add pdfs, update lab07

courses/lab01-correction.Rmd

@@ -7,6 +7,7 @@ output: html_document
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+library(ggplot2)
 ```
 
 ## Ouvrir le dataset "mtcars"

courses/lab01-ggplot-intro.Rmd

@@ -13,9 +13,6 @@ output:
       self-contained: true
       beforeInit: "addons/macros.js"
       highlightLines: true
-  pdf_document:
-    seal: false
-  
 ---
 
 ```{r setup, include=FALSE}

courses/lab02-perception-colors.Rmd

@@ -25,6 +25,18 @@ library(see)
 library(RColorBrewer)
 ```
 
+class: center, middle, title
+
+# Lab 2: Perception et couleurs
+
+### 2019-2020
+
+## Dr. Antoine Neuraz
+
+### AHU Informatique médicale
+#### Hôpital Necker-Enfants malades, </br> Université de Paris
+
+
 ---
 class: inverse, center, middle
 # Perception des différentes marques dans ggplot2
@@ -240,6 +252,14 @@ class: full
 
 #### changer la palette par défaut vers une autre palette disponible
 
+---
+```{r}
+dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
+ggplot(dsamp, aes(carat, price)) +
+  geom_point(aes(colour = color)) +
+  scale_color_brewer(palette = "Set3") + 
+  facet_wrap(~color)
+```
 
 
 ---
@@ -254,17 +274,8 @@ class: full
 #### 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 = color)) +
-  scale_color_brewer(palette = "Set3") + 
-  facet_wrap(~color)
-```
+
 
 ```{r}
 
@@ -273,7 +284,8 @@ ggplot(dsamp, aes(carat, price)) +
   scale_color_distiller(palette="RdYlBu")
 ```
 
-```{r}
+---
+```{r, eval = F}
 
 #showtext_auto()
 #font_add_google("Schoolbell", "bell")
@@ -307,7 +319,40 @@ ggplot(dsamp, aes(carat, price)) +
         legend.position = "none") 
 ```
 
+---
+```{r, echo = F}
+
+#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") 
+```
 
 
 

courses/lab02-perception-colors.html

@@ -13,6 +13,18 @@
 
 
 
+class: center, middle, title
+
+# Lab 2: Perception et couleurs
+
+### 2019-2020
+
+## Dr. Antoine Neuraz
+
+### AHU Informatique médicale
+#### Hôpital Necker-Enfants malades, </br> Université de Paris
+
+
 ---
 class: inverse, center, middle
 # Perception des différentes marques dans ggplot2
@@ -236,6 +248,19 @@ class: full
 
 #### changer la palette par défaut vers une autre palette disponible
 
+---
+
+```r
+dsamp <- 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)<!-- -->
+
+
 ---
 ## TODO: couleurs 2
 
@@ -248,20 +273,8 @@ class: full
 #### 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 = color)) +
-  scale_color_brewer(palette = "Set3") + 
-  facet_wrap(~color)
-```
-
-![](lab02-perception-colors_files/figure-html/unnamed-chunk-12-1.png)<!-- -->
 
 
 ```r
@@ -272,6 +285,7 @@ ggplot(dsamp, aes(carat, price)) +
 
 ![](lab02-perception-colors_files/figure-html/unnamed-chunk-13-1.png)<!-- -->
 
+---
 
 ```r
 #showtext_auto()
@@ -306,7 +320,8 @@ ggplot(dsamp, aes(carat, price)) +
         legend.position = "none") 
 ```
 
-![](lab02-perception-colors_files/figure-html/unnamed-chunk-14-1.png)<!-- -->
+---
+![](lab02-perception-colors_files/figure-html/unnamed-chunk-15-1.png)<!-- -->
     </textarea>
 <style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
 <script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>

courses/lab03-tabulaire.html

@@ -44,8 +44,8 @@ class: center, middle, title
 read_csv("lab03_data/notes.csv") -> notes
 ```
 
