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‎converstion_and_excerises‎

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@@ -22,6 +22,7 @@ TASK ONE:
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7. You can perform trend analysis or any type of time series analysis you would like.
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TASK TWO:
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2D maps
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‎images/Variables.png‎

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‎images/matrix_raster_terra.png‎

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‎index.adoc‎

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@@ -196,6 +196,7 @@ considered. Examples, atmospheric data, oceanic data, and soil profiles.
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NOTE: All these dimensions can additionally include the time axis.
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== Variables and constants
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image:images/Variables.png[height=10]
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* Variables are used to store data, which can be changed afterwards
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* The name given to a variable is known as _identifier_
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* Will go in more detail with `ggplot2` -> allows more modifications
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== Exercise I
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. Pick a location (i.e. longitude and latitude), where you want to apply your analysis.
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. List all netCDF files (except files in `final task` folder) using `list.files.` Check the options `full.names` & `recursive`
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. Loop over the listed files and use the function `get_ts_as_csv` to get your csv files (i.e. time series)
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. Load CSV files of your choice and:
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.. Plot different types of plots
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.. Run some statistical tests.
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.. Explore the climate conditions of your area
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. You may do some aggregation, e.g., monthly, seasonally, and annually
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. You can perform trend analysis or any time series analysis you would like.
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[IMPORTANT]
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.Climate Variables:
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====
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. sfcWind -> Surface wind [m/s]
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. pr -> Precipitation [kg m-2 s-1]
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. tas -> Surface temperature [k]
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====
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== Tidyverse
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[quote, tidiverse.org]
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* There is a great amount of packages to work with spatial data
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* Might not be as user friendly as QGIS, but really pays off to learn
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* Packages needed:
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** `rgdal`
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** `raster`
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** `sp`
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** `terra`
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** `sf`
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* Some of those packages need installation of other software outside of R
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** This might be time consuming...
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--
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[source,R]
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----
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library(raster)
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library(terra)
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# Creating a raster from a matrix
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r1 <- raster(matrix(rnorm(19*13), nrow = 19),
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xmn=5, xmx=15, ymn=-5, ymx=10,
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crs = sp::CRS(SRS_string = "EPSG:4326"))
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r1 <- rast(matrix(rnorm(19*13), nrow = 19), crs = "EPSG:4326")
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# define extent
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ext(r1)<-c(xmin=5, xmax=15, ymin=-5, ymax=10)
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r1
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class : RasterLayer
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dimensions : 19, 13, 247 (nrow, ncol, ncell)
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resolution : 0.7692308, 0.7894737 (x, y)
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extent : 5, 15, -5, 10 (xmin, xmax, ymin, ymax)
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crs : +proj=longlat +datum=WGS84 +no_defs
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source : memory
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names : layer
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values : -2.990121, 2.558331 (min, max)
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class : SpatRaster
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dimensions : 19, 13, 1 (nrow, ncol, nlyr)
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resolution : 0.7692308, 0.7894737 (x, y)
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extent : 5, 15, -5, 10 (xmin, xmax, ymin, ymax)
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coord. ref. : lon/lat WGS 84 (EPSG:4326)
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source : memory
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name : lyr.1
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min value : -2.777259
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max value : 2.850702
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plot(r1, main = "Raster made from a matrix")
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# Plot the center of the pixels
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points(coordinates(r1), pch=3, cex=0.5)
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points(crds(r1), pch=3, cex=0.5)
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----
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* For other sources check `?raster`
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* For other sources check `?terra`
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--
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[.column]
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--
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image::matrix_raster.png[height=900]
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image::matrix_raster_terra.png[height=900]
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--
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=== Read raster data

