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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | Spatial data in R: Using R as a GIS 3 | ======================================================== 4 | 5 | 6 | A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps. 7 | 8 | 9 | [Francisco Rodriguez-Sanchez](http://sites.google.com/site/rodriguezsanchezf) 10 | 11 | 12 | v 2.2 13 | 14 | 27-01-2015 15 | 16 | Licence: [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/) 17 | 18 | 19 | Check out latest version at [http://pakillo.github.io/R-GIS-tutorial](http://pakillo.github.io/R-GIS-tutorial) 20 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | 36.1.-5.65.png 5 | 36.1.-5.65.png.rda 6 | 53.086.-2.31.png 7 | 53.086.-2.31.png.rda 8 | ESP2_msk_alt.grd 9 | ESP2_msk_alt.gri 10 | ESP2_msk_alt.vrt 11 | ESP_msk_alt.grd 12 | ESP_msk_alt.gri 13 | ESP_msk_alt.vrt 14 | France.gfw 15 | France.gif 16 | France.prj 17 | G_2013-02-25_185157_55492.kmz 18 | R-GIS tutorial.Rproj 19 | R-GIS_tutorial.html 20 | README.html 21 | gmap.png 22 | gmap.png.rda 23 | locsgb.dbf 24 | locsgb.kml 25 | locsgb.shp 26 | locsgb.shx 27 | newmap.png 28 | newmap.png.rda 29 | newmap2.png 30 | newmap2.png.rda 31 | newmap3.png 32 | newmap3.png.rda 33 | tmin.all.grd 34 | tmin.all.gri 35 | tmin1.c.grd 36 | tmin1.c.gri 37 | tmin1.kmz 38 | wc10 39 | BES_MacroEcology_SpatialRFlyer.jpg 40 | G_2013-12-18_150958_77388.kmz 41 | G_2013-12-19_012141_82937.kmz 42 | G_2013-12-19_123019_21910.kmz 43 | -------------------------------------------------------------------------------- /R-GIS_tutorial.Rmd: -------------------------------------------------------------------------------- 1 | 2 | Spatial data in R: Using R as a GIS 3 | ======================================================== 4 | 5 | 6 | A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps. 7 | 8 | 9 | [Francisco Rodriguez-Sanchez](http://sites.google.com/site/rodriguezsanchezf) 10 | 11 | 12 | v 2.2 13 | 14 | 27-01-2015 15 | 16 | Licence: [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/) 17 | 18 | 19 | Check out code and latest version at [GitHub](https://github.com/Pakillo/R-GIS-tutorial/blob/master/R-GIS_tutorial.md) 20 | 21 | 22 | 23 |
24 |
25 |
26 | 27 | CONTENTS 28 | ========= 29 | 30 |
31 |
32 | 33 | [1. INTRODUCTION](#intro) 34 |
35 |
36 | 37 | 38 | [2. GENERIC MAPPING](#mapping) 39 |
40 | 41 | * [Retrieving base maps from Google with `gmap` function in package `dismo`](#gmap) 42 | 43 | * [`RgoogleMaps`: Map your data onto Google Map tiles](#rgooglemaps) 44 | 45 | * [`googleVis`: visualise data in a web browser using Google Visualisation API](#googlevis) 46 | 47 | * [`RWorldMap`: mapping global data](#rworldmap) 48 |
49 |
50 | 51 | 52 | [3. SPATIAL VECTOR DATA (points, lines, polygons)](#vector) 53 |
54 | 55 | * [Example dataset: retrieve point occurrence data from GBIF](#gbif) 56 | 57 | * [Making data 'spatial'](#spatial) 58 | 59 | * [Define spatial projection](#projection) 60 | 61 | * [Quickly plotting point data on a map](#plot) 62 | 63 | * [Subsetting and mapping again](#subset) 64 | 65 | * [Mapping vectorial data (points, polygons, polylines)](#mapvector) 66 | 67 | * [Drawing polygons and polylines (e.g. for digitising)](#digitise) 68 | 69 | * [Converting between formats, reading in, and saving spatial vector data](#iovec) 70 | 71 | * [Changing projection of spatial vector data](#changeproj) 72 |
73 |
74 | 75 | 76 | [4. USING RASTER (GRID) DATA](#raster) 77 |
78 | 79 | * [Downloading raster climate data from internet](#getdata) 80 | 81 | * [Loading a raster layer](#loadraster) 82 | 83 | * [Creating a raster stack](#rasterstack) 84 | 85 | * [Raster bricks](#rasterbrick) 86 | 87 | * [Crop rasters](#cropraster) 88 | 89 | * [Define spatial projection of the rasters](#projectionraster) 90 | 91 | * [Changing projection](#changeprojraster) 92 | 93 | * [Plotting raster data](#plotraster) 94 | 95 | * [Spatial autocorrelation](#autocorrelation) 96 | 97 | * [Extract values from raster](#extract) 98 | 99 | * [Rasterize vector data (points, lines or polygons)](#rasterize) 100 | 101 | * [Changing raster resolution](#resolution) 102 | 103 | * [Spline interpolation](#interpolation) 104 | 105 | * [Setting all rasters to the same extent, projection and resolution all in one](#spatialsync) 106 | 107 | * [Elevations, slope, aspect, etc](#elevation) 108 | 109 | * [Saving and exporting raster data](#saveraster) 110 |
111 |
112 | 113 | 114 | 115 | 116 | [5. SPATIAL STATISTICS](#spatstats) 117 |
118 | 119 | * [Point pattern analysis](#pointpatterns) 120 | 121 | * [Geostatistics](#geostatistics) 122 |
123 |
124 | 125 | 126 | 127 | [6. INTERACTING WITH OTHER GIS](#othergis) 128 |
129 |
130 | 131 | [7. OTHER USEFUL PACKAGES](#otherpackages) 132 |
133 |
134 | 135 | [8. TO LEARN MORE](#tolearnmore) 136 | 137 | 138 |
139 |
140 |
141 |
142 | 143 | 144 | 145 | 146 | 147 | 1. INTRODUCTION 148 | =============== 149 | 150 |
151 | 152 | R is great not only for doing statistics, but also for many other tasks, including GIS analysis and working with spatial data. For instance, R is capable of doing wonderful maps such as [this](http://spatialanalysis.co.uk/wp-content/uploads/2012/02/bike_ggplot.png) or [this](http://oscarperpinan.github.io/spacetime-vis/images/airMadrid_stamen.png). In this tutorial I will show some basic GIS functionality in R. 153 | 154 | 155 | 156 | #### Basic packages 157 | 158 | ```{r message=FALSE} 159 | 160 | library(sp) # classes for spatial data 161 | library(raster) # grids, rasters 162 | library(rasterVis) # raster visualisation 163 | library(maptools) 164 | library(rgeos) 165 | # and their dependencies 166 | ``` 167 | 168 | There are many other useful packages, e.g. check [CRAN Spatial Task View](http://cran.r-project.org/web/views/Spatial.html). Some of them will be used below. 169 | 170 |
171 | [Back to Contents](#contents) 172 |
173 |
174 |
175 |
176 | 177 | 178 | 179 | 2. GENERIC MAPPING 180 | ================== 181 | 182 |
183 | 184 | 185 | Retrieving base maps from Google with `gmap` function in package `dismo` 186 | ------------------------------------------------------------------------ 187 | 188 | Some examples: 189 | 190 | Getting maps for countries: 191 | 192 | ```{r gmap1, message=FALSE} 193 | 194 | library(dismo) 195 | 196 | mymap <- gmap("France") # choose whatever country 197 | plot(mymap) 198 | ``` 199 | 200 | Choose map type: 201 | ```{r gmap2, message=FALSE} 202 | mymap <- gmap("France", type="satellite") 203 | plot(mymap) 204 | ``` 205 | 206 | Choose zoom level: 207 | ```{r gmap3, message=FALSE} 208 | mymap <- gmap("France", type="satellite", exp=3) 209 | plot(mymap) 210 | ``` 211 | 212 | Save the map as a file in your working directory for future use 213 | ```{r message=FALSE} 214 | mymap <- gmap("France", type="satellite", filename="France.gmap") 215 | ``` 216 | 217 | 218 | Now get a map for a region drawn at hand 219 | 220 | ```{r eval=FALSE} 221 | 222 | mymap <- gmap("Europe") 223 | plot(mymap) 224 | 225 | select.area <- drawExtent() 226 | # now click 2 times on the map to select your region 227 | mymap <- gmap(select.area) 228 | plot(mymap) 229 | # See ?gmap for many other possibilities 230 | ``` 231 | 232 | 233 | 234 |
235 |
236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | `RgoogleMaps`: Map your data onto Google Map tiles 245 | ------------------------------------------------ 246 | 247 | ```{r message=FALSE, results='hide'} 248 | library(RgoogleMaps) 249 | ``` 250 | 251 | 252 | Get base maps from Google (a file will be saved in your working directory) 253 | ```{r message=FALSE, results='hide'} 254 | newmap <- GetMap(center=c(36.7,-5.9), zoom =10, destfile = "newmap.png", maptype = "satellite") 255 | 256 | # Now using bounding box instead of center coordinates: 257 | newmap2 <- GetMap.bbox(lonR=c(-5, -6), latR=c(36, 37), destfile = "newmap2.png", maptype="terrain") 258 | 259 | # Try different maptypes 260 | newmap3 <- GetMap.bbox(lonR=c(-5, -6), latR=c(36, 37), destfile = "newmap3.png", maptype="satellite") 261 | ``` 262 | 263 | 264 | Now plot data onto these maps, e.g. these 3 points 265 | ```{r} 266 | PlotOnStaticMap(lat = c(36.3, 35.8, 36.4), lon = c(-5.5, -5.6, -5.8), zoom= 10, 267 | cex=4, pch= 19, col="red", FUN = points, add=F) 268 | 269 | ``` 270 | 271 | 272 | 273 | 274 | 275 | 276 |
277 |
278 | 279 | 280 | 281 | 282 | 283 | `googleVis`: visualise data in a web browser using Google Visualisation API 284 | --------------------------------------------------------------------------- 285 | 286 | ```{r message=FALSE} 287 | library(googleVis) 288 | ``` 289 | 290 | Run `demo(googleVis)` to see all the possibilities 291 | 292 | 293 | ```{r setOptions, echo=FALSE} 294 | op <- options(gvis.plot.tag = "chart") 295 | # necessary so that googleVis works with knitr, see http://lamages.blogspot.co.uk/2012/10/googlevis-032-is-released-better.html 296 | ``` 297 | 298 |
299 | 300 | ### Example: plot country-level data 301 | ```{r results='asis', tidy=FALSE, eval=TRUE} 302 | data(Exports) # a simple data frame 303 | Geo <- gvisGeoMap(Exports, locationvar="Country", numvar="Profit", 304 | options=list(height=400, dataMode='regions')) 305 | plot(Geo) 306 | ``` 307 | Using `print(Geo)` we can get the HTML code to embed the map in a web page! 308 | 309 |
