class: inverse background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover # Extending ggplot2 statistical geometries ## MAA Metro New York ### Dr. Evangeline 'Gina' Reynolds ### Saturday May 1, 2021, 11AM #### Photo Credit: Mike Lewis HeadSmart Media ---  --- class: inverse, center middle # If visualization packages used this approach they would be able to "draw *every* statistical graphic". ??? The grammar of graphics framework, proposed by Leland Wilkinson in 1999, that identified 'seven orthogonal components' in the creation of data visualizations.\ Wilkinson asserted that if data visualization packages were created using a separation of concerns approach -- dividing decision making surrounding these components --- the packages would be able to "draw every statistical graphic". The grammar of graphic principles were incredibly powerful and gave rise to a number of visualization platforms including Tableau, vega-lite, and ggplot2. --- # Creators enjoy freedom, fine control -- # Grammar of graphics inspired: -- #- Tableau #- vega-lite #- ggplot2 --- ### Use ggplot2 in the classroom because R, and ... --  > The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a **powerful way of thinking about visualisation**, as a way of mapping between variables and the visual properties of geometric objects that you can perceive. ??? Statistical educators that introduce students to one of these tools arguably are doing more than constructing one-off plots to discuss statistical principles with students: they are introducing students to 'a powerful way of thinking about data visualization'. Statistical educators often use ggplot2 as their grammar-of-graphics-based data visualization tool as students can learn it along side the rich statistical ecosystem of the R programming language. The R programming language thus may serve as a one-stop-shop for statistical tooling; with recent developments in packages and IDEs writing code is becoming more accessible and welcoming to newcomers. --- ??? Still, using ggplot2 for statistical education can be a challenge at times. When it is used to discuss statistical concepts, sometimes it feels as though getting something done with the plotting library derails the focus on discussion of statistical concepts and material. --- class: inverse, center, middle # The problem -- ## sometimes ggplot2 is hard --- ## The status quo: adding the mean <img src="talk_maa_metro_ny_files/figure-html/unnamed-chunk-2-1.png" width="504" /> ??? Consider for example, a the seemingly simple enterprise of adding a vertical line at the mean of x, perhaps atop a histogram or density plot. --- count: false ### Adding the mean at x w/ base ggplot2 .panel1-basic-auto[ ```r *ggplot(data = airquality) ``` ] .panel2-basic-auto[ <img src="talk_maa_metro_ny_files/figure-html/basic_auto_01_output-1.png" width="504" /> ] --- count: false ### Adding the mean at x w/ base ggplot2 .panel1-basic-auto[ ```r ggplot(data = airquality) + * aes(x = Ozone) ``` ] .panel2-basic-auto[ <img src="talk_maa_metro_ny_files/figure-html/basic_auto_02_output-1.png" width="504" /> ] --- count: false ### Adding the mean at x w/ base ggplot2 .panel1-basic-auto[ ```r ggplot(data = airquality) + aes(x = Ozone) + * geom_histogram() ``` ] .panel2-basic-auto[ <img src="talk_maa_metro_ny_files/figure-html/basic_auto_03_output-1.png" width="504" /> ] --- count: false ### Adding the mean at x w/ base ggplot2 .panel1-basic-auto[ ```r ggplot(data = airquality) + aes(x = Ozone) + geom_histogram() + * geom_vline( * xintercept = * mean(airquality$Ozone, * na.rm = T) * ) ``` ] .panel2-basic-auto[ <img src="talk_maa_metro_ny_files/figure-html/basic_auto_04_output-1.png" width="504" /> ] <style> .panel1-basic-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-basic-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-basic-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> <img