highlight_key() to assign the number of cylinders to each point so that when a particular point is ‘queried’ all points with the same number of cylinders are highlighted (the number of cylinders is displayed with text just for demonstration purposes). In that case, one should consider linking views server-side, as covered in Section 17. That’s because, the statistical R functions that ggplot2 relies on to generate the graphical layers can’t necessarily be recomputed with different input data in your web browser. One subtlety to be aware of in terms of what makes Figure 16.2 possible is that every point along a line may have a different data value assigned to it. static.html files). For example, in Figure 16.25, persistent selection with dynamic colors allows one to first highlight earthquakes with a magnitude of 5 or higher in red, then earthquakes with a magnitude of 4.5 or lower, and the corresponding earthquakes are highlighted in the leaflet map. For the interactive, see https://plotly-r.com/interactives/storms.html. By querying a few different cities in Figure 16.3, one obvious thing we can learn is that not every city has complete pricing information (e.g., South Padre Island, San Marcos, etc). Is it considered plagiarism when you modify your professor's proof when solving a problem in a homework assignment?
Selecting points on one of the iris plots doesn’t affect the plots in the quakes group, and vice versa.
As Figure 16.23 shows, sometimes you’ll want to fix certain parameters of the summary (e.g., number of bins in a histogram) to ensure the selection layer is comparable to original layer. FIGURE 16.18: Using statistical queries to perform a 2-way ANOVA on the mpg dataset.
FIGURE 16.13: Graphical querying in a facetted worm chart of English Premier League football teams between 2007 and 2015. How does the highlight.js change affect Stack Overflow specifically? With plotly, one can write R code to pose graphical queries that operate entirely client-side in a web browser (i.e., no special web server or callback to R is required). FIGURE 16.10: Combining filtering and highlighting with non-unique querying variables. The biggest advantage of drawing an entirely new graphical layer with the filtered data is that it becomes easy to leverage statistical trace types for producing summaries that are conditional on the query. FIGURE 16.4: Linking a dot-plot of the number of missing housing prices with the raw time series. Currently one point is highlighted and the other points are visible but faded. To learn more, see our tips on writing great answers. At the moment, graphical queries work best with leaflet and DT.
For the interactive, see https://plotly-r.com/interactives/ggplotly-linked-densities.html. Thanks for contributing an answer to Stack Overflow! Within each panel, we’ll wrap the house price time series by year by putting the month on the x-axis and grouping by year. Perhaps more widely useful is the ability to highlight individual observations since most of these diagnostics are designed to identify highly influential or unusual observations. For the interactive, see https://plotly-r.com/interactives/tour-USArrests.html. Figure 16.7 demonstrates the main difference in logic between filter and highlight events. This sort of idea relates closely to the notion of generalized selections as described in Heer, Agrawala, and Willett (2008).↩︎, The title that appears in the dropdown can be controlled via the group argument in the highlight_key() function.
Another neat consequence of drawing a completely new layer is that we can control the plotly.js attributes in that layer through the selected argument of the highlight() function. In addition, the dynamic and persistent color brush allows us to query other teams to compare both within and across years. Each panel represents one year of English Premier League standings across time and each line represents a team (the querying variable). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Sievert, Carson. Figure 16.11 extends Figure 14.1 by layering on linear models specific to each frame and specifying continent as a key variable.
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