README.RMD

---
title: "Trabajo con mapas"
output: github_document
---
# Introducción
En este documento trabajaremos para explorar la identidad de plantas de *tacsonia* del Perú

## trabajando con los datos
Vamos a cargar las librerias
```{r}
library(pacman)
library(ggplot2)
library(sf)
require(pacman)
pacman::p_load(tidyverse, writexl, readr, writexl, raster,
               readxl, gstat, sp, rworldxtra, sf, rgdal, cowplot, 
               googleway, ggplot2, ggrepel, ggspatial, rnaturalearth, rnaturalearthdata, lwgeom)
#rm(list = ls())

```

Ahora voy a leer los datos
```{r}
plants <- read_csv("Datos.csv", locale=locale(encoding="latin1"))
ambiente <- read_csv("ambiente.csv", locale=locale(encoding="latin1"))

huamachucoensis_ambiente <- read_csv("Passiflora_huamachucoensis_processed_occs.csv", locale=locale(encoding="latin1"))

manicata_ambiente <- read_csv("Passiflora_manicata_processed_occs.csv", locale=locale(encoding="latin1"))

mathewsii_ambiente <- read_csv("Passiflora_mathewsii_processed_occs.csv", locale=locale(encoding="latin1"))

mixta_ambiente <- read_csv("Passiflora_mixta_processed_occs.csv", locale=locale(encoding="latin1"))

parvifolia_ambiente <- read_csv("Passiflora_parvifolia_processed_occs.csv", locale=locale(encoding="latin1"))

peduncularis_ambiente <- read_csv("Passiflora_peduncularis_processed_occs.csv", locale=locale(encoding="latin1"))

pinnatistipula_ambiente <- read_csv("Passiflora_pinnatistipula_processed_occs.csv", locale=locale(encoding="latin1"))

tarminiana_ambiente <- read_csv("Passiflora_tarminiana_processed_occs.csv", locale=locale(encoding="latin1"))

trifoliata_ambiente <- read_csv("Passiflora_trifoliata_processed_occs.csv", locale=locale(encoding="latin1"))

tripartita_ambiente <- read_csv("Passiflora_tripartita_processed_occs.csv", locale=locale(encoding="latin1"))

trisecta_ambiente <- read_csv("Passiflora_trisecta_processed_occs.csv", locale=locale(encoding="latin1"))

weberbaueri_ambiente <- read_csv("Passiflora_weberbaueri_processed_occs.csv", locale=locale(encoding="latin1"))

weigendii_ambiente <- read_csv("Passiflora_weigendii_processed_occs.csv", locale=locale(encoding="latin1"))

xrosea_ambiente <- read_csv("Passiflora_xrosea_processed_occs.csv", locale=locale(encoding="latin1"))

ensifera_ambiente <- read_csv("Ensifera_ensifera_processed_occs.csv", locale=locale(encoding="latin1"))

amazonica_ambiente <- read_csv("Passiflora_amazonica_processed_occs.csv", locale=locale(encoding="latin1"))

anastomosans_ambiente <- read_csv("Passiflora_anastomosans_processed_occs.csv", locale=locale(encoding="latin1"))

cumbalensis_ambiente <- read_csv("Passiflora_cumbalensis_processed_occs.csv", locale=locale(encoding="latin1"))

glaberrima_ambiente <- read_csv("Passiflora_glaberrima_processed_occs.csv", locale=locale(encoding="latin1"))

gracilens_ambiente <- read_csv("Passiflora_gracilens_processed_occs.csv", locale=locale(encoding="latin1"))