310 | 311 | ### Example: Plotting point data onto a google map (internet) 312 | ```{r results='asis', tidy=FALSE, eval=TRUE} 313 | data(Andrew) 314 | M1 <- gvisMap(Andrew, "LatLong", "Tip", 315 | options=list(showTip=TRUE, showLine=F, enableScrollWheel=TRUE, 316 | mapType='satellite', useMapTypeControl=TRUE, width=800,height=400)) 317 | plot(M1) 318 | ``` 319 | 320 | 321 | 322 | 323 |
324 |
325 | 326 | 327 | 328 | 329 | 330 | `RWorldMap`: mapping global data 331 | -------------------------------- 332 | 333 | Some examples 334 | 335 | ```{r message=FALSE, warning=FALSE} 336 | 337 | library(rworldmap) 338 | 339 | newmap <- getMap(resolution="coarse") # different resolutions available 340 | plot(newmap) 341 | ``` 342 | 343 | ```{r message=FALSE} 344 | mapCountryData() 345 | ``` 346 | ```{r message=FALSE} 347 | mapCountryData(mapRegion="europe") 348 | ``` 349 | ```{r message=FALSE} 350 | mapGriddedData() 351 | ``` 352 | ```{r message=FALSE} 353 | mapGriddedData(mapRegion="europe") 354 | 355 | ``` 356 | 357 | 358 | 359 | 360 |
361 | [Back to Contents](#contents) 362 |
363 |
364 |
365 |
366 |
367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 3. SPATIAL VECTOR DATA (points, lines, polygons) 379 | ================================================ 380 | 381 |
382 |
383 | 384 | ### Example dataset: retrieve point occurrence data from GBIF 385 | 386 | Let's create an example dataset: retrieve occurrence data 387 | for the laurel tree (Laurus nobilis) from the 388 | [Global Biodiversity Information Facility (GBIF)](http://gbif.org) 389 | ```{r message=FALSE} 390 | library(dismo) # check also the nice "rgbif" package! 391 | laurus <- gbif("Laurus", "nobilis") 392 | # get data frame with spatial coordinates (points) 393 | locs <- subset(laurus, select=c("country", "lat", "lon")) 394 | head(locs) # a simple data frame with coordinates 395 | 396 | # Discard data with errors in coordinates: 397 | locs <- subset(locs, locs$lat<90) 398 | ``` 399 | 400 |
401 | 402 | ### Making data 'spatial' 403 | 404 | So we have got a simple dataframe containing spatial coordinates. 405 | Let's make these data explicitly *spatial* 406 | ```{r} 407 | coordinates(locs) <- c("lon", "lat") # set spatial coordinates 408 | plot(locs) 409 | ``` 410 | 411 | 412 | ### Define spatial projection 413 | 414 | Important: define geographical projection. 415 | Consult the appropriate PROJ.4 description here: 416 | [http://www.spatialreference.org/](http://www.spatialreference.org/) 417 | ```{r} 418 | crs.geo <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84") # geographical, datum WGS84 419 | proj4string(locs) <- crs.geo # define projection system of our data 420 | summary(locs) 421 | ``` 422 | 423 |
424 | 425 | ### Quickly plotting point data on a map 426 | ```{r} 427 | plot(locs, pch=20, col="steelblue") 428 | library(rworldmap) 429 | # library rworldmap provides different types of global maps, e.g: 430 | data(coastsCoarse) 431 | data(countriesLow) 432 | plot(coastsCoarse, add=T) 433 | ``` 434 | 435 | 436 | ### Subsetting and mapping again 437 | ```{r} 438 | table(locs$country) # see localities of Laurus nobilis by country 439 | 440 | locs.gb <- subset(locs, locs$country=="United Kingdom") # select only locs in UK 441 | plot(locs.gb, pch=20, cex=2, col="steelblue") 442 | title("Laurus nobilis occurrences in UK") 443 | plot(countriesLow, add=T) 444 | summary(locs.gb) 445 | 446 | ``` 447 | 448 | 449 |
450 | 451 | Mapping vectorial data (points, polygons, polylines) 452 | --------------------------------------------------------------------- 453 | 454 |
455 | 456 | ### Mapping vectorial data using `gmap` from `dismo` 457 | 458 | 459 | ```{r} 460 | gbmap <- gmap(locs.gb, type="satellite") 461 | locs.gb.merc <- Mercator(locs.gb) # Google Maps are in Mercator projection. 462 | # This function projects the points to that projection to enable mapping 463 | plot(gbmap) 464 | points(locs.gb.merc, pch=20, col="red") 465 | ``` 466 | 467 | 468 | 469 | 470 | 471 |
472 | 473 | ### Mapping vectorial data with `RgoogleMaps` 474 | 475 | 476 | ```{r message=FALSE} 477 | 478 | require(RgoogleMaps) 479 | 480 | locs.gb.coords <- as.data.frame(coordinates(locs.gb)) # retrieves coordinates 481 | # (1st column for longitude, 2nd column for latitude) 482 | PlotOnStaticMap(lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, 483 | zoom= 5, cex=1.4, pch= 19, col="red", FUN = points, add=F) 484 | ``` 485 | 486 | Download base map from Google Maps and plot onto it 487 | ```{r message=FALSE} 488 | map.lim <- qbbox (locs.gb.coords$lat, locs.gb.coords$lon, TYPE="all") # define region 489 | # of interest (bounding box) 490 | mymap <- GetMap.bbox(map.lim$lonR, map.lim$latR, destfile = "gmap.png", maptype="satellite") 491 | # see the file in the wd 492 | PlotOnStaticMap(mymap, lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, 493 | zoom= NULL, cex=1.3, pch= 19, col="red", FUN = points, add=F) 494 | ``` 495 | 496 |
497 |
498 | 499 | Using different background (base map) 500 | ```{r message=FALSE} 501 | mymap <- GetMap.bbox(map.lim$lonR, map.lim$latR, destfile = "gmap.png", maptype="hybrid") 502 | PlotOnStaticMap(mymap, lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, 503 | zoom= NULL, cex=1.3, pch= 19, col="red", FUN = points, add=F) 504 | 505 | ``` 506 | 507 | 508 | 509 | 510 | 511 | 512 | 513 | 514 |
515 |
516 | 517 | ### Map vectorial data with `googleVis` (internet) 518 | 519 | 520 | ```{r results='asis', tidy=FALSE, eval=TRUE} 521 | points.gb <- as.data.frame(locs.gb) 522 | points.gb$latlon <- paste(points.gb$lat, points.gb$lon, sep=":") 523 | map.gb <- gvisMap(points.gb, locationvar="latlon", tipvar="country", 524 | options = list(showTip=T, showLine=F, enableScrollWheel=TRUE, 525 | useMapTypeControl=T, width=1400,height=800)) 526 | plot(map.gb) 527 | #print(map.gb) # get HTML suitable for a webpage 528 | ``` 529 | 530 | 531 | 532 | 533 |
534 |
535 |
536 | 537 | ### Drawing polygons and polylines (e.g. for digitising) 538 | 539 | 540 | ```{r eval=FALSE} 541 | plot(gbmap) 542 | mypolygon <- drawPoly() # click on the map to draw a polygon and press ESC when finished 543 | summary(mypolygon) # now you have a spatial polygon! Easy, isn't it? 544 | ``` 545 | 546 | 547 |
548 |
549 |
550 |
551 | 552 | Converting between formats, reading in, and saving spatial vector data 553 | ------------------------------------------------------------------- 554 | 555 |
556 | 557 | ### Exporting KML (Google Earth) 558 | ```{r} 559 | writeOGR(locs.gb, dsn="locsgb.kml", layer="locs.gb", driver="KML") 560 | ``` 561 | 562 | ### Reading KML 563 | ```{r} 564 | newmap <- readOGR("locsgb.kml", layer="locs.gb") 565 | ``` 566 | 567 | ### Save as shapefile 568 | ```{r} 569 | writePointsShape(locs.gb, "locsgb") 570 | ``` 571 | 572 | ### Reading shapefiles 573 | ```{r} 574 | gb.shape <- readShapePoints("locsgb.shp") 575 | plot(gb.shape) 576 | ``` 577 | Use `readShapePoly` to read polygon shapefiles, and `readShapeLines` to read polylines. 578 | See also `shapefile` in `raster` package. 579 | 580 | 581 | 582 |
583 |
584 |
585 | 586 | Changing projection of spatial vector data 587 | ------------------------------------------- 588 | 589 | `spTransform` (package `sp`) will do the projection as long as the original and new projection are correctly specified. 590 | 591 |
592 | 593 | ### Projecting point dataset 594 | 595 | To illustrate, let's project the dataframe with Laurus nobilis coordinates that we obtained above: 596 | ```{r} 597 | summary(locs) 598 | ``` 599 | The original coordinates are in lat lon format. Let's define the new desired projection: 600 | Lambert Azimuthal Equal Area in this case 601 | (look up parameters at [http://spatialreference.org](http://spatialreference.org)) 602 | ```{r} 603 | crs.laea <- CRS("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs") # Lambert Azimuthal Equal Area 604 | locs.laea <- spTransform(locs, crs.laea) # spTransform makes the projection 605 | ``` 606 | 607 |
608 | 609 | ### Projecting shapefile of countries 610 | ```{r} 611 | plot(countriesLow) # countries map in geographical projection 612 | country.laea <- spTransform(countriesLow, crs.laea) # project 613 | ``` 614 | 615 | Let's plot this: 616 | ```{r} 617 | plot(locs.laea, pch=20, col="steelblue") 618 | plot(country.laea, add=T) 619 | 620 | # define spatial limits for plotting 621 | plot(locs.laea, pch=20, col="steelblue", xlim=c(1800000, 3900000), ylim=c(1000000, 3000000)) 622 | plot(country.laea, add=T) 623 | ``` 624 | 625 | 626 | 627 | 628 |
629 | [Back to Contents](#contents) 630 |
631 |
632 |
633 |
634 |
635 |
636 | 637 | 638 | 639 | 640 | 641 | 642 | 643 | 4. USING RASTER (GRID) DATA 644 | =========================== 645 | 646 | 647 |
648 | 649 | ### Downloading raster climate data from internet 650 | The `getData` function from the `dismo` package will easily retrieve climate data, elevation, administrative boundaries, etc. Check also the excellent [rWBclimate package](http://ropensci.org/packages/rwbclimate.html) by rOpenSci with additional functionality. 651 | 652 | ```{r} 653 | tmin <- getData("worldclim", var="tmin", res=10) # this will download 654 | # global data on minimum temperature at 10' resolution 655 | ``` 656 | 657 |
658 | 659 | ### Loading a raster layer 660 | 661 | ```{r} 662 | tmin1 <- raster(paste(getwd(), "/wc10/tmin1.bil", sep="")) # Tmin for January 663 | ``` 664 | Easy! The `raster` function reads many different formats, including Arc ASCII grids or netcdf files (see raster help). And values are stored on disk instead of memory! (useful for large rasters) 665 | ```{r} 666 | fromDisk(tmin1) 667 | ``` 668 | 669 | Let's examine the raster layer: 670 | ```{r} 671 | tmin1 <- tmin1/10 # Worldclim temperature data come in decimal degrees 672 | tmin1 # look at the info 673 | plot(tmin1) 674 | ``` 675 | 676 |
677 | 678 | ### Creating a raster stack 679 | 680 | A raster stack is collection of many raster layers with the same projection, spatial extent and resolution. 681 | Let's collect several raster files from disk and read them as a single raster stack: 682 | 683 | ```{r message=FALSE, warning=FALSE} 684 | 685 | library(gtools) 686 | file.remove(paste(getwd(), "/wc10/", "tmin_10m_bil.zip", sep="")) 687 | list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep=""), full.names=T, pattern=".bil")) 688 | list.ras # I have just collected a list of the files containing monthly temperature values 689 | tmin.all <- stack(list.ras) 690 | tmin.all 691 | tmin.all <- tmin.all/10 692 | plot(tmin.all) 693 | ``` 694 | 695 | 696 |
697 | 698 | ### Raster bricks 699 | 700 | A rasterbrick is similar to a raster stack (i.e. multiple layers with the same extent and resolution), but all the data must be stored in a single file on disk. 701 | 702 | ```{r} 703 | tmin.brick <- brick(tmin.all) # creates rasterbrick 704 | ``` 705 | 706 |
707 | 708 | ### Crop rasters 709 | 710 | Crop raster manually (drawing region of interest): 711 | 712 | ```{r eval=FALSE} 713 | plot(tmin1) 714 | newext <- drawExtent() # click twice on the map to select the region of interest 715 | tmin1.c <- crop(tmin1, newext) 716 | plot(tmin1.c) 717 | ``` 718 | 719 | Alternatively, provide coordinates for the limits of the region of interest: 720 | ```{r} 721 | newext <- c(-10, 10, 30, 50) 722 | tmin1.c <- crop(tmin1, newext) 723 | plot(tmin1.c) 724 | 725 | tmin.all.c <- crop(tmin.all, newext) 726 | plot(tmin.all.c) 727 | ``` 728 | 729 |
730 | 731 | ### Define spatial projection of the rasters 732 | 733 | ```{r} 734 | crs.geo # defined above 735 | projection(tmin1.c) <- crs.geo 736 | projection(tmin.all.c) <- crs.geo 737 | tmin1.c # notice info at coord.ref. 738 | ``` 739 | 740 |
741 | 742 | ### Changing projection 743 | 744 | Use `projectRaster` function: 745 | ```{r} 746 | tmin1.proj <- projectRaster(tmin1.c, crs="+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +a=6378137 +b=6378137 +units=m +no_defs") # can also use a template raster, see ?projectRaster 747 | tmin1.proj # notice info at coord.ref. 748 | plot(tmin1.proj) 749 | ``` 750 | 751 | 752 |
753 | 754 | ### Plotting raster data 755 | 756 | Different plotting functions: 757 | ```{r} 758 | histogram(tmin1.c) 759 | pairs(tmin.all.c) 760 | persp(tmin1.c) 761 | contour(tmin1.c) 762 | contourplot(tmin1.c) 763 | levelplot(tmin1.c) 764 | #plot3D(tmin1.c) 765 | bwplot(tmin.all.c) 766 | densityplot(tmin1.c) 767 | ``` 768 | 769 | 770 | ### Spatial autocorrelation 771 | 772 | ```{r} 773 | Moran(tmin1.c) # global Moran's I 774 | tmin1.Moran <- MoranLocal(tmin1.c) 775 | plot(tmin1.Moran) 776 | ``` 777 | 778 | 779 | ### Extract values from raster 780 | 781 | Use `extract` function: 782 | ```{r} 783 | head(locs) # we'll obtain tmin values for our points 784 | projection(tmin1) <- crs.geo 785 | locs$tmin1 <- extract(tmin1, locs) # raster values 786 | # are incorporated to the dataframe 787 | head(locs) 788 | ``` 789 | 790 | You can also extract values for a given region instead of the whole raster: 791 | ```{r eval=FALSE} 792 | plot(tmin1.c) 793 | reg.clim <- extract(tmin1.c, drawExtent()) # click twice to 794 | # draw extent of the region of interest 795 | summary(reg.clim) 796 | ``` 797 | 798 | Using `rasterToPoints`: 799 | ```{r} 800 | # rasterToPoints 801 | tminvals <- rasterToPoints(tmin1.c) 802 | head(tminvals) 803 | ``` 804 | 805 | And also, the `click` function will get values from particular locations in the map 806 | ```{r eval=FALSE} 807 | plot(tmin1.c) 808 | click(tmin1.c, n=3) # click n times in the map to get values 809 | ``` 810 | 811 |
812 | 813 | ### Rasterize points, lines or polygons 814 | 815 | ```{r} 816 | locs2ras <- rasterize(locs.gb, tmin1, field=rep(1,nrow(locs.gb))) 817 | locs2ras 818 | plot(locs2ras, xlim=c(-10,10), ylim=c(45, 60), legend=F) 819 | data(wrld_simpl) 820 | plot(wrld_simpl, add=T) 821 | ``` 822 | 823 | 824 |
825 | 826 | ### Changing raster resolution 827 | 828 | Use `aggregate` function: 829 | ```{r} 830 | tmin1.lowres <- aggregate(tmin1.c, fact=2, fun=mean) 831 | tmin1.lowres 832 | tmin1.c # compare 833 | par(mfcol=c(1,2)) 834 | plot(tmin1.c, main="original") 835 | plot(tmin1.lowres, main="low resolution") 836 | ``` 837 | 838 | 839 | ### Spline interpolation 840 | 841 | ```{r message=FALSE, warning=FALSE} 842 | xy <- data.frame(xyFromCell(tmin1.lowres, 1:ncell(tmin1.lowres))) # get raster cell coordinates 843 | head(xy) 844 | vals <- getValues(tmin1.lowres) 845 | library(fields) 846 | spline <- Tps(xy, vals) # thin plate spline 847 | intras <- interpolate(tmin1.c, spline) 848 | intras # note new resolution 849 | plot(intras) 850 | intras <- mask(intras, tmin1.c) # mask to land areas only 851 | plot(intras) 852 | title("Interpolated raster") 853 | ``` 854 | 855 | ### Setting all rasters to the same extent, projection and resolution all in one 856 | 857 | See `spatial_sync_raster` function from `spatial.tools` package. 858 | 859 |
860 | 861 | 862 | ### Elevations, slope, aspect, etc 863 | 864 |
865 | Download elevation data from internet: 866 | ```{r} 867 | elevation <- getData('alt', country='ESP') 868 | ``` 869 | 870 | Some quick maps: 871 | ```{r} 872 | x <- terrain(elevation, opt=c('slope', 'aspect'), unit='degrees') 873 | plot(x) 874 | 875 | slope <- terrain(elevation, opt='slope') 876 | aspect <- terrain(elevation, opt='aspect') 877 | hill <- hillShade(slope, aspect, 40, 270) 878 | plot(hill, col=grey(0:100/100), legend=FALSE, main='Spain') 879 | plot(elevation, col=rainbow(25, alpha=0.35), add=TRUE) 880 | ``` 881 | 882 | 883 | ### Saving and exporting raster data 884 | 885 | 886 | Saving raster to file: 887 | 888 | ```{r} 889 | writeRaster(tmin1.c, filename="tmin1.c.grd") 890 | writeRaster(tmin.all.c, filename="tmin.all.grd") 891 | ``` 892 | `writeRaster` can export to many different file types, see help. 893 | 894 |
895 | 896 | Exporting to KML (Google Earth) 897 | ```{r} 898 | tmin1.c <- raster(tmin.all.c, 1) 899 | KML(tmin1.c, file="tmin1.kml") 900 | KML(tmin.all.c) # can export multiple layers 901 | 902 | ``` 903 | 904 |
905 | [Back to Contents](#contents) 906 |
907 |
908 |
909 |
910 | 911 | 5. SPATIAL STATISTICS (just a glance) 912 | ===================================== 913 | 914 |
915 | 916 | ### Point pattern analysis 917 | 918 | Some useful packages: 919 | 920 | ```{r message=FALSE} 921 | library(spatial) 922 | library(spatstat) 923 | library(spatgraphs) 924 | library(ecespa) # ecological focus 925 | ``` 926 | See [CRAN Spatial Task View](http://cran.r-project.org/web/views/Spatial.html). 927 | 928 | Let's do a quick example with Ripley's K function: 929 | ```{r} 930 | data(fig1) 931 | plot(fig1) # point pattern 932 | data(Helianthemum) 933 | cosa12 <- K1K2(Helianthemum, j="deadpl", i="survpl", r=seq(0,200,le=201), 934 | nsim=99, nrank=1, correction="isotropic") 935 | plot(cosa12$k1k2, lty=c(2, 1, 2), col=c(2, 1, 2), xlim=c(0, 200), 936 | main= "survival- death",ylab=expression(K[1]-K[2]), legend=FALSE) 937 | 938 | ``` 939 | 940 |
941 | 942 | ### Geostatistics 943 | 944 | Some useful packages: 945 | 946 | ```{r message=FALSE, eval=FALSE} 947 | library(gstat) 948 | library(geoR) 949 | library(akima) # for spline interpolation 950 | library(spdep) # dealing with spatial dependence 951 | ``` 952 | See [CRAN Spatial Task View](http://cran.r-project.org/web/views/Spatial.html). 953 | 954 | 955 |
956 | [Back to Contents](#contents) 957 |
958 |
959 |
960 |
961 | 962 | 6. INTERACTING WITH OTHER GIS 963 | =============================================== 964 | 965 | ```{r message=F, eval=F} 966 | library(spgrass6) # GRASS 967 | library(RPyGeo) # ArcGis (Python) 968 | library(RSAGA) # SAGA 969 | library(spsextante) # Sextante 970 | 971 | ``` 972 | 973 |
974 | [Back to Contents](#contents) 975 |
976 |
977 |
978 |
979 | 980 | 981 | 7. OTHER USEFUL PACKAGES 982 | ========================= 983 | 984 | ```{r message=FALSE, eval=FALSE} 985 | library(Metadata) # automatically collates data from online GIS datasets (land cover, pop density, etc) for a given set of coordinates 986 | 987 | #library(GeoXp) # Interactive exploratory spatial data analysis 988 | example(columbus) 989 | histomap(columbus,"CRIME") 990 | 991 | library(maptools) 992 | # readGPS 993 | 994 | library(rangeMapper) # plotting species distributions, richness and traits 995 | 996 | 997 | # Species Distribution Modelling 998 | library(dismo) 999 | library(biomod2) 1000 | library(SDMTools) 1001 | 1002 | library(BioCalc) # computes 19 bioclimatic variables from monthly climatic values (tmin, tmax, prec) 1003 | 1004 | ``` 1005 | 1006 |
