Self-containing html widgets in RMarkdown

Question

I'm currently writing a statistical study on rmarkdown (rmdformat readthedown) which contains a lot of plotly graphs. I had the issue that plotly graphs where not rendering and just leaving blank spaces after the second/third graph I was inclunding. I found a clever - or I thought it was - way to bypass it by using HTML widget as in the block below :

ggplot(graph) +
  aes(
    x = ab,
    y = ord,
    fill = fill
  ) +
  geom_boxplot() +
  scale_fill_brewer(palette = "Set1", direction = 1) +
  labs(
    title = ""
  ) +
  theme_minimal() +
  theme(legend.position = "none") -> GME
ggplotly(GME)->GMED

htmlwidgets::saveWidget(GMED, 
                        file.path(results_dir, 'GMED.html'))

and then using an iframe to include it in the doc

<embed scrolling='no' seamless='seamless' style='border:none'
src='results\GMED.html' width='800' height='500'></EMBED> 

But my dumbass didn't think that html widgets aren't self-contained so I can't juste send a single html document to my clients or else it just doesn't appear.

Is there a way to go around this ? Maybe I should just try to resolve the plotly not displaying issue but I spent several hours on it and just couldn't do it.

Thanks in advance for you time and help.

Answer 1

As I was asked to provide an example of the plotly issue, here is the code I use. Both graphs in the section "D.Notes" do not appear on the final document.

It gets worse if I replace the Text blocs with actual text.

Please let me know if I can make it any easier for it to be a reproducible example.

Thanks for all your insights

---
title: "test file for stackoverflow"
author: "Julien Kalamon"
date: "`r Sys.Date()`"
output:
  rmdformats::readthedown:
    highlight: kate 
    css : style.css
---

{r setup, include=FALSE}
## Global options
knitr::opts_chunk$set(cache = TRUE, echo=FALSE)
library(ggplot2)
library(esquisse) 
library(readxl)
library(dplyr)
library(plotly)
library(knitr)
library(htmltools)
library(IRdisplay)
assur <- read_excel("C:/Users/LENOVO/Downloads/Benchmark appli assurance.xlsx")



<style>
p {
    font-size: 16px;
    line-height: 24px;
    margin: 0px 0px 12px 0px;
}

h1, h2, h3, h4, h5, h6, legend {
    font-family: Arial, sans-serif;
    font-weight: 700;
    color: #1A658F;
} 
body {
text-align: justify; 
}
</style>

# 1. Présentation de l'étude 

## 1.1 Motivation & Objectifs

### A. Constats de départ

Text

### B. Objectifs

Text

### C. Hypothèses de départ 

Text

## 1.2 Périmètre & Définitions

### A. Définitions

Text

### Périmètre d'étude 

Text

{r, echo = FALSE}
library(rmarkdown)
paged_table(assur)
assur$`Téléchargements (en k)` <- factor(assur$`Téléchargements (en k)`, levels = c("5-10","10-50","50-100","100-500","500-1000","5000-10000"))



# 2. Phénomènes observés 

## 2.1 Présentation du groupe d'étude

{r aldo, echo=FALSE}
ggplot(assur) +
  aes(x = Type) +
  geom_bar(fill = "#1A658F") +
  labs(
    y = "Total",
    title = "Répartition du groupe test par type de structure"
  ) +
  theme_minimal() -> d
ggplotly(d)



text
 
### A. Applications

{r adio, echo=FALSE}
ggplot(assur) +
  aes(x = Applications, fill = Type) +
  geom_histogram(bins = 5L) +
  scale_fill_hue(direction = 1) +
  labs(x = "Nombre d'applications", y = "Total", title = "Nombre d'apllications clients par assureur", 
       fill = "Type d'assureur") +
  theme_minimal() -> p1
ggplotly(p1) ->p1 
p1


Text

### B. Chiffre d'affaires

{r ada, echo=FALSE}
ggplot(assur) +
 aes(x = "", y = `Chiffre d'affaire (en M\200)`) +
 geom_boxplot(fill = "#1A658F") +
 labs(x = "groupe test", 
 title = "Répartition des chiffres d'affaires dans le groupe test") +
 theme_minimal() -> p3
ggplotly(p3) 