```

Exploraremos la base de datos. Solo usaremos las columnas de departamento, elevación, especie, longitud y latitud para cada una de las especies.
```{r}
huamachucoensis <- plants %>% 
  dplyr::filter(ESPECIES == "P. huamachucoensis L.K. Escobar") %>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)  
  writexl::write_xlsx(huamachucoensis, "huamachucoensis.xlsx")

nueva <- plants %>% 
  dplyr::filter(ESPECIES == "P. nueva")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(nueva, "nueva.xlsx")

amazonica <- plants %>% 
  dplyr::filter(ESPECIES == "P. amazonica L. K. Escobar")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(amazonica, "amazonica.xlsx")

anastomosans <- plants %>% 
  dplyr::filter(ESPECIES == "P. anastomosans (Lamb. Ex Dc.) Killip")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(anastomosans, "anastomosans.xlsx")

cumbalensis <- plants %>% 
  dplyr::filter(ESPECIES == "P. cumbalensis (H. Karst.) Harms")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(cumbalensis, "cumbalensis.xlsx")

glaberrima <- plants %>% 
  dplyr::filter(ESPECIES == "P. glaberrima (Juss.) Poir")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(glaberrima, "glaberrima.xlsx")

gracilens <- plants %>% 
  dplyr::filter(ESPECIES == "P. gracilens (A. Gray) Harms")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(gracilens, "gracilens.xlsx")

kuethiana <- plants %>% 
  dplyr::filter(ESPECIES == "P. kuethiana B. Esquerre")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(kuethiana, "kuethiana.xlsx")

lanceolata <- plants %>% 
  dplyr::filter(ESPECIES == "P. lanceolata (Mast.) Harms")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(lanceolata, "lanceolata.xlsx")

mandonii <- plants %>% 
  dplyr::filter(ESPECIES == "P. mandonii (Mast.) Killip")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(mandonii, "mandonii.xlsx")

manicata <- plants %>% 
  dplyr::filter(ESPECIES == "P. manicata (Juss.) Pers.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(manicata, "manicata.xlsx")

mathewsii <- plants %>% 
  dplyr::filter(ESPECIES == "P. mathewsii (Mast.) Killip")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(mathewsii, "mathewsii.xlsx")

mixta <- plants %>% 
  dplyr::filter(ESPECIES == "P. mixta L.f.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(mixta, "mixta.xlsx")

parvifolia <- plants %>% 
  dplyr::filter(ESPECIES == "P. parvifolia (DC.) Harms")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(parvifolia, "parvifolia.xlsx")

peduncularis <- plants %>% 
  dplyr::filter(ESPECIES == "P. peduncularis Cav.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(peduncularis, "peduncularis.xlsx")

pinnatistipula <- plants %>% 
  dplyr::filter(ESPECIES == "P. pinnatistipula Cav.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(pinnatistipula, "pinnatistipula.xlsx")

runa <- plants %>% 
  dplyr::filter(ESPECIES == "P. runa L. K Escobar")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(runa, "runa.xlsx")

salpoense <- plants %>% 
  dplyr::filter(ESPECIES == "P. salpoense S. Leiva & Tantalean")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(salpoense, "salpoense.xlsx")

tarminiana <- plants %>% 
  dplyr::filter(ESPECIES == "P. tarminiana Coppens y V. E. Barney")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(tarminiana, "tarminiana.xlsx")

trifoliata <- plants %>% 
  dplyr::filter(ESPECIES == "P. trifoliata Cav.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI) 
writexl::write_xlsx(trifoliata, "trifoliata.xlsx")

tripartita <- plants %>% 
  dplyr::filter(ESPECIES == "P. tripartita (Juss.) Poir.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(tripartita, "tripartita.xlsx")

trisecta <- plants %>% 
  dplyr::filter(ESPECIES == "P. trisecta Mast.")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(trisecta, "trisecta.xlsx")

weberbaueri <- plants %>% 
  dplyr::filter(ESPECIES == "P. weberbaueri Harms")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(weberbaueri, "weberbaueri.xlsx")

weigendii <- plants %>% 
  dplyr::filter(ESPECIES == "P. weigendii T. Ulmer & Schwerdtferger")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(weigendii, "weigendii.xlsx")

xrosea <- plants %>% 
  dplyr::filter(ESPECIES == "P. x rosea (H. Karst.) Killip")%>% 
  dplyr::select(ESPECIES, DEPARTA, ELEVA, LONGI, LATI)
writexl::write_xlsx(xrosea, "xrosea.xlsx")