-<div id="htmlwidget-a9f909b507366e2763fd" style="width:100%;height:auto;" class="datatables html-widget"></div>
-<script type="application/json" data-for="htmlwidget-a9f909b507366e2763fd">{"x":{"filter":"none","data":[["1/1/2019","1/15/2019","2/1/2019","2/15/2019","3/1/2019","3/15/2019","4/1/2019","4/15/2019","5/1/2019","5/15/2019"],[14,16,15,17,14,15,13,15,16,17],[10,11,11,10,15,13,12,12,13,11],[18,19,18,19,19,20,19,19,17,18],[9,10,12,11,14,13,14,15,14,15]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Date<\/th>\n      <th>Alice<\/th>\n      <th>Bob<\/th>\n      <th>Claire<\/th>\n      <th>David<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"info":false,"searching":false,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-94c0f5d1381e56fb5881" style="width:100%;height:auto;" class="datatables html-widget"></div>
+<script type="application/json" data-for="htmlwidget-94c0f5d1381e56fb5881">{"x":{"filter":"none","data":[["1/1/2019","1/15/2019","2/1/2019","2/15/2019","3/1/2019","3/15/2019","4/1/2019","4/15/2019","5/1/2019","5/15/2019"],[14,16,15,17,14,15,13,15,16,17],[10,11,11,10,15,13,12,12,13,11],[18,19,18,19,19,20,19,19,17,18],[9,10,12,11,14,13,14,15,14,15]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Date<\/th>\n      <th>Alice<\/th>\n      <th>Bob<\/th>\n      <th>Claire<\/th>\n      <th>David<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"info":false,"searching":false,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 
 ---
 
@@ -61,8 +61,8 @@ pivot_longer(notes,
              values_to = "Note") -&gt; notes_long
 ```
 
-<div id="htmlwidget-744a3944844d231f7996" style="width:100%;height:auto;" class="datatables html-widget"></div>
-<script type="application/json" data-for="htmlwidget-744a3944844d231f7996">{"x":{"filter":"none","data":[["1/1/2019","1/1/2019","1/1/2019","1/1/2019","1/15/2019","1/15/2019","1/15/2019","1/15/2019","2/1/2019","2/1/2019","2/1/2019","2/1/2019","2/15/2019","2/15/2019","2/15/2019","2/15/2019","3/1/2019","3/1/2019","3/1/2019","3/1/2019","3/15/2019","3/15/2019","3/15/2019","3/15/2019","4/1/2019","4/1/2019","4/1/2019","4/1/2019","4/15/2019","4/15/2019","4/15/2019","4/15/2019","5/1/2019","5/1/2019","5/1/2019","5/1/2019","5/15/2019","5/15/2019","5/15/2019","5/15/2019"],["Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David"],[14,10,18,9,16,11,19,10,15,11,18,12,17,10,19,11,14,15,19,14,15,13,20,13,13,12,19,14,15,12,19,15,16,13,17,14,17,11,18,15]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Date<\/th>\n      <th>Prénom<\/th>\n      <th>Note<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"info":false,"searching":false,"columnDefs":[{"className":"dt-right","targets":2}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-3265d78dc11fed97ef02" style="width:100%;height:auto;" class="datatables html-widget"></div>
+<script type="application/json" data-for="htmlwidget-3265d78dc11fed97ef02">{"x":{"filter":"none","data":[["1/1/2019","1/1/2019","1/1/2019","1/1/2019","1/15/2019","1/15/2019","1/15/2019","1/15/2019","2/1/2019","2/1/2019","2/1/2019","2/1/2019","2/15/2019","2/15/2019","2/15/2019","2/15/2019","3/1/2019","3/1/2019","3/1/2019","3/1/2019","3/15/2019","3/15/2019","3/15/2019","3/15/2019","4/1/2019","4/1/2019","4/1/2019","4/1/2019","4/15/2019","4/15/2019","4/15/2019","4/15/2019","5/1/2019","5/1/2019","5/1/2019","5/1/2019","5/15/2019","5/15/2019","5/15/2019","5/15/2019"],["Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David","Alice","Bob","Claire","David"],[14,10,18,9,16,11,19,10,15,11,18,12,17,10,19,11,14,15,19,14,15,13,20,13,13,12,19,14,15,12,19,15,16,13,17,14,17,11,18,15]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Date<\/th>\n      <th>Prénom<\/th>\n      <th>Note<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"info":false,"searching":false,"columnDefs":[{"className":"dt-right","targets":2}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 
 ---
 
@@ -77,8 +77,8 @@ pivot_wider(notes_long,
             values_from = Note)
 ```
 
