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<h1 class="title toc-ignore">P4 Spatial data analysis: introduction to raster processing (part-3)</h1>
<h4 class="author"><em>Joao Goncalves</em></h4>
<h4 class="date"><em>17 November 2017</em></h4>
</div>
<div id="background" class="section level3">
<h3>Background</h3>
<hr />
<p>In the third part of this tutorial series on spatial data analysis using the <code>raster</code> package, we will explore more functionalities, namely:</p>
<ul>
<li>Masking,<br />
</li>
<li>Aggregation,</li>
<li>Zonal analysis, and,</li>
<li>Cross-tabulation.</li>
</ul>
<p>For more information on raster data processing see <a href="http://r-exercises.com/tags/raster-data">here</a>, as well as the <a href="https://www.r-exercises.com/2017/11/29/spatial-data-analysis-introduction-to-raster-processing-part-1">tutorial part-1</a>, and, <a href="https://www.r-exercises.com/2017/12/13/spatial-data-analysis-introduction-to-raster-processing-part-2">tutorial part-2</a> of this series.</p>
</div>
<div id="raster-masking" class="section level3">
<h3>Raster masking</h3>
<hr />
<p>Masking a raster is often required when we want to represent and/or analyze only the pixels included in a specific area or region. In turn, the remaining pixels are transformed into <code>NA</code>’s (or other user-defined value).</p>
<p>For this purpose we can use the <code>mask</code> function. This requires to input a ‘mask’ layer that can be either a <code>Raster*</code> object (with the same extent and resolution), or a <code>Spatial*</code> object (e.g., <code>SpatialPolygons</code>) in which case, all cells that are not covered by this object are set to <code>updatevalue</code> (<code>NA</code> by default).</p>
<p>We will start by downloading, uncompressing and loading the sample data. A <code>SpatialPolygons*</code> layer will be used as the mask layer. The objective of this example is to mask elevation values that are inside the Peneda-Geres National Park (NW Portugal). First up, let’s prepare the elevation data:</p>
<pre class="r"><code>library(raster)
## Create a folder named data-raw inside the working directory to place downloaded data
if(!dir.exists("./data-raw")) dir.create("./data-raw")
## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/srtm_pnpg.zip", "./data-raw/srtm_pnpg.zip", method = "auto")
unzip("./data-raw/srtm_pnpg.zip", exdir = "./data-raw")
rst <- raster("./data-raw/srtm_pnpg.tif")</code></pre>
<p>Now, let’s download and read the mask layer (using <code>rgdal</code>):</p>
<pre class="r"><code>library(sp)
library(rgdal)
## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/BOUNDS_PNPG.zip", "./data-raw/BOUNDS_PNPG.zip", method = "auto")
unzip("./data-raw/BOUNDS_PNPG.zip", exdir = "./data-raw")
# Read the mask layer and convert it to a 'simpler' SpatialPolygons dataset
maskLayer <- as(readOGR(dsn = "./data-raw", layer = "pnpg_bounds"), "SpatialPolygons")</code></pre>
<pre><code>## OGR data source with driver: ESRI Shapefile
## Source: "./data-raw", layer: "pnpg_bounds"
## with 1 features
## It has 5 fields</code></pre>
<p>Plot the data to see if everything is OK:</p>
<pre class="r"><code>plot(rst, main="Elevation (meters) for Peneda-Geres\n National Park", xlab="X-coordinates",
ylab = "Y-coordinates")
plot(maskLayer, add=TRUE)</code></pre>
<div class="figure">
<img src="https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/img/P4_plot_data-1.png" />
</div>
<p>Finally, let’s mask the values for the PG National Park boundaries:</p>
<pre class="r"><code>rstMasked <- mask(rst, maskLayer)
plot(rstMasked, main="Elevation (meters) for Peneda-Geres\n National Park", xlab="X-coordinates",
ylab = "Y-coordinates")
plot(maskLayer, add=TRUE)</code></pre>
<div class="figure">
<img src="https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/img/P4_mask_raster-1.png" />
</div>
<p>From the image, we can see that only pixels occurring inside the Park are represented or ‘masked’. Exactly what we wanted! 😄 👍 😉</p>