```

##Ahora creamos shapefile de todas las especies por separado. También plotearemos las ocurrencias en un shapefile de Perú. Por último guardaremos el shapefile de los datos de ocurrencia para cada especie.
```{r}
huamachucoensis <- read_xlsx('huamachucoensis.xlsx')
xy <- huamachucoensis[,4:5]
huamachucoensis_shp <- SpatialPointsDataFrame(coords = xy, data = huamachucoensis)
proj4string(huamachucoensis_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(huamachucoensis_shp, ad=TRUE)
shapefile(huamachucoensis_shp, 'huamachucoensis.shp', overwrite = TRUE)

amazonica <- read_xlsx('amazonica.xlsx')
xy <- amazonica[,4:5]
amazonica_shp <- SpatialPointsDataFrame(coords = xy, data = amazonica)
proj4string(amazonica_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(amazonica_shp, ad=TRUE)
shapefile(amazonica_shp, 'amazonica.shp', overwrite = TRUE)

anastomosans <- read_xlsx('anastomosans.xlsx')
xy <- anastomosans[,4:5]
anastomosans_shp <- SpatialPointsDataFrame(coords = xy, data = anastomosans)
proj4string(anastomosans_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(anastomosans_shp, ad=TRUE)
shapefile(anastomosans_shp, 'anastomosans.shp', overwrite = TRUE)

cumbalensis <- read_xlsx('cumbalensis.xlsx')
xy <- cumbalensis[,4:5]
cumbalensis_shp <- SpatialPointsDataFrame(coords = xy, data = cumbalensis)
proj4string(cumbalensis_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(cumbalensis_shp, ad=TRUE)
shapefile(cumbalensis_shp, 'cumbalensis.shp', overwrite = TRUE)

glaberrima <- read_xlsx('glaberrima.xlsx')
xy <- glaberrima[,4:5]
glaberrima_shp <- SpatialPointsDataFrame(coords = xy, data = glaberrima)
proj4string(glaberrima_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(glaberrima_shp, ad=TRUE)
shapefile(glaberrima_shp, 'glaberrima.shp', overwrite = TRUE)

gracilens <- read_xlsx('gracilens.xlsx')
xy <- gracilens[,4:5]
gracilens_shp <- SpatialPointsDataFrame(coords = xy, data = gracilens)
proj4string(gracilens_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(gracilens_shp, ad=TRUE)
shapefile(gracilens_shp, 'gracilens.shp', overwrite = TRUE)

kuethiana <- read_xlsx('kuethiana.xlsx')
xy <- kuethiana[,4:5]
kuethiana_shp <- SpatialPointsDataFrame(coords = xy, data = kuethiana)
proj4string(kuethiana_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(kuethiana_shp, ad=TRUE)
shapefile(kuethiana_shp, 'kuethiana.shp', overwrite = TRUE)

lanceolata <- read_xlsx('lanceolata.xlsx')
xy <- lanceolata[,4:5]
lanceolata_shp <- SpatialPointsDataFrame(coords = xy, data = lanceolata)
proj4string(lanceolata_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(lanceolata_shp, ad=TRUE)
shapefile(lanceolata_shp, 'lanceolata.shp', overwrite = TRUE)

mandonii <- read_xlsx('mandonii.xlsx')
xy <- mandonii[,4:5]
mandonii_shp <- SpatialPointsDataFrame(coords = xy, data = mandonii)
proj4string(mandonii_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(mandonii_shp, ad=TRUE)
shapefile(mandonii_shp, 'mandonii.shp', overwrite = TRUE)

mathewsii <- read_xlsx('mathewsii.xlsx')
xy <- mathewsii[,4:5]
mathewsii_shp <- SpatialPointsDataFrame(coords = xy, data = mathewsii)
proj4string(mathewsii_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(mathewsii_shp, ad=TRUE)
shapefile(mathewsii_shp, 'mathewsii.shp', overwrite = TRUE)

mixta <- read_xlsx('mixta.xlsx')
xy <- mixta[,4:5]
mixta_shp <- SpatialPointsDataFrame(coords = xy, data = mixta)
proj4string(mixta_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(mixta_shp, ad=TRUE)
shapefile(mixta_shp, 'mixta.shp', overwrite = TRUE)

nueva <- read_xlsx('nueva.xlsx')
xy <- nueva [,4:5]
nueva_shp <- SpatialPointsDataFrame(coords = xy, data = nueva)
proj4string(nueva_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(nueva_shp, ad=TRUE)
shapefile(nueva_shp, 'nueva.shp', overwrite = TRUE)

parvifolia <- read_xlsx('parvifolia.xlsx')
xy <- parvifolia [,4:5]
