First Draft Review #4
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Making some progress. Some suggestions: 1) don't worry about shiny - it will make the analysis much more complicated. If you really want to show a timeseries, you could probably do it with grouplayers in leaflet as shown [here](https://rstudio.github.io/leaflet/showhide.html). So you would bin them (by decade, maybe?) and then you could turn the markers on and off together) 2) I'd like you to add some more spatial analysis of some kind. At a minimum, this could be a convex polygon around each species (showing the approximate range).
adammwilson
commented
Nov 21, 2019
| Invasive species are organisms that cause ecological or economic harm in environments in which they are not native. As globalization has spread and the world has grown exponentially smaller, invasive species have become one of the greatest challenges facing environmental managers today. Coupled with constant influx of imported goods is the ever-looming spectre of climate change. Biodiversity loss caused by climate change may leave room in an ecosystem in which the invader has no natural predators, causing species hierarchies to shift, and the invasive species to outcompete native species already vulnerable due to climate induced stress. While there are numerous other factors involved in the spread of invasive species, this map seeks to ask the question: **Is the spread of invasive species correlated with an increase in mean temperature? Land usage or land cover?** This project aims to build an interactive tool that allows users to trace the spread of numerous invasive species in order to further understand the factors that allow invasive species to spread. | ||
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| # Materials and methods | ||
| # Materials and methods |
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Once you finalize them, fill this in with more details about your specific methods.
adammwilson
commented
Nov 21, 2019
| @@ -38,7 +38,7 @@ library(shinydashboard) | |||
| knitr::opts_chunk$set(cache=TRUE) # cache the results for quick compiling | |||
| ``` | |||
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| ## Download and clean all required data | |||
| ## Download and clean all required data | |||
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you can put in a vector of species names and make a single call. e.g.
species=c("sp1","sp2")
spdf=occ(species, has_coords=TRUE, limit=1000000)%>%occ2df()
adammwilson
commented
Nov 21, 2019
| @@ -59,7 +59,7 @@ SWWdf<-occ2df(SWW) | |||
| ``` | |||
| Spocc downloads data via "occ" and converts to a dataframe via "occ2df". | |||
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| # Results | |||
| # Results | |||
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see my notes in the pull request - I would encourage you to not add a shiny app until you have your analysis worked out. I suspect that will take most of your time.
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Making some progress. Some suggestions: