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<h1 class="title toc-ignore">exercise solutions</h1>
</div>
<p><strong>Last updated:</strong> 2018-09-04</p>
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<p></details></p>
<hr />
<div id="vector-spatial-data" class="section level2">
<h2>Vector spatial data</h2>
<div id="add-a-nose" class="section level4">
<h4>1) add a nose!</h4>
<p>Create a nose geometry, combine all the shapes into a single <code>sf</code> and then plot the face.</p>
<pre class="r"><code>nose <- st_linestring(x = matrix(c(1, -1, 1, -2), ncol = 2, byrow = T))
sfc <- st_sfc(points, line, nose)
face <- st_sf(data.frame(shape = c("eyes", "mouth", "nose"), geom = sfc))
plot(face)</code></pre>
<p><img src="figure/exercise_solutions.Rmd/unnamed-chunk-2-1.png" width="672" style="display: block; margin: auto;" /></p>
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</div>
<div id="what-are-the-coordinates-for-the-10th-point-in-the-mexico-polygon" class="section level4">
<h4>2) What are the coordinates for the 10th point in the Mexico polygon?</h4>
<pre class="r"><code>mx_coords <- world %>% filter(iso_a2 == "MX") %>% st_coordinates()
mx_coords[10, c("X", "Y")]</code></pre>
<pre><code> X Y
-106.50759 31.75452 </code></pre>
</div>
<div id="how-about-in-crs-mexico-itrf92-utm-zone-15n" class="section level4">
<h4>3) How about in CRS <a href="https://epsg.io/4488">Mexico ITRF92 / UTM zone 15N</a></h4>
<p>(hint: use <code>st_transform</code> to change the projection first)</p>
<pre class="r"><code>mx_utm15 <- world %>%
st_transform(crs = 4488)
mx_coords <- mx_utm15 %>%
filter(iso_a2 == "MX") %>%
st_coordinates()
mx_coords[10, c("X", "Y")]</code></pre>
<pre><code> X Y
-784546.6 3593838.9 </code></pre>
</div>
<div id="are-these-coordinates-projected-or-not-can-you-tell-by-just-looking-at-the-crs-in-the-transformed-sf-object" class="section level4">
<h4>4) Are these coordinates projected or not? Can you tell by just looking at the CRS in the transformed <code>sf</code> object?</h4>
<pre class="r"><code>mx_utm15</code></pre>
<pre><code>Simple feature collection with 177 features and 10 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -11779910 ymin: -19977280 xmax: 17183260 ymax: 19983850
epsg (SRID): 4488
proj4string: +proj=utm +zone=15 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
First 10 features:
