Distributions discover.Rmd

---
title: "Distributon Explore"
author: "BradMcFall"
date: "November 4, 2018"
output: html_document
---

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

#

#-------------------Librarys used---------------
library(rgbif)
library(tidyverse)
library(knitr)

```

## R Markdown


```{r}

#------------------------------Sets and Creates a directory to store files
setwd("C:/Users/Brad McFall/Documents/R/Distributions")
dir.create("GbifEurycea")

#------------------------------Creates list to read in and sets each name as unique

#------------------------------Creates list to read in and sets each name as unique
 
Upload_name_list<-c("Eurycea lucifuga","Eurycea longicauda", "Eurycea tynerensis", "Eurycea multiplicata","Eurycea spelaea",'Cheilanthes lanosa','Dodecatheon frenchii',
'Luxilus pilsbryi',
'Luxilus zonatus',
'Luxilus coccogenis',
'Luxilus zonistius',
'Plethodon angusticlavius',
'Plethodon caddoensis',
'Plethodon ouchitae',
'Plethodon fourchensis',
'Plethodon albagua',
'Desmognathus brimleyorum',
'Plethodon serratus')

Upload_name_list2<-c('Plethodon ouachitae','Plethodon albagula', 'Eurycea quadridigitata')

sp_name<-unique(Upload_name_list2)


```


This is a

```{r}

readgbif<- function (name, limit){ 
  #reads in GBIF data using package:rgbif, takes two parameters(scientific name(Genus space species as string) and a limit number on the amount of records to obtain, returns a GBIFclass object named with the species name.
  
  name<-occ_search(scientificName = name, limit = limit)
  
  return(name)
}


```

## Including 

You can also

```{r pressure, echo=FALSE}


#------------------------------loop----- reads in GBIF data for names in a list (only loops over list2)
for(sp_name in Upload_name_list2){

   name<-occ_search(scientificName = sp_name, limit=400) #calls rgbif occ_serach
   df<-as_tibble(name$data)
   write_csv(df, paste("GbifEurycea/",sp_name, sep=""))
   
}



```

```{r}

#setwd("C:/Users/Brad McFall/Documents/R/Distributions/GbifEurycea")

#reads csv file of GBIF data into a species named tibble
  
new_df2<-NULL
path="C:/Users/Brad McFall/Documents/R/Distributions/"  
new_names2<- unique(Upload_name_list2)
for (new_names2 in Upload_name_list2) {
  new_df2[[new_names2]]<-read_csv(paste(path,"GbifEurycea/",new_names2, sep=""))
}
#map(list, read_csv



```



```{r}

for (i in seq_along(1:length(Upload_name_list))){
nm[[i]]<-Upload_name_list[[i]]
}



#for (i in seq_along(1:length(Upload_name_list))){


i=1
while (i< length(Upload_name_list)) {
   print(i)
   name<-nm[[i]]
    if (i==4){
      
   ggplot(new_df[[i]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
   labs(title="Distribution")
      
    }
   i=i+1
  
}

   ggplot(new_df[["Eurycea tynerensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
  
 
 
  ggplot(new_df[["Plethodon angusticlavius"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")



#------------------------------


 ggplot(new_df[["Plethodon serratus"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
 
 


   ggplot(new_df[["Eurycea tynerensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
  
 
 
  ggplot(new_df[["Plethodon angusticlavius"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
  
   ggplot(new_df[["Eurycea multiplicata"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
  
    ggplot(new_df[["Desmognathus brimleyorum"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
   
  ggplot(new_df[["Plethodon fourchensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
  
  
   ggplot(new_df[["Plethodon caddoensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")
  
   
   
  ##-----------
 
   
    ggplot(new_df[["Plethodon serratus"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.05)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
       coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
  
    ggplot(new_df2[["Plethodon ouachitae"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.3)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
       coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
  
    ggplot(new_df[["Plethodon fourchensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
   
     ggplot(new_df[["Plethodon caddoensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
       coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
  
     ggplot(new_df[["Desmognathus brimleyorum"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
   coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
     
     
       ggplot(new_df2[["Plethodon albagula"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
   #--------------------------  
 
  Pfo<-merge(new_df[["Plethodon fourchensis"]],new_df2[["Plethodon ouachitae"]], all=TRUE)
  
  
        ggplot(Pfo, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
        
  
   
   Pfoc<-merge(new_df[["Plethodon caddoensis"]],Pfo, all=TRUE)
   
      ggplot(Pfoc, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()
 
      
  PfocEmDb<-merge(EmDb, Pfoc, all=TRUE)    
  
     ggplot(PfocEmDb, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))
     
       
  
 Pfocs<-merge(new_df[["Plethodon serratus"]],Pfoc, all=TRUE)
 
     ggplot(Pfocs, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))
     
  Pfocsa<-merge(new_df2[["Plethodon albagula"]], Pfocs, all=TRUE)
  
    ggplot(Pfocsa, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))
     
     
     