1007 | [Back to Contents](#contents) 1008 |
1009 |
1010 |
1011 |
1012 | 1013 | 8. TO LEARN MORE 1014 | ================ 1015 | 1016 | * [ASDAR book](http://www.asdar-book.org/) 1017 | 1018 | * Packages help and vignettes, especially 1019 | 1020 | http://cran.r-project.org/web/packages/raster/vignettes/Raster.pdf 1021 | http://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf 1022 | http://cran.r-project.org/web/packages/sp/vignettes/sp.pdf 1023 | 1024 | * [CRAN Task View: Analysis of Spatial Data](http://cran.r-project.org/web/views/Spatial.html) 1025 | 1026 | 1027 | * [Introduction to Spatial Data and ggplot2](http://rpubs.com/RobinLovelace/intro-spatial) 1028 | 1029 | * [R spatial tips](http://spatial.ly/category/r-spatial-data-hints/) 1030 | 1031 | * [R wiki: tips for spatial data](http://rwiki.sciviews.org/doku.php?id=tips:spatial-data&s=spatial) 1032 | 1033 | * [Spatial analysis in R](http://www.maths.lancs.ac.uk/~rowlings/Teaching/Sheffield2013/index.html) 1034 | 1035 | * [Displaying time series, spatial and space-time data with R](http://oscarperpinan.github.io/spacetime-vis/) 1036 | 1037 | * [Notes on Spatial Data Operations in R](https://dl.dropboxusercontent.com/u/9577903/broomspatial.pdf) 1038 | 1039 | * [Analysing spatial point patterns in R](http://www.csiro.au/resources/pf16h) 1040 | 1041 | * [Spatial data in R](http://science.nature.nps.gov/im/datamgmt/statistics/r/advanced/Spatial.cfm) 1042 | 1043 | * [NCEAS Geospatial use cases](http://www.nceas.ucsb.edu/scicomp/usecases) 1044 | 1045 | * [Spatial Analyst](http://spatial-analyst.net) 1046 | 1047 | * [Making maps with R](http://www.molecularecologist.com/2012/09/making-maps-with-r/) 1048 | 1049 | * [The Visual Raster Cheat Sheet](http://www.rpubs.com/etiennebr/visualraster) 1050 | 1051 | * [R-SIG-Geo mailing list](https://stat.ethz.ch/mailman/listinfo/R-SIG-Geo) 1052 | 1053 | * [Geospatial data processing and analysis in R (slides)](http://rpubs.com/ajlyons/rspatialdata) 1054 | 1055 | * [rMaps](http://rmaps.github.io/) 1056 | 1057 | 1058 | 1059 | 1060 | 1061 |
1062 | [Back to Contents](#contents) 1063 |
1064 |
1065 |
1066 |
-------------------------------------------------------------------------------- /R-GIS_tutorial.md: -------------------------------------------------------------------------------- 1 | 2 | Spatial data in R: Using R as a GIS 3 | ======================================================== 4 | 5 | 6 | A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps. 7 | 8 | 9 | [Francisco Rodriguez-Sanchez](http://sites.google.com/site/rodriguezsanchezf) 10 | 11 | 12 | v 2.2 13 | 14 | 27-01-2015 15 | 16 | Licence: [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/) 17 | 18 | 19 | Check out code and latest version at [GitHub](https://github.com/Pakillo/R-GIS-tutorial/blob/master/R-GIS_tutorial.md) 20 | 21 | 22 | 23 |
24 |
25 |
26 | 27 | CONTENTS 28 | ========= 29 | 30 |
31 |
32 | 33 | [1. INTRODUCTION](#intro) 34 |
35 |
36 | 37 | 38 | [2. GENERIC MAPPING](#mapping) 39 |
40 | 41 | * [Retrieving base maps from Google with `gmap` function in package `dismo`](#gmap) 42 | 43 | * [`RgoogleMaps`: Map your data onto Google Map tiles](#rgooglemaps) 44 | 45 | * [`googleVis`: visualise data in a web browser using Google Visualisation API](#googlevis) 46 | 47 | * [`RWorldMap`: mapping global data](#rworldmap) 48 |
49 |
50 | 51 | 52 | [3. SPATIAL VECTOR DATA (points, lines, polygons)](#vector) 53 |
54 | 55 | * [Example dataset: retrieve point occurrence data from GBIF](#gbif) 56 | 57 | * [Making data 'spatial'](#spatial) 58 | 59 | * [Define spatial projection](#projection) 60 | 61 | * [Quickly plotting point data on a map](#plot) 62 | 63 | * [Subsetting and mapping again](#subset) 64 | 65 | * [Mapping vectorial data (points, polygons, polylines)](#mapvector) 66 | 67 | * [Drawing polygons and polylines (e.g. for digitising)](#digitise) 68 | 69 | * [Converting between formats, reading in, and saving spatial vector data](#iovec) 70 | 71 | * [Changing projection of spatial vector data](#changeproj) 72 |
73 |
74 | 75 | 76 | [4. USING RASTER (GRID) DATA](#raster) 77 |
78 | 79 | * [Downloading raster climate data from internet](#getdata) 80 | 81 | * [Loading a raster layer](#loadraster) 82 | 83 | * [Creating a raster stack](#rasterstack) 84 | 85 | * [Raster bricks](#rasterbrick) 86 | 87 | * [Crop rasters](#cropraster) 88 | 89 | * [Define spatial projection of the rasters](#projectionraster) 90 | 91 | * [Changing projection](#changeprojraster) 92 | 93 | * [Plotting raster data](#plotraster) 94 | 95 | * [Spatial autocorrelation](#autocorrelation) 96 | 97 | * [Extract values from raster](#extract) 98 | 99 | * [Rasterize vector data (points, lines or polygons)](#rasterize) 100 | 101 | * [Changing raster resolution](#resolution) 102 | 103 | * [Spline interpolation](#interpolation) 104 | 105 | * [Setting all rasters to the same extent, projection and resolution all in one](#spatialsync) 106 | 107 | * [Elevations, slope, aspect, etc](#elevation) 108 | 109 | * [Saving and exporting raster data](#saveraster) 110 |
111 |
112 | 113 | 114 | 115 | 116 | [5. SPATIAL STATISTICS](#spatstats) 117 |
118 | 119 | * [Point pattern analysis](#pointpatterns) 120 | 121 | * [Geostatistics](#geostatistics) 122 |
123 |
124 | 125 | 126 | 127 | [6. INTERACTING WITH OTHER GIS](#othergis) 128 |
129 |
130 | 131 | [7. OTHER USEFUL PACKAGES](#otherpackages) 132 |
133 |
134 | 135 | [8. TO LEARN MORE](#tolearnmore) 136 | 137 | 138 |
139 |
140 |
141 |
142 | 143 | 144 | 145 | 146 | 147 | 1. INTRODUCTION 148 | =============== 149 | 150 |
151 | 152 | R is great not only for doing statistics, but also for many other tasks, including GIS analysis and working with spatial data. For instance, R is capable of doing wonderful maps such as [this](http://spatialanalysis.co.uk/wp-content/uploads/2012/02/bike_ggplot.png) or [this](http://oscarperpinan.github.io/spacetime-vis/images/airMadrid_stamen.png). In this tutorial I will show some basic GIS functionality in R. 153 | 154 | 155 | 156 | #### Basic packages 157 | 158 | 159 | ```r 160 | 161 | library(sp) # classes for spatial data 162 | library(raster) # grids, rasters 163 | library(rasterVis) # raster visualisation 164 | library(maptools) 165 | library(rgeos) 166 | # and their dependencies 167 | ``` 168 | 169 | 170 | There are many other useful packages, e.g. check [CRAN Spatial Task View](http://cran.r-project.org/web/views/Spatial.html). Some of them will be used below. 171 | 172 |
173 | [Back to Contents](#contents) 174 |
175 |
176 |
177 |
178 | 179 | 180 | 181 | 2. GENERIC MAPPING 182 | ================== 183 | 184 |
185 | 186 | 187 | Retrieving base maps from Google with `gmap` function in package `dismo` 188 | ------------------------------------------------------------------------ 189 | 190 | Some examples: 191 | 192 | Getting maps for countries: 193 | 194 | 195 | ```r 196 | 197 | library(dismo) 198 | 199 | mymap <- gmap("France") # choose whatever country 200 | plot(mymap) 201 | ``` 202 | 203 | ![plot of chunk gmap1](figure/gmap11.png) ![plot of chunk gmap1](figure/gmap12.png) 204 | 205 | 206 | Choose map type: 207 | 208 | ```r 209 | mymap <- gmap("France", type = "satellite") 210 | plot(mymap) 211 | ``` 212 | 213 | ![plot of chunk gmap2](figure/gmap21.png) ![plot of chunk gmap2](figure/gmap22.png) 214 | 215 | 216 | Choose zoom level: 217 | 218 | ```r 219 | mymap <- gmap("France", type = "satellite", exp = 3) 220 | plot(mymap) 221 | ``` 222 | 223 | ![plot of chunk gmap3](figure/gmap31.png) ![plot of chunk gmap3](figure/gmap32.png) 224 | 225 | 226 | Save the map as a file in your working directory for future use 227 | 228 | ```r 229 | mymap <- gmap("France", type = "satellite", filename = "France.gmap") 230 | ``` 231 | 232 | 233 | 234 | Now get a map for a region drawn at hand 235 | 236 | 237 | ```r 238 | 239 | mymap <- gmap("Europe") 240 | plot(mymap) 241 | 242 | select.area <- drawExtent() 243 | # now click 2 times on the map to select your region 244 | mymap <- gmap(select.area) 245 | plot(mymap) 246 | # See ?gmap for many other possibilities 247 | ``` 248 | 249 | 250 | 251 | 252 |
253 |
254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | `RgoogleMaps`: Map your data onto Google Map tiles 263 | ------------------------------------------------ 264 | 265 | 266 | ```r 267 | library(RgoogleMaps) 268 | ``` 269 | 270 | 271 | 272 | Get base maps from Google (a file will be saved in your working directory) 273 | 274 | ```r 275 | newmap <- GetMap(center = c(36.7, -5.9), zoom = 10, destfile = "newmap.png", 276 | maptype = "satellite") 277 | 278 | # Now using bounding box instead of center coordinates: 279 | newmap2 <- GetMap.bbox(lonR = c(-5, -6), latR = c(36, 37), destfile = "newmap2.png", 280 | maptype = "terrain") 281 | 282 | # Try different maptypes 283 | newmap3 <- GetMap.bbox(lonR = c(-5, -6), latR = c(36, 37), destfile = "newmap3.png", 284 | maptype = "satellite") 285 | ``` 286 | 287 | 288 | 289 | Now plot data onto these maps, e.g. these 3 points 290 | 291 | ```r 292 | PlotOnStaticMap(lat = c(36.3, 35.8, 36.4), lon = c(-5.5, -5.6, -5.8), zoom = 10, 293 | cex = 4, pch = 19, col = "red", FUN = points, add = F) 294 | ``` 295 | 296 | ![plot of chunk unnamed-chunk-6](figure/unnamed-chunk-6.png) 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 |
