widget_ggiraph.Rmd

---
title: "ggiraph, plotly"
author: "State of the R"
date: "24-28/08/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,  message = FALSE, warning = FALSE)
```

## Un premier exemple: nuages de points

```{r, message=FALSE}
library(palmerpenguins)
library(tidyverse)
library(ggiraph)
library(cowplot)

theme_set(theme_minimal())
```

On va travailler avec le jeu de données des pingouins de Palmer. 

```{r}
penguins

ggplot(penguins) +
  aes(x =  bill_length_mm, y = bill_depth_mm, color = species) +
  geom_point() +
  scale_color_viridis_d()
```

Nous allons animer le graphe précédent pour que lorsqu'on passe la souris sur le point, un texte indiquant l'île s'affiche et tous les pinguoins de la même île se mettent à briller.

```{r}
p <-
  ggplot(penguins) +
  aes(x =  bill_length_mm, y = bill_depth_mm, color = species) +
  geom_point_interactive(aes(tooltip = str_c("Island: ", island), data_id = island)) +
  scale_color_viridis_d()

girafe(ggobj = p)
```

Nous allons dessiner deux nuages de points (donc quatre axes plus la couleur), et lorsqu'on passe sur un point dans un des deux nuages, sa représentation dans l'autre graphe se mette à briller.

```{r}
penguins <- rowid_to_column(penguins)

p1 <-
  ggplot(penguins) +
  aes(x = bill_length_mm, y = bill_depth_mm, color = species) +
  geom_point_interactive(aes(tooltip = str_c("Island: ", island), data_id = rowid)) +
  scale_color_viridis_d() +
  theme(legend.position = "none")

p2 <-
  ggplot(penguins) +
  aes(x = flipper_length_mm, y = body_mass_g , color = species) +
  geom_point_interactive(aes(tooltip = str_c("Island: ", island), data_id = rowid)) +
  scale_color_viridis_d() +
  theme(legend.position = "none")

girafe(ggobj = plot_grid(p1, p2))
```


## Un deuxième exemple: "volcano plot"

Our goal is to add some interactivity to a "volcano plot": 

- clicking on a gene opens the GeneCards page 
- hovering on a gene shows other genes from the same pathway
- ...

### Data

Microarray data from a differential gene expression study (Chiaretti et al 2005)

```{r load-data}
diff_exp <- readRDS("data/diff_exp.rds")
head(diff_exp)
```

### Using plotly

Setup:
```{r load-packages, results="hide"}
library("plotly")
library("htmlwidgets")
```


```{r}
p <- plot_ly(data = diff_exp, x = ~meanDiff, y = ~logp) %>%
    add_markers(
        text = diff_exp[["geneName"]],
        customdata = paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=", diff_exp[["geneName"]]))
onRender(
    p, "
  function(el) {
    el.on('plotly_click', function(d) {
      var url = d.points[0].customdata;
      window.open(url);
    });
  }
")
```

### Using ggiraph

```{r}
library("ggplot2")
library("ggiraph")
```


```{r}
diff_exp$onclick <- sprintf("window.open(\"%s%s\")",
  "https://www.genecards.org/cgi-bin/carddisp.pl?gene=", diff_exp[["geneName"]])
```

```{r}
p <- ggplot(diff_exp, 
            aes(meanDiff, logp)) +
    geom_point()
p
```

```{r}
ip <- p + geom_point_interactive(
        aes(data_id = geneName, 
            tooltip = geneName,
            onclick = onclick))

girafe(ggobj = ip)
```


## Session information
```{r session-info}
sessionInfo()
```