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    <title>BES QUANTITATIVE ECOLOGY GROUP on BES QUANTITATIVE ECOLOGY GROUP</title>
    <link>https://BES-QSIG.github.io/</link>
    <description>Recent content in BES QUANTITATIVE ECOLOGY GROUP on BES QUANTITATIVE ECOLOGY GROUP</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-us</language>
    <copyright>&amp;copy; 2021</copyright>
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    <item>
      <title>We&#39;re hiring!</title>
      <link>https://BES-QSIG.github.io/post/sig-secretary/</link>
      <pubDate>Mon, 01 Nov 2021 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/post/sig-secretary/</guid>
      <description>&lt;p&gt;The Quantitative Ecology Special Interest Group is looking for our new
Secretary. Do you work in quantitative ecology and want to help shape
the future of the SIG? If so, this role is for you! You will be
responsible for planning future events which meet the aims of the SIG:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Support quantitative skills development for all ecologists&lt;/li&gt;
&lt;li&gt;Improve dissemination of new quantitative methods to their users&lt;/li&gt;
&lt;li&gt;Provide a forum to advance quantitative ecology in its own right&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you are interested in applying, please send a brief covering letter
and CV to
&lt;a href=&#34;mailto:quantitative@britishecologicalsociety.org&#34; target=&#34;_blank&#34;&gt;quantitative@britishecologicalsociety.org&lt;/a&gt;
by Friday 19th November 2021. Your covering letter should address the
following points:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Experience of organising events.&lt;/p&gt;&lt;/li&gt;

&lt;li&gt;&lt;p&gt;Experience in managing budgets.&lt;/p&gt;&lt;/li&gt;

&lt;li&gt;&lt;p&gt;The kinds of ideas you have for future SIG events.&lt;/p&gt;&lt;/li&gt;

&lt;li&gt;&lt;p&gt;Ability to travel to SIG meetings and events.&lt;/p&gt;&lt;/li&gt;

&lt;li&gt;&lt;p&gt;Evidence of ability to promote the SIG within and outside of BES.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
</description>
    </item>
    
    <item>
      <title>Quantitative Ecology SIG Annual (Virtual) Meeting 2021</title>
      <link>https://BES-QSIG.github.io/events/annualmeet_2021/</link>
      <pubDate>Tue, 27 Jul 2021 00:00:00 +0100</pubDate>
      
      <guid>https://BES-QSIG.github.io/events/annualmeet_2021/</guid>
      <description>&lt;h2 class=&#34;subtitle&#34;&gt;Programme&lt;/h2&gt;

&lt;p&gt;This year’s meeting will take place across three days, with themes of:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Integrating models and data in ecology&lt;/li&gt;
&lt;li&gt;Bringing together mathematicians and quantitative ecologists&lt;/li&gt;
&lt;li&gt;Interdisciplinary quantitative ecology&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;To accommodate time zone differences, the conference will take place at different times each day as follows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Tuesday 31st August: 8am – 11am BST (GMT +1)&lt;/li&gt;
&lt;li&gt;Wednesday 1st September: 5pm – 8pm BST
*Thursday 2nd September: 11am – 2pm BST&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In addition, we will have two keynote speakers on each day, at least one of whom is an ECR, there will be short talks, networking and a virtual poster session.&lt;/p&gt;

&lt;h2 class=&#34;subtitle&#34;&gt;Keynote speakers&lt;/h2&gt;

&lt;p&gt;Confirmed keynote speakers:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href=&#34;https://www.ufz.de/index.php?en=45284&#34; target=&#34;_blank&#34;&gt;Diana Bowler&lt;/a&gt;&lt;/strong&gt;, German Centre for Integrative Biodiversity Research (iDiv)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href=&#34;https://www.gla.ac.uk/researchinstitutes/bahcm/staff/richardreeve/#biography&#34; target=&#34;_blank&#34;&gt;Richard Reeve&lt;/a&gt;&lt;/strong&gt;, University of Glasgow&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href=&#34;http://natashaellison.co.uk/&#34; target=&#34;_blank&#34;&gt;Natasha Ellison&lt;/a&gt;&lt;/strong&gt;, University of Sheffield&lt;/li&gt;
&lt;/ul&gt;