src="talk_maa_metro_ny_files/figure-html/basic-1.png" width="504" /> ??? Creating this plot requires greater focus on ggplot2 *syntax*, likely detracting from discussion of *the mean* that statistical instructors desire. It may require a discussion about dollar sign syntax and how geom_vline is actually a special geom -- an annotation -- rather than being mapped to the data. None of this is relevant to the point you as an instructor aim to make: maybe that the the mean is the balancing point of the data or maybe a comment about skewness. --- ### Adding the conditional means <img src="talk_maa_metro_ny_files/figure-html/cond_means_hard-1.png" width="504" /> ??? Further, for the case of adding a vertical line at the mean for different subsets of the data, a different approach is required. This enterprise may take instructor/analyst/student on an even larger detour -- possibly googling, and maybe landing on the following stack overflow page where 11,000 analytics souls (some repeats to be sure) have landed: <https://stackoverflow.com/questions/1644661/add-a-vertical-line-with-different-intercept-for-each-panel-in-ggplot2> The solutions to this problem involve data manipulation *prior* to plotting the data. The solution disrupts the forward flow ggplot build. One must take a pause, which may involve toggling back and forth between stack overflow solutions, disrupting momentum you are working on to talk about the pooled mean and the conditional mean. --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r *airquality ``` ] .panel2-cond_means_hard-auto[ ``` Ozone Solar.R Wind Temp Month Day 1 41 190 7.4 67 5 1 2 36 118 8.0 72 5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4 5 NA NA 14.3 56 5 5 6 28 NA 14.9 66 5 6 7 23 299 8.6 65 5 7 8 19 99 13.8 59 5 8 9 8 19 20.1 61 5 9 10 NA 194 8.6 69 5 10 11 7 NA 6.9 74 5 11 12 16 256 9.7 69 5 12 13 11 290 9.2 66 5 13 14 14 274 10.9 68 5 14 15 18 65 13.2 58 5 15 16 14 334 11.5 64 5 16 17 34 307 12.0 66 5 17 18 6 78 18.4 57 5 18 19 30 322 11.5 68 5 19 20 11 44 9.7 62 5 20 21 1 8 9.7 59 5 21 22 11 320 16.6 73 5 22 23 4 25 9.7 61 5 23 24 32 92 12.0 61 5 24 25 NA 66 16.6 57 5 25 26 NA 266 14.9 58 5 26 27 NA NA 8.0 57 5 27 28 23 13 12.0 67 5 28 29 45 252 14.9 81 5 29 30 115 223 5.7 79 5 30 31 37 279 7.4 76 5 31 32 NA 286 8.6 78 6 1 33 NA 287 9.7 74 6 2 34 NA 242 16.1 67 6 3 35 NA 186 9.2 84 6 4 36 NA 220 8.6 85 6 5 37 NA 264 14.3 79 6 6 38 29 127 9.7 82 6 7 39 NA 273 6.9 87 6 8 40 71 291 13.8 90 6 9 41 39 323 11.5 87 6 10 42 NA 259 10.9 93 6 11 43 NA 250 9.2 92 6 12 44 23 148 8.0 82 6 13 45 NA 332 13.8 80 6 14 46 NA 322 11.5 79 6 15 47 21 191 14.9 77 6 16 48 37 284 20.7 72 6 17 49 20 37 9.2 65 6 18 50 12 120 11.5 73 6 19 51 13 137 10.3 76 6 20 52 NA 150 6.3 77 6 21 53 NA 59 1.7 76 6 22 54 NA 91 4.6 76 6 23 55 NA 250 6.3 76 6 24 56 NA 135 8.0 75 6 25 57 NA 127 8.0 78 6 26 58 NA 47 10.3 73 6 27 59 NA 98 11.5 80 6 28 60 NA 31 14.9 77 6 29 61 NA 138 8.0 83 6 30 62 135 269 4.1 84 7 1 63 49 248 9.2 85 7 2 64 32 236 9.2 81 7 3 65 NA 101 10.9 84 7 4 66 64 175 4.6 83 7 5 67 40 314 10.9 83 7 6 68 77 276 5.1 88 7 7 69 97 267 6.3 92 7 8 70 97 272 5.7 92 7 9 71 85 175 7.4 89 7 10 72 NA 139 8.6 82 7 11 73 10 264 14.3 73 7 12 74 27 175 14.9 81 7 13 75 NA 291 14.9 91 7 14 76 7 48 14.3 80 7 15 77 48 260 6.9 81 7 16 78 35 274 10.3 82 7 17 79 61 285 6.3 84 7 18 80 79 187 5.1 87 7 19 81 63 220 11.5 85 7 20 82 16 7 6.9 74 7 21 83 NA 258 9.7 81 7 22 84 NA 295 11.5 82 7 23 85 80 294 8.6 86 7 24 86 108 223 8.0 85 7 25 87 20 81 8.6 82 7 26 88 52 82 12.0 86 7 27 89 82 213 7.4 88 7 28 90 50 275 7.4 86 7 29 91 64 253 7.4 83 7 30 92 59 254 9.2 81 7 31 93 39 83 6.9 81 8 1 94 9 24 13.8 81 8 2 95 16 77 7.4 82 