-<div id="htmlwidget-206da931c15e14bd710f" style="width:100%;height:auto;" class="datatables html-widget"></div>
-<script type="application/json" data-for="htmlwidget-206da931c15e14bd710f">{"x":{"filter":"none","data":[["1/1/2019","1/15/2019","2/1/2019","2/15/2019","3/1/2019","3/15/2019","4/1/2019","4/15/2019","5/1/2019","5/15/2019"],[14,16,15,17,14,15,13,15,16,17],[10,11,11,10,15,13,12,12,13,11],[18,19,18,19,19,20,19,19,17,18],[9,10,12,11,14,13,14,15,14,15]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Date<\/th>\n      <th>Alice<\/th>\n      <th>Bob<\/th>\n      <th>Claire<\/th>\n      <th>David<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"info":false,"searching":false,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-1496fd73ac98b39c4291" style="width:100%;height:auto;" class="datatables html-widget"></div>
+<script type="application/json" data-for="htmlwidget-1496fd73ac98b39c4291">{"x":{"filter":"none","data":[["1/1/2019","1/15/2019","2/1/2019","2/15/2019","3/1/2019","3/15/2019","4/1/2019","4/15/2019","5/1/2019","5/15/2019"],[14,16,15,17,14,15,13,15,16,17],[10,11,11,10,15,13,12,12,13,11],[18,19,18,19,19,20,19,19,17,18],[9,10,12,11,14,13,14,15,14,15]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Date<\/th>\n      <th>Alice<\/th>\n      <th>Bob<\/th>\n      <th>Claire<\/th>\n      <th>David<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"info":false,"searching":false,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 
 ---
 