</div>
<div id="raster-aggregation" class="section level3">
<h3>Raster aggregation</h3>
<hr />
<p>Raster aggregation is the process of creating a new <code>RasterLayer</code> by grouping cell values in a rectangular area to create larger/coarser cells. This grouping can employ any user-defined function to summarize multiple values (in the rectangular area) and provide a single value (e.g., mean, sd, min, max, sum). This ‘upsampling’ allows to represent and analyze the spatial distribution of cell values inside each rectangular area.</p>
<p>The <code>aggregate</code> function will be used for this purpose. The ‘coarseness’ of the aggregation is controlled by the <code>fact</code> parameter (aggregation factor) which expresses the number of cells in each direction (horizontal and vertical). Alternatively, two integers can be used to separately express the horizontal and vertical aggregation factors.</p>
<p>Let’s see how this works out with different aggregation factors:</p>
<pre><code>* Aggregation factor 2 - pixel size 160(m)
* Aggregation factor 7 - pixel size 560(m)</code></pre>
<pre class="r"><code># Aggregation factor = 2
rstAggFact2Mean <- aggregate(rst, fact = 2, fun = mean)
rstAggFact2SD <- aggregate(rst, fact = 2, fun = sd)
# Aggregation factor = 7
rstAggFact7Mean <- aggregate(rst, fact = 7, fun = mean)
rstAggFact7SD <- aggregate(rst, fact = 7, fun = sd)
# Plot the newly aggregated rasters
par(mfrow = c(2,2))
plot(rstAggFact2Mean, main = "Aggregation factor = 2 | Mean")
plot(rstAggFact2SD, main = "Aggregation factor = 2 | SD")
plot(rstAggFact7Mean, main = "Aggregation factor = 7 | Mean")
plot(rstAggFact7SD, main = "Aggregation factor = 7 | SD")</code></pre>
<div class="figure">
<img src="https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/img/P4_raster_aggregation-1.png" />
</div>
<p>Notice the change in coarseness as we move from an aggregation factor of 2 to 7, with much less detail in the latter. In this particular example (a DEM analysis), and for larger factors, it allows us to understand general land-forms (mean) and also topographic heterogeneity (standard-deviation).</p>
</div>
<div id="zonal-analysis" class="section level3">
<h3>Zonal analysis</h3>
<hr />
<p>For this part of the tutorial we will address zonal analysis. This method allows to summarize the values in a <code>Raster*</code> object for each “zone” included in a <code>RasterLayer</code> (typically defined by an integer code). The <code>zonal</code> function is used for this objective. Notice that both <code>Raster*</code> input objects must have the same extent, resolution, and, CRS.</p>
<p>Applications of this technique include summarizing cell values for administrative regions (like in the example explored below) or, calculating summary statistics for raster segments (useful in an object-based image analysis approach).</p>
<p>In the example we will calculate zonal statistics for elevation data for each civil parish (N=17) within the Peneda-Geres National Park (PGNP - NW Portugal).</p>
<p>As customary, we will start by downloading, uncompressing and loading civil parish data:</p>
<pre class="r"><code>## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/CIVPARISH_PNPG.zip", "./data-raw/CIVPARISH_PNPG.zip", method = "auto")
unzip("./data-raw/CIVPARISH_PNPG.zip", exdir = "./data-raw")
rstCivPar <- raster("./data-raw/PNPG_CivilParishes.tif")</code></pre>
<p>Plot the data to see if it’s OK:</p>
<pre class="r"><code>par(mfrow = c(1,2))
plot(rst, main="Elevation (meters)")
plot(maskLayer, add=TRUE)
plot(rstCivPar, main="Civil parishes PGNP")
plot(maskLayer, add=TRUE)</code></pre>
<div class="figure">
<img src="https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/img/P4_civil_parish_data_plot-1.png" />
</div>
