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41001 1643587468</code></pre></div></div></div></section><sectionid="_other_ways_of_importing"><h2>Other ways of importing</h2><divclass="slide-content"><divclass="ulist"><ul><li><p><em>File</em> → <em>Import dataset</em> → <em>From text</em></p><divclass="ulist"><ul><li><p><em>(base)</em> → same as before but with visual help</p></li><li><p><em>(readr)</em> → using the <em>readr</em> library</p></li></ul></div></li></ul></div>
<divclass="imageblock"><imgsrc="./images/readr.png" alt="readr" height="750"></div></div></section><sectionid="_exercise_i"><h2>Exercise I</h2><divclass="slide-content"><divclass="olist arabic"><olclass="arabic"><li><p>List all CSV files using <code>list.files.</code> Check the options <code>full.names</code> & <code>recursive</code></p></li><li><p>Loop over the listed files and read them as dataframes or time series</p></li><li><p>Pick CSV files of your choice and:</p><divclass="olist loweralpha"><olclass="loweralpha" type="a"><li><p>Plot different types of plots</p></li><li><p>Run some statistical tests.</p></li><li><p>Explore the climate conditions of your area</p></li></ol></div></li><li><p>You may do some aggregation, e.g., monthly, seasonally, and annually</p></li><li><p>You can perform trend analysis or any time series analysis you would like.</p></li><li><p>You may convert the variables to common units such as Celsius or mm/day</p></li></ol></div>
<section><sectionid="_factors"><h2>Factors</h2><divclass="slide-content"><divclass="ulist"><ul><li><p><em>Factors</em> categorize the data and store it as levels</p></li><li><p>Use strings and integers</p></li><li><p>Will prove very useful with <em>tidyverse</em> and plotting with <em>ggplot2</em></p></li></ul></div><divclass="listingblock"><divclass="content"><preclass="highlightjs highlight"><codeclass="language-R hljs" data-noescape="true" data-lang="R">data <- c("East","West","East","North","North","East","West","West","West","East","North")
<sectionid="_last_remarks"><h2>Last remarks</h2><divclass="slide-content"><divclass="ulist"><ul><li><p>There usually is more than one way to achieve similar results</p></li><li><p>What was shown here was just a short overview of what can<br>
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be done with spatial data</p><divclass="ulist"><ul><li><p>Most of the functions in <em>QGIS</em> are available in R</p></li></ul></div></li><li><p>There are many more useful functions on <code>sf</code> to explore</p></li><li><p>There is much more to learn about <code>ggplot2</code> and <code>tidyverse</code></p></li><li><p>Hydrological analysis can be carried out with R</p><divclass="ulist"><ul><li><p>Watershed delineation</p></li><li><p>Voronoi polygons</p></li><li><p>Extreme events distribution fit and analysis</p></li></ul></div></li></ul></div></div></section>
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<sectionid="_exercise_i"><h2>Exercise I</h2><divclass="slide-content"><divclass="olist arabic"><olclass="arabic"><li><p>List all CSV files using <code>list.files.</code> Check the options <code>full.names</code> & <code>recursive</code></p></li><li><p>Loop over the listed files and read them as dataframes or time series</p></li><li><p>Pick CSV files of your choice and:</p><divclass="olist loweralpha"><olclass="loweralpha" type="a"><li><p>Plot different types of plots</p></li><li><p>Run some statistical tests.</p></li><li><p>Explore the climate conditions of your area</p></li></ol></div></li><li><p>You may do some aggregation, e.g., monthly, seasonally, and annually</p></li><li><p>You can perform trend analysis or any time series analysis you would like.</p></li><li><p>You may convert the variables to common units such as Celsius or mm/day</p></li></ol></div>
<sectionid="_exercise_ii"><h2>Exercise II</h2><divclass="slide-content"><divclass="olist arabic"><olclass="arabic"><li><p>List all CSV files using <code>list.files.</code> Check the options <code>full.names</code> & <code>recursive</code></p></li><li><p>Loop over the listed files and read them as dataframes or time series</p></li><li><p>Pick CSV files of your choice and:</p><divclass="olist loweralpha"><olclass="loweralpha" type="a"><li><p>Plot different types of plots</p></li><li><p>Run some statistical tests.</p></li><li><p>Explore the climate conditions of your area</p></li></ol></div></li><li><p>You may do some aggregation, e.g., monthly, seasonally, and annually</p></li><li><p>You can perform trend analysis or any time series analysis you would like.</p></li><li><p>You may convert the variables to common units such as Celsius or mm/day</p></li></ol></div>
<sectionid="_exercise_iii"><h2>Exercise III</h2><divclass="slide-content"><divclass="olist arabic"><olclass="arabic"><li><p>Pick a location (i.e. longitude and latitude), where you want to apply your analysis.</p></li><li><p>List all netCDF files in <code>final task</code> using <code>list.files.</code> Check the options <code>full.names</code> & <code>recursive</code></p></li><li><p>Access the files using <code>terra</code> and obtain the vertical profiles for your location.</p></li><li><p>The netCDF files include variables such u and v wind components<br>
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