parvifolia_shp <- SpatialPointsDataFrame(coords = xy, data = parvifolia)
proj4string(parvifolia_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(parvifolia_shp, ad=TRUE)
shapefile(parvifolia_shp, 'parvifolia.shp', overwrite = TRUE)

peduncularis <- read_xlsx('peduncularis.xlsx')
xy <- peduncularis [,4:5]
peduncularis_shp <- SpatialPointsDataFrame(coords = xy, data = peduncularis)
proj4string(peduncularis_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(peduncularis_shp, ad=TRUE)
shapefile(peduncularis_shp, 'peduncularis.shp', overwrite = TRUE)

pinnatistipula <- read_xlsx('pinnatistipula.xlsx')
xy <- pinnatistipula [,4:5]
pinnatistipula_shp <- SpatialPointsDataFrame(coords = xy, data = pinnatistipula)
proj4string(pinnatistipula_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(pinnatistipula_shp, ad=TRUE)
shapefile(pinnatistipula_shp, 'pinnatistipula.shp', overwrite = TRUE)

runa <- read_xlsx('runa.xlsx')
xy <- runa [,4:5]
runa_shp <- SpatialPointsDataFrame(coords = xy, data = runa)
proj4string(runa_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(runa_shp, ad=TRUE)
shapefile(runa_shp, 'runa.shp', overwrite = TRUE)

salpoense <- read_xlsx('salpoense.xlsx')
xy <- salpoense [,4:5]
salpoense_shp <- SpatialPointsDataFrame(coords = xy, data = salpoense)
proj4string(salpoense_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(salpoense_shp, ad=TRUE)
shapefile(salpoense_shp, 'salpoense.shp', overwrite = TRUE)

tarminiana <- read_xlsx('tarminiana.xlsx')
xy <- tarminiana [,4:5]
tarminiana_shp <- SpatialPointsDataFrame(coords = xy, data = tarminiana)
proj4string(tarminiana_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(tarminiana_shp, ad=TRUE)
shapefile(tarminiana_shp, 'tarminiana.shp', overwrite = TRUE)

trifoliata <- read_xlsx('trifoliata.xlsx')
xy <- trifoliata [,4:5]
trifoliata_shp <- SpatialPointsDataFrame(coords = xy, data = trifoliata)
proj4string(trifoliata_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(trifoliata_shp, ad=TRUE)
shapefile(trifoliata_shp, 'trifoliata.shp', overwrite = TRUE)

tripartita <- read_xlsx('tripartita.xlsx')
xy <- tripartita [,4:5]
tripartita_shp <- SpatialPointsDataFrame(coords = xy, data = tripartita)
proj4string(tripartita_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(tripartita_shp, ad=TRUE)
shapefile(tripartita_shp, 'tripartita.shp', overwrite = TRUE)

trisecta <- read_xlsx('trisecta.xlsx')
xy <- trisecta [,4:5]
trisecta_shp <- SpatialPointsDataFrame(coords = xy, data = trisecta)
proj4string(trisecta_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(trisecta_shp, ad=TRUE)
shapefile(trisecta_shp, 'trisecta.shp', overwrite = TRUE)

weberbaueri <- read_xlsx('weberbaueri.xlsx')
xy <- weberbaueri [,4:5]
weberbaueri_shp <- SpatialPointsDataFrame(coords = xy, data = weberbaueri)
proj4string(weberbaueri_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(weberbaueri_shp, ad=TRUE)
shapefile(weberbaueri_shp, 'weberbaueri.shp', overwrite = TRUE)

weigendii <- read_xlsx('weigendii.xlsx')
xy <- weigendii [,4:5]
weigendii_shp <- SpatialPointsDataFrame(coords = xy, data = weigendii)
proj4string(weigendii_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(weigendii_shp, ad=TRUE)
shapefile(weigendii_shp, 'weigendii.shp', overwrite = TRUE)

xrosea <- read_xlsx('xrosea.xlsx')
xy <- xrosea [,4:5]
xrosea_shp <- SpatialPointsDataFrame(coords = xy, data = xrosea)
proj4string(xrosea_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(xrosea_shp, ad=TRUE)
shapefile(xrosea_shp, 'xrosea.shp', overwrite = TRUE)

manicata <- read_xlsx('manicata.xlsx')
xy <- manicata [,4:5]
manicata_shp <- SpatialPointsDataFrame(coords = xy, data = manicata)
proj4string(manicata_shp) = CRS('+proj=longlat + datum=WGS84 +no_defs')
PERU <- shapefile('PER_adm1.shp')
plot(PERU)
plot(manicata_shp, ad=TRUE)
shapefile(manicata_shp, 'manicata.shp', overwrite = TRUE)