<p>We can now calculate elevation statistics for each civil parish (identified by an integer number). <code>zonal</code> accepts summarizing functions (in argument <code>fun</code>) with single or multiple outputs. In the example below we will calculate the mean.</p>
<pre class="r"><code># Single-output
zonal(rst, rstCivPar, fun=mean)</code></pre>
<pre><code>## zone value
## [1,] 1 1041.2407
## [2,] 2 308.6019
## [3,] 3 622.7727
## [4,] 4 790.4464
## [5,] 5 970.8552
## [6,] 6 787.0810
## [7,] 7 549.3588
## [8,] 8 963.2459
## [9,] 9 307.2516
## [10,] 10 1106.3882
## [11,] 11 1156.9009
## [12,] 12 713.7014
## [13,] 13 1044.0795
## [14,] 14 641.9319
## [15,] 15 1023.6904
## [16,] 16 867.7098
## [17,] 17 930.2353</code></pre>
<p>In the next example, column #1 equals the median and column #2 the median-absolute deviation which is obtained by a specific multi-output function:</p>
<pre class="r"><code># Multi-output
zonal(rst, rstCivPar, fun=function(x,...) c(MED=median(x,...), MAD=mad(x,...)))</code></pre>
<pre><code>## zone value_1 value_2
## [1,] 1 1059.0 171.9816
## [2,] 2 271.0 131.9514
## [3,] 3 637.0 191.2554
## [4,] 4 813.5 405.4911
## [5,] 5 956.0 293.5548
## [6,] 6 765.0 392.8890
## [7,] 7 555.5 306.1569
## [8,] 8 954.0 219.4248
## [9,] 9 289.0 182.3598
## [10,] 10 1125.0 123.0558
## [11,] 11 1162.0 96.3690
## [12,] 12 698.0 332.1024
## [13,] 13 1024.0 240.1812
## [14,] 14 617.0 265.3854
## [15,] 15 1017.0 195.7032
## [16,] 16 875.0 413.6454
## [17,] 17 977.0 189.7728</code></pre>
</div>
<div id="cross-tabulation-of-two-discretecategorical-raster-datasets" class="section level3">
<h3>Cross-tabulation of two discrete/categorical raster datasets</h3>
<hr />
<p>Performing a cross-tabulation of two raster datasets is very useful when, for example, you want to assess land cover changes between two different dates. It is also a preliminary step for generating a confusion matrix from which several classification performance metrics can be calculated.</p>
<p>In this example, we will use Corine Land Cover (CLC), a dataset from the European Environmental Agency (EEA) for years 2006 and 2012, to analyze changes in land cover composition. In this case, we have two categorical rasters with integer values corresponding to different land cover classes (see details in the table below).</p>
<pre class="r"><code>clcLeg <- read.csv(url("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/legend_clc.csv"),
stringsAsFactors = FALSE)
clcLeg <- data.frame(clcLeg[,1:2],
CLC_abr=toupper(abbreviate(gsub("-"," ",clcLeg[,3]), 6)),
Label=clcLeg[,3], row.names = 1:nrow(clcLeg))
knitr::kable(clcLeg)</code></pre>
<table>
<thead>
<tr class="header">
<th align="right">Raster_value</th>
<th align="right">CLC_code</th>
<th align="left">CLC_abr</th>
<th align="left">Label</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">1</td>
<td align="right">111</td>
<td align="left">CNTNUF</td>
<td align="left">Continuous urban fabric</td>
</tr>
<tr class="even">
<td align="right">2</td>
<td align="right">112</td>
<td align="left">DSCNUF</td>
<td align="left">Discontinuous urban fabric</td>
</tr>
<tr class="odd">
<td align="right">7</td>
<td align="right">131</td>
<td align="left">MNRLES</td>
<td align="left">Mineral extraction sites</td>
</tr>
<tr class="even">
<td align="right">12</td>
<td align="right">211</td>
<td align="left">NNIRAL</td>
<td align="left">Non-irrigated arable land</td>
</tr>
<tr class="odd">
<td align="right">13</td>
<td align="right">212</td>
<td align="left">PRMNIL</td>
<td align="left">Permanently irrigated land</td>
</tr>
<tr class="even">
<td align="right">15</td>
<td align="right">221</td>
<td align="left">VNYRDS</td>
<td align="left">Vineyards</td>
</tr>
<tr class="odd">
<td align="right">18</td>
<td align="right">231</td>
<td align="left">PASTRS</td>
<td align="left">Pastures</td>
</tr>
<tr class="even">
<td align="right">19</td>
<td align="right">241</td>
<td align="left">ANCWPC</td>
<td align="left">Annual crops with permanent crops</td>
</tr>
<tr class="odd">