courses/lab06_graphes.html

@@ -233,8 +233,8 @@ graph.star(n = 10, mode = "out")
 read_csv("lab06_data/OMIM.csv") -> OMIM
 ```
 
-<div id="htmlwidget-47d834c4dcc97d74e7ee" style="width:100%;height:auto;" class="datatables html-widget"></div>
-<script type="application/json" data-for="htmlwidget-47d834c4dcc97d74e7ee">{"x":{"filter":"none","data":[["ACYL-CoA DEHYDROGENASE, SHORT-CHAIN, DEFICIENCY OF","ADAMS-OLIVER SYNDROME 1","ADAMS-OLIVER SYNDROME 2","ADAMS-OLIVER SYNDROME 3","ADENINE PHOSPHORIBOSYLTRANSFERASE DEFICIENCY","LUNG CANCER","LUNG CANCER","LUNG CANCER","LUNG CANCER","LUNG CANCER","LUNG CANCER"],["ACADS","ARHGAP31","DOCK6","RBPJ","APRT","CYP2A6","EGFR","TNFSF6","IRF1","BRAF","ERBB2"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Disease<\/th>\n      <th>Gene<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"search":false,"info":false,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-e2536eee24b80cd7b4a4" style="width:100%;height:auto;" class="datatables html-widget"></div>
+<script type="application/json" data-for="htmlwidget-e2536eee24b80cd7b4a4">{"x":{"filter":"none","data":[["ACYL-CoA DEHYDROGENASE, SHORT-CHAIN, DEFICIENCY OF","ADAMS-OLIVER SYNDROME 1","ADAMS-OLIVER SYNDROME 2","ADAMS-OLIVER SYNDROME 3","ADENINE PHOSPHORIBOSYLTRANSFERASE DEFICIENCY","LUNG CANCER","LUNG CANCER","LUNG CANCER","LUNG CANCER","LUNG CANCER","LUNG CANCER"],["ACADS","ARHGAP31","DOCK6","RBPJ","APRT","CYP2A6","EGFR","TNFSF6","IRF1","BRAF","ERBB2"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Disease<\/th>\n      <th>Gene<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"paging":false,"search":false,"info":false,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 
 ---
 # Chargement du graphe
@@ -246,9 +246,9 @@ graph.data.frame(OMIM, directed = F) -&gt; graphe
 
 
 ```
-## IGRAPH 01bad3b UN-- 6288 4234 -- 
+## IGRAPH d3876e8 UN-- 6288 4234 -- 
 ## + attr: name (v/c)
-## + edges from 01bad3b (vertex names):
+## + edges from d3876e8 (vertex names):
 ## [1] ADRENAL HYPERPLASIA, CONGENITAL, DUE TO 17-ALPHA-HYDROXYLASE DEFICIENCY--CYP17A1 
 ## [2] 17-BETA-HYDROXYSTEROID DEHYDROGENASE X DEFICIENCY                      --HSD17B10
 ## [3] 2-METHYLBUTYRYL-CoA DEHYDROGENASE DEFICIENCY                           --ACADSB  
@@ -304,7 +304,7 @@ neighbors(graphe, V(graphe)[2019])
 ```
 
 ```
-## + 1/6288 vertex, named, from 01bad3b:
+## + 1/6288 vertex, named, from d3876e8:
 ## [1] SLC25A3
 ```
 
@@ -333,9 +333,9 @@ HDN
 ```
 
 ```
-## IGRAPH 8db9c9b UNW- 3512 2839 -- 
+## IGRAPH 58fb0bd UNW- 3512 2839 -- 
 ## + attr: name (v/c), weight (e/n)
-## + edges from 8db9c9b (vertex names):
+## + edges from 58fb0bd (vertex names):
 ## [1] 17-BETA-HYDROXYSTEROID DEHYDROGENASE X DEFICIENCY--MENTAL RETARDATION, X-LINKED 17           
 ## [2] 17-BETA-HYDROXYSTEROID DEHYDROGENASE X DEFICIENCY--MENTAL RETARDATION, X-LINKED, SYNDROMIC 10
 ## [3] 3-HYDROXYACYL-CoA DEHYDROGENASE DEFICIENCY       --HYPERINSULINEMIC HYPOGLYCEMIA, FAMILIAL, 4
@@ -353,9 +353,9 @@ HGN
 ```
 
 ```
-## IGRAPH 65dfdde UNW- 2776 2810 -- 
+## IGRAPH f5f35d8 UNW- 2776 2810 -- 
 ## + attr: name (v/c), weight (e/n)
-## + edges from 65dfdde (vertex names):
+## + edges from f5f35d8 (vertex names):
 ##  [1] AKR1C2--AKR1C4   LMNA  --MYBPC3   LMNA  --ZMPSTE24 GNAS  --SSTR5   
 ##  [5] GNAS  --AIP      GNAS  --STX16    GNAS  --GNASAS1  COL2A1--COL11A2 
 ##  [9] FGFR3 --KRAS     FGFR3 --HRAS     FGFR3 --RB1      FGFR3 --PIK3CA  

courses/lab7-temporal_data.Rmd

@@ -21,6 +21,8 @@ library(ggplot2)
 library(gghighlight)
 library(dplyr)
 library(ggTimeSeries)
+library(hrbrthemes)
+library(gganimate)
 ```
 
