importation_quantitative_Léa.Rmd

---
title: "primates"
author: "Junyi Léa Enora"
output: html_document
editor_options: 
  chunk_output_type: console
---
```{r}
rm(list=ls())
library("ggplot2") ; theme_set(theme_minimal())
library("ggmap")
library("dplyr") ; options(dplyr.summarise.inform = FALSE)
library("rnaturalearth")
library("rnaturalearthdata")
library(RColorBrewer)
library("plotly")
library(scales)
```

```{r}
#setwd("C:/Users/mimi/Desktop/M2/Visualisation de données/PROJET")
quantitative <- read.csv("./Dataset/ATLANTIC-PR_Quantitative.csv", header = TRUE, sep = ";")
occurrence <- read.csv("./Dataset/ATLANTIC-PR_Occurrence.csv", header = TRUE, sep = ";")
community <- read.csv("./Dataset/ATLANTIC-PR_Community.csv", header = TRUE, sep = ";")

occurrence$SPECIES <- as.factor(gsub("^\\s+|\\s+$", "", occurrence$SPECIES)) # Enlève les espaces superflus

# print("occurence")
# dim(occurrence)
# str(occurrence)
# 
# print("quantitative")
# dim(quantitative)
# str(quantitative)
# 
# print("community")
# dim(community)
# str(community)
```

```{r}
for (i in 29:35){
	quantitative[,i] <- as.numeric(quantitative[,i])
}
summary(quantitative[,29:35])
```


```{r}
summary(occurrence$ANNUAL_RAIN)
summary(occurrence$ALTITUDE)
summary(occurrence$ANNUAL_TEMP)
```
# Partie barplot

```{r}
occurrence %>% 
  group_by(SPECIES) %>% 
	summarise(n = n()) %>%
	mutate(SPECIES = factor(SPECIES, levels = SPECIES[order(n)])) %>% 
	mutate(FAMILY = gsub(" .*$", "", SPECIES)) %>% 
  ggplot(aes(x= SPECIES, y = n, fill = FAMILY))+
  geom_bar(stat = "identity")+
  coord_flip()+
  theme_minimal() +
	scale_fill_brewer(palette = "Dark2")
```

# Partie carto

```{r, fig.height=10}

sbbox <- make_bbox(lon = c(min(occurrence$LONGITUDE_X), max(occurrence$LONGITUDE_X)), 
									 lat = c(min(occurrence$LATITUDE_Y), max(occurrence$LATITUDE_Y)),
									 f = 0.01)
southamerica <- get_map(location = sbbox, zoom = 7, maptype = "terrain") # résolution à améliorer
southamerica_map = ggmap(southamerica)
southamerica_map
```

```{r}
pal_iucn <- palette(c(
	  "#000000", # "Extinct",
		"#4A3852", # "EW",
		"#755A82", # "RE",
		"#8A322D", # "Critically Endangered",
		"#A15A2D", # "Endangered",
		"#BF9231", # "Vulnerable",
		"#A39F37", # "Near Threatened",
		"#438A32", # "Least Concern",
		"#C9C9C9", # "Data Deficient",
		"#9C9C9C", # "NA",
		"#6E6E6E"  # "NE"
		))
```

```{r fig.height=10}
map_with_species <- southamerica_map + 
    geom_point(data = occurrence,
               aes(x = LONGITUDE_X, y = LATITUDE_Y, col = SPECIES, text = paste0("espèce: ",SPECIES)), size = 1)+
   theme (legend.position = "none")+
    scale_col_manual(values = pal_iucn)
 

ggplotly(p = map_with_species, tooltip = "text")
```


```{r}

```



# Partie analyse
```{r}
dta <- occurrence[,c(4,16:18)]

dfcompleted <-
	missMDA::imputePCA(dta, ncp = 2, quali.sup = c(1))$completeObs
res.PCA <- PCA(dfcompleted,
							 ncp = 2,
							 quali.sup = c(1),
							 graph = T)

res.HCPC <- HCPC(
	res.PCA,
	nb.clust = 3,
	kk = 100,
	consol = FALSE,
	graph = FALSE
)
plot.HCPC(res.HCPC, choice = 'tree', title = 'Arbre hiérarchique')
plot.HCPC(res.HCPC,
					choice = 'map',
					draw.tree = FALSE,
					title = 'Plan factoriel')

plot.HCPC(
	res.HCPC,
	choice = '3D.map',
	ind.names = FALSE,
	centers.plot = FALSE,
	angle = 60,
	title = 'Arbre hiérarchique sur le plan factoriel'
)
```

```{r}
data_classifie <- as.data.frame(res.HCPC$data.clust)
head(data_classifie)
table(data_classifie$SPECIES, data_classifie$clust)


nesp = length(levels(data_classifie$SPECIES))
sp_clust <- table(data_classifie$SPECIES, data_classifie$clust)
sp_proba_clust <- data.frame(proba1 = rep(NA, nesp),
														 proba2 = rep(NA, nesp),
														 proba3 = rep(NA, nesp))
```