<td align="right">20</td>
<td align="right">242</td>
<td align="left">CMPLCP</td>
<td align="left">Complex cultivation patterns</td>
</tr>
<tr class="even">
<td align="right">21</td>
<td align="right">243</td>
<td align="left">AGRWNV</td>
<td align="left">Agriculture with natural vegetation</td>
</tr>
<tr class="odd">
<td align="right">23</td>
<td align="right">311</td>
<td align="left">BRDLVF</td>
<td align="left">Broad-leaved forest</td>
</tr>
<tr class="even">
<td align="right">24</td>
<td align="right">312</td>
<td align="left">CNFRSF</td>
<td align="left">Coniferous forest</td>
</tr>
<tr class="odd">
<td align="right">25</td>
<td align="right">313</td>
<td align="left">MXDFRS</td>
<td align="left">Mixed forest</td>
</tr>
<tr class="even">
<td align="right">26</td>
<td align="right">321</td>
<td align="left">NTRLGR</td>
<td align="left">Natural grasslands</td>
</tr>
<tr class="odd">
<td align="right">27</td>
<td align="right">322</td>
<td align="left">MRSANH</td>
<td align="left">Moors and heathland</td>
</tr>
<tr class="even">
<td align="right">29</td>
<td align="right">324</td>
<td align="left">TRNSWS</td>
<td align="left">Transitional woodland-shrub</td>
</tr>
<tr class="odd">
<td align="right">31</td>
<td align="right">332</td>
<td align="left">BRRCKS</td>
<td align="left">Bare rocks</td>
</tr>
<tr class="even">
<td align="right">32</td>
<td align="right">333</td>
<td align="left">SPRSVA</td>
<td align="left">Sparsely vegetated areas</td>
</tr>
<tr class="odd">
<td align="right">33</td>
<td align="right">334</td>
<td align="left">BRNTAR</td>
<td align="left">Burnt areas</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">511</td>
<td align="left">WTRCRS</td>
<td align="left">Water courses</td>
</tr>
<tr class="odd">
<td align="right">41</td>
<td align="right">512</td>
<td align="left">WTRBDS</td>
<td align="left">Water bodies</td>
</tr>
</tbody>
</table>
<p>Now, let’s download, uncompress and load the raster data into R and then perform the cross-tabulation:</p>
<pre class="r"><code>## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/CLC_06_12.zip", "./data-raw/CLC_06_12.zip", method = "auto")
unzip("./data-raw/CLC_06_12.zip", exdir = "./data-raw")
# Load the Corine Land cover dataset for 2006 and 2012
clc06 <- raster("./data-raw/clc2006_100m.tif")
clc12 <- raster("./data-raw/clc2012_100m.tif")
# 'Ratify' the rasters, i.e., inform that these are
# categorical/discrete datasets
clc06 <- ratify(clc06)
clc12 <- ratify(clc12)
# Perform the crosstab
ct <- crosstab(clc06, clc12, long = TRUE)
# Plot the contingency table
knitr::kable(ct)</code></pre>
<table>
<thead>
<tr class="header">
<th></th>
<th align="left">clc2006_100m</th>
<th align="left">clc2012_100m</th>
<th align="right">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>1</td>
<td align="left">1</td>
<td align="left">1</td>
<td align="right">28</td>
</tr>
<tr class="even">
<td>25</td>
<td align="left">2</td>
<td align="left">2</td>
<td align="right">503</td>
</tr>
<tr class="odd">
<td>49</td>
<td align="left">7</td>
<td align="left">7</td>
<td align="right">50</td>
</tr>
<tr class="even">
<td>73</td>
<td align="left">12</td>
<td align="left">12</td>
<td align="right">1966</td>
</tr>
<tr class="odd">
<td>97</td>
<td align="left">13</td>
<td align="left">13</td>
<td align="right">206</td>
</tr>
<tr class="even">
<td>121</td>
<td align="left">15</td>
<td align="left">15</td>
<td align="right">196</td>
</tr>
<tr class="odd">
<td>145</td>
<td align="left">18</td>
<td align="left">18</td>
<td align="right">3242</td>
</tr>
<tr class="even">
<td>169</td>
<td align="left">19</td>
<td align="left">19</td>
<td align="right">6229</td>
</tr>
<tr class="odd">
<td>193</td>
<td align="left">20</td>
<td align="left">20</td>
<td align="right">5638</td>
</tr>
<tr class="even">
<td>197</td>
<td align="left">25</td>
<td align="left">20</td>
<td align="right">1</td>
</tr>
<tr class="odd">
<td>217</td>
<td align="left">21</td>