305 |
306 | 307 | 308 | 309 | 310 | 311 | `googleVis`: visualise data in a web browser using Google Visualisation API 312 | --------------------------------------------------------------------------- 313 | 314 | 315 | ```r 316 | library(googleVis) 317 | ``` 318 | 319 | 320 | Run `demo(googleVis)` to see all the possibilities 321 | 322 | 323 | 324 | 325 | 326 |
327 | 328 | ### Example: plot country-level data 329 | 330 | ```r 331 | data(Exports) # a simple data frame 332 | Geo <- gvisGeoMap(Exports, locationvar="Country", numvar="Profit", 333 | options=list(height=400, dataMode='regions')) 334 | plot(Geo) 335 | ``` 336 | 337 | 338 | 339 | 340 | 341 | 342 | 453 | 454 | 455 | 456 | 457 | 458 | 459 |
461 |
462 | 463 | Using `print(Geo)` we can get the HTML code to embed the map in a web page! 464 | 465 |
466 | 467 | ### Example: Plotting point data onto a google map (internet) 468 | 469 | ```r 470 | data(Andrew) 471 | M1 <- gvisMap(Andrew, "LatLong", "Tip", 472 | options=list(showTip=TRUE, showLine=F, enableScrollWheel=TRUE, 473 | mapType='satellite', useMapTypeControl=TRUE, width=800,height=400)) 474 | plot(M1) 475 | ``` 476 | 477 | 478 | 479 | 480 | 481 | 482 | 793 | 794 | 795 | 796 | 797 | 798 | 799 |
801 |
802 | 803 | 804 | 805 | 806 | 807 |
808 |
809 | 810 | 811 | 812 | 813 | 814 | `RWorldMap`: mapping global data 815 | -------------------------------- 816 | 817 | Some examples 818 | 819 | 820 | ```r 821 | 822 | library(rworldmap) 823 | 824 | newmap <- getMap(resolution = "coarse") # different resolutions available 825 | plot(newmap) 826 | ``` 827 | 828 | ![plot of chunk unnamed-chunk-10](figure/unnamed-chunk-10.png) 829 | 830 | 831 | 832 | ```r 833 | mapCountryData() 834 | ``` 835 | 836 | ![plot of chunk unnamed-chunk-11](figure/unnamed-chunk-11.png) 837 | 838 | 839 | ```r 840 | mapCountryData(mapRegion = "europe") 841 | ``` 842 | 843 | ![plot of chunk unnamed-chunk-12](figure/unnamed-chunk-12.png) 844 | 845 | 846 | ```r 847 | mapGriddedData() 848 | ``` 849 | 850 | ![plot of chunk unnamed-chunk-13](figure/unnamed-chunk-13.png) 851 | 852 | 853 | ```r 854 | mapGriddedData(mapRegion = "europe") 855 | ``` 856 | 857 | ![plot of chunk unnamed-chunk-14](figure/unnamed-chunk-14.png) 858 | 859 | 860 | 861 | 862 | 863 |
864 | [Back to Contents](#contents) 865 |
866 |
867 |
868 |
869 |
870 | 871 | 872 | 873 | 874 | 875 | 876 | 877 | 878 | 879 | 880 | 881 | 3. SPATIAL VECTOR DATA (points, lines, polygons) 882 | ================================================ 883 | 884 |
885 |
886 | 887 | ### Example dataset: retrieve point occurrence data from GBIF 888 | 889 | Let's create an example dataset: retrieve occurrence data 890 | for the laurel tree (Laurus nobilis) from the 891 | [Global Biodiversity Information Facility (GBIF)](http://gbif.org) 892 | 893 | ```r 894 | library(dismo) # check also the nice 'rgbif' package! 895 | laurus <- gbif("Laurus", "nobilis") 896 | ``` 897 | 898 | ``` 899 | ## Laurus nobilis : 2120 occurrences found 900 | ## 1-1000-2000-2120 901 | ``` 902 | 903 | ```r 904 | # get data frame with spatial coordinates (points) 905 | locs <- subset(laurus, select = c("country", "lat", "lon")) 906 | head(locs) # a simple data frame with coordinates 907 | ``` 908 | 909 | ``` 910 | ## country lat lon 911 | ## 1 Spain 36.12 -5.579 912 | ## 2 Spain 38.26 -5.207 913 | ## 3 Spain 36.11 -5.534 914 | ## 4 Spain 36.87 -5.312 915 | ## 5 Spain 37.30 -1.918 916 | ## 6 Spain 36.10 -5.545 917 | ``` 918 | 919 | ```r 920 | 921 | # Discard data with errors in coordinates: 922 | locs <- subset(locs, locs$lat < 90) 923 | ``` 924 | 925 | 926 |
927 | 928 | ### Making data 'spatial' 929 | 930 | So we have got a simple dataframe containing spatial coordinates. 931 | Let's make these data explicitly *spatial* 932 | 933 | ```r 934 | coordinates(locs) <- c("lon", "lat") # set spatial coordinates 935 | plot(locs) 936 | ``` 937 | 938 | ![plot of chunk unnamed-chunk-16](figure/unnamed-chunk-16.png) 939 | 940 | 941 | 942 | ### Define spatial projection 943 | 944 | Important: define geographical projection. 945 | Consult the appropriate PROJ.4 description here: 946 | [http://www.spatialreference.org/](http://www.spatialreference.org/) 947 | 948 | ```r 949 | crs.geo <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84") # geographical, datum WGS84 950 | proj4string(locs) <- crs.geo # define projection system of our data 951 | summary(locs) 952 | ``` 953 | 954 | ``` 955 | ## Object of class SpatialPointsDataFrame 956 | ## Coordinates: 957 | ## min max 958 | ## lon -123.25 145.04 959 | ## lat -37.78 59.84 960 | ## Is projected: FALSE 961 | ## proj4string : 962 | ## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0] 963 | ## Number of points: 2109 964 | ## Data attributes: 965 | ## Length Class Mode 966 | ## 2109 character character 967 | ``` 968 | 969 | 970 |
971 | 972 | ### Quickly plotting point data on a map 973 | 974 | ```r 975 | plot(locs, pch = 20, col = "steelblue") 976 | library(rworldmap) 977 | # library rworldmap provides different types of global maps, e.g: 978 | data(coastsCoarse) 979 | data(countriesLow) 980 | plot(coastsCoarse, add = T) 981 | ``` 982 | 983 | ![plot of chunk unnamed-chunk-18](figure/unnamed-chunk-18.png) 984 | 985 | 986 | 987 | ### Subsetting and mapping again 988 | 989 | ```r 990 | table(locs$country) # see localities of Laurus nobilis by country 991 | ``` 992 | 993 | ``` 994 | ## 995 | ## Australia Canada Croatia France Germany 996 | ## 2 1 1 1 1 997 | ## Greece Ireland Israel Italy Spain 998 | ## 5 69 1231 2 206 999 | ## Sweden United Kingdom United States 1000 | ## 2 578 10 1001 | ``` 1002 | 1003 | ```r 1004 | 1005 | locs.gb <- subset(locs, locs$country == "United Kingdom") # select only locs in UK 1006 | plot(locs.gb, pch = 20, cex = 2, col = "steelblue") 1007 | title("Laurus nobilis occurrences in UK") 1008 | plot(countriesLow, add = T) 1009 | ``` 1010 | 1011 | ![plot of chunk unnamed-chunk-19](figure/unnamed-chunk-19.png) 1012 | 1013 | ```r 1014 | summary(locs.gb) 1015 | ``` 1016 | 1017 | ``` 1018 | ## Object of class SpatialPointsDataFrame 1019 | ## Coordinates: 1020 | ## min max 1021 | ## lon -6.392 1.772 1022 | ## lat 49.951 56.221 1023 | ## Is projected: FALSE 1024 | ## proj4string : 1025 | ## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0] 1026 | ## Number of points: 578 1027 | ## Data attributes: 1028 | ## Length Class Mode 1029 | ## 578 character character 1030 | ``` 1031 | 1032 | 1033 | 1034 |
1035 | 1036 | Mapping vectorial data (points, polygons, polylines) 1037 | --------------------------------------------------------------------- 1038 | 1039 |
1040 | 1041 | ### Mapping vectorial data using `gmap` from `dismo` 1042 | 1043 | 1044 | 1045 | ```r 1046 | gbmap <- gmap(locs.gb, type = "satellite") 1047 | locs.gb.merc <- Mercator(locs.gb) # Google Maps are in Mercator projection. 1048 | # This function projects the points to that projection to enable mapping 1049 | plot(gbmap) 1050 | ``` 1051 | 1052 | ![plot of chunk unnamed-chunk-20](figure/unnamed-chunk-201.png) 1053 | 1054 | ```r 1055 | points(locs.gb.merc, pch = 20, col = "red") 1056 | ``` 1057 | 1058 | ![plot of chunk unnamed-chunk-20](figure/unnamed-chunk-202.png) 1059 | 1060 | 1061 | 1062 | 1063 | 1064 | 1065 |
1066 | 1067 | ### Mapping vectorial data with `RgoogleMaps` 1068 | 1069 | 1070 | 1071 | ```r 1072 | 1073 | require(RgoogleMaps) 1074 | 1075 | locs.gb.coords <- as.data.frame(coordinates(locs.gb)) # retrieves coordinates 1076 | # (1st column for longitude, 2nd column for latitude) 1077 | PlotOnStaticMap(lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, zoom = 5, 1078 | cex = 1.4, pch = 19, col = "red", FUN = points, add = F) 1079 | ``` 1080 | 1081 | ![plot of chunk unnamed-chunk-21](figure/unnamed-chunk-21.png) 1082 | 1083 | 1084 | Download base map from Google Maps and plot onto it 1085 | 1086 | ```r 1087 | map.lim <- qbbox(locs.gb.coords$lat, locs.gb.coords$lon, TYPE = "all") # define region 1088 | # of interest (bounding box) 1089 | mymap <- GetMap.bbox(map.lim$lonR, map.lim$latR, destfile = "gmap.png", maptype = "satellite") 1090 | ``` 1091 | 1092 | ``` 1093 | ## [1] "http://maps.google.com/maps/api/staticmap?center=53.086237,-2.30987445&zoom=6&size=640x640&maptype=satellite&format=png32&sensor=true" 1094 | ``` 1095 | 1096 | ```r 1097 | # see the file in the wd 1098 | PlotOnStaticMap(mymap, lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, zoom = NULL, 1099 | cex = 1.3, pch = 19, col = "red", FUN = points, add = F) 1100 | ``` 1101 | 1102 | ![plot of chunk unnamed-chunk-22](figure/unnamed-chunk-22.png) 1103 | 1104 | 1105 |
1106 |