iso_a2 name_long continent region_un subregion
1 FJ Fiji Oceania Oceania Melanesia
2 TZ Tanzania Africa Africa Eastern Africa
3 EH Western Sahara Africa Africa Northern Africa
4 CA Canada North America Americas Northern America
5 US United States North America Americas Northern America
6 KZ Kazakhstan Asia Asia Central Asia
7 UZ Uzbekistan Asia Asia Central Asia
8 PG Papua New Guinea Oceania Oceania Melanesia
9 ID Indonesia Asia Asia South-Eastern Asia
10 AR Argentina South America Americas South America
type area_km2 pop lifeExp gdpPercap
1 Sovereign country 19289.97 885806 69.96000 8222.254
2 Sovereign country 932745.79 52234869 64.16300 2402.099
3 Indeterminate 96270.60 NA NA NA
4 Sovereign country 10036042.98 35535348 81.95305 43079.143
5 Country 9510743.74 318622525 78.84146 51921.985
6 Sovereign country 2729810.51 17288285 71.62000 23587.338
7 Sovereign country 461410.26 30757700 71.03900 5370.866
8 Sovereign country 464520.07 7755785 65.23000 3709.082
9 Sovereign country 1819251.33 255131116 68.85600 10003.089
10 Sovereign country 2784468.59 42981515 76.25200 18797.548
geom
1 MULTIPOLYGON (((-11776327 -...
2 MULTIPOLYGON (((7508185 -19...
3 MULTIPOLYGON (((9294103 882...
4 MULTIPOLYGON (((-1664512 58...
5 MULTIPOLYGON (((-1664512 58...
6 MULTIPOLYGON (((473784.4 14...
7 MULTIPOLYGON (((3108708 149...
8 MULTIPOLYGON (((-6664974 -1...
9 MULTIPOLYGON (((-6664974 -1...
10 MULTIPOLYGON (((2134436 -61...</code></pre>
<p>We’ve already talked about UTM being projected CRSs but there is also a hint in the <code>proj4string</code>, in particular <code>+units=m</code> indicating that the units are linear (m).</p>
<p>We can also extract the units from an <code>sf</code>s crs using function <code>sf::st_crs()</code> and accessing the units</p>
<pre class="r"><code>st_crs(mx_utm15)$units</code></pre>
<pre><code>[1] "m"</code></pre>
</div>
</div>
<div id="raster-spatial-data" class="section level2">
<h2>Raster spatial data</h2>
<div id="create-and-plot-a-new-rasterlayer-of-rough-mean-temperature-in-degrees-c" class="section level4">
<h4>1) Create and plot a new <code>rasterLayer</code> of rough mean temperature in degrees C</h4>
<p>(rough because it would be much better to use more data at higher temporal resolution, eg at least monthly, not extremes).</p>
<p>We can do this using a simple mean calculation:</p>
<pre class="r"><code>rough_mean <- ((full_stack[["mx.bio_5"]] + full_stack[["mx.bio_6"]])/2)/10</code></pre>
<p>But we can even use r functions, in this case <code>mean()</code></p>
<pre class="r"><code>rough_mean <- mean(full_stack[["mx.bio_5"]], full_stack[["mx.bio_6"]])/10</code></pre>
<pre class="r"><code>rough_mean</code></pre>
<pre><code>class : RasterLayer
dimensions : 563, 811, 456593 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -101.85, -95.09167, 16.94167, 21.63333 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : /Users/Anna/Documents/workflows/workshops/intro-r-gis/data/raster/rough_mean.tif
names : rough_mean
values : -1.45, 29.6 (min, max)</code></pre>
<pre class="r"><code>levelplot(rough_mean, margin = F)</code></pre>
<p><img src="figure/exercise_solutions.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
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</div>
<div id="calculate-mean-precipitation-seasonality-for-the-extraction-bounding-box-area.-what-is-the-value" class="section level4">
<h4>2) Calculate mean precipitation seasonality for the <strong>extraction bounding box area</strong>. What is the value?</h4>
<pre class="r"><code>mean_prec_seasonality <- extract(env_stack[["prec_seasonality"]],
as_Spatial(extract_bbox),
fun = mean,
na.rm = T)
mean_prec_seasonality</code></pre>
<pre><code> [,1]
[1,] 87.78957</code></pre>
</div>
<div id="add-a-column-indicating-whether-data-points-are-greater-than-true-or-less-than-false-extraction-area-mean-precipitation-seasonality." class="section level4">
<h4>3) Add a column indicating whether data points are greater than (<code>TRUE</code>) or less than (<code>FALSE</code>) extraction area mean precipitation seasonality.</h4>
<pre class="r"><code>mol_env_sf <- mol_env_sf %>%
mutate(greater_mean_ps =
prec_seasonality > as.vector(mean_prec_seasonality))
mol_env_sf %>% select(locality, greater_mean_ps)</code></pre>
<pre><code>Simple feature collection with 15 features and 2 fields
geometry type: POINT
dimension: XY
bbox: xmin: -99.84806 ymin: 18.94194 xmax: -97.09056 ymax: 19.63083
epsg (SRID): 4326
proj4string: +proj=longlat +datum=WGS84 +no_defs
# A tibble: 15 x 3
locality greater_mean_ps geometry
<chr> <lgl> <POINT [°]>
1 Nevado de Toluca FALSE (-99.84806 19.19361)
2 Texcalyacac TRUE (-99.5 19.12056)
3 Desierto de los Leones TRUE (-99.30056 19.26667)
4 Ajusco TRUE (-99.3 19.18278)
5 Calpan TRUE (-98.59167 19.13139)
6 Atzompa TRUE (-98.55972 19.18056)
7 Llano Grande FALSE (-98.72056 19.33889)
8 Rio Frio FALSE (-98.69472 19.36611)
9 Nanacamilpa FALSE (-98.59611 19.48028)
10 MalincheS FALSE (-98.02194 19.18722)
11 MalincheW FALSE (-98.095 19.25778)
12 MalincheE FALSE (-97.975 19.23)
13 Texmalaquilla FALSE (-97.29 18.94194)
14 Xometla FALSE (-97.19056 18.975)
15 Vigas FALSE (-97.09056 19.63083)</code></pre>
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 spData_0.2.9.3 ggplot2_3.0.0
[4] dplyr_0.7.6 sf_0.6-3 rasterVis_0.45
[7] latticeExtra_0.6-28 RColorBrewer_1.1-2 lattice_0.20-35
[10] raster_2.6-7 sp_1.2-5
loaded via a namespace (and not attached):
[1] zoo_1.8-3 tidyselect_0.2.4 purrr_0.2.5
[4] colorspace_1.3-2 htmltools_0.3.6 viridisLite_0.3.0
[7] yaml_2.1.19 utf8_1.1.3 rlang_0.2.1
[10] R.oo_1.21.0 e1071_1.6-8 hexbin_1.27.1
[13] pillar_1.2.1 glue_1.2.0.9000 withr_2.1.2
[16] DBI_1.0.0 R.utils_2.6.0 bindr_0.1.1
[19] plyr_1.8.4 stringr_1.3.1 munsell_0.5.0
[22] gtable_0.2.0 workflowr_1.0.1 R.methodsS3_1.7.1
[25] evaluate_0.11 knitr_1.20 parallel_3.4.4
[28] class_7.3-14 Rcpp_0.12.18 backports_1.1.2
[31] scales_1.0.0 classInt_0.1-24 digest_0.6.15
[34] stringi_1.2.4 grid_3.4.4 rprojroot_1.3-2
[37] cli_1.0.0 rgdal_1.3-4 here_0.1
[40] tools_3.4.4 magrittr_1.5 lazyeval_0.2.1
[43] tibble_1.4.2 crayon_1.3.4 whisker_0.3-2
[46] pkgconfig_2.0.2 assertthat_0.2.0 rmarkdown_1.10
[49] R6_2.2.2 units_0.6-0 git2r_0.21.0
[52] compiler_3.4.4 </code></pre>
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