Date: Thu, 28 Mar 2024 20:11:00 +0000 (UTC)
Message-ID: <2112915858.3.1711656660858@26c3e9866691>
Subject: Exported From Confluence
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To explore the cell values of raster layers several types of graphical s=
ummaries can be used, such as boxplots, and density plots. Scatter plots ca=
n be used to explore the relationships between the values of different rast=
er layers.
EXAMPLES
Below examples of the generartion graphical summaries of the values and =
relationships between raster layers in R are presented. These examples are =
taken from the =E2=80=9CYasi Effects on Green Cover at Mission Beach=E2=80=
=9D tutorial. Putting the code snipp=
ets in context, by looking at a broader section of the R script, could be b=
enefitial. Boxes with grey ba=
ckground contain code snippets, and boxes with white background containt code (text) outputs.
We create boxplots and scatter plots (with loess) using the 'bwplot' and=
'splom ' functions in the 'rasterVis' package. To create density plots we =
use two methods: (1) using the 'densityplot' function in the 'rasterVis' pa=
ckage, and (2) the 'ggplot' function in the 'ggplot2' package. The latter a=
pproach produce nicer graphs, but requires more elaborated coding. Finally,=
we use the 'grid.arrange' function in the 'gridExtra' package to plot=
multiple grobs (i.e. grid graphical objects) on a page (i.e. in =
a single graph).
Boxplots and scatter plots
The 'bwplot' and 'splom' functions require a RasterStack or&nb=
sp;RasterBrick object. In our example we already have a rasterBrick wi=
th raw (in %) values (i.e. for 2010-Winter and 2011-Winter). However, =
we need to create an analogous rasterBrick with standardised (i.e. in =
SDs) values.
#=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
SPGC.StudyArea.Std2010q3_2011q3.rb =3D brick(normImage(SPGC.StudyArea.2010q=
3.rl), normImage(SPGC.StudyArea.2011q3.rl))
names(SPGC.StudyArea.Std2010q3_2011q3.rb) =3D c("SPGC.SA.Std2010q3", "SPGC.SA.Std2011q3") =20
SPGC.StudyArea.Std2010q3_2011q3.rb
## class : RasterBrick=20
## dimensions : 295, 206, 60770, 2 (nrow, ncol, ncell, nlayers)
## resolution : 30, 30 (x, y)
## extent : 1484025, 1490205, -1996985, -1988135 (xmin, xmax, ymin, y=
max)
## coord. ref. : +proj=3Daea +lat_1=3D-18 +lat_2=3D-36 +lat_0=3D0 +lon_0=3D=
132 +x_0=3D0 +y_0=3D0 +ellps=3DGRS80 +units=3Dm +no_defs=20
## data source : in memory
## names : SPGC.SA.Std2010q3, SPGC.SA.Std2011q3=20
## min values : -9.645614, -10.521245=20
## max values : 3.048260, 2.860489
#------------------------------------------------------=
----------------------------
RawSPGC.IndvYrs.bwplot =3D bwplot(SPGC.StudyArea.2010q3_2011q3.rb, main=3D<=
span data-colorid=3D"n2xx05al1m" class=3D"hljs-string">"Raw SPGCs")
StdSPGC.IndvYrs.bwplot =3D bwplot(SPGC.StudyArea.Std2010q3_2011q3.rb, main=
=3D"Standardised SP=
GCs")
RawSPGC.IndvYrs.splot =3D splom(SPGC.StudyArea.2010q3_2011q3.rb, plot.loess=
=3DTRUE, xl=
ab=3D'')
StdSPGC.IndvYrs.splot =3D splom(SPGC.StudyArea.Std2010q3_2011q3.rb, main=3D=
"Standardised SPGCs=
", plot.loess=3DTRUE, xlab=3D'')
grid.arrange( RawSPGC.IndvYrs.bwplot, StdSPGC.IndvYrs.bwplot,=20
RawSPGC.IndvYrs.splot, StdSPGC.IndvYrs.splot,
nrow=3D2, top=3D"=
SPGC - Individuals Years" )
Density plots
Method 1: Using =E2=80=98densityplot=E2=80=99 from the =E2=80=98rast=
erVis=E2=80=99 package
#------------------------------------------------------=
---------------------------
Method 2: Using =E2=80=98ggplot=E2=80=99 from the =E2=80=98ggplot2=
=E2=80=99 package
To create the the density plot using the 'ggplot' function, we first nee=
d to convert the raster cell values to a vector of values (i.e. a vari=
able) using the 'values' function in the 'raster' package. Then we combine =
all variables into a data frame. Finally, convert the wide format data fram=
e to long format (i.e. combining both year values in the same column w=
ith an additional column/variable containing the year-season), as this form=
at is required in the arguments of the 'ggplot' function.
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<=
/span>
RawSPGC.2010q3 =3D values(SPGC.StudyArea.2010q3.rl)
RawSPGC.2011q3 =3D values(SPGC.StudyArea.2011q3.rl)
RawSPGC.Diff =3D values(SPGC.StudyArea.Diffq3.rl)
StdSPGC.2010q3 =3D values(normImage(SPGC.StudyArea.2010q3.rl))
StdSPGC.2011q3 =3D values(normImage(SPGC.StudyArea.2011q3.rl))
StdSPGC.Diff =3D values(SPGC.StudyArea.StdDiffq3.rl)
SPGCs.df =3D data.frame( RawSPGC.2010q3, RawSPGC.2011q3, RawSPGC.Diff,=20
StdSPGC.2010q3, StdSPGC.2011q3, StdSPGC.Diff )
=20
RawSPGC.IndvYrs.dflf =3D melt(SPGCs.df[c("RawSPGC.2010q3","RawSPGC.2011q3")])
RawSPGC.IndvYrs.dp2 =3D ggplot(RawSPGC.IndvYrs.dflf, aes(x=3Dvalue, fill=3D=
variable)) +
geom_density(alpha=3D0.25)
=20
RawSPGC.Diff.dp2 =3D ggplot(SPGCs.df, aes(x=3DRawSPGC.Diff)) +=20
geom_density(alpha=3D0.25,color=3D"darkgreen", fill=3D"lightgreen")
=20
StdSPGC.IndvYrs.dflf =3D melt(SPGCs.df[c("StdSPGC.2010q3","StdSPGC.2011q3")])
StdSPGC.IndvYrs.dp2 =3D ggplot(StdSPGC.IndvYrs.dflf, aes(x=3Dvalue, fill=3D=
variable)) +
geom_density(alpha=3D0.25)
=20
StdSPGC.Diff.dp2 =3D ggplot(SPGCs.df, aes(x=3DStdSPGC.Diff)) +=20
geom_density(alpha=3D0.25,color=3D"darkgreen", fill=3D"lightgreen")
=20
grid.arrange( RawSPGC.IndvYrs.dp2, RawSPGC.Diff.dp2, StdSPGC.IndvYrs.dp2, =
StdSPGC.Diff.dp2,
nrow=3D2, top=3D"=
SPGC: Individuals Years & Change" )
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