In RS-eco/ggmap2: R package to easily create nice maps

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, comment=NA, message=FALSE, fig.path="figures/")

Install ggmap2 package

To use the package, it can be installed directly from GitHub using the remotes package.

# Install packages from Github
#install.packages("remotes")
remotes::install_github("RS-eco/ggmap2", build_vignettes = TRUE)

After installation, load the package

library(ggmap2)
library(sf)

If you encounter a bug or if you have any problems, please file an issue on Github.

Data

Outline

outline is a shapefile of the world's land regions:

# Load outline
data(outline)

# Plot outline of Europe
plot(outline, xlim = c(-11, 43), ylim = c(34, 68), asp = 1)

Continents

# Load continents
data(continents)

# Plot map of Africa
plot(continents[1])

Islands

# Load large islands data
data(largeislands)

# Plot map of Mauritius
plot(sf::st_geometry(largeislands[largeislands$Island=="Mauritius",]))

# Load small islands data
data(smallislands)

# Plot map of Maldives (islands smaller than 1 km2)
maldives <- smallislands[grep(smallislands$NAME_ENGLI, pattern="Maldives"),]
plot(sf::st_geometry(maldives))

Functions

The ggmap2 package currently contains the following functions:

• ggmap2, a function to easily create nice maps
• climograph, a function to automatically produce a climograph of any region in the world.
• distanceToCoast, a function to calculate the distance to coast/shore.
• getData2, this is a copy of the getData function from the raster package, but with some very helpful additional data resources (i.e. GEBCO, Bio-Oracle, MarSpec, WDPA, gimms3g) implemented.
• downloadNDVI, a function for downloading the GIMMS3g ndvi data.
• ndvi3g, a function to read the GIMMS3g ndvi data, which was downloaded manually or using the downloadNDVI function.
• ggRegression, a function to produce maps of the slope, R², Adjusted R² and p-Value of point-wise linear model based on a time-series raster object.

ggmap2

ggmap2 is a function to create global or regional maps, either standalone or combining multiple plots into a facet or grid. In addition, it automatically adds the outline of the world's land mass to each plot, if country is specified as TRUE.

# Load raster library
library(raster)

# Create data
set.seed(54321)
r <- raster(nrow=36, ncol=72)
s1 <- stack(lapply(1:2, function(i) setValues(r, rnorm(ncell(r), 0, 1))))
s2 <- stack(lapply(1:5, function(i) setValues(r, rnorm(ncell(r), i, 3))))

# Turn raster into data.frame
#df <- as.data.frame(rasterToPoints(s2))

# Create single plot
ggmap2(s1[[1]], name="Single", split=FALSE, ncol=1, country=TRUE)

# Also works if you use a data.frame as input
#ggmap2(df[,c(1,2,3)], name="Single", split=FALSE, ncol=1, country=TRUE)

ggmap2(s1[[1]], name="Single", split=FALSE, ncol=1, extent=c(-11, 43, 34, 68))
# Same as df[,c(1,2,3,4)] as data input

ggmap2(s1, name="Facet", split=FALSE, ncol=1, country=FALSE)
# Same as df[,c(1,2,3,4)] as data input

# Create multiplot with grid.draw
# This requires that the number of plots is dividable by the number of columns
ggmap2(s2[[1:4]], name=c("Split", "Split_Lon", "Sp_Sh", "Sp_Mid"), 
       split=TRUE, ncol=2, country=FALSE)
# Also works using df[,c(1,2,3,4,5,6)] as data input

# Create multiplot with grid.arrange
ggmap2(s2, name=c("Split1", "Split2", "Split3", "Split4", "Split5"), 
       split=TRUE, ncol=2, country=FALSE)
# Also works using df as input

# Create multiplot with one column
ggmap2(s2[[1:2]], name=c("Split", "Sp_Sh"), split=TRUE, ncol=1, country=FALSE)
# Also works with df[,c(1,2,3,5)] as data input

climograph

climograph is a function to plot the monthly temperature and precipitation of a given location.

climograph(lon=39.5, lat=40.5, res=10, path="/home/matt/Documents/", save=FALSE)

getData2

getData2("WDPA", country="LIE")

downloadNDVI & ndvi3g

Download global GIMMS ndvi3g data and load it into R

# Download data
#downloadNDVI(startyear=1981, endyear=1990, version="v0", path="/home/matt/Documents/GIMMS")

# Read downloaded data
gimms3g_v0_bav <- ndvi3g(extent = c(8, 14, 47, 51), version="v0", startyear=1981, endyear=2011, 
                         path="/home/matt/Documents/GIMMS")

# Mask by outline of bavaria
library(bavDC)
data(bavaria)
gimms3g_v0_bav <- raster::mask(gimms3g_v0_bav, bavaria)

Create animation of GIMMS ndvi3g data

# Plot data
library(sf)
for(x in 1:nlayers(gimms3g_v0_bav)){
  raster::plot(gimms3g_v0_bav[[x]], breaks=seq(0, 1, by=0.1), col=rev(terrain.colors(10)), 
               main=zoo::as.yearmon(seq(as.Date('1981-01-15'), as.Date('2011-12-15'), 'month'))[x])
  plot(sf::st_geometry(bavaria), add=T)
  Sys.sleep(0.02)
}

ggRegression

Perform a point-wise regression on a time-series raster stack and plot maps of Slope, R2, Adjusted R2 and p-value.

ggRegression(data=gimms3g_v0_bav)

RS-eco/ggmap2 documentation built on Dec. 7, 2022, 2:02 a.m.