8 3 96 78 NA 6.9 86 8 4 97 35 NA 7.4 85 8 5 98 66 NA 4.6 87 8 6 99 122 255 4.0 89 8 7 100 89 229 10.3 90 8 8 101 110 207 8.0 90 8 9 102 NA 222 8.6 92 8 10 103 NA 137 11.5 86 8 11 104 44 192 11.5 86 8 12 105 28 273 11.5 82 8 13 106 65 157 9.7 80 8 14 107 NA 64 11.5 79 8 15 108 22 71 10.3 77 8 16 109 59 51 6.3 79 8 17 110 23 115 7.4 76 8 18 111 31 244 10.9 78 8 19 112 44 190 10.3 78 8 20 113 21 259 15.5 77 8 21 114 9 36 14.3 72 8 22 115 NA 255 12.6 75 8 23 116 45 212 9.7 79 8 24 117 168 238 3.4 81 8 25 118 73 215 8.0 86 8 26 119 NA 153 5.7 88 8 27 120 76 203 9.7 97 8 28 121 118 225 2.3 94 8 29 122 84 237 6.3 96 8 30 123 85 188 6.3 94 8 31 124 96 167 6.9 91 9 1 125 78 197 5.1 92 9 2 126 73 183 2.8 93 9 3 127 91 189 4.6 93 9 4 128 47 95 7.4 87 9 5 129 32 92 15.5 84 9 6 130 20 252 10.9 80 9 7 131 23 220 10.3 78 9 8 132 21 230 10.9 75 9 9 133 24 259 9.7 73 9 10 134 44 236 14.9 81 9 11 135 21 259 15.5 76 9 12 136 28 238 6.3 77 9 13 137 9 24 10.9 71 9 14 138 13 112 11.5 71 9 15 139 46 237 6.9 78 9 16 140 18 224 13.8 67 9 17 141 13 27 10.3 76 9 18 142 24 238 10.3 68 9 19 143 16 201 8.0 82 9 20 144 13 238 12.6 64 9 21 145 23 14 9.2 71 9 22 146 36 139 10.3 81 9 23 147 7 49 10.3 69 9 24 148 14 20 16.6 63 9 25 149 30 193 6.9 70 9 26 150 NA 145 13.2 77 9 27 151 14 191 14.3 75 9 28 152 18 131 8.0 76 9 29 153 20 223 11.5 68 9 30 ``` ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% * group_by(Month) ``` ] .panel2-cond_means_hard-auto[ ``` # A tibble: 153 x 6 # Groups: Month [5] Ozone Solar.R Wind Temp Month Day <int> <int> <dbl> <int> <int> <int> 1 41 190 7.4 67 5 1 2 36 118 8 72 5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4 5 NA NA 14.3 56 5 5 6 28 NA 14.9 66 5 6 7 23 299 8.6 65 5 7 8 19 99 13.8 59 5 8 9 8 19 20.1 61 5 9 10 NA 194 8.6 69 5 10 # … with 143 more rows ``` ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% * summarise( * Ozone_mean = * mean(Ozone, na.rm = T) * ) ``` ] .panel2-cond_means_hard-auto[ ``` # A tibble: 5 x 2 Month Ozone_mean <int> <dbl> 1 5 23.6 2 6 29.4 3 7 59.1 4 8 60.0 5 9 31.4 ``` ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> *airquality_by_month ``` ] .panel2-cond_means_hard-auto[ ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> airquality_by_month *ggplot(airquality) ``` ] .panel2-cond_means_hard-auto[ <img src="talk_maa_metro_ny_files/figure-html/cond_means_hard_auto_05_output-1.png" width="504" /> ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> airquality_by_month ggplot(airquality) + * aes(x = Ozone) ``` ] .panel2-cond_means_hard-auto[ <img src="talk_maa_metro_ny_files/figure-html/cond_means_hard_auto_06_output-1.png" width="504" /> ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> airquality_by_month ggplot(airquality) + aes(x = Ozone) + * geom_histogram() ``` ] .panel2-cond_means_hard-auto[ <img src="talk_maa_metro_ny_files/figure-html/cond_means_hard_auto_07_output-1.png" width="504" /> ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> airquality_by_month ggplot(airquality) + aes(x = Ozone) + geom_histogram() + * facet_grid(rows = vars(Month)) ``` ] .panel2-cond_means_hard-auto[ <img src="talk_maa_metro_ny_files/figure-html/cond_means_hard_auto_08_output-1.png" width="504" /> ] --- count: false #### Conditional means (may require a trip to stackoverflow!) .panel1-cond_means_hard-auto[ ```r airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> airquality_by_month ggplot(airquality) + aes(x = Ozone) + geom_histogram() + facet_grid(rows = vars(Month)) + * geom_vline(data = airquality_by_month, * aes(xintercept = * Ozone_mean)) ``` ] .panel2-cond_means_hard-auto[ <img src="talk_maa_metro_ny_files/figure-html/cond_means_hard_auto_09_output-1.png" width="504" /> ] <style> .panel1-cond_means_hard-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-cond_means_hard-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-cond_means_hard-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- # "...mastery