EmDb<-merge(new_df[["Eurycea multiplicata"]], new_df[["Desmognathus brimleyorum"]], all=TRUE)

  ggplot(EmDb, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
    geom_smooth()
  
  
AllSArkSal<- merge(EmDb,Pfocsa, all=TRUE)  

ggplot(EmDb, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))
```



```{r}

  
AllSArkSal<- merge(EmDb,Pfocsa, all=TRUE)  

ggplot(AllSArkSal, aes(x=decimalLongitude, y=decimalLatitude, color=name ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
  geom_smooth()
```



```{r}

    ggplot(new_df[["Plethodon fourchensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()

Pfpoints<-new_df[["Plethodon fourchensis"]]%>%
  select(name, decimalLongitude, decimalLatitude)

#Pfmod$coefficients[[1]]
Pfmod<- lm( decimalLatitude~ decimalLongitude , data=Pfpoints)

ggplot(Pfpoints, aes(x=decimalLongitude, y=decimalLatitude)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])+
  geom_smooth()+
  geom_density2d()


```


```{r}
library(igraph)
library(geosphere)


vv= as.data.frame(Pfpoints%>%
                    filter(!is.na(decimalLongitude),!is.na(decimalLatitude))%>%
                    select(decimalLongitude,decimalLatitude))



v=data.frame(
  ids = 1:nrow(vv),
  x=vv$decimalLongitude,
  y=vv$decimalLatitude)



D <- distm(vv, vv, fun=distVincentyEllipsoid) 



mat2list <- function(D) {
  n = dim(D)[1]
  k <- 1
  e <- matrix(ncol = 3,nrow = n*(n-1)/2)
  for (i in 1:(n-1)) {
    for (j in (i+1):n) {
      e[k,] = c(i,j,D[i,j])
      k<-k+1
    }
  }
  return(e)
}

eD = mat2list(D/1000)

net <- graph.data.frame(eD[,1:2],directed = FALSE, vertices = v)
E(net)$weight <- eD[,3] # Important use edge weight rather than net$weight
mst <- minimum.spanning.tree(net)

par(mfrow=c(1,2), mar=c(0,1,0.75,0)) 
plot(net,vertex.label=NA)
plot(mst, vertex.shape="none",edge.label=round(E(mst)$weight))





```


```{r}

## grourp the distance matrix in to lines for use with ggplot geom_path

mst2lines <- function(mst, vv) {
  me = get.edges(mst,1:ecount(mst))
  R = data.frame(lon=NULL,lat=NULL,group=NULL)
  for (k in 1:ecount(mst)) {
    A = vv[me[k,],]
    A$group = k
    R <- rbind(R,A)
  }
  rownames(R) <- NULL
  return(R)
}

R = mst2lines(mst,vv)
## plot with geom_path and quick map
v%>%
ggplot()+
  geom_path(aes(x = decimalLongitude, y =decimalLatitude, group=group), data = R, colour = 'red', size = 1)+
  geom_point(data = v, aes(x = x, y = y), color="gold", size=3, alpha=0.5)+
  geom_smooth(aes(x=x, y=y))+
  borders("state")+
  coord_quickmap()+
  coord_cartesian(xlim = c(-94.3,-93.8), ylim=c(34.55,34.8))+
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])
 

ggplot(v, aes(x=x, y=y)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])+
  geom_density2d()+
  geom_path(aes(x = decimalLongitude, y =decimalLatitude, group=group), data = R, colour = 'green', size = 1)+
  geom_point(data = v, aes(x = x, y = y), color="black", size=3, alpha=0.5)+
  theme_classic()
 


```

```{r}
   ggplot(new_df[["Plethodon caddoensis"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()

Pfpoints<-new_df[["Plethodon caddoensis"]]%>%
  select(name, decimalLongitude, decimalLatitude)

#Pfmod$coefficients[[1]]
Pfmod<- lm( decimalLatitude~ decimalLongitude , data=Pfpoints)

ggplot(Pfpoints, aes(x=decimalLongitude, y=decimalLatitude)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])+
  geom_smooth()+
  geom_density2d()

vv= as.data.frame(Pfpoints%>%
                    filter(!is.na(decimalLongitude),!is.na(decimalLatitude))%>%
                    select(decimalLongitude,decimalLatitude))



v=data.frame(
  ids = 1:nrow(vv),
  x=vv$decimalLongitude,
  y=vv$decimalLatitude)



D <- distm(vv, vv, fun=distVincentyEllipsoid) 



mat2list <- function(D) {
  n = dim(D)[1]
  k <- 1
  e <- matrix(ncol = 3,nrow = n*(n-1)/2)
  for (i in 1:(n-1)) {
    for (j in (i+1):n) {
      e[k,] = c(i,j,D[i,j])
      k<-k+1
    }
  }
  return(e)
}

eD = mat2list(D/1000)

net <- graph.data.frame(eD[,1:2],directed = FALSE, vertices = v)
E(net)$weight <- eD[,3] # Important use edge weight rather than net$weight
mst <- minimum.spanning.tree(net)

par(mfrow=c(1,2), mar=c(0,1,0.75,0)) 
plot(net,vertex.label=NA)
plot(mst, vertex.shape="none",edge.label=round(E(mst)$weight))

