gbifmap_list: Make a simple map to visualize GBIF point data.

Description

This function is deprecated.

Usage

gbifmap_list(input = NULL, mapdatabase = "world",
    region = ".", geom = geom_point, jitter = NULL,
    customize = NULL)

Arguments

input

Either a single data.frame or a list of data.frame's (e.g., from different speies). The data.frame has to have, in addition to any other columns, columns named exactly "decimalLatitude" and "decimalLongitude".

mapdatabase

The map database to use in mapping. What you choose here determines what you can choose in the region parameter. One of: county, state, usa, world, world2, france, italy, or nz.

region

The region of the world to map. From the maps package, run sort(unique(map_data("world")$region)) to see region names for the world database layer, or e.g., sort(unique(map_data("state")$region)) for the state layer.

geom

The geom to use, one of geom_point or geom_jitter. Don't quote them.

jitter

If you use jitterposition, the amount by which to jitter points in width, height, or both.

customize

Further arguments passed on to ggplot.

Value

Map (using ggplot2 package) of points on a map or tiles on a map.

Details

gbifmap takes care of cleaning up the data.frame (removing NA's, etc.) returned from rgbif functions, and creating the map. This function gives a simple map of your data. You can look at the code behing the function itself if you want to build on it to make a map according to your specfications.

Note that this function removes values that are impossible on the globe, and those rows that have both lat and long as NA or zeros.

Examples

Run this code

# Point map, using output from occurrencelist, example 1
out <- occurrencelist(scientificname = 'Accipiter erythronemius',
   coordinatestatus = TRUE, maxresults = 100)
gbifmap_list(input = out) # make a map using vertmap

# Point map, using output from occurrencelist, example 2, a species with more data
out <- occurrencelist(scientificname = 'Puma concolor', coordinatestatus = TRUE,
   maxresults = 100)
gbifmap_list(input = out) # make a map
gbifmap_list(input = out, region = 'USA') # make a map, just using the US map

# Point map, using output from occurrencelist, many species
splist <- c('Accipiter erythronemius', 'Junco hyemalis', 'Aix sponsa')
out <- occurrencelist_many(splist, coordinatestatus = TRUE, maxresults = 20)
gbifmap_list(out)

# Point map, using output from occurrencelist, many species
splist <- c('Accipiter erythronemius', 'Junco hyemalis', 'Aix sponsa', 'Ceyx fallax',
   'Picoides lignarius', 'Campephilus leucopogon')
out <- occurrencelist_many(splist, coordinatestatus = TRUE, maxresults = 100)
gbifmap_list(out)

# Get occurrences or density by area, using min/max lat/long coordinates
# Setting scientificname="*" so we just get any species
out <- occurrencelist(scientificname="*", minlatitude=30, maxlatitude=35,
   minlongitude=-100, maxlongitude=-95, coordinatestatus = TRUE, maxresults = 500)

# Using `geom_point`
gbifmap_list(out, "state", "texas", geom_point)

# Using geom_jitter to move the points apart from one another
gbifmap_list(out, "state", "texas", geom_jitter, position_jitter(width = 0.3,
   height = 0.3))

# And move points a lot
gbifmap_list(out, "state", "texas", geom_jitter, position_jitter(width = 1, height = 1))

# Customize the plot by passing options to `ggplot()`
mycustom <- function(){
   list(geom_point(size=9)
       )}
out <- occurrencelist(scientificname = 'Accipiter erythronemius',
   coordinatestatus = TRUE, maxresults = 100)
gbifmap_list(out, customize = mycustom())