&lt;h2 class=&#34;subtitle&#34;&gt;Talks and posters&lt;/h2&gt;

&lt;p&gt;We invite contributed talks which will be 5 minutes in length. These can either be presented live or pre-recorded. Please submit your title when registering for the meeting, and choose which day/time slot you would prefer. If you would like to present a poster, please submit your title when registering for the meeting, and choose which day/time slot you would prefer.&lt;/p&gt;

&lt;p&gt;Talk and poster title deadline is &lt;strong&gt;23:59 BST (GMT+1) 13 August 2021&lt;/strong&gt;. We will contact attendees after this to confirm talk slots.&lt;/p&gt;

&lt;h2 class=&#34;subtitle&#34;&gt;Registration&lt;/h2&gt;

&lt;p&gt;Registration deadline is &lt;strong&gt;23:59 BST, 16 August 2021&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Register &lt;a href=&#34;https://www.eventbrite.co.uk/e/quantitative-sig-annual-meeting-2021-tickets-163389130287&#34; target=&#34;_blank&#34;&gt;&lt;strong&gt;here&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Quantitative Ecology Virtual Meeting 2020</title>
      <link>https://BES-QSIG.github.io/events/annualmeet_2020/</link>
      <pubDate>Wed, 01 Jul 2020 00:00:00 +0100</pubDate>
      
      <guid>https://BES-QSIG.github.io/events/annualmeet_2020/</guid>
      <description>&lt;p&gt;Join the BES Quantitative Ecology SIG for their 3rd Annual Meeting, and first virtual meeting. There will be 4 sessions across the week, and each will have a keynote, 5-minute lightning talks, and a discussion around the session’s theme. There will also be a “coffee” break in each session to give you the chance to talk to other delegates, and there will be some networking activities, including a quiz on the Wednesday evening (BST).&lt;/p&gt;

&lt;p&gt;The aim of the Quantitative Ecology SIG is threefold:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Support quantitative skills development for all ecologists&lt;/li&gt;
&lt;li&gt;Improve dissemination of new quantitative methods to their users&lt;/li&gt;
&lt;li&gt;Provide a forum to advance quantitative ecology in its own right&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This event is for any ecologist who wants to share new quantitative methods, or original research using quantitative methods; who wants to broaden their understanding of quantitative ecology; or who wants to join the discussion around how we can move quantitative ecology forward.&lt;/p&gt;

&lt;h2 class=&#34;subtitle&#34;&gt;Programme&lt;/h2&gt;

&lt;p&gt;&lt;strong&gt;Monday 27th July (4PM – 7PM, BST, click link for time zone conversion)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Session theme: Network Ecology&lt;br /&gt;
Keynote speaker: Laura Dee, University of Colorado, Boulder&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Tuesday 28th July (10AM – 1PM)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Session theme: Systems Ecology&lt;br /&gt;
Keynote speaker: Jean-Francois Arnoldi, Trinity College Dublin&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Wednesday 29th July (4PM – 7PM)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Session theme: Disease Ecology&lt;br /&gt;
Keynote speaker: Lewis Bartlett, University of Georgia&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Thursday 30th July (10AM – 1PM)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Session theme: Teaching and Learning Quantitative Ecology&lt;br /&gt;
Keynote speaker: Gergana Daskalova, University of Edinburgh&lt;/p&gt;

&lt;h2 class=&#34;subtitle&#34;&gt;Lightning talks&lt;/h2&gt;

&lt;p&gt;If you would like to be considered to give a 5-minute lightning talk, please submit a title and select the appropriate session when prompted in the registration form.&lt;/p&gt;

