class: center, middle, inverse, title-slide # Tips for effective data visualization <br> 💅 ### Dr. Çetinkaya-Rundel --- layout: true <div class="my-footer"> <span> Dr. Mine Çetinkaya-Rundel - <a href="http://www2.stat.duke.edu/courses/Fall18/sta112.01/schedule" target="_blank">stat.duke.edu/courses/Fall18/sta112.01 </a> </span> </div> --- ## Announcements - Make sure you're in the GitHub org and on Slack - Make sure *all* of your changed files are committed and pushed to GitHub - Contingency plan for Thursday: Presentations will be moved to next Tuesday, content for Thursday's class will be delivered via video --- class: center, middle # Data visualization --- ## What is data visualization? Anything that converts data sources into a visual representation - charts - plots - maps - tables - etc. .footnote[ Source: https://guides.library.duke.edu/datavis ] --- class: center, middle # Why do we visualize? --- ## Data: `datasaurus_dozen` Below is an exceprt from the `datasaurus_dozen` dataset: ``` ## # A tibble: 142 x 8 ## away_x away_y bullseye_x bullseye_y circle_x circle_y dino_x dino_y ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 32.3 61.4 51.2 83.3 56.0 79.3 55.4 97.2 ## 2 53.4 26.2 59.0 85.5 50.0 79.0 51.5 96.0 ## 3 63.9 30.8 51.9 85.8 51.3 82.4 46.2 94.5 ## 4 70.3 82.5 48.2 85.0 51.2 79.2 42.8 91.4 ## 5 34.1 45.7 41.7 84.0 44.4 78.2 40.8 88.3 ## 6 67.7 37.1 37.9 82.6 45.0 77.9 38.7 84.9 ## 7 53.3 97.5 39.5 80.8 48.6 78.8 35.6 79.9 ## 8 63.5 25.1 39.6 82.7 42.1 76.9 33.1 77.6 ## 9 68.0 81.0 34.8 80.0 41.0 76.4 29.0 74.5 ## 10 67.4 29.7 27.6 72.8 34.6 72.7 26.2 71.4 ## # ... with 132 more rows ``` --- ## Summary statistics .question[ .midi[ Write pseudo-code for calculating the correlation of (`x`,`y`) for each of the thirteen `dataset`s? Based on the summary statistics, how similar do the relationships between `x` and `y` in the thirteen datasets look? ] ] .pull-left[ ``` ## # A tibble: 1,846 x 3 ## dataset x y ## <chr> <dbl> <dbl> ## 1 dino 55.4 97.2 ## 2 dino 51.5 96.0 ## 3 dino 46.2 94.5 ## 4 dino 42.8 91.4 ## 5 dino 40.8 88.3 ## 6 dino 38.7 84.9 ## 7 dino 35.6 79.9 ## 8 dino 33.1 77.6 ## 9 dino 29.0 74.5 ## 10 dino 26.2 71.4 ## # ... with 1,836 more rows ``` ] -- .pull-right[ ```r datasaurus_dozen %>% group_by(dataset) %>% summarise(r = cor(x, y)) ``` ``` ## # A tibble: 13 x 2 ## dataset r ## <chr> <dbl> ## 1 away -0.0641 ## 2 bullseye -0.0686 ## 3 circle -0.0683 ## 4 dino -0.0645 ## 5 dots -0.0603 ## 6 h_lines -0.0617 ## 7 high_lines -0.0685 ## 8 slant_down -0.0690 ## 9 slant_up -0.0686 ## 10 star -0.0630 ## 11 v_lines -0.0694 ## 12 wide_lines -0.0666 ## 13 x_shape -0.0656 ``` ] --- ## Visualization .question[ Based on the summary statistics, how similar do the relationships between `x` and `y` in the thirteen datasets look? ] <!