of advanced programming skills should not be allowed to crowd out data analysis skills or statistical thinking." -- GAISE report --  --- class: inverse, center, bottom background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover # But other statistical geometries in ggplot2 flow! -- ... ggplot is known for being able to 'speak your plot into existence'. --- # Declarative -- ## "Let there be a a box plot..." -- ## and there was a boxplot. -- ## "Let there be LOESS smoothing..." -- ## and there was loess smoothing --- count: false .panel1-base_gg_flow-auto[ ```r *ggplot(cars) ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_01_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + * aes(x = speed) ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_02_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + * aes(y = dist) ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_03_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + * geom_point() ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_04_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + * geom_smooth() ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_05_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> *g1 ``` ] .panel2-base_gg_flow-auto[ ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 *library(palmerpenguins) ``` ] .panel2-base_gg_flow-auto[ ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 library(palmerpenguins) *ggplot(penguins) ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_08_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 library(palmerpenguins) ggplot(penguins) + * aes(x = species) ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_09_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 library(palmerpenguins) ggplot(penguins) + aes(x = species) + * aes(y = bill_length_mm) ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_10_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 library(palmerpenguins) ggplot(penguins) + aes(x = species) + aes(y = bill_length_mm) + * geom_point() ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_11_output-1.png" width="504" /> ] --- count: false .panel1-base_gg_flow-auto[ ```r ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 library(palmerpenguins) ggplot(penguins) + aes(x = species) + aes(y = bill_length_mm) + geom_point() + * geom_boxplot() ``` ] .panel2-base_gg_flow-auto[ <img src="talk_maa_metro_ny_files/figure-html/base_gg_flow_auto_12_output-1.png" width="504" /> ] <style> .panel1-base_gg_flow-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-base_gg_flow-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-base_gg_flow-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- # But, infrastructure to *extend* ggplot2 ... -- --  --- ## Build new statistical geometries that deliver the *flow* experience! -- - # for students -- - # for instructors -- - # for analysts --- class: center, inverse, middle # Introducing {ggxmean} -- https://github.com/EvaMaeRey/ggxmean --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r *library(ggplot2) ``` ] .panel2-geom_x_mean-auto[ ] --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r library(ggplot2) *library(ggxmean) ``` ] .panel2-geom_x_mean-auto[ ] --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r library(ggplot2) library(ggxmean) *ggplot(airquality) ``` ] .panel2-geom_x_mean-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_x_mean_auto_03_output-1.png" width="504" /> ] --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r library(ggplot2) library(ggxmean) ggplot(airquality) + * aes(x = Ozone) ``` ] .panel2-geom_x_mean-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_x_mean_auto_04_output-1.png" width="504" /> ] --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r library(ggplot2) library(ggxmean) ggplot(airquality) + aes(x = Ozone) + * geom_histogram() ``` ] .panel2-geom_x_mean-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_x_mean_auto_05_output-1.png" width="504" /> ] --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r library(ggplot2) library(ggxmean) ggplot(airquality) + aes(x = Ozone) + geom_histogram() + * geom_x_mean() ``` ] .panel2-geom_x_mean-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_x_mean_auto_06_output-1.png" width="504" /> ] --- count: false geom_x_mean() .panel1-geom_x_mean-auto[ ```r library(ggplot2) library(ggxmean) ggplot(airquality) + aes(x = Ozone) + geom_histogram() + geom_x_mean() + * facet_grid(rows = * vars(Month)) ``` ] .panel2-geom_x_mean-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_x_mean_auto_07_output-1.png" width="504" /> ] <style> .panel1-geom_x_mean-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-geom_x_mean-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-geom_x_mean-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false ### Other univariate markers .panel1-univariate-auto[ ```r *ggplot(airquality) ``` ] .panel2-univariate-auto[ <img src="talk_maa_metro_ny_files/figure-html/univariate_auto_01_output-1.png" width="504" /> ] --- count: false ### Other univariate markers .panel1-univariate-auto[ ```r ggplot(airquality) + * aes(x = Ozone) ``` ] .panel2-univariate-auto[ <img src="talk_maa_metro_ny_files/figure-html/univariate_auto_02_output-1.png" width="504" /> ] --- count: false ### Other univariate markers .panel1-univariate-auto[ ```r ggplot(airquality) + aes(x = Ozone) + * geom_histogram() ``` ] .panel2-univariate-auto[ <img src="talk_maa_metro_ny_files/figure-html/univariate_auto_03_output-1.png" width="504" /> ] --- count: false ### Other univariate markers .panel1-univariate-auto[ ```r ggplot(airquality) + aes(x = Ozone) + geom_histogram() + * geom_x_median() ``` ] .panel2-univariate-auto[ <img src="talk_maa_metro_ny_files/figure-html/univariate_auto_04_output-1.png" width="504" /> ] --- count: false ### Other univariate markers .panel1-univariate-auto[ ```r ggplot(airquality) + aes(x = Ozone) + geom_histogram() + geom_x_median() + * geom_x_quantile( * quantile = .25, * linetype = "dashed" * ) ``` ] .panel2-univariate-auto[ <img src="talk_maa_metro_ny_files/figure-html/univariate_auto_05_output-1.png" width="504" /> ] --- count: false ### Other univariate markers .panel1-univariate-auto[ ```r ggplot(airquality) + aes(x = Ozone) + geom_histogram() + geom_x_median() + geom_x_quantile( quantile = .25, linetype = "dashed" ) + * geom_x_percentile( * percentile = 100, * linetype = "dotted" * ) ``` ] .panel2-univariate-auto[ <img src="talk_maa_metro_ny_files/figure-html/univariate_auto_06_output-1.png" width="504" /> ] <style> .panel1-univariate-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-univariate-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-univariate-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r *ggplot(data = cars) ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_01_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + * aes(speed, dist) ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_02_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + * geom_point() ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_03_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + geom_point() + * ggxmean::geom_lm() ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_04_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + geom_point() + ggxmean::geom_lm() + * ggxmean::geom_lm_fitted(color = "blue", * size = 3) ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_05_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + geom_point() + ggxmean::geom_lm() + ggxmean::geom_lm_fitted(color = "blue", size = 3) + * ggxmean::geom_lm_residuals() ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_06_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + geom_point() + ggxmean::geom_lm() + ggxmean::geom_lm_fitted(color = "blue", size = 3) + ggxmean::geom_lm_residuals() + * ggxmean::geom_lm_conf_int() ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_07_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + geom_point() + ggxmean::geom_lm() + ggxmean::geom_lm_fitted(color = "blue", size = 3) + ggxmean::geom_lm_residuals() + ggxmean::geom_lm_conf_int() + * ggxmean::geom_lm_intercept(color = "red", * size = 5) ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_08_output-1.png" width="504" /> ] --- count: false ### Relevant to OLS .panel1-lm_sequence-auto[ ```r ggplot(data = cars) + aes(speed, dist) + geom_point() + ggxmean::geom_lm() + ggxmean::geom_lm_fitted(color = "blue", size = 3) + ggxmean::geom_lm_residuals() + ggxmean::geom_lm_conf_int() + ggxmean::geom_lm_intercept(color = "red", size = 5) + * ggxmean::geom_lm_formula(size = 10) ``` ] .panel2-lm_sequence-auto[ <img src="talk_maa_metro_ny_files/figure-html/lm_sequence_auto_09_output-1.png" width="504" /> ] <style> .panel1-lm_sequence-auto { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-lm_sequence-auto { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-lm_sequence-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false ### fitting distributions .panel1-geom_normal_dist-auto[ ```r *ggplot(data = faithful) ``` ] .panel2-geom_normal_dist-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_normal_dist_auto_01_output-1.png" width="504" /> ] --- count: false ### fitting distributions .panel1-geom_normal_dist-auto[ ```r ggplot(data = faithful) + * aes(waiting) ``` ] .panel2-geom_normal_dist-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_normal_dist_auto_02_output-1.png" width="504" /> ] --- count: false ### fitting distributions .panel1-geom_normal_dist-auto[ ```r ggplot(data = faithful) + aes(waiting) + * geom_rug() ``` ] .panel2-geom_normal_dist-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_normal_dist_auto_03_output-1.png" width="504" /> ] --- count: false ### fitting distributions .panel1-geom_normal_dist-auto[ ```r ggplot(data = faithful) + aes(waiting) + geom_rug() + * geom_histogram( * aes(y = ..density..)) ``` ] .panel2-geom_normal_dist-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_normal_dist_auto_04_output-1.png" width="504" /> ] --- count: false ### fitting distributions .panel1-geom_normal_dist-auto[ ```r ggplot(data = faithful) + aes(waiting) + geom_rug() + geom_histogram( aes(y = ..density..)) + * geom_normal_dist(fill = "blue") ``` ] .panel2-geom_normal_dist-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_normal_dist_auto_05_output-1.png" width="504" /> ] --- count: false ### fitting distributions .panel1-geom_normal_dist-auto[ ```r ggplot(data = faithful) + aes(waiting) + geom_rug() + geom_histogram( aes(y = ..density..)) + geom_normal_dist(fill = "blue") + * facet_grid(rows = * vars(eruptions > 3)) ``` ] .panel2-geom_normal_dist-auto[ <img src="talk_maa_metro_ny_files/figure-html/geom_normal_dist_auto_06_output-1.png" width="504" /> ] <style> .panel1-geom_normal_dist-auto { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-geom_normal_dist-auto { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-geom_normal_dist-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- --- class: inverse, center, bottom background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover # Thank you! -- ## {ggxmean} development package: https://evamaerey.github.io/ggxmean/ -- # Feedback welcome!