Dataviz Notebook #7

Dans ce notebook, nous allons créer une représentation en treemap des flux d’artificialisation.

Cette représentation est assez originale pour représenter des proportions.

Chargeons les données et reconstitutions notre palette de couleurs précédente :

library(tidyverse)

flux <- read_csv("../data/obs_artif_conso_com_2009_2020_V2.csv", na = c("", "NULL"))

# Palette 
colorBlue    <- hcl(h = 220, c = 50, l = 80, fixup = TRUE)
colorRed     <- hcl(h = 4, c = 50, l = 80, fixup = TRUE)
colorMagenta <- hcl(h = 300, c = 50, l = 80, fixup = TRUE)
colorGrey    <- hcl(h = 0, c = 0, l = 80, fixup = TRUE)

myPalette <- c("blue" = colorBlue,
                "red" = colorRed,
                "magenta" = colorMagenta,
                "grey" = colorGrey)

Créons la fonction makeTreemap. On s’en servira dans l’application Shiny :

library(plotly)

## Warning: package 'plotly' was built under R version 4.0.5

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

makeTreemap <- function(flux, codeInsee) {
  df <- flux %>% filter(idcom == codeInsee)
  
  df <- df %>% gather("variable",
                      "value",
                      c("arthab0920", "artact0920", "artmix0920", "artinc0920"))
  
  df$variable <- case_when(
    df$variable == "arthab0920" ~ "Habitat",
    df$variable == "artact0920" ~ "Activité",
    df$variable == "artmix0920" ~ "Mixte",
    df$variable == "artinc0920" ~ "NC"
  )
  
  # Par. treemap
  labels = df$variable
  parents = rep("", nrow(df))
  values = df$value
  
  fig <- plot_ly(
    type="treemap",
    labels=labels,
    parents=parents,
    values=values,
    marker=list(colors = myPalette))
  
  fig
}

Faisons la treemap de la commune d’Aix en Provence :

flux %>% makeTreemap("13001")

Faisons la treemap sur Toulon :

flux %>% makeTreemap("83137")