 ## TODO
@@ -47,27 +49,16 @@ library(ggTimeSeries)
 ---
 
 ```{r}
-
 data("us_city_populations")
 
 n_cities = 5
 
-# top_cities <- 
-#   us_city_populations %>% 
-#   filter(Rank <= n_cities) %>%
-#   select(City, State, Region) %>% 
-#   distinct()
-# 
-# to_plot <- filter(us_city_populations, City %in% top_cities$City)
-
-#to_plot <- us_city_populations
-
 last_ranks <- us_city_populations %>% 
   filter(Year == max(Year)) %>%
   mutate(last_rank = Rank) %>% 
   select(City, last_rank)
 
-to_plot <- left_join(us_city_populations, last_ranks, by= 'City')
+to_plot <- left_join(us_city_populations, last_ranks, by= 'City') 
 
 right_axis <-  to_plot %>% 
   group_by(City) %>% 
@@ -85,14 +76,16 @@ labels <-  right_axis %>%
 
 ---
 class: full
-```{r, echo = FALSE}
-ggplot(to_plot, aes(x=Year, y = Population, group = City, color = City)) + 
+```{r, echo = TRUE}
+p <- ggplot(to_plot, aes(x=Year, y = Population, 
+                         group = City, color = City)) + 
   geom_line(size=1) + 
-  #geom_text(data = subset(to_plot, Year == 2010), aes(x=Inf, y = Population, label=City), hjust = 1) +
   scale_x_continuous("", expand=c(0,0))+
   scale_y_continuous("",
                      labels=scales::comma_format(big.mark = " "),
-                     sec.axis = sec_axis(~ ., breaks = ends, labels = labels ))+
+                     sec.axis = sec_axis(~ ., 
+                                         breaks = ends, 
+                                         labels = labels ))+
   scale_color_viridis_d()+
   theme_elegant_dark()+
   theme(legend.position = "none",
@@ -101,14 +94,23 @@ ggplot(to_plot, aes(x=Year, y = Population, group = City, color = City)) +
         axis.line.x = element_blank(), 
         axis.ticks.x = element_line(), 
         panel.grid.major.y = element_line(color= 'grey30', size = .2) ) + 
-  gghighlight(max(last_rank) <= n_cities, use_direct_label = FALSE, label_key = City,unhighlighted_colour = "grey20")
+  gghighlight(max(last_rank) <= n_cities, 
+              use_direct_label = FALSE, 
+              label_key = City,
+              unhighlighted_colour = "grey20")
   
 ```
 ---
 class: full
-```{r, echo = FALSE}
+```{r, echo = TRUE}
+p
+```
+---
+
+class: full
+```{r, echo = TRUE}
 library(ggTimeSeries)
-to_plot %>% filter(City %in% labels) %>%
+p <- to_plot %>% filter(City %in% labels) %>%
   ggplot(aes(x = Year, y = Population, group = City, fill = City)) +
   scale_y_continuous("", labels = scales::comma_format(big.mark = " "))+
   stat_steamgraph() +
@@ -121,7 +123,122 @@ to_plot %>% filter(City %in% labels) %>%
         panel.grid.major.y = element_line(color= 'grey30', size = .2) ) 
 
 ```
+
+---
+class: full 
+```{r}
+p
+```
 ---
+class: inverse, center, middle
+# Barchart race
+
+---
+## Load data
+```{r load_data}
+data("us_city_populations")
+
+n_cities = 10
+ 
+top_cities <-us_city_populations %>% filter(Rank <= n_cities) %>%
+  select(City, State, Region) %>% distinct()
+
+```
+
+---
+## Create all missing dates
+```{r, combine_dates}
+# create a data frame with all the years between min and max Year
+all_years <- data.frame(Year = seq(min(us_city_populations$Year), 
+                                   max(us_city_populations$Year), 1))
+
+# combine top_cities and all_years
+all_combos <- merge(top_cities, all_years, all = T)
+
+# combine all_combos with the original dataset
+res_interp <- merge(us_city_populations, all_combos, all.y = T)