```
## Ahora visualizaremos mejor los mapas de ocurrencia con cada shapefile creados previamente
```{r}
huamachucoensis_sf <- huamachucoensis %>% st_as_sf(coords = c(4,5), crs = 4326) 
amazonica_sf <- amazonica %>% st_as_sf(coords = c(4,5), crs = 4326) 
anastomosans_sf <- anastomosans %>% st_as_sf(coords = c(4,5), crs = 4326)
cumbalensis_sf <- cumbalensis %>% st_as_sf(coords = c(4,5), crs = 4326) 
glaberrima_sf <- glaberrima %>% st_as_sf(coords = c(4,5), crs = 4326) 
gracilens_sf <- gracilens %>% st_as_sf(coords = c(4,5), crs = 4326) 
kuethiana_sf <- kuethiana %>% st_as_sf(coords = c(4,5), crs = 4326) 
lanceolata_sf <- lanceolata %>% st_as_sf(coords = c(4,5), crs = 4326) 
mandonii_sf <- mandonii %>% st_as_sf(coords = c(4,5), crs = 4326) 
manicata_sf <- manicata %>% st_as_sf(coords = c(4,5), crs = 4326) 
mathewsii_sf <- mathewsii %>% st_as_sf(coords = c(4,5), crs = 4326) 
mixta_sf <- mixta %>% st_as_sf(coords = c(4,5), crs = 4326) 
nueva_sf <- nueva %>% st_as_sf(coords = c(4,5), crs = 4326) 
parvifolia_sf <- parvifolia %>% st_as_sf(coords = c(4,5), crs = 4326) 
peduncularis_sf <- peduncularis %>% st_as_sf(coords = c(4,5), crs = 4326)
pinnatistipula_sf <- pinnatistipula %>% st_as_sf(coords = c(4,5), crs = 4326) 
runa_sf <- runa %>% st_as_sf(coords = c(4,5), crs = 4326) 
salpoense_sf <- salpoense %>% st_as_sf(coords = c(4,5), crs = 4326) 
tarminiana_sf <- tarminiana %>% st_as_sf(coords = c(4,5), crs = 4326)
trifoliata_sf <- trifoliata %>% st_as_sf(coords = c(4,5), crs = 4326) 
tripartita_sf <- tripartita %>% st_as_sf(coords = c(4,5), crs = 4326) 
trisecta_sf <- trisecta %>% st_as_sf(coords = c(4,5), crs = 4326) 
weberbaueri_sf <- weberbaueri %>% st_as_sf(coords = c(4,5), crs = 4326) 
weigendii_sf <- weigendii %>% st_as_sf(coords = c(4,5), crs = 4326) 
xrosea_sf <- xrosea %>% st_as_sf(coords = c(4,5), crs = 4326)

ggplot() + 
  geom_sf(data = huamachucoensis_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = xrosea_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()


ggplot() + 
  geom_sf(data = amazonica_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = anastomosans_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = cumbalensis_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = glaberrima_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = gracilens_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = kuethiana_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = lanceolata_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = mandonii_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = manicata_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = mathewsii_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = mixta_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = nueva_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = parvifolia_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = peduncularis_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = pinnatistipula_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = runa_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = salpoense_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = tarminiana_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = trifoliata_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = tripartita_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = trisecta_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = weberbaueri_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = weigendii_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

ggplot() + 
  geom_sf(data = xrosea_sf, aes(color = ELEVA)) +
  scale_color_viridis_c()