## grourp the distance matrix in to lines for use with ggplot geom_path

mst2lines <- function(mst, vv) {
  me = get.edges(mst,1:ecount(mst))
  R = data.frame(lon=NULL,lat=NULL,group=NULL)
  for (k in 1:ecount(mst)) {
    A = vv[me[k,],]
    A$group = k
    R <- rbind(R,A)
  }
  rownames(R) <- NULL
  return(R)
}

R = mst2lines(mst,vv)
## plot with geom_path and quick map
v%>%
ggplot()+
  geom_path(aes(x = decimalLongitude, y =decimalLatitude, group=group), data = R, colour = 'red', size = 1)+
  geom_point(data = v, aes(x = x, y = y), color="gold", size=3, alpha=0.5)+
  geom_smooth(aes(x=x, y=y))+
  borders("state")+
  coord_quickmap()+
  coord_cartesian(xlim = c(-94.3,-93.8), ylim=c(34.55,34.8))+
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])
 

ggplot(v, aes(x=x, y=y)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])+
  geom_smooth()+
  geom_density2d()+
  geom_path(aes(x = decimalLongitude, y =decimalLatitude, group=group), data = R, colour = 'red', size = 1)+
  geom_point(data = v, aes(x = x, y = y), color="gold", size=3, alpha=0.5)
 



```


```{r}
ggplot(new_df2[["Plethodon ouachitae"]], aes(x=decimalLongitude, y=decimalLatitude ))+
   geom_point(alpha=.5)+
   borders("state")+
   coord_quickmap()+
  labs(title="Distribution")+
     coord_cartesian(xlim = c(-94.5,-93.5), ylim=c(34,35))+
     geom_smooth()

Pfpoints<-new_df2[["Plethodon ouachitae"]]%>%
  select(name, decimalLongitude, decimalLatitude)

#Pfmod$coefficients[[1]]
Pfmod<- lm( decimalLatitude~ decimalLongitude , data=Pfpoints)

ggplot(Pfpoints, aes(x=decimalLongitude, y=decimalLatitude)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])+
  geom_smooth()+
  geom_density2d()

vv= as.data.frame(Pfpoints%>%
                    filter(!is.na(decimalLongitude),!is.na(decimalLatitude))%>%
                    select(decimalLongitude,decimalLatitude))



v=data.frame(
  ids = 1:nrow(vv),
  x=vv$decimalLongitude,
  y=vv$decimalLatitude)



D <- distm(vv, vv, fun=distVincentyEllipsoid) 



mat2list <- function(D) {
  n = dim(D)[1]
  k <- 1
  e <- matrix(ncol = 3,nrow = n*(n-1)/2)
  for (i in 1:(n-1)) {
    for (j in (i+1):n) {
      e[k,] = c(i,j,D[i,j])
      k<-k+1
    }
  }
  return(e)
}

eD = mat2list(D/1000)

net <- graph.data.frame(eD[,1:2],directed = FALSE, vertices = v)
E(net)$weight <- eD[,3] # Important use edge weight rather than net$weight
mst <- minimum.spanning.tree(net)

par(mfrow=c(1,2), mar=c(0,1,0.75,0)) 
plot(net,vertex.label=NA)
plot(mst, vertex.shape="none",edge.label=round(E(mst)$weight))

## grourp the distance matrix in to lines for use with ggplot geom_path

mst2lines <- function(mst, vv) {
  me = get.edges(mst,1:ecount(mst))
  R = data.frame(lon=NULL,lat=NULL,group=NULL)
  for (k in 1:ecount(mst)) {
    A = vv[me[k,],]
    A$group = k
    R <- rbind(R,A)
  }
  rownames(R) <- NULL
  return(R)
}

R = mst2lines(mst,vv)
## plot with geom_path and quick map
v%>%
ggplot()+
  geom_path(aes(x = decimalLongitude, y =decimalLatitude, group=group), data = R, colour = 'red', size = 1)+
  geom_point(data = v, aes(x = x, y = y), color="gold", size=3, alpha=0.5)+
  geom_smooth(aes(x=x, y=y))+
  borders("state")+
  coord_quickmap()+
  coord_cartesian(xlim = c(-94.3,-93.8), ylim=c(34.55,34.8))+
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])
 

ggplot(v, aes(x=x, y=y)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = Pfmod$coefficients[[1]], slope = Pfmod$coefficients[[2]])+
  geom_smooth()+
  geom_density2d()+
  geom_path(aes(x = decimalLongitude, y =decimalLatitude, group=group), data = R, colour = 'red', size = 1)+
  geom_point(data = v, aes(x = x, y = y), color="gold", size=3, alpha=0.5)
 


```