+```
+
+## Interpolate the Populations when missing (linear interpolation here)
+```{r, interpolate}
+res_interp  <- res_interp  %>%
+  group_by(City) %>%
+  mutate(Population=approx(Year,Population,Year)$y)
+```
+
+---
+## Filter data
+```{r, filter_for_plot}
+to_plot <- res_interp %>%
+  group_by(Year) %>%
+  arrange(-Population) %>%
+  mutate(Rank=row_number()) %>%
+  filter(Rank<=n_cities)
+
+```
+
+---
+## Ease transitions
+
+```{r}
+to_plot_trans <- to_plot %>%
+  group_by(City) %>%
+  arrange(Year) %>%
+  mutate(lag_rank = lag(Rank, 1),
+         change = ifelse(Rank > lag(Rank, 1), 1, 0),
+         change = ifelse(Rank < lag(Rank, 1), -1, 0)) %>%
+  mutate(transition = ifelse(lead(change, 1) == -1, -.9, 0),
+         transition = ifelse(lead(change,2) == -1, -.5, transition),
+         transition = ifelse(lead(change,3) == -1, -.3, transition),
+         transition = ifelse(lead(change, 1) == 1, .9, transition),
+         transition = ifelse(lead(change,2) == 1, .5, transition),
+         transition = ifelse(lead(change,3) == 1, .3, transition)) %>%
+  mutate(trans_rank = Rank + transition)
+  
+```
+
+
+---
+## Make the plot
+.small[
+```{r, make_plot}
+p <- to_plot_trans %>%
+  ggplot(aes(x = -trans_rank,y = Population, group =City)) +
+   geom_tile(aes(y = Population / 2, height = Population, fill = Region), 
+             width = 0.9) +
+  geom_text(aes(label = City), 
+            hjust = "right", colour = "white", 
+            fontface="bold", nudge_y = -100000) +
+  geom_text(aes(label = scales::comma(Population,big.mark = ' ')), 
+            hjust = "left", nudge_y = 100000, colour = "grey90") +
+  coord_flip(clip="off") +
+  hrbrthemes::scale_fill_ipsum() +
+  scale_x_discrete("") +
+  scale_y_continuous("",labels=scales::comma_format(big.mark = " ")) +
+  theme_elegant_dark(base_size = 20) +
+  theme(
+    panel.grid.minor.x=element_blank(),
+    axis.line = element_blank(),
+    panel.grid.major= element_line(color='lightgrey', size=.2),
+    legend.position = c(0.6, 0.2),
+    plot.margin = margin(1,1,1,2,"cm"),
+    plot.title = element_text(hjust = 0),
+    axis.text.y=element_blank(),
+    legend.text = element_text(size = 15),
+    legend.background = element_blank()) +
+  # gganimate code to transition by year:
+  transition_time(Year) +
+  ease_aes('cubic-in-out') +
+  labs(title='Evolution des plus grandes villes US',
+       subtitle='Population en {round(frame_time,0)}')
+
+```
+]
+---
+```{r, animate, eval = FALSE}
+animate(p, nframes = 400, fps = 25, end_pause = 30, width = 1200)
+anim_save("bar_race.gif", animation = last_animation())
+```
 
-![](bar_race.gif)
+![:scale 80%](bar_race.gif)
 

courses/lab7-temporal_data.html

@@ -1,8 +1,8 @@
 <!DOCTYPE html>
-<html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
   <head>
     <title>Lab 07 - Données temporelles et textuelles</title>
-    <meta charset="utf-8">
+    <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" />
@@ -42,22 +42,12 @@ data("us_city_populations")
 
 n_cities = 5
 
-# top_cities &lt;- 
-#   us_city_populations %&gt;% 
-#   filter(Rank &lt;= n_cities) %&gt;%
-#   select(City, State, Region) %&gt;% 
-#   distinct()
-# 
-# to_plot &lt;- filter(us_city_populations, City %in% top_cities$City)
-
-#to_plot &lt;- us_city_populations
-
 last_ranks &lt;- us_city_populations %&gt;% 
   filter(Year == max(Year)) %&gt;%