&lt;h2 class=&#34;subtitle&#34;&gt;Registration&lt;/h2&gt;

&lt;p&gt;This event will take place on Zoom, and a link will be sent out to all attendees prior to the event.&lt;/p&gt;

&lt;p&gt;The registration and abstract submission deadline is &lt;strong&gt;Friday 17 July.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Register &lt;a href=&#34;https://www.eventbrite.co.uk/e/bes-quantitative-ecology-virtual-meeting-2020-tickets-109796596576&#34; target=&#34;_blank&#34;&gt;here&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Quantitative Ecology Speed-Share-Your-Research</title>
      <link>https://BES-QSIG.github.io/events/bes2019_social/</link>
      <pubDate>Fri, 06 Dec 2019 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/events/bes2019_social/</guid>
      <description>&lt;p&gt;Get to meet more quantitative ecologists and learn about the wide range of work done by members of our SIG. Then relax and enjoy food, drinks and live music for the rest of the evening in a local Belfast venue.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Open committee positions</title>
      <link>https://BES-QSIG.github.io/post/open-committee-positions/</link>
      <pubDate>Mon, 02 Dec 2019 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/post/open-committee-positions/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Deputy Secretary&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;We are looking for a Quantitative Ecologist to step into the deputy secretary role and provide some continuity to the SIG leadership. The deputy secretary role involves:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Supporting the secretary in the running of the SIG - helping with budgets, suggesting events etc.&lt;/li&gt;
&lt;li&gt;Attending meetings at BES HQ twice a year (travel &amp;amp; accommodation covered)&lt;/li&gt;
&lt;li&gt;Seek to expand the SIG and promote quantitative ecology within the BES&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Policy Rep&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Do you work in policy, or do policy-focused research and consider yourself a quantitative ecologist? We are looking to take on someone to be our policy rep. The policy rep will aim to develop events (with the support of the rest of the committee) that bring together policy makers and quantitative ecologists, or that are focused around policy issues.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;To apply&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Please send a copy of your CV, and a few sentences about what you would do in the role to &lt;a href=&#34;mailto:quantitative@britishecologicalsociety.org&#34; target=&#34;_blank&#34;&gt;quantitative@britishecologicalsociety.org&lt;/a&gt;. If you have any questions at all, do not hesitate to get in touch.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Interdisciplinary model integration thematic session at BES2019</title>
      <link>https://BES-QSIG.github.io/events/imi-bes2019/</link>
      <pubDate>Fri, 26 Jul 2019 00:00:00 +0100</pubDate>
      
      <guid>https://BES-QSIG.github.io/events/imi-bes2019/</guid>
      <description>&lt;p&gt;We are lucky to be joining a fantastic session line-up with our thematic session on &lt;strong&gt;Interdisciplinary model integration to better understand biodiversity change&lt;/strong&gt; at this year’s BES annual meeting in Belfast.&lt;/p&gt;

&lt;p&gt;The main drivers of global biodiversity change are climate change, land-use change, pollution and overfishing, which result directly from human-mediated activities, transgress ecosystems and political borders, and act globally. For a more complete understanding of biodiversity changes, integrating models from different disciplines at large spatial and temporal scales is crucial. Interdisciplinary approaches to biodiversity assessment have received increasing attention in recent years, particularly with the launch of the new BES People and Nature journal. The surge in efforts to provide a more complete picture of the socioeconomic and biophysical drivers of biodiversity change highlights new computational possibilities, unprecedented data availability, and a fast-growing acknowledgement of the importance of driver interactions across disciplines and spatial scales.&lt;/p&gt;