```

##vamos a crear shapefile de cada especie para las variables climáticas bio
```{r}
huamachucoensis_bio <- huamachucoensis_ambiente %>% dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)  
  writexl::write_xlsx(huamachucoensis_bio, "huamachucoensis_bio.xlsx")

#nueva_bio <- ambiente %>% 
 # dplyr::filter(scientific_name == "Passiflora nueva")%>% 
#  dplyr::select(bio01, bio02, bio03, bio04, bio05, bio06, bio07, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17)
#writexl::write_xlsx(nueva_bio, "nueva_bio.xlsx")

amazonica_bio <- amazonica_ambiente %>%
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)  
writexl::write_xlsx(amazonica_bio, "amazonica_bio.xlsx")

anastomosans_bio <- anastomosans_ambiente %>% 
 dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(anastomosans_bio, "anastomosans_bio.xlsx")

cumbalensis_bio <- cumbalensis_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(cumbalensis_bio, "cumbalensis_bio.xlsx")

glaberrima_bio <- glaberrima_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(glaberrima_bio, "glaberrima_bio.xlsx")

gracilens_bio <- gracilens_ambiente %>% 
 dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(gracilens_bio, "gracilens_bio.xlsx")

#kuethiana_bio <- ambiente %>% 
 # dplyr::filter(scientific_name == "Passiflora kuethiana")%>% 
  #dplyr::select(bio01, bio02, bio03, bio04, bio05, bio06, bio07, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17)
#writexl::write_xlsx(kuethiana_bio, "kuethiana_bio.xlsx")

#lanceolata_bio <- ambiente %>% 
 # dplyr::filter(scientific_name == "Passiflora lanceolata")%>% 
  #dplyr::select(bio01, bio02, bio03, bio04, bio05, bio06, bio07, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17)
#writexl::write_xlsx(lanceolata_bio, "lanceolata_bio.xlsx")

#mandonii_bio <- ambiente %>% 
 # dplyr::filter(scientific_name == "Passiflora mandonii")%>% 
  #dplyr::select(bio01, bio02, bio03, bio04, bio05, bio06, bio07, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17)
#writexl::write_xlsx(mandonii_bio, "mandonii_bio.xlsx")

manicata_bio <- manicata_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(manicata_bio, "manicata_bio.xlsx")

mathewsii_bio <- mathewsii_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(mathewsii_bio, "mathewsii_bio.xlsx")

mixta_bio <- mixta_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(mixta_bio, "mixta_bio.xlsx")

parvifolia_bio <- parvifolia_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(parvifolia_bio, "parvifolia_bio.xlsx")

peduncularis_bio <- peduncularis_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(peduncularis_bio, "peduncularis_bio.xlsx")

pinnatistipula_bio <- pinnatistipula_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(pinnatistipula_bio, "pinnatistipula_bio.xlsx")

#runa_bio <- ambiente %>% 
 # dplyr::filter(scientific_name == "Passiflora runa")%>% 
  #dplyr::select(bio01, bio02, bio03, bio04, bio05, bio06, bio07, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17)
#writexl::write_xlsx(runa_bio, "runa_bio.xlsx")

#salpoense_bio <- ambiente %>% 
 # dplyr::filter(scientific_name == "Passiflora salpoense")%>% 
  #dplyr::select(bio01, bio02, bio03, bio04, bio05, bio06, bio07, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17)
#writexl::write_xlsx(salpoense_bio, "salpoense_bio.xlsx")

tarminiana_bio <- tarminiana_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(tarminiana_bio, "tarminiana_bio.xlsx")

trifoliata_bio <- trifoliata_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15) 
writexl::write_xlsx(trifoliata_bio, "trifoliata_bio.xlsx")

tripartita_bio <- tripartita_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15) 
writexl::write_xlsx(tripartita_bio, "tripartita_bio.xlsx")

trisecta_bio <- trisecta_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15) 
writexl::write_xlsx(trisecta_bio, "trisecta_bio.xlsx")

weberbaueri_bio <- weberbaueri_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(weberbaueri_bio, "weberbaueri_bio.xlsx")

weigendii_bio <- weigendii_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(weigendii_bio, "weigendii_bio.xlsx")

xrosea_bio <- xrosea_ambiente %>% 
  dplyr::select(occID, longitude, latitude, cells, bio01, bio02, bio03, bio04, bio12, bio15)
writexl::write_xlsx(xrosea_bio, "xrosea_bio.xlsx")