   mutate(last_rank = Rank) %&gt;% 
   select(City, last_rank)
 
-to_plot &lt;- left_join(us_city_populations, last_ranks, by= 'City')
+to_plot &lt;- left_join(us_city_populations, last_ranks, by= 'City') 
 
 right_axis &lt;-  to_plot %&gt;% 
   group_by(City) %&gt;% 
@@ -74,14 +64,182 @@ labels &lt;-  right_axis %&gt;%
 
 ---
 class: full
-![](lab7-temporal_data_files/figure-html/unnamed-chunk-2-1.png)&lt;!-- --&gt;
+
+```r
+p &lt;- ggplot(to_plot, aes(x=Year, y = Population, 
+                         group = City, color = City)) + 
+  geom_line(size=1) + 
+  scale_x_continuous("", expand=c(0,0))+
+  scale_y_continuous("",
+                     labels=scales::comma_format(big.mark = " "),
+                     sec.axis = sec_axis(~ ., 
+                                         breaks = ends, 
+                                         labels = labels ))+
+  scale_color_viridis_d()+
+  theme_elegant_dark()+
+  theme(legend.position = "none",
+        plot.margin = unit(c(1,3,1,1), "lines"), 
+        axis.line.y = element_blank(),
+        axis.line.x = element_blank(), 
+        axis.ticks.x = element_line(), 
+        panel.grid.major.y = element_line(color= 'grey30', size = .2) ) + 
+  gghighlight(max(last_rank) &lt;= n_cities, 
+              use_direct_label = FALSE, 
+              label_key = City,
+              unhighlighted_colour = "grey20")
+```
 ---
 class: full
+
+```r
+p
+```
+
 ![](lab7-temporal_data_files/figure-html/unnamed-chunk-3-1.png)&lt;!-- --&gt;
 ---
 
-![](bar_race.gif)
+class: full
+
+```r
+library(ggTimeSeries)
+p &lt;- to_plot %&gt;% filter(City %in% labels) %&gt;%
+  ggplot(aes(x = Year, y = Population, group = City, fill = City)) +
+  scale_y_continuous("", labels = scales::comma_format(big.mark = " "))+
+  stat_steamgraph() +
+  theme_elegant_dark() + 
+  scale_fill_viridis_d() + 
+  theme(plot.margin = unit(c(1,3,1,1), "lines"), 
+        axis.line.y = element_blank(),
+        axis.line.x = element_blank(), 
+        axis.ticks.x = element_line(), 
+        panel.grid.major.y = element_line(color= 'grey30', size = .2) ) 
+```
+
+---
+class: full 
+
+```r
+p
+```
+
+![](lab7-temporal_data_files/figure-html/unnamed-chunk-5-1.png)&lt;!-- --&gt;
+---
+class: inverse, center, middle
+# Barchart race
+
+---
+## Load data
+
+```r
+data("us_city_populations")
+
+n_cities = 10
+ 
+top_cities &lt;-us_city_populations %&gt;% filter(Rank &lt;= n_cities) %&gt;%
+  select(City, State, Region) %&gt;% distinct()
+```
+
+---
+## Create all missing dates
+
+```r
+# create a data frame with all the years between min and max Year
+all_years &lt;- data.frame(Year = seq(min(us_city_populations$Year), 
+                                   max(us_city_populations$Year), 1))
+
+# combine top_cities and all_years
+all_combos &lt;- merge(top_cities, all_years, all = T)
+
+# combine all_combos with the original dataset
+res_interp &lt;- merge(us_city_populations, all_combos, all.y = T)
+```
+
+## Interpolate the Populations when missing (linear interpolation here)
+
+```r
+res_interp  &lt;- res_interp  %&gt;%
+  group_by(City) %&gt;%
+  mutate(Population=approx(Year,Population,Year)$y)
+```
+
+---
+## Filter data
+
+```r
+to_plot &lt;- res_interp %&gt;%
+  group_by(Year) %&gt;%
+  arrange(-Population) %&gt;%
+  mutate(Rank=row_number()) %&gt;%
+  filter(Rank&lt;=n_cities)
+```
+
+---
+## Ease transitions
+
+
+```r
