continue lab2

courses/lab02-perception-colors.Rmd

@@ -15,34 +15,269 @@ output:
 ---
 
 ```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(echo = TRUE, fig.asp = .6)
 library(ggplot2)
-library(showtext)
+#library(showtext)
+library(vizoR)
+library(dplyr)
+library(patchwork)
+library(see) 
+library(RColorBrewer)
 ```
 
 ---
-## Les couleurs dans ggplot2
+class: inverse, center, middle
+# Perception des différentes marques dans ggplot2
 
+---
+## TODO: échauffement
+
+#### Générer un dataset aléatoire avec la fonction vizoR::generate_dataset_uniform
+```{r}
+size <- list(100, 2)
+min_x <- 0
+max_x <- 1
+seed <- 34
+```
+---
+## TODO: perception
+
+#### 1. Réaliser des plots avec les échelles suivantes sur la variable group : 
+- couleur
+- forme
+- angle
+- taille
+- luminosité
+- courbe
+- encapsulage
+- remplissage
+
+Certaines échelles sont très simples à mettre en place (e.g. couleur, forme) mais d'autres n'existent pas directement. Il faut trouver une alternative. 
+
+#### 2. Comparer l'efficacité des différentes échelles pour distinguer les 2 groupes
+
+---
+## Couleur
+
+
+```{r, echo = F}
+
+dt <- generate_dataset_uniform(
+  dataset_size = size,
+  min_x = min_x,
+  max_x = max_x,
+  seed = seed
+)
+```
+
+--
+.small[
+```{r, echo = T}
+
+p_color <- ggplot( data = dt, 
+                   aes(x = x, y = y, color = group)) +
+  geom_point(size = 3, alpha = .6) +
+  see::scale_color_material_d() +
+  vizoR::theme_void_complete() +
+  labs(subtitle= "Couleur")
+
+p_color
+```
+]
+
+
+---
+## Angle 
+
+--
+
+.small[
+```{r}
+p_angle <- dt %>%
+  mutate(angle = ifelse(group == "group1", 0, pi / 3)) %>%
+  ggplot(
+    data = ., aes( x = x, y = y, angle = angle ) ) +
+  geom_spoke(radius = 0.02,size = .8, alpha = .6 ) +
+  theme_void_complete() +
+  scale_color_material_d() +
+  ggtitle("Angle")
+p_angle
+```
+]
+---
+## Taille
+
+--
+
+.small[
+```{r}
+p_size <- dt %>%
+  mutate(size = ifelse(group == "group1", 2, 3)) %>%
+  ggplot( data = .,
+          aes( x = x,y = y,size = size)) +
+  geom_point(alpha = .6) +
+  theme_void_complete() +
+  scale_size(range = c(1, 3)) +
+  ggtitle("Taille")
+p_size
+```
+]
+
+---
+## Luminosité
+
+--
+
+.small[
+```{r}
+p_grey <- dt %>%
+  ggplot(
+    data = .,aes(x = x,y = y,color = group)) +
+  geom_point(size = 3, alpha = .6) +
+  theme_void_complete() +
+  #scale_color_grey() +
+  scale_color_grey(start=.8, end=.2)+
+  ggtitle("Luminosité")
+p_grey
+```
+]
+---
+## Courbe
+
+--
+
+.small[
+```{r}
+dt <- dt %>%
+  mutate(curvature = ifelse(group == "group1", 0, 1))
+
+p_curve <- dt %>%
+  ggplot(data = .,aes(x = x,y = y,xend = x, yend = y+max_x/50, curvature = curvature)) +
+  #geom_curve()+
+  geom_curve(data = subset(dt, group == 'group1'), curvature = 0, alpha = .7) +
+  geom_curve(data = subset(dt, group == 'group2'), curvature = .7, alpha = .7) +
+  scale_color_material_d() +
+  theme_void_complete() +
+  ggtitle("Courbe")
+p_curve
+```
+]
+---
+## Encapsulage
+
+--
+
+.small[
+```{r}
+p_box <- dt %>%
+  ggplot(data = .,aes(x = x,xend = x+max_x/50,y = y,yend = y, group = group)) +
+  geom_point(data = subset(dt, group=='group2'),aes(x = x+max_x/100), shape = 22, size = 13) + 
+  geom_segment() +
+  scale_color_material_d() +
+  theme_void_complete() +
+  ggtitle("Encapsulage")
+p_box
+```
+]
+
+---
+## Forme
+
+--
+
+.small[
+```{r}
+p_shape <- dt %>%
+  ggplot(data = .,aes(x = x,y = y,shape = group)) +
+  geom_point(size = 3, alpha = .6) +
+  theme_void_complete() +
+  ggtitle("Forme")
+p_shape
+```
+]
+---
+## Remplissage
+
+--
+
+.small[
+```{r}
+p_fill <- dt %>%
+  ggplot(data = ., aes( x = x,y = y,fill = group)) +
+  geom_point(size = 3, shape = 21, alpha = .7) +
+  scale_fill_manual(values = c('group2' = 'black', 'group1' = 'white')) +
+  theme_void_complete() +
+  ggtitle("Remplissage")
+p_fill
+```
+]
+
+---
+class: full, center, middle
+```{r, echo = F,  out.width = '2000px'}
+p_color + p_angle + 
+  p_size + p_grey + 
+  p_curve + p_box + 
+  p_shape + p_fill + 
+  plot_layout(ncol = 2) 
+
+
+```
+
+
+---
+class: full
+### Les couleurs dans ggplot2 `display.brewer.all()`
+
+![:scale 80%](img/palettes.jpg)
+
+---
+## TODO: couleurs
+
+#### Charger le dataset diamonds et créer un sous-dataset aléatoire de 1000 lignes
+
+#### Plot carat en fonction du prix et de la couleur
+
+#### changer la palette par défaut vers une autre palette disponible
+
+---
+## TODO: couleurs 2
+
+#### Plot carat en fonction du prix avec carat en double encodage
+
+#### Aller sur [http://colorbrewer2.org]() et trouver une palette divergente
+
+#### Créer une palette custom basée sur cette palette et l'appliquer au plot précédent
+
+#### Caler la palette sur le carat moyen 
+
+#### Annoter le plot avec une ligne désignant le carat moyen et un texte expliquant cette ligne
+
+
+
+---
 ```{r}
 dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
 ggplot(dsamp, aes(carat, price)) +
-  geom_point(aes(colour = clarity)) +
-  scale_color_brewer(palette = "Set3")
+  geom_point(aes(colour = color)) +
+  scale_color_brewer(palette = "Set3") + 
+  facet_wrap(~color)
 ```
 
 ```{r}
 
 ggplot(dsamp, aes(carat, price)) +
-  geom_point(aes(colour = carat))) +
+  geom_point(aes(colour = carat)) +
   scale_color_distiller(palette="RdYlBu")
 ```
 
 ```{r}
 
-font_add_google("Gochi Hand", "gochi")
-showtext_auto()
+#showtext_auto()
+#font_add_google("Schoolbell", "bell")
+
+font_family = "sans"
 annotate_color = "grey50"
-font_add_google("Schoolbell", "bell")
 
 midpoint = (max(dsamp$carat)-min(dsamp$carat))/2
 
@@ -56,18 +291,22 @@ ggplot(dsamp, aes(carat, price)) +
   annotate("text", 
            x=.78, y=15000, hjust=1, srt=40,
            label ="this is the midpoint",  
-           family="bell", 
+           family=font_family, 
            color=annotate_color) +
   annotate("curve", 
            x = .8, xend=midpoint-.01, y=15000, yend = 14000,
            curvature = -.5, 
            color=annotate_color ,
            arrow=arrow(length = unit(0.03, "npc") )) + 
-  theme_minimal() +
-  theme(panel.grid.minor = element_blank(), 
-        panel.grid.major.x = element_blank()) + ggthemes::theme_tufte()
 
+  theme_elegant() +
+  theme(panel.grid.minor = element_blank(), 
+        panel.grid.major.x = element_blank(), 
+        legend.position = "none") 
 ```
 