&lt;p&gt;Our thematic session will put the spotlight on this emerging area of research and help identify the potential for future directions and rapid developments in the field. Specifically, we will:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;provide a unique showcase of conceptual approaches and methodological tools currently used in integrated biodiversity assessment,&lt;/li&gt;
&lt;li&gt;contribute to a better understanding of interdisciplinary system complexity in such assessments,&lt;/li&gt;
&lt;li&gt;highlight strategies to reduce model and prediction uncertainty, and&lt;/li&gt;
&lt;li&gt;enhance the set of robust tools available to inform policy.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Further details and the session schedule will be published through the official BES2019 programme release. A full list of thematic sessions is available &lt;a href=&#34;https://www.britishecologicalsociety.org/events/annual-meeting-2019/call-thematic-sessions/&#34; target=&#34;_blank&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>2019 BES Quantitative &amp; Movement Ecology SIG Annual Meeting</title>
      <link>https://BES-QSIG.github.io/events/quantmove2019/</link>
      <pubDate>Wed, 22 May 2019 00:00:00 +0100</pubDate>
      
      <guid>https://BES-QSIG.github.io/events/quantmove2019/</guid>
      <description>

&lt;p&gt;&lt;img src=&#34;QuantMove2019.png&#34; alt=&#34;Caption&#34; /&gt;&lt;/p&gt;

&lt;p&gt;The BES Quantitative and Movement Ecology special interest groups will be holding their annual meetings back to back &lt;strong&gt;on 9th &amp;amp; 10th July 2019 in Sheffield&lt;/strong&gt;. This joint meeting will take advantage of shared strengths as well as shared challenges between the two groups, and foster links between them.&lt;/p&gt;

&lt;p&gt;The meeting will be along the common theme of Analysing big datasets while answering big ecological questions and the challenges associated with it. We welcome talks, posters and will have discussions on general issues with computation; integrating different datasets; and propagating uncertainty in ecological analyses.&lt;/p&gt;

&lt;p&gt;Attendees will have the opportunity to attend one, or both annual meetings. Note that it is cheaper to join the BES as a member and register for a BES member ticket, than to register for the non-BES member ticket.&lt;/p&gt;