Text

{r pressure, echo=FALSE}
assur %>%
 filter(Type %in% c("Généraliste", "Prévoyance")) %>%
 ggplot() +
 aes(x = Type, y = `Chiffre d'affaire (en M\200)`, fill = Type) +
 geom_boxplot() +
 scale_fill_hue(direction = 1) +
 labs(x = "Type d'assureur", title = "Répartition des chiffres d'affaires") +
 theme_minimal() +
 theme(legend.position = "none")->p5
ggplotly(p5)


Text

### 

{r pressure2, echo=FALSE}
assur %>%
 filter(Type %in% c("Startup", "Courtier")) %>%
 ggplot() +
 aes(x = Type, y = `Chiffre d'affaire (en M\200)`, fill = Type) +
 geom_boxplot() +
 scale_fill_viridis_d(option = "viridis", 
 direction = 1) +
 labs(x = "Type d'assureur", title = "Répartition des chiffres d'affaires") +
 theme_minimal() +
 theme(legend.position = "none") -> p4
ggplotly(p4)


Text

### C. Téléchargements 

{r}
results_dir <- 'results'
if(!dir.exists(results_dir)) dir.create(results_dir)

ggplot(assur) +
 aes(x = `Téléchargements (en k)`) +
 geom_bar(fill = "#1A658F") +
 labs(y = "Total", title = "Nombre de téléchargements des applications du groupe test") +
 theme_minimal() -> dark
ggplotly(dark) 


Text

### D. Notes

{r}

ggplot(assur) +
 aes(x = "", y = `Note moyenne`) +
 geom_boxplot(fill = "#185F87") +
 labs(x = "Groupe test", 
 title = "Répartition des notes dans le groupe test") +
 theme_minimal() -> deli 
ggplotly(deli)




Text

{r}

assur %>%
 filter(!(Type %in% "Auto")) %>%
 ggplot() +
 aes(x = Type, y = `Note moyenne`, fill = Type) +
 geom_boxplot() +
 scale_fill_hue(direction = 1) +
 labs(x = "Type d'assureur", title = "Répartition des notes par type d'assureur") +
 theme_minimal() +
 theme(legend.position = "none") -> BOOKER
ggplotly(BOOKER)





Text 



## 2.2 Principales observations

### A. Notes & Chiffre d'affaires

{r}
ggplot(assur) +
  aes(
    x = `Chiffre d'affaire (en M\200)`,
    y = `Note moyenne`,
    colour = Type,
    label = Assureur
  ) +
  geom_jitter(shape = "circle", size = 2) +
  scale_color_hue(direction = 1) +
  labs(
    title = "Note moyenne en fonction du chiffre d'affaire de l'assureur"
  ) +
  theme_minimal()-> Ayton
ggplotly(Ayton)



Text

{r, echo=FALSE, warning=FALSE}
library(car)
scatterplot(`Note moyenne`~`Chiffre d'affaire (en M\200)`, data=assur) 


Text

### B. Notes & Nombre d'applications

{r, echo=FALSE, warning=FALSE}
ggplot(assur) +
  aes(x = Applications, y = `Note moyenne`, colour = Type, label = Assureur) +
  geom_point(shape = "circle", size = 2L) +
  scale_color_viridis_d(option = "viridis", direction = 1) +
  labs(
    title = "Nombre d'applications utilisateurs & note moyenne"
  ) +
  theme_minimal()-> blop 

ggplotly(blop)


Text 

{r,warning=FALSE}
as.character(assur$Applications)->assur$Applications
ggplot(assur) +
  aes(
    x = Applications,
    y = `Note moyenne`,
    fill = Applications
  ) +
  geom_boxplot() +
  scale_fill_brewer(palette = "Set1", direction = 1) +
  labs(
    title = "Répartition des notes par nombre d'applications"
  ) +
  theme_minimal() +
  theme(legend.position = "none") -> GME
ggplotly(GME)





text