<td align="left">21</td>
<td align="right">14263</td>
</tr>
<tr class="even">
<td>219</td>
<td align="left">24</td>
<td align="left">21</td>
<td align="right">6</td>
</tr>
<tr class="odd">
<td>222</td>
<td align="left">27</td>
<td align="left">21</td>
<td align="right">9</td>
</tr>
<tr class="even">
<td>223</td>
<td align="left">29</td>
<td align="left">21</td>
<td align="right">7</td>
</tr>
<tr class="odd">
<td>228</td>
<td align="left">41</td>
<td align="left">21</td>
<td align="right">1</td>
</tr>
<tr class="even">
<td>240</td>
<td align="left">21</td>
<td align="left">23</td>
<td align="right">4</td>
</tr>
<tr class="odd">
<td>241</td>
<td align="left">23</td>
<td align="left">23</td>
<td align="right">14763</td>
</tr>
<tr class="even">
<td>243</td>
<td align="left">25</td>
<td align="left">23</td>
<td align="right">5</td>
</tr>
<tr class="odd">
<td>246</td>
<td align="left">29</td>
<td align="left">23</td>
<td align="right">167</td>
</tr>
<tr class="even">
<td>249</td>
<td align="left">33</td>
<td align="left">23</td>
<td align="right">28</td>
</tr>
<tr class="odd">
<td>265</td>
<td align="left">24</td>
<td align="left">24</td>
<td align="right">6761</td>
</tr>
<tr class="even">
<td>266</td>
<td align="left">25</td>
<td align="left">24</td>
<td align="right">26</td>
</tr>
<tr class="odd">
<td>269</td>
<td align="left">29</td>
<td align="left">24</td>
<td align="right">563</td>
</tr>
<tr class="even">
<td>286</td>
<td align="left">21</td>
<td align="left">25</td>
<td align="right">1</td>
</tr>
<tr class="odd">
<td>288</td>
<td align="left">24</td>
<td align="left">25</td>
<td align="right">44</td>
</tr>
<tr class="even">
<td>289</td>
<td align="left">25</td>
<td align="left">25</td>
<td align="right">9183</td>
</tr>
<tr class="odd">
<td>292</td>
<td align="left">29</td>
<td align="left">25</td>
<td align="right">637</td>
</tr>
<tr class="even">
<td>313</td>
<td align="left">26</td>
<td align="left">26</td>
<td align="right">12791</td>
</tr>
<tr class="odd">
<td>315</td>
<td align="left">29</td>
<td align="left">26</td>
<td align="right">4</td>
</tr>
<tr class="even">
<td>318</td>
<td align="left">33</td>
<td align="left">26</td>
<td align="right">112</td>
</tr>
<tr class="odd">
<td>333</td>
<td align="left">23</td>
<td align="left">27</td>
<td align="right">24</td>
</tr>
<tr class="even">
<td>334</td>
<td align="left">24</td>
<td align="left">27</td>
<td align="right">103</td>
</tr>
<tr class="odd">
<td>335</td>
<td align="left">25</td>
<td align="left">27</td>
<td align="right">101</td>
</tr>
<tr class="even">
<td>337</td>
<td align="left">27</td>
<td align="left">27</td>
<td align="right">58718</td>
</tr>
<tr class="odd">
<td>338</td>
<td align="left">29</td>
<td align="left">27</td>
<td align="right">54</td>
</tr>
<tr class="even">
<td>341</td>
<td align="left">33</td>
<td align="left">27</td>
<td align="right">172</td>
</tr>
<tr class="odd">
<td>353</td>
<td align="left">19</td>
<td align="left">29</td>
<td align="right">1</td>
</tr>
<tr class="even">
<td>356</td>
<td align="left">23</td>
<td align="left">29</td>
<td align="right">371</td>
</tr>
<tr class="odd">
<td>357</td>
<td align="left">24</td>
<td align="left">29</td>
<td align="right">1398</td>
</tr>
<tr class="even">
<td>358</td>
<td align="left">25</td>
<td align="left">29</td>
<td align="right">1466</td>
</tr>
<tr class="odd">
<td>359</td>
<td align="left">26</td>
<td align="left">29</td>
<td align="right">2</td>
</tr>
<tr class="even">
<td>360</td>
<td align="left">27</td>
<td align="left">29</td>
<td align="right">96</td>
</tr>
<tr class="odd">
<td>361</td>
<td align="left">29</td>
<td align="left">29</td>
<td align="right">23358</td>
</tr>
<tr class="even">
<td>363</td>
<td align="left">32</td>
<td align="left">29</td>
<td align="right">1</td>
</tr>
<tr class="odd">
<td>364</td>
<td align="left">33</td>
<td align="left">29</td>
<td align="right">390</td>
</tr>
<tr class="even">