 
 
+
+
+

courses/lab02-perception-colors.html

@@ -0,0 +1,401 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
+  <head>
+    <title>LAB 2: Perception et couleurs</title>
+    <meta charset="utf-8" />
+    <meta name="author" content="Antoine Neuraz" />
+    <link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
+    <link rel="stylesheet" href="css/my_style.css" type="text/css" />
+  </head>
+  <body>
+    <textarea id="source">
+
+
+
+
+---
+class: inverse, center, middle
+# Perception des différentes marques dans ggplot2
+
+---
+## TODO: échauffement
+
+#### Générer un dataset aléatoire avec la fonction vizoR::generate_dataset_uniform
+
+```r
+size &lt;- list(100, 2)
+min_x &lt;- 0
+max_x &lt;- 1
+seed &lt;- 34
+```
+---
+## TODO: perception
+
+#### 1. Réaliser des plots avec les échelles suivantes sur la variable group : 
+- couleur
+- forme
+- angle
+- taille
+- luminosité
+- courbe
+- encapsulage
+- remplissage
+
+Certaines échelles sont très simples à mettre en place (e.g. couleur, forme) mais d'autres n'existent pas directement. Il faut trouver une alternative. 
+
+#### 2. Comparer l'efficacité des différentes échelles pour distinguer les 2 groupes
+
+---
+## Couleur
+
+
+
+
+--
+.small[
+
+```r
+p_color &lt;- ggplot( data = dt, 
+                   aes(x = x, y = y, color = group)) +
+  geom_point(size = 3, alpha = .6) +
+  see::scale_color_material_d() +
+  vizoR::theme_void_complete() +
+  labs(subtitle= "Couleur")
+
+p_color
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-3-1.png)&lt;!-- --&gt;
+]
+
+
+---
+## Angle 
+
+--
+
+.small[
+
+```r
+p_angle &lt;- dt %&gt;%
+  mutate(angle = ifelse(group == "group1", 0, pi / 3)) %&gt;%
+  ggplot(
+    data = ., aes( x = x, y = y, angle = angle ) ) +
+  geom_spoke(radius = 0.02,size = .8, alpha = .6 ) +
+  theme_void_complete() +
+  scale_color_material_d() +
+  ggtitle("Angle")
+p_angle
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-4-1.png)&lt;!-- --&gt;
+]
+---
+## Taille
+
+--
+
+.small[
+
+```r
+p_size &lt;- dt %&gt;%
+  mutate(size = ifelse(group == "group1", 2, 3)) %&gt;%
+  ggplot( data = .,
+          aes( x = x,y = y,size = size)) +
+  geom_point(alpha = .6) +
+  theme_void_complete() +
+  scale_size(range = c(1, 3)) +
+  ggtitle("Taille")
+p_size
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-5-1.png)&lt;!-- --&gt;
+]
+
+---
+## Luminosité
+
+--
+
+.small[
+
+```r
+p_grey &lt;- dt %&gt;%
+  ggplot(
+    data = .,aes(x = x,y = y,color = group)) +
+  geom_point(size = 3, alpha = .6) +
+  theme_void_complete() +
+  #scale_color_grey() +
+  scale_color_grey(start=.8, end=.2)+
+  ggtitle("Luminosité")
+p_grey
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-6-1.png)&lt;!-- --&gt;
+]
+---
+## Courbe
+
+--
+
+.small[
+
+```r
+dt &lt;- dt %&gt;%
+  mutate(curvature = ifelse(group == "group1", 0, 1))
+
+p_curve &lt;- dt %&gt;%
+  ggplot(data = .,aes(x = x,y = y,xend = x, yend = y+max_x/50, curvature = curvature)) +
+  #geom_curve()+
+  geom_curve(data = subset(dt, group == 'group1'), curvature = 0, alpha = .7) +
+  geom_curve(data = subset(dt, group == 'group2'), curvature = .7, alpha = .7) +
+  scale_color_material_d() +
+  theme_void_complete() +
+  ggtitle("Courbe")
+p_curve
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-7-1.png)&lt;!-- --&gt;
+]
+---
+## Encapsulage
+
+--
+
+.small[
+
+```r
+p_box &lt;- dt %&gt;%
+  ggplot(data = .,aes(x = x,xend = x+max_x/50,y = y,yend = y, group = group)) +
+  geom_point(data = subset(dt, group=='group2'),aes(x = x+max_x/100), shape = 22, size = 13) + 