1107 | 1108 | Using different background (base map) 1109 | 1110 | ```r 1111 | mymap <- GetMap.bbox(map.lim$lonR, map.lim$latR, destfile = "gmap.png", maptype = "hybrid") 1112 | ``` 1113 | 1114 | ``` 1115 | ## [1] "http://maps.google.com/maps/api/staticmap?center=53.086237,-2.30987445&zoom=6&size=640x640&maptype=hybrid&format=png32&sensor=true" 1116 | ``` 1117 | 1118 | ```r 1119 | PlotOnStaticMap(mymap, lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, zoom = NULL, 1120 | cex = 1.3, pch = 19, col = "red", FUN = points, add = F) 1121 | ``` 1122 | 1123 | ![plot of chunk unnamed-chunk-23](figure/unnamed-chunk-23.png) 1124 | 1125 | 1126 | 1127 | 1128 | 1129 | 1130 | 1131 | 1132 | 1133 |
1134 |
1135 | 1136 | ### Map vectorial data with `googleVis` (internet) 1137 | 1138 | 1139 | 1140 | ```r 1141 | points.gb <- as.data.frame(locs.gb) 1142 | points.gb$latlon <- paste(points.gb$lat, points.gb$lon, sep=":") 1143 | map.gb <- gvisMap(points.gb, locationvar="latlon", tipvar="country", 1144 | options = list(showTip=T, showLine=F, enableScrollWheel=TRUE, 1145 | useMapTypeControl=T, width=1400,height=800)) 1146 | plot(map.gb) 1147 | ``` 1148 | 1149 | 1150 | 1151 | 1152 | 1153 | 1154 | 4119 | 4120 | 4121 | 4122 | 4123 | 4124 | 4125 |
4127 |
4128 | 4129 | ```r 4130 | #print(map.gb) # get HTML suitable for a webpage 4131 | ``` 4132 | 4133 | 4134 | 4135 | 4136 | 4137 |
4138 |
4139 |
4140 | 4141 | ### Drawing polygons and polylines (e.g. for digitising) 4142 | 4143 | 4144 | 4145 | ```r 4146 | plot(gbmap) 4147 | mypolygon <- drawPoly() # click on the map to draw a polygon and press ESC when finished 4148 | summary(mypolygon) # now you have a spatial polygon! Easy, isn't it? 4149 | ``` 4150 | 4151 | 4152 | 4153 |
4154 |
4155 |
4156 |
4157 | 4158 | Converting between formats, reading in, and saving spatial vector data 4159 | ------------------------------------------------------------------- 4160 | 4161 |
4162 | 4163 | ### Exporting KML (Google Earth) 4164 | 4165 | ```r 4166 | writeOGR(locs.gb, dsn = "locsgb.kml", layer = "locs.gb", driver = "KML") 4167 | ``` 4168 | 4169 | 4170 | ### Reading KML 4171 | 4172 | ```r 4173 | newmap <- readOGR("locsgb.kml", layer = "locs.gb") 4174 | ``` 4175 | 4176 | ``` 4177 | ## OGR data source with driver: KML 4178 | ## Source: "locsgb.kml", layer: "locs.gb" 4179 | ## with 578 features and 2 fields 4180 | ## Feature type: wkbPoint with 2 dimensions 4181 | ``` 4182 | 4183 | 4184 | ### Save as shapefile 4185 | 4186 | ```r 4187 | writePointsShape(locs.gb, "locsgb") 4188 | ``` 4189 | 4190 | 4191 | ### Reading shapefiles 4192 | 4193 | ```r 4194 | gb.shape <- readShapePoints("locsgb.shp") 4195 | plot(gb.shape) 4196 | ``` 4197 | 4198 | ![plot of chunk unnamed-chunk-29](figure/unnamed-chunk-29.png) 4199 | 4200 | Use `readShapePoly` to read polygon shapefiles, and `readShapeLines` to read polylines. 4201 | See also `shapefile` in `raster` package. 4202 | 4203 | 4204 | 4205 |
4206 |
4207 |
4208 | 4209 | Changing projection of spatial vector data 4210 | ------------------------------------------- 4211 | 4212 | `spTransform` (package `sp`) will do the projection as long as the original and new projection are correctly specified. 4213 | 4214 |
4215 | 4216 | ### Projecting point dataset 4217 | 4218 | To illustrate, let's project the dataframe with Laurus nobilis coordinates that we obtained above: 4219 | 4220 | ```r 4221 | summary(locs) 4222 | ``` 4223 | 4224 | ``` 4225 | ## Object of class SpatialPointsDataFrame 4226 | ## Coordinates: 4227 | ## min max 4228 | ## lon -123.25 145.04 4229 | ## lat -37.78 59.84 4230 | ## Is projected: FALSE 4231 | ## proj4string : 4232 | ## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0] 4233 | ## Number of points: 2109 4234 | ## Data attributes: 4235 | ## Length Class Mode 4236 | ## 2109 character character 4237 | ``` 4238 | 4239 | The original coordinates are in lat lon format. Let's define the new desired projection: 4240 | Lambert Azimuthal Equal Area in this case 4241 | (look up parameters at [http://spatialreference.org](http://spatialreference.org)) 4242 | 4243 | ```r 4244 | crs.laea <- CRS("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs") # Lambert Azimuthal Equal Area 4245 | locs.laea <- spTransform(locs, crs.laea) # spTransform makes the projection 4246 | ``` 4247 | 4248 | 4249 |
4250 | 4251 | ### Projecting shapefile of countries 4252 | 4253 | ```r 4254 | plot(countriesLow) # countries map in geographical projection 4255 | ``` 4256 | 4257 | ![plot of chunk unnamed-chunk-32](figure/unnamed-chunk-32.png) 4258 | 4259 | ```r 4260 | country.laea <- spTransform(countriesLow, crs.laea) # project 4261 | ``` 4262 | 4263 | 4264 | Let's plot this: 4265 | 4266 | ```r 4267 | plot(locs.laea, pch = 20, col = "steelblue") 4268 | plot(country.laea, add = T) 4269 | ``` 4270 | 4271 | ![plot of chunk unnamed-chunk-33](figure/unnamed-chunk-331.png) 4272 | 4273 | ```r 4274 | 4275 | # define spatial limits for plotting 4276 | plot(locs.laea, pch = 20, col = "steelblue", xlim = c(1800000, 3900000), ylim = c(1e+06, 4277 | 3e+06)) 4278 | plot(country.laea, add = T) 4279 | ``` 4280 | 4281 | ![plot of chunk unnamed-chunk-33](figure/unnamed-chunk-332.png) 4282 | 4283 | 4284 | 4285 | 4286 | 4287 |
4288 | [Back to Contents](#contents) 4289 |
4290 |
4291 |
4292 |
4293 |
4294 |
4295 | 4296 | 4297 | 4298 | 4299 | 4300 | 4301 | 4302 | 4. USING RASTER (GRID) DATA 4303 | =========================== 4304 | 4305 | 4306 |
4307 | 4308 | ### Downloading raster climate data from internet 4309 | The `getData` function from the `dismo` package will easily retrieve climate data, elevation, administrative boundaries, etc. Check also the excellent [rWBclimate package](http://ropensci.org/packages/rwbclimate.html) by rOpenSci with additional functionality. 4310 | 4311 | 4312 | ```r 4313 | tmin <- getData("worldclim", var = "tmin", res = 10) # this will download 4314 | # global data on minimum temperature at 10' resolution 4315 | ``` 4316 | 4317 | 4318 |
4319 | 4320 | ### Loading a raster layer 4321 | 4322 | 4323 | ```r 4324 | tmin1 <- raster(paste(getwd(), "/wc10/tmin1.bil", sep = "")) # Tmin for January 4325 | ``` 4326 | 4327 | Easy! The `raster` function reads many different formats, including Arc ASCII grids or netcdf files (see raster help). And values are stored on disk instead of memory! (useful for large rasters) 4328 | 4329 | ```r 4330 | fromDisk(tmin1) 4331 | ``` 4332 | 4333 | ``` 4334 | ## [1] TRUE 4335 | ``` 4336 | 4337 | 4338 | Let's examine the raster layer: 4339 | 4340 | ```r 4341 | tmin1 <- tmin1/10 # Worldclim temperature data come in decimal degrees 4342 | tmin1 # look at the info 4343 | ``` 4344 | 4345 | ``` 4346 | ## class : RasterLayer 4347 | ## dimensions : 900, 2160, 1944000 (nrow, ncol, ncell) 4348 | ## resolution : 0.1667, 0.1667 (x, y) 4349 | ## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) 4350 | ## coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs 4351 | ## data source : in memory 4352 | ## names : tmin1 4353 | ## values : -54.7, 26.6 (min, max) 4354 | ``` 4355 | 4356 | ```r 4357 | plot(tmin1) 4358 | ``` 4359 | 4360 | ![plot of chunk unnamed-chunk-37](figure/unnamed-chunk-37.png) 4361 | 4362 | 4363 |
4364 | 4365 | ### Creating a raster stack 4366 | 4367 | A raster stack is collection of many raster layers with the same projection, spatial extent and resolution. 4368 | Let's collect several raster files from disk and read them as a single raster stack: 4369 | 4370 | 4371 | ```r 4372 | 4373 | library(gtools) 4374 | file.remove(paste(getwd(), "/wc10/", "tmin_10m_bil.zip", sep = "")) 4375 | ``` 4376 | 4377 | ``` 4378 | ## [1] TRUE 4379 | ``` 4380 | 4381 | ```r 4382 | list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep = ""), full.names = T, 4383 | pattern = ".bil")) 4384 | list.ras # I have just collected a list of the files containing monthly temperature values 4385 | ``` 4386 | 4387 | ``` 4388 | ## [1] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin1.bil" 4389 | ## [2] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin2.bil" 4390 | ## [3] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin3.bil" 4391 | ## [4] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin4.bil" 4392 | ## [5] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin5.bil" 4393 | ## [6] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin6.bil" 4394 | ## [7] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin7.bil" 4395 | ## [8] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin8.bil" 4396 | ## [9] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin9.bil" 4397 | ## [10] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin10.bil" 4398 | ## [11] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin11.bil" 4399 | ## [12] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin12.bil" 4400 | ``` 4401 | 4402 | ```r 4403 | tmin.all <- stack(list.ras) 4404 | tmin.all 4405 | ``` 4406 | 4407 | ``` 4408 | ## class : RasterStack 4409 | ## dimensions : 900, 2160, 1944000, 12 (nrow, ncol, ncell, nlayers) 4410 | ## resolution : 0.1667, 0.1667 (x, y) 4411 | ## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) 4412 | ## coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs 4413 | ## names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12 4414 | ## min values : -547, -525, -468, -379, -225, -170, -171, -178, -192, -302, -449, -522 4415 | ## max values : 266, 273, 277, 283, 295, 312, 311, 312, 300, 268, 267, 268 4416 | ``` 4417 | 4418 | ```r 4419 | tmin.all <- tmin.all/10 4420 | plot(tmin.all) 4421 | ``` 4422 | 4423 | ![plot of chunk unnamed-chunk-38](figure/unnamed-chunk-38.png) 4424 | 4425 | 4426 | 4427 |