+to_plot_trans &lt;- to_plot %&gt;%
+  group_by(City) %&gt;%
+  arrange(Year) %&gt;%
+  mutate(lag_rank = lag(Rank, 1),
+         change = ifelse(Rank &gt; lag(Rank, 1), 1, 0),
+         change = ifelse(Rank &lt; lag(Rank, 1), -1, 0)) %&gt;%
+  mutate(transition = ifelse(lead(change, 1) == -1, -.9, 0),
+         transition = ifelse(lead(change,2) == -1, -.5, transition),
+         transition = ifelse(lead(change,3) == -1, -.3, transition),
+         transition = ifelse(lead(change, 1) == 1, .9, transition),
+         transition = ifelse(lead(change,2) == 1, .5, transition),
+         transition = ifelse(lead(change,3) == 1, .3, transition)) %&gt;%
+  mutate(trans_rank = Rank + transition)
+```
+
+
+---
+## Make the plot
+.small[
+
+```r
+p &lt;- to_plot_trans %&gt;%
+  ggplot(aes(x = -trans_rank,y = Population, group =City)) +
+   geom_tile(aes(y = Population / 2, height = Population, fill = Region), 
+             width = 0.9) +
+  geom_text(aes(label = City), 
+            hjust = "right", colour = "white", 
+            fontface="bold", nudge_y = -100000) +
+  geom_text(aes(label = scales::comma(Population,big.mark = ' ')), 
+            hjust = "left", nudge_y = 100000, colour = "grey90") +
+  coord_flip(clip="off") +
+  hrbrthemes::scale_fill_ipsum() +
+  scale_x_discrete("") +
+  scale_y_continuous("",labels=scales::comma_format(big.mark = " ")) +
+  theme_elegant_dark(base_size = 20) +
+  theme(
+    panel.grid.minor.x=element_blank(),
+    axis.line = element_blank(),
+    panel.grid.major= element_line(color='lightgrey', size=.2),
+    legend.position = c(0.6, 0.2),
+    plot.margin = margin(1,1,1,2,"cm"),
+    plot.title = element_text(hjust = 0),
+    axis.text.y=element_blank(),
+    legend.text = element_text(size = 15),
+    legend.background = element_blank()) +
+  # gganimate code to transition by year:
+  transition_time(Year) +
+  ease_aes('cubic-in-out') +
+  labs(title='Evolution des plus grandes villes US',
+       subtitle='Population en {round(frame_time,0)}')
+```
+]
+---
+
+```r
+animate(p, nframes = 400, fps = 25, end_pause = 30, width = 1200)
+anim_save("bar_race.gif", animation = last_animation())
+```
+
+![:scale 80%](bar_race.gif)
     </textarea>
+<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
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+(function(d) {
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+  var s = d.createElement("style");
+  s.type = "text/css"; s.innerHTML = "@media print { .has-continuation { display: none; } }";
+  d.head.appendChild(s);
+})(document);
+// delete the temporary CSS (for displaying all slides initially) when the user
+// starts to view slides
+(function() {
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+  slideshow.on('beforeShowSlide', function(slide) {
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+      node.parentNode.removeChild(node);
+    }
+    deleted = true;
+  });
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 (function() {
-  var i, text, code, codes = document.getElementsByTagName('code');
+  var links = document.getElementsByTagName('a');
+  for (var i = 0; i < links.length; i++) {
+    if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
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@@ -115,7 +314,8 @@ if (window.HTMLWidgets) slideshow.on('afterShowSlide', function (slide) {
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