&lt;h2 id=&#34;quantitative-ecology-keynotes&#34;&gt;Quantitative Ecology keynotes&lt;/h2&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;JamesGrecian.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;
    &lt;table&gt;&lt;b&gt;&lt;a href = &#34;http://www.jamesgrecian.com/&#34;&gt;James Grecian&lt;/a&gt;&lt;/b&gt;&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of St Andrews&lt;/span&gt;&lt;/table&gt;
      &lt;p&gt; James&#39; research focuses on understanding how marine predators interact with their environment, particularly the effect that human impacts such as offshore energy extraction, fisheries and climate change have on foraging behaviour and migration.&lt;/p&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;RachelMcCrea.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;
    &lt;table&gt;&lt;b&gt;&lt;a href = &#34;https://www.kent.ac.uk/mathematics-statistics-actuarial-science/people/1060/mccrea-rachel&#34;&gt;Rachel McCrea&lt;/a&gt;&lt;/b&gt;&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Kent&lt;/span&gt;&lt;/table&gt;
      &lt;p&gt; Rachel is an applied statistician working closely with ecologists as well as statisticians, developing new statistical methodology for the modelling of ecological data. She has worked extensively with capture-recapture data and is particularly interested in the potential of multi-state models. Her research has included the development of goodness-of-fit assessment and model selection strategies for complex data sets.  Modelling population dynamics, integrated population modelling and survey design are also current areas of interest.&lt;/p&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;h2 id=&#34;movement-ecology-keynotes&#34;&gt;Movement Ecology Keynotes&lt;/h2&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;SamanthaPatrick.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;
    &lt;table&gt;&lt;b&gt;&lt;a href = &#34;https://samanthacpatrick.wixsite.com/home&#34;&gt;Samantha Patrick&lt;/a&gt;&lt;/b&gt;&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Liverpool&lt;/span&gt;&lt;/table&gt;
      &lt;p&gt; Samantha is a behavioural ecologist interested in individual differences in mating strategies. She focuses on reproductive tactics and foraging behaviour and considers how and why alternative strategies are maintained in the population. She has worked mainly on avian systems, from great tits to albatrosses, and examined the causes and consequence of individual variation.&lt;/p&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;KarineHeerah.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;
    &lt;table&gt;&lt;b&gt;&lt;a href = &#34;https://www.researchgate.net/profile/Karine_Heerah&#34;&gt;Karine Heerah&lt;/a&gt;&lt;/b&gt;&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;Aarhus University&lt;/span&gt;&lt;/table&gt;
      &lt;p&gt; Karine is a Marie Sklodowska Curie Fellow working in the lab of Prof. Peter Madsen at Aarhus University.&lt;/p&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;TheoMichelot.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;
    &lt;table&gt;&lt;b&gt;&lt;a href = &#34;http://theo-michelot.postgrad.shef.ac.uk/&#34;&gt;Théo Michelot&lt;/a&gt;&lt;/b&gt;&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of St Andrews&lt;/span&gt;&lt;/table&gt;
      &lt;p&gt; Théo is a newly appointed Research Fellow in the School of Mathematics and Statistics at the University of St Andrews. He specialises in analyses of animal movement.&lt;/p&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;GrantHopcraft.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;
    &lt;table&gt;&lt;b&gt;&lt;a href = &#34;https://www.gla.ac.uk/researchinstitutes/bahcm/staff/granthopcraft/&#34;&gt;Grant Hopcraft&lt;/a&gt;&lt;/b&gt;&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Glasgow&lt;/span&gt;&lt;/table&gt;
      &lt;p&gt; Grant&#39;s research broadly investigates landscape-scale ecological processes that determine the distribution and abundance of animals. He focuses on African savannahs as model ecosystems to investigate how underlying biogeographical gradients, such as rainfall and soil fertility, influence animal nutrition and predation rates, and how these gradients structure community assemblages.&lt;/p&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;h2 id=&#34;talks-and-posters&#34;&gt;TALKS AND POSTERS&lt;/h2&gt;

&lt;p&gt;We will have slots for talks and space for posters at the meeting!&lt;/p&gt;

&lt;p&gt;Talks will be &lt;strong&gt;5 minutes&lt;/strong&gt; long. There may not be enough talk slots for everyone, so priority will be given to early career researchers. Please include a rough title when you register so we have an idea of the balance of topics (you can change it later). Students will also be given the option to put themselves forward for the student plenary talk (20 minutes). We highly recommend applying; it&amp;rsquo;s fun and a great thing to put on your CV.&lt;/p&gt;

&lt;h2 id=&#34;accessibility&#34;&gt;ACCESSIBILITY&lt;/h2&gt;

&lt;p&gt;All facilities, rooms and spaces in the conference venue are wheelchair-accessible and there are disabled parking spaces nearby. See &lt;a href=&#34;https://www.accessable.co.uk/venues/alfred-denny-building&#34; target=&#34;_blank&#34;&gt;https://www.accessable.co.uk/venues/alfred-denny-building&lt;/a&gt; for more information and please contact us if you have any questions.&lt;/p&gt;

&lt;h2 id=&#34;families-and-childcare&#34;&gt;FAMILIES AND CHILDCARE&lt;/h2&gt;

&lt;p&gt;If you plan to bring your baby or young child to the meeting, please get in touch with us to let us know what facilities you would require, for example, a room for nursing or créche facilities. We have not made any specific arrangements for families but we will be happy to help as much as we can.&lt;/p&gt;

&lt;h2 id=&#34;accommodation&#34;&gt;ACCOMMODATION&lt;/h2&gt;

&lt;p&gt;&lt;a href=&#34;https://www.sheffield.ac.uk/accommodation/visitors&#34; target=&#34;_blank&#34;&gt;https://www.sheffield.ac.uk/