4428 | 4429 | ### Raster bricks 4430 | 4431 | A rasterbrick is similar to a raster stack (i.e. multiple layers with the same extent and resolution), but all the data must be stored in a single file on disk. 4432 | 4433 | 4434 | ```r 4435 | tmin.brick <- brick(tmin.all) # creates rasterbrick 4436 | ``` 4437 | 4438 | 4439 |
4440 | 4441 | ### Crop rasters 4442 | 4443 | Crop raster manually (drawing region of interest): 4444 | 4445 | 4446 | ```r 4447 | plot(tmin1) 4448 | newext <- drawExtent() # click twice on the map to select the region of interest 4449 | tmin1.c <- crop(tmin1, newext) 4450 | plot(tmin1.c) 4451 | ``` 4452 | 4453 | 4454 | Alternatively, provide coordinates for the limits of the region of interest: 4455 | 4456 | ```r 4457 | newext <- c(-10, 10, 30, 50) 4458 | tmin1.c <- crop(tmin1, newext) 4459 | plot(tmin1.c) 4460 | ``` 4461 | 4462 | ![plot of chunk unnamed-chunk-41](figure/unnamed-chunk-411.png) 4463 | 4464 | ```r 4465 | 4466 | tmin.all.c <- crop(tmin.all, newext) 4467 | plot(tmin.all.c) 4468 | ``` 4469 | 4470 | ![plot of chunk unnamed-chunk-41](figure/unnamed-chunk-412.png) 4471 | 4472 | 4473 |
4474 | 4475 | ### Define spatial projection of the rasters 4476 | 4477 | 4478 | ```r 4479 | crs.geo # defined above 4480 | ``` 4481 | 4482 | ``` 4483 | ## CRS arguments: 4484 | ## +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4485 | ``` 4486 | 4487 | ```r 4488 | projection(tmin1.c) <- crs.geo 4489 | projection(tmin.all.c) <- crs.geo 4490 | tmin1.c # notice info at coord.ref. 4491 | ``` 4492 | 4493 | ``` 4494 | ## class : RasterLayer 4495 | ## dimensions : 120, 120, 14400 (nrow, ncol, ncell) 4496 | ## resolution : 0.1667, 0.1667 (x, y) 4497 | ## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax) 4498 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4499 | ## data source : in memory 4500 | ## names : tmin1 4501 | ## values : -12.3, 10.3 (min, max) 4502 | ``` 4503 | 4504 | 4505 |
4506 | 4507 | ### Changing projection 4508 | 4509 | Use `projectRaster` function: 4510 | 4511 | ```r 4512 | tmin1.proj <- projectRaster(tmin1.c, crs = "+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +a=6378137 +b=6378137 +units=m +no_defs") # can also use a template raster, see ?projectRaster 4513 | tmin1.proj # notice info at coord.ref. 4514 | ``` 4515 | 4516 | ``` 4517 | ## class : RasterLayer 4518 | ## dimensions : 132, 134, 17688 (nrow, ncol, ncell) 4519 | ## resolution : 18600, 24200 (x, y) 4520 | ## extent : -1243395, 1249005, 3372876, 6567276 (xmin, xmax, ymin, ymax) 4521 | ## coord. ref. : +proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +a=6378137 +b=6378137 +units=m +no_defs 4522 | ## data source : in memory 4523 | ## names : tmin1 4524 | ## values : -11.59, 10.3 (min, max) 4525 | ``` 4526 | 4527 | ```r 4528 | plot(tmin1.proj) 4529 | ``` 4530 | 4531 | ![plot of chunk unnamed-chunk-43](figure/unnamed-chunk-43.png) 4532 | 4533 | 4534 | 4535 |
4536 | 4537 | ### Plotting raster data 4538 | 4539 | Different plotting functions: 4540 | 4541 | ```r 4542 | histogram(tmin1.c) 4543 | ``` 4544 | 4545 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-441.png) 4546 | 4547 | ```r 4548 | pairs(tmin.all.c) 4549 | ``` 4550 | 4551 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-442.png) 4552 | 4553 | ```r 4554 | persp(tmin1.c) 4555 | ``` 4556 | 4557 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-443.png) 4558 | 4559 | ```r 4560 | contour(tmin1.c) 4561 | ``` 4562 | 4563 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-444.png) 4564 | 4565 | ```r 4566 | contourplot(tmin1.c) 4567 | ``` 4568 | 4569 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-445.png) 4570 | 4571 | ```r 4572 | levelplot(tmin1.c) 4573 | ``` 4574 | 4575 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-446.png) 4576 | 4577 | ```r 4578 | # plot3D(tmin1.c) 4579 | bwplot(tmin.all.c) 4580 | ``` 4581 | 4582 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-447.png) 4583 | 4584 | ```r 4585 | densityplot(tmin1.c) 4586 | ``` 4587 | 4588 | ![plot of chunk unnamed-chunk-44](figure/unnamed-chunk-448.png) 4589 | 4590 | 4591 | 4592 | ### Spatial autocorrelation 4593 | 4594 | 4595 | ```r 4596 | Moran(tmin1.c) # global Moran's I 4597 | ``` 4598 | 4599 | ``` 4600 | ## [1] 0.9099 4601 | ``` 4602 | 4603 | ```r 4604 | tmin1.Moran <- MoranLocal(tmin1.c) 4605 | plot(tmin1.Moran) 4606 | ``` 4607 | 4608 | ![plot of chunk unnamed-chunk-45](figure/unnamed-chunk-45.png) 4609 | 4610 | 4611 | 4612 | ### Extract values from raster 4613 | 4614 | Use `extract` function: 4615 | 4616 | ```r 4617 | head(locs) # we'll obtain tmin values for our points 4618 | ``` 4619 | 4620 | ``` 4621 | ## country 4622 | ## 1 Spain 4623 | ## 2 Spain 4624 | ## 3 Spain 4625 | ## 4 Spain 4626 | ## 5 Spain 4627 | ## 6 Spain 4628 | ``` 4629 | 4630 | ```r 4631 | projection(tmin1) <- crs.geo 4632 | locs$tmin1 <- extract(tmin1, locs) # raster values 4633 | # are incorporated to the dataframe 4634 | head(locs) 4635 | ``` 4636 | 4637 | ``` 4638 | ## country tmin1 4639 | ## 1 Spain 6.7 4640 | ## 2 Spain 2.1 4641 | ## 3 Spain 6.7 4642 | ## 4 Spain 4.2 4643 | ## 5 Spain 6.2 4644 | ## 6 Spain 6.7 4645 | ``` 4646 | 4647 | 4648 | You can also extract values for a given region instead of the whole raster: 4649 | 4650 | ```r 4651 | plot(tmin1.c) 4652 | reg.clim <- extract(tmin1.c, drawExtent()) # click twice to 4653 | # draw extent of the region of interest 4654 | summary(reg.clim) 4655 | ``` 4656 | 4657 | 4658 | Using `rasterToPoints`: 4659 | 4660 | ```r 4661 | # rasterToPoints 4662 | tminvals <- rasterToPoints(tmin1.c) 4663 | head(tminvals) 4664 | ``` 4665 | 4666 | ``` 4667 | ## x y tmin1 4668 | ## [1,] -6.4167 49.92 4.2 4669 | ## [2,] -6.2500 49.92 4.2 4670 | ## [3,] -5.2500 49.92 2.4 4671 | ## [4,] 0.5833 49.92 1.0 4672 | ## [5,] 0.7500 49.92 1.0 4673 | ## [6,] 0.9167 49.92 1.0 4674 | ``` 4675 | 4676 | 4677 | And also, the `click` function will get values from particular locations in the map 4678 | 4679 | ```r 4680 | plot(tmin1.c) 4681 | click(tmin1.c, n = 3) # click n times in the map to get values 4682 | ``` 4683 | 4684 | 4685 |
4686 | 4687 | ### Rasterize points, lines or polygons 4688 | 4689 | 4690 | ```r 4691 | locs2ras <- rasterize(locs.gb, tmin1, field = rep(1, nrow(locs.gb))) 4692 | locs2ras 4693 | ``` 4694 | 4695 | ``` 4696 | ## class : RasterLayer 4697 | ## dimensions : 900, 2160, 1944000 (nrow, ncol, ncell) 4698 | ## resolution : 0.1667, 0.1667 (x, y) 4699 | ## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) 4700 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4701 | ## data source : in memory 4702 | ## names : layer 4703 | ## values : 1, 1 (min, max) 4704 | ``` 4705 | 4706 | ```r 4707 | plot(locs2ras, xlim = c(-10, 10), ylim = c(45, 60), legend = F) 4708 | data(wrld_simpl) 4709 | plot(wrld_simpl, add = T) 4710 | ``` 4711 | 4712 | ![plot of chunk unnamed-chunk-50](figure/unnamed-chunk-50.png) 4713 | 4714 | 4715 | 4716 |
4717 | 4718 | ### Changing raster resolution 4719 | 4720 | Use `aggregate` function: 4721 | 4722 | ```r 4723 | tmin1.lowres <- aggregate(tmin1.c, fact = 2, fun = mean) 4724 | tmin1.lowres 4725 | ``` 4726 | 4727 | ``` 4728 | ## class : RasterLayer 4729 | ## dimensions : 60, 60, 3600 (nrow, ncol, ncell) 4730 | ## resolution : 0.3333, 0.3333 (x, y) 4731 | ## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax) 4732 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4733 | ## data source : in memory 4734 | ## names : tmin1 4735 | ## values : -10.57, 10.1 (min, max) 4736 | ``` 4737 | 4738 | ```r 4739 | tmin1.c # compare 4740 | ``` 4741 | 4742 | ``` 4743 | ## class : RasterLayer 4744 | ## dimensions : 120, 120, 14400 (nrow, ncol, ncell) 4745 | ## resolution : 0.1667, 0.1667 (x, y) 4746 | ## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax) 4747 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4748 | ## data source : in memory 4749 | ## names : tmin1 4750 | ## values : -12.3, 10.3 (min, max) 4751 | ``` 4752 | 4753 | ```r 4754 | par(mfcol = c(1, 2)) 4755 | plot(tmin1.c, main = "original") 4756 | plot(tmin1.lowres, main = "low resolution") 4757 | ``` 4758 | 4759 | ![plot of chunk unnamed-chunk-51](figure/unnamed-chunk-51.png) 4760 | 4761 | 4762 | 4763 | ### Spline interpolation 4764 | 4765 | 4766 | ```r 4767 | xy <- data.frame(xyFromCell(tmin1.lowres, 1:ncell(tmin1.lowres))) # get raster cell coordinates 4768 | head(xy) 4769 | ``` 4770 | 4771 | ``` 4772 | ## x y 4773 | ## 1 -9.833 49.83 4774 | ## 2 -9.500 49.83 4775 | ## 3 -9.167 49.83 4776 | ## 4 -8.833 49.83 4777 | ## 5 -8.500 49.83 4778 | ## 6 -8.167 49.83 4779 | ``` 4780 | 4781 | ```r 4782 | vals <- getValues(tmin1.lowres) 4783 | library(fields) 4784 | spline <- Tps(xy, vals) # thin plate spline 4785 | intras <- interpolate(tmin1.c, spline) 4786 | intras # note new resolution 4787 | ``` 4788 | 4789 | ``` 4790 | ## class : RasterLayer 4791 | ## dimensions : 120, 120, 14400 (nrow, ncol, ncell) 4792 | ## resolution : 0.1667, 0.1667 (x, y) 4793 | ## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax) 4794 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4795 | ## data source : in memory 4796 | ## names : layer 4797 | ## values : -10.43, 13.16 (min, max) 4798 | ``` 4799 | 4800 | ```r 4801 | plot(intras) 4802 | ``` 4803 | 4804 | ![plot of chunk unnamed-chunk-52](figure/unnamed-chunk-521.png) 4805 | 4806 | ```r 4807 | intras <- mask(intras, tmin1.c) # mask to land areas only 4808 | plot(intras) 4809 | title("Interpolated raster") 4810 | ``` 4811 | 4812 | ![plot of chunk unnamed-chunk-52](figure/unnamed-chunk-522.png) 4813 | 4814 | 4815 | ### Setting all rasters to the same extent, projection and resolution all in one 4816 | 4817 | See `spatial_sync_raster` function from `spatial.tools` package. 4818 | 4819 |