+  geom_segment() +
+  scale_color_material_d() +
+  theme_void_complete() +
+  ggtitle("Encapsulage")
+p_box
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-8-1.png)&lt;!-- --&gt;
+]
+
+---
+## Forme
+
+--
+
+.small[
+
+```r
+p_shape &lt;- dt %&gt;%
+  ggplot(data = .,aes(x = x,y = y,shape = group)) +
+  geom_point(size = 3, alpha = .6) +
+  theme_void_complete() +
+  ggtitle("Forme")
+p_shape
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-9-1.png)&lt;!-- --&gt;
+]
+---
+## Remplissage
+
+--
+
+.small[
+
+```r
+p_fill &lt;- dt %&gt;%
+  ggplot(data = ., aes( x = x,y = y,fill = group)) +
+  geom_point(size = 3, shape = 21, alpha = .7) +
+  scale_fill_manual(values = c('group2' = 'black', 'group1' = 'white')) +
+  theme_void_complete() +
+  ggtitle("Remplissage")
+p_fill
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-10-1.png)&lt;!-- --&gt;
+]
+
+---
+class: full, center, middle
+&lt;img src="lab02-perception-colors_files/figure-html/unnamed-chunk-11-1.png" width="2000px" /&gt;
+
+
+---
+class: full
+### Les couleurs dans ggplot2 `display.brewer.all()`
+
+![:scale 80%](img/palettes.jpg)
+
+---
+## TODO: couleurs
+
+#### Charger le dataset diamonds et créer un sous-dataset aléatoire de 1000 lignes
+
+#### Plot carat en fonction du prix et de la couleur
+
+#### changer la palette par défaut vers une autre palette disponible
+
+---
+## TODO: couleurs 2
+
+#### Plot carat en fonction du prix avec carat en double encodage
+
+#### Aller sur [http://colorbrewer2.org]() et trouver une palette divergente
+
+#### Créer une palette custom basée sur cette palette et l'appliquer au plot précédent
+
+#### Caler la palette sur le carat moyen 
+
+#### Annoter le plot avec une ligne désignant le carat moyen et un texte expliquant cette ligne
+
+
+
+---
+
+```r
+dsamp &lt;- diamonds[sample(nrow(diamonds), 1000), ]
+ggplot(dsamp, aes(carat, price)) +
+  geom_point(aes(colour = color)) +
+  scale_color_brewer(palette = "Set3") + 
+  facet_wrap(~color)
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-12-1.png)&lt;!-- --&gt;
+
+
+```r
+ggplot(dsamp, aes(carat, price)) +
+  geom_point(aes(colour = carat)) +
+  scale_color_distiller(palette="RdYlBu")
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-13-1.png)&lt;!-- --&gt;
+
+
+```r
+#showtext_auto()
+#font_add_google("Schoolbell", "bell")
+
+font_family = "sans"
+annotate_color = "grey50"
+
+midpoint = (max(dsamp$carat)-min(dsamp$carat))/2
+
+ggplot(dsamp, aes(carat, price)) +
+  geom_vline(xintercept = midpoint, color = annotate_color) +
+  geom_point(aes(colour = carat)) +
+  scale_color_gradient2(low = "#d8b365", 
+                        mid="#f5f5f5", 
+                        high="#5ab4ac", 
+                        midpoint = midpoint) +
+  annotate("text", 
+           x=.78, y=15000, hjust=1, srt=40,
+           label ="this is the midpoint",  
+           family=font_family, 
+           color=annotate_color) +
+  annotate("curve", 
+           x = .8, xend=midpoint-.01, y=15000, yend = 14000,
+           curvature = -.5, 
+           color=annotate_color ,
+           arrow=arrow(length = unit(0.03, "npc") )) + 
+
+  theme_elegant() +
+  theme(panel.grid.minor = element_blank(), 
+        panel.grid.major.x = element_blank(), 
+        legend.position = "none") 
+```
+
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-14-1.png)&lt;!-- --&gt;
+    </textarea>
+<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
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courses/minard.Rmd