```
## Ahora vamos a hacer variogramas para las bio1 y bio12
```{r}
huamachucoensis_bio <- read_xlsx('huamachucoensis_bio.xlsx')
coordinates(huamachucoensis_bio) <- ~longitude + latitude
proj4string(huamachucoensis_bio) = CRS("+init=epsg:4326")

v_huamachucoensis_null <- variogram(log(bio01) ~1, data = huamachucoensis_bio) %>% mutate(Modelo = 'Nulo')

v_huamachucoensis_espacial <- variogram(log(bio01) ~ longitude + latitude, data = huamachucoensis_bio) %>% mutate(Modelo = 'Espacial')

v_huamachucoensis_plot <- rbind(v_huamachucoensis_null, v_huamachucoensis_espacial)

ggplot(v_huamachucoensis_plot, aes(x = dist, y = gamma, color = Modelo)) + geom_path()+ geom_point() + theme_bw()

v_huamachucoensis_fit_espaci <- fit.variogram(v_huamachucoensis_espacial,model = vgm(1, "Sph", 700, 1))
v_huamachucoensis_fit_null <- fit.variogram(v_huamachucoensis_null, model = vgm(1, "Sph", 
    700, 1))


ggplot(v_huamachucoensis_espacial, aes(x = dist, y = gamma, color = Modelo))+ geom_point() + geom_hline(yintercept = 1.0000000 + 0.9999842, lty = 2) +
  geom_vline(xintercept = 699.9889, lty = 2) +
  theme_bw()


#v_huamachucoensis_sqrt <- variogram(log(bio01) ~sqrt(dist), huamachucoensis_bio) %>% 
   # mutate(Modelo = "sqrt(dist)")

#v_huamachucoensis_distancia2 <- variogram(log(bio12) ~ dist.m, data = huamachucoensis_bio) %>% mutate(Modelo = 'Distancia')

v_huamachucoensis_null2 <- variogram(log(bio12) ~1, data = huamachucoensis_bio) %>% mutate(Modelo = 'Nulo')

v_huamachucoensis_espacial2 <- variogram(log(bio12) ~ longitude + latitude, data = huamachucoensis_bio) %>% mutate(Modelo = 'Espacial')

#v_huamachucoensis_distancia <- variogram(log(bio01) ~ dist.m, data = huamachucoensis_bio) %>% mutate(Modelo = 'Distancia')

plot(v_huamachucoensis_null2)
plot(v_huamachucoensis_espacial2, ad=TRUE)

v_huamachucoensis <- variogram(log(bio01) ~1, data = huamachucoensis_bio2)
plot(v_huamachucoensis)

```




```

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