-- --> --- ## Anscombe's quartet ```r library(Tmisc) quartet ``` .pull-left[ ``` ## set x y ## 1 I 10 8.04 ## 2 I 8 6.95 ## 3 I 13 7.58 ## 4 I 9 8.81 ## 5 I 11 8.33 ## 6 I 14 9.96 ## 7 I 6 7.24 ## 8 I 4 4.26 ## 9 I 12 10.84 ## 10 I 7 4.82 ## 11 I 5 5.68 ## 12 II 10 9.14 ## 13 II 8 8.14 ## 14 II 13 8.74 ## 15 II 9 8.77 ## 16 II 11 9.26 ## 17 II 14 8.10 ## 18 II 6 6.13 ## 19 II 4 3.10 ## 20 II 12 9.13 ## 21 II 7 7.26 ## 22 II 5 4.74 ``` ] .pull-right[ ``` ## set x y ## 23 III 10 7.46 ## 24 III 8 6.77 ## 25 III 13 12.74 ## 26 III 9 7.11 ## 27 III 11 7.81 ## 28 III 14 8.84 ## 29 III 6 6.08 ## 30 III 4 5.39 ## 31 III 12 8.15 ## 32 III 7 6.42 ## 33 III 5 5.73 ## 34 IV 8 6.58 ## 35 IV 8 5.76 ## 36 IV 8 7.71 ## 37 IV 8 8.84 ## 38 IV 8 8.47 ## 39 IV 8 7.04 ## 40 IV 8 5.25 ## 41 IV 19 12.50 ## 42 IV 8 5.56 ## 43 IV 8 7.91 ## 44 IV 8 6.89 ``` ] --- ## Summarising Anscombe's quartet ```r quartet %>% group_by(set) %>% summarise( mean_x = mean(x), mean_y = mean(y), sd_x = sd(x), sd_y = sd(y), r = cor(x, y) ) ``` ``` ## # A tibble: 4 x 6 ## set mean_x mean_y sd_x sd_y r ## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 I 9 7.50 3.32 2.03 0.816 ## 2 II 9 7.50 3.32 2.03 0.816 ## 3 III 9 7.5 3.32 2.03 0.816 ## 4 IV 9 7.50 3.32 2.03 0.817 ``` --- ## Visualizing Anscombe's quartet ```r ggplot(quartet, aes(x = x, y = y)) + geom_point() + facet_wrap(~ set, ncol = 4) ``` <!-- --> --- ## Age at first kiss .question[ Do you see anything out of the ordinary? ] ```r ggplot(student_survey, aes(x = first_kiss)) + geom_histogram(binwidth = 1) + labs(title = "How old were you when you had your first kiss?") ``` ``` ## Warning: Removed 6 rows containing non-finite values (stat_bin). ``` <!-- --> --- ## Facebook visits .question[ How are people reporting lower vs. higher values of FB visits? ] ```r ggplot(student_survey, aes(x = fb_visits_per_day)) + geom_dotplot(binwidth = 5, dotsize = 0.4) + labs(title = "How many times do you go on Facebook per day?") ``` <!-- --> --- class: center, middle # Designing effective visualizations --- ## Keep it simple <img src="img/pie-3d.jpg" width="300" style="display: block; margin: auto;" /> <img src="u1_d04-effective-data-viz-pt1_files/figure-html/pie-to-bar-1.png" width="600" style="display: block; margin: auto;" /> --- ## Use color to draw attention <img src="u1_d04-effective-data-viz-pt1_files/figure-html/unnamed-chunk-4-1.png" width="500" style="display: block; margin: auto;" /> <img src="u1_d04-effective-data-viz-pt1_files/figure-html/unnamed-chunk-5-1.png" width="600" style="display: block; margin: auto;" /> --- ## Tell a story <img src="img/time-series.story.png" width="800" style="display: block; margin: auto;" /> .footnote[ Credit: Angela Zoss and Eric Monson, Duke DVS ] --- <center> <iframe width="768" height="432" src="https://www.youtube.com/embed/Z8t4k0Q8e8Y" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> </center> ---