4820 | 4821 | 4822 | ### Elevations, slope, aspect, etc 4823 | 4824 |
4825 | Download elevation data from internet: 4826 | 4827 | ```r 4828 | elevation <- getData("alt", country = "ESP") 4829 | ``` 4830 | 4831 | 4832 | Some quick maps: 4833 | 4834 | ```r 4835 | x <- terrain(elevation, opt = c("slope", "aspect"), unit = "degrees") 4836 | plot(x) 4837 | ``` 4838 | 4839 | ![plot of chunk unnamed-chunk-54](figure/unnamed-chunk-541.png) 4840 | 4841 | ```r 4842 | 4843 | slope <- terrain(elevation, opt = "slope") 4844 | aspect <- terrain(elevation, opt = "aspect") 4845 | hill <- hillShade(slope, aspect, 40, 270) 4846 | plot(hill, col = grey(0:100/100), legend = FALSE, main = "Spain") 4847 | plot(elevation, col = rainbow(25, alpha = 0.35), add = TRUE) 4848 | ``` 4849 | 4850 | ![plot of chunk unnamed-chunk-54](figure/unnamed-chunk-542.png) 4851 | 4852 | 4853 | 4854 | ### Saving and exporting raster data 4855 | 4856 | 4857 | Saving raster to file: 4858 | 4859 | 4860 | ```r 4861 | writeRaster(tmin1.c, filename = "tmin1.c.grd") 4862 | ``` 4863 | 4864 | ``` 4865 | ## class : RasterLayer 4866 | ## dimensions : 120, 120, 14400 (nrow, ncol, ncell) 4867 | ## resolution : 0.1667, 0.1667 (x, y) 4868 | ## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax) 4869 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4870 | ## data source : C:\Users\FRS\Dropbox\R.scripts\my.Rcode\R-GIS tutorial\tmin1.c.grd 4871 | ## names : tmin1 4872 | ## values : -12.3, 10.3 (min, max) 4873 | ``` 4874 | 4875 | ```r 4876 | writeRaster(tmin.all.c, filename = "tmin.all.grd") 4877 | ``` 4878 | 4879 | ``` 4880 | ## class : RasterBrick 4881 | ## dimensions : 120, 120, 14400, 12 (nrow, ncol, ncell, nlayers) 4882 | ## resolution : 0.1667, 0.1667 (x, y) 4883 | ## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax) 4884 | ## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0 4885 | ## data source : C:\Users\FRS\Dropbox\R.scripts\my.Rcode\R-GIS tutorial\tmin.all.grd 4886 | ## names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12 4887 | ## min values : -12.3, -12.5, -10.8, -8.6, -4.2, -0.8, 1.8, 1.6, -0.1, -3.3, -8.1, -10.8 4888 | ## max values : 10.3, 10.8, 12.5, 14.5, 19.7, 24.7, 27.6, 26.7, 22.9, 16.9, 13.7, 11.3 4889 | ``` 4890 | 4891 | `writeRaster` can export to many different file types, see help. 4892 | 4893 |
4894 | 4895 | Exporting to KML (Google Earth) 4896 | 4897 | ```r 4898 | tmin1.c <- raster(tmin.all.c, 1) 4899 | KML(tmin1.c, file = "tmin1.kml") 4900 | KML(tmin.all.c) # can export multiple layers 4901 | ``` 4902 | 4903 | 4904 |
4905 | [Back to Contents](#contents) 4906 |
4907 |
4908 |
4909 |
4910 | 4911 | 5. SPATIAL STATISTICS (just a glance) 4912 | ===================================== 4913 | 4914 |
4915 | 4916 | ### Point pattern analysis 4917 | 4918 | Some useful packages: 4919 | 4920 | 4921 | ```r 4922 | library(spatial) 4923 | library(spatstat) 4924 | library(spatgraphs) 4925 | library(ecespa) # ecological focus 4926 | ``` 4927 | 4928 | See [CRAN Spatial Task View](http://cran.r-project.org/web/views/Spatial.html). 4929 | 4930 | Let's do a quick example with Ripley's K function: 4931 | 4932 | ```r 4933 | data(fig1) 4934 | plot(fig1) # point pattern 4935 | ``` 4936 | 4937 | ![plot of chunk unnamed-chunk-58](figure/unnamed-chunk-581.png) 4938 | 4939 | ```r 4940 | data(Helianthemum) 4941 | cosa12 <- K1K2(Helianthemum, j = "deadpl", i = "survpl", r = seq(0, 200, le = 201), 4942 | nsim = 99, nrank = 1, correction = "isotropic") 4943 | ``` 4944 | 4945 | ``` 4946 | ## 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 4947 | ## 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 4948 | ## 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 4949 | ## 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 4950 | ## 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 4951 | ## 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 4952 | ## 91, 92, 93, 94, 95, 96, 97, 98, 99. 4953 | ``` 4954 | 4955 | ```r 4956 | plot(cosa12$k1k2, lty = c(2, 1, 2), col = c(2, 1, 2), xlim = c(0, 200), main = "survival- death", 4957 | ylab = expression(K[1] - K[2]), legend = FALSE) 4958 | ``` 4959 | 4960 | ![plot of chunk unnamed-chunk-58](figure/unnamed-chunk-582.png) 4961 | 4962 | ``` 4963 | ## lty col key label 4964 | ## lo 2 2 lo lo(r) 4965 | ## K1-K2 1 1 K1-K2 K1(r) - K2(r) 4966 | ## hi 2 2 hi hi(r) 4967 | ## meaning 4968 | ## lo lower pointwise envelope of simulations 4969 | ## K1-K2 differences of Ripley isotropic correction estimate of expression(K[1] - K[2]) 4970 | ## hi upper pointwise envelope of simulations 4971 | ``` 4972 | 4973 | 4974 |
4975 | 4976 | ### Geostatistics 4977 | 4978 | Some useful packages: 4979 | 4980 | 4981 | ```r 4982 | library(gstat) 4983 | library(geoR) 4984 | library(akima) # for spline interpolation 4985 | library(spdep) # dealing with spatial dependence 4986 | ``` 4987 | 4988 | See [CRAN Spatial Task View](http://cran.r-project.org/web/views/Spatial.html). 4989 | 4990 | 4991 |
4992 | [Back to Contents](#contents) 4993 |
4994 |
4995 |
4996 |
4997 | 4998 | 6. INTERACTING WITH OTHER GIS 4999 | =============================================== 5000 | 5001 | 5002 | ```r 5003 | library(spgrass6) # GRASS 5004 | library(RPyGeo) # ArcGis (Python) 5005 | library(RSAGA) # SAGA 5006 | library(spsextante) # Sextante 5007 | 5008 | ``` 5009 | 5010 | 5011 |
5012 | [Back to Contents](#contents) 5013 |
5014 |
5015 |
5016 |
5017 | 5018 | 5019 | 7. OTHER USEFUL PACKAGES 5020 | ========================= 5021 | 5022 | 5023 | ```r 5024 | library(Metadata) # automatically collates data from online GIS datasets (land cover, pop density, etc) for a given set of coordinates 5025 | 5026 | # library(GeoXp) # Interactive exploratory spatial data analysis 5027 | example(columbus) 5028 | histomap(columbus, "CRIME") 5029 | 5030 | library(maptools) 5031 | # readGPS 5032 | 5033 | library(rangeMapper) # plotting species distributions, richness and traits 5034 | 5035 | 5036 | # Species Distribution Modelling 5037 | library(dismo) 5038 | library(biomod2) 5039 | library(SDMTools) 5040 | 5041 | library(BioCalc) # computes 19 bioclimatic variables from monthly climatic values (tmin, tmax, prec) 5042 | 5043 | ``` 5044 | 5045 | 5046 |
5047 | [Back to Contents](#contents) 5048 |
5049 |
5050 |
5051 |
5052 | 5053 | 8. TO LEARN MORE 5054 | ================ 5055 | 5056 | * [ASDAR book](http://www.asdar-book.org/) 5057 | 5058 | * Packages help and vignettes, especially 5059 | 5060 | http://cran.r-project.org/web/packages/raster/vignettes/Raster.pdf 5061 | http://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf 5062 | http://cran.r-project.org/web/packages/sp/vignettes/sp.pdf 5063 | 5064 | * [CRAN Task View: Analysis of Spatial Data](http://cran.r-project.org/web/views/Spatial.html) 5065 | 5066 | 5067 | * [Introduction to Spatial Data and ggplot2](http://rpubs.com/RobinLovelace/intro-spatial) 5068 | 5069 | * [R spatial tips](http://spatial.ly/category/r-spatial-data-hints/) 5070 | 5071 | * [R wiki: tips for spatial data](http://rwiki.sciviews.org/doku.php?id=tips:spatial-data&s=spatial) 5072 | 5073 | * [Spatial analysis in R](http://www.maths.lancs.ac.uk/~rowlings/Teaching/Sheffield2013/index.html) 5074 | 5075 | * [Displaying time series, spatial and space-time data with R](http://oscarperpinan.github.io/spacetime-vis/) 5076 | 5077 | * [Notes on Spatial Data Operations in R](https://dl.dropboxusercontent.com/u/9577903/broomspatial.pdf) 5078 | 5079 | * [Analysing spatial point patterns in R](http://www.csiro.au/resources/pf16h) 5080 | 5081 | * [Spatial data in R](http://science.nature.nps.gov/im/datamgmt/statistics/r/advanced/Spatial.cfm) 5082 | 5083 | * [NCEAS Geospatial use cases](http://www.nceas.ucsb.edu/scicomp/usecases) 5084 | 5085 | * [Spatial Analyst](http://spatial-analyst.net) 5086 | 5087 | * [Making maps with R](http://www.molecularecologist.com/2012/09/making-maps-with-r/) 5088 | 5089 | * [The Visual Raster Cheat Sheet](http://www.rpubs.com/etiennebr/visualraster) 5090 | 5091 | * [R-SIG-Geo mailing list](https://stat.ethz.ch/mailman/listinfo/R-SIG-Geo) 5092 | 5093 | 5094 | 5095 | 5096 | 5097 |
5098 | [Back to Contents](#contents) 5099 |
5100 |
5101 |
5102 |
5103 | --------------------------------------------------------------------------------