ggplotly_geopos: Converts a ggplot2 object from ggplot_geopos() to plotly

In RchivalTag: Analyzing and Interactive Visualization of Archival Tagging Data

Converts a ggplot2 object from ggplot_geopos() to plotly

Description

This function converts a ggplot2 object created by RchivalTag::ggplot_geopos() to a plotly object.

Usage

ggplotly_geopos(ggobj, fixedrange=F, grid=F,expand=10)

Arguments

ggobj	Character string identifying regions predefined by the region_definitions-dataset, Raster* or Extent object (corresponds to v_area of the v-function). If missing, region is derived from geographical coordinates, denoted by lat and lon. See add.region to define new region definitions and delete.region to delete unproper region definitions.
fixedrange	Vector returning longitude coordinates of the area to be plotted.
grid	whether a grid should be plotted (default is TRUE)
expand	By default, the underlying ggplotly-function does not stick to the plotting region of the ggobj, but extends it. This can result in missing countries or islands. The expand-argument extends the plotly-plotting window in each direction in order to cover the corresponding landmasks.)

Details

ggoplotmaply uses the ggplotly functions to convert the ggplot object into the plotly format.

Author(s)

Robert K. Bauer

See Also

leaflet_geopos, ggplot_geopos, ggplotmap, ggplotly

Examples

# ## example 1a) line plot from several csv-files:
# library(oceanmap)
# csv_file <- system.file("example_files/15P1019-104659-1-GPE3.csv",package="RchivalTag")
# pos <- get_geopos(csv_file)  ## show tracks as line plot
# ggobj <- ggplot_geopos(pos)
# ggobj
# ggplotly_geopos(ggobj)
# 
# ## load second file and add to plot:
# csv_file2 <- system.file("example_files/14P0911-46177-1-GPE3.csv",package="RchivalTag")
# pos2 <- get_geopos(csv_file2)  ## show tracks as line plot
# ggobj2 <- ggplot_geopos(pos2)
# ggplotly_geopos(ggobj2)
# 
# pos3 <- rbind(pos,pos2)
# ggobj3 <- ggplot_geopos(pos3,type = "l")
# # ggobj3 <- ggplot_geopos(pos3,type = "b")
# # ggobj3 <- ggplot_geopos(pos3,type = "p")
# ggplotly_geopos(ggobj3)
# 
# 
# ## example 1b) scatter plot from csv-file on existing landmask:
# ggobj <- oceanmap::ggplotmap('lion',grid.res = 5) # use keyword to derive area limits
# ggobj4 <- ggplot_geopos(csv_file,ggobj)
# ggplotly_geopos(ggobj4)
# 
# ## alternatives:
# pos <- get_geopos(csv_file)
# r <- oceanmap::regions("lion")
# ggobj5 <- ggplot_geopos(pos, xlim = r$xlim, ylim = r$ylim)
# ggplotly_geopos(ggobj5)
# 
# 
# ## example 2) probability surfaces of horizontal tracks from nc-file:
# ## this can take some time as it inlcudes time consuming data processing
# nc_file <- system.file("example_files/15P1019-104659-1-GPE3.nc",package="RchivalTag")
# ggobj6 <- ggplot_geopos(nc_file)
# ggobj6
# ggplotly_geopos(ggobj6)
# 
# 
# ## alternative:
# pols_df <- get_geopos(nc_file)
# ggplot_geopos(pols_df)
# 
# 
# ## example 3) probability surfaces of horizontal tracks from kmz-file:
# kmz_file <- system.file("example_files/15P1019-104659-1-GPE3.kmz",package="RchivalTag")
# ggobj7 <- ggplot_geopos(kmz_file)
# ggobj7
# ggplotly_geopos(ggobj7)
# 
# 
# kmz_file2 <- system.file("example_files/15P0986-15P0986-2-GPE3.kmz",package="RchivalTag")
# ggobj8 <- ggplot_geopos(kmz_file)
# ggobj8
# ggplotly_geopos(ggobj8)
# 
# ## example 4) combine polygon tracks:
# k1 = get_geopos(kmz_file)
# k2 = get_geopos(kmz_file2)
# 
# ggobj <- ggplotmap("mednw4")
# ## p1 <- ggplot_geopos(k1,ggobj = ggobj) ## not working, need to change date format:
# p1 <- ggplot_geopos(k1,date_format = "%d-%b-%Y %H:%M:%S")
# p1
# p2 <- ggplot_geopos(k2,p1,zlim = as.Date(range(c(k1$datetime,k2$datetime))),
#       date_format = "%d-%b-%Y %H:%M:%S")
# ggplotly_geopos(p2)
# 
# ## change plot window:
# p1b <- ggplot_geopos(k1,ggobj = ggobj, date_format = "%d-%b-%Y %H:%M:%S")
# p2b <- ggplot_geopos(k2,p1b,zlim = as.Date(range(c(k1$datetime,k2$datetime))),
#                     date_format = "%d-%b-%Y %H:%M:%S")
# p2b
# ggplotly_geopos(p2b)

RchivalTag documentation built on Nov. 10, 2023, 5:06 p.m.