‎removed_slides.adoc‎

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== Spatial data in image:r-seeklogo.svg[height=45]
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[%step]
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* There is a great amount of packages to work with spatial data
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* Might not be as user friendly as QGIS, but really pays off to learn
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* Packages needed:
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** `rgdal`
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** `raster`
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** `sp`
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** `sf`
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* Some of those packages need installation of other software outside of R
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** This might be time consuming...
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* Both _vector_ and _raster_ data can be:
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** Read to R
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** Modified
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** Created from scratch
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** Saved into desired format
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[.columns.is-vcentered]
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== _Rasters_
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[.column]
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--
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[source,R]
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----
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library(raster)
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# Creating a raster from a matrix
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r1 <- raster(matrix(rnorm(19*13), nrow = 19),
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xmn=5, xmx=15, ymn=-5, ymx=10,
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crs = sp::CRS(SRS_string = "EPSG:4326"))
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r1
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class : RasterLayer
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dimensions : 19, 13, 247 (nrow, ncol, ncell)
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resolution : 0.7692308, 0.7894737 (x, y)
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extent : 5, 15, -5, 10 (xmin, xmax, ymin, ymax)
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crs : +proj=longlat +datum=WGS84 +no_defs
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source : memory
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names : layer
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values : -2.990121, 2.558331 (min, max)
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plot(r1, main = "Raster made from a matrix")
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# Plot the center of the pixels
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points(coordinates(r1), pch=3, cex=0.5)
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----
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* For other sources check `?raster`
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--
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[.column]
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--
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image::matrix_raster.png[height=900]
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--
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=== Read raster data
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[source,R]
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----
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# Run these 4 lines in this order to install the "hires" version of "rnaturalearth"
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install.packages("Rtools")
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install.packages("devtools")
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devtools::install_github("ropenscilabs/rnaturalearth")
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devtools::install_github("ropenscilabs/rnaturalearthhires")
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library(sf)
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library(raster)
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library(rgdal)
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library(rnaturalearth)
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setwd("/home/dqc/Documents/PhD/Students/R_course/FRM/spatial/")
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de_dem <- raster("deutschland_dgm.asc")
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crs(de_dem) <- sp::CRS(SRS_string = "ESRI:31494")
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print(de_dem)
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class : RasterLayer
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dimensions : 910, 720, 655200 (nrow, ncol, ncell)
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resolution : 1000, 1000 (x, y)
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extent : 4030000, 4750000, 5230000, 6140000 (xmin, xmax, ymin, ymax)
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crs : +proj=tmerc +lat_0=0 +lon_0=12 +k=1 +x_0=4500000 +y_0=0 +ellps=bessel +units=m +no_defs
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source : /mnt/Linux/Documents/PhD/Data/Sachsen/deutschland_dgm.asc
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names : deutschland_dgm
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----

‎replace_rast.R‎

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source : deutschland_dgm.asc
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name : deutschland_dgm
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min value : -178.46
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max value : 2770.35
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max value : 2770.35
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# PDF slide 87 ------------------------------------------------------------
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sqrt(de_dem)
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class : SpatRaster
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dimensions : 910, 720, 1 (nrow, ncol, nlyr)
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resolution : 1000, 1000 (x, y)
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extent : 4030000, 4750000, 5230000, 6140000 (xmin, xmax, ymin, ymax)
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coord. ref. : Germany_Zone_4 (ESRI:31494)
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source : memory
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name : deutschland_dgm
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min value : 0.00000
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max value : 52.63412
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de_dem + de_dem*4 # Need to have same dimensions
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class : SpatRaster
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dimensions : 910, 720, 1 (nrow, ncol, nlyr)
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resolution : 1000, 1000 (x, y)
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extent : 4030000, 4750000, 5230000, 6140000 (xmin, xmax, ymin, ymax)
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coord. ref. : Germany_Zone_4 (ESRI:31494)
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source : memory
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name : deutschland_dgm
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min value : -892.30
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max value : 13851.75
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# PDF slide 88------------------------------------------------------------
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par(mfrow=c(1,3))
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terra::hist(de_dem, main="Distribution of elevation \n values",
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breaks=40,maxcell=1000000)
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terra::boxplot(de_dem, ylab= "Elevation", main = "Boxplot")
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terra::plot(de_dem, main = "Basic plot",
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col = RColorBrewer::brewer.pal(7, "BrBG"),
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range = c(0, 2500),
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ylim = ext(de_dem)[c(3,4)])
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list.files()

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