<td>366</td>
<td align="left">41</td>
<td align="left">29</td>
<td align="right">1</td>
</tr>
<tr class="odd">
<td>385</td>
<td align="left">31</td>
<td align="left">31</td>
<td align="right">1706</td>
</tr>
<tr class="even">
<td>406</td>
<td align="left">27</td>
<td align="left">32</td>
<td align="right">15</td>
</tr>
<tr class="odd">
<td>407</td>
<td align="left">29</td>
<td align="left">32</td>
<td align="right">1</td>
</tr>
<tr class="even">
<td>409</td>
<td align="left">32</td>
<td align="left">32</td>
<td align="right">35083</td>
</tr>
<tr class="odd">
<td>410</td>
<td align="left">33</td>
<td align="left">32</td>
<td align="right">24</td>
</tr>
<tr class="even">
<td>425</td>
<td align="left">23</td>
<td align="left">33</td>
<td align="right">25</td>
</tr>
<tr class="odd">
<td>426</td>
<td align="left">24</td>
<td align="left">33</td>
<td align="right">294</td>
</tr>
<tr class="even">
<td>427</td>
<td align="left">25</td>
<td align="left">33</td>
<td align="right">50</td>
</tr>
<tr class="odd">
<td>429</td>
<td align="left">27</td>
<td align="left">33</td>
<td align="right">457</td>
</tr>
<tr class="even">
<td>430</td>
<td align="left">29</td>
<td align="left">33</td>
<td align="right">392</td>
</tr>
<tr class="odd">
<td>457</td>
<td align="left">40</td>
<td align="left">40</td>
<td align="right">98</td>
</tr>
<tr class="even">
<td>471</td>
<td align="left">23</td>
<td align="left">41</td>
<td align="right">9</td>
</tr>
<tr class="odd">
<td>481</td>
<td align="left">41</td>
<td align="left">41</td>
<td align="right">3800</td>
</tr>
<tr class="even">
<td>505</td>
<td align="left">128</td>
<td align="left">128</td>
<td align="right">97250</td>
</tr>
</tbody>
</table>
<p>The first two columns of the table show respectively the land cover class in year 2006 and in 2012. The third column shows the number of pixels (frequency). In cases where values for both column 1 and 2 coincide then, no land cover transition occurred. On the opposite hand, different values evidence changes.</p>
<p>We can also convert the contingency table into a confusion matrix with the following (not-so-pretty) code. Confusion matrices are sometimes easier to analyze than contingency tables… 😉</p>
<pre class="r"><code># Get the class integer codes and size
lv <- unique(c(levels(ct[,1]), levels(ct[,2])))
n <- length(lv)
# Create the square confusion matrix filled with 0's
cm <- matrix(0, nrow = n, ncol = n, dimnames = list(lv,lv))
# Fill the matrix following each line of the contingency table
for(i in 1:nrow(ct)){
cm[ct[i,1], ct[i,2]] <- ct[i,3]
}
knitr::kable(cm)</code></pre>
<table>
<thead>
<tr class="header">
<th></th>
<th align="right">1</th>
<th align="right">2</th>
<th align="right">7</th>
<th align="right">12</th>
<th align="right">13</th>
<th align="right">15</th>
<th align="right">18</th>
<th align="right">19</th>
<th align="right">20</th>
<th align="right">21</th>
<th align="right">23</th>
<th align="right">24</th>
<th align="right">25</th>
<th align="right">26</th>
<th align="right">27</th>
<th align="right">29</th>
<th align="right">31</th>
<th align="right">32</th>
<th align="right">33</th>
<th align="right">40</th>
<th align="right">41</th>
<th align="right">128</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>1</td>
<td align="right">28</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
</tr>
<tr class="even">
<td>2</td>
<td align="right">0</td>
<td align="right">503</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
</tr>
<tr class="odd">
<td>7</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">50</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
</tr>
<tr class="even">
<td>12</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">1966</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
</tr>
<tr class="odd">
<td>13</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">206</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
</tr>
<tr class="even">