@@ -17,20 +17,17 @@ library(ggrepel)
 library(gridExtra)
 library(pander)
 library(ggmap)
+library(vizoR)
 
 # https://www.andrewheiss.com/blog/2017/08/10/exploring-minards-1812-plot-with-ggplot2/
 
-cities <- read.table("data/minard/cities.txt",
-                     header = TRUE, stringsAsFactors = FALSE)
+data("minard_cities")
 
-troops <- read.table("data/minard/troops.txt",
-                     header = TRUE, stringsAsFactors = FALSE)
+data("minard_troops")
 
-temps <- read.table("data/minard/temps.txt",
-                    header = TRUE, stringsAsFactors = FALSE) %>%
-  mutate(date = dmy(date))  # Convert string to actual date
+data('minard_temps')
 
-temps.nice <- temps %>%
+temps.nice <- minard_temps %>%
   mutate(nice.label = paste0(temp, "°, ", month, ". ", day))
 
 ```
@@ -44,12 +41,12 @@ color_troops_fw = "#DFC17E"
 color_troops_bw = "#252523"
 # No map this time
 march.1812.plot.simple <- ggplot() +
-  geom_path(data = troops, aes(x = long, y = lat, group = group, 
+  geom_path(data = minard_troops, aes(x = long, y = lat, group = group, 
                                color = direction, size = survivors),
             lineend = "round") +
-  geom_point(data = cities, aes(x = long, y = lat),
+  geom_point(data = minard_cities, aes(x = long, y = lat),
              color = color_cities) +
-  geom_text_repel(data = cities, aes(x = long, y = lat, label = city),
+  geom_text_repel(data = minard_cities, aes(x = long, y = lat, label = city),
                   color = color_cities, family = font_family) +
   scale_size(range = c(0.5, 15)) + 
   scale_colour_manual(values = c(color_troops_fw,color_troops_bw )) +
@@ -59,7 +56,7 @@ march.1812.plot.simple <- ggplot() +
 # Change the x-axis limits to match the simple map
 temps.1812.plot <- ggplot(data = temps.nice, aes(x = long, y = temp)) +
   geom_line() +
-  geom_text_repel(aes(label = nice.label),
+  geom_label(aes(label = nice.label),
             family = font_family, size = 2.5) + 
   labs(x = NULL, y = "° Celsius") +
   scale_x_continuous(limits = ggplot_build(march.1812.plot.simple)$layout$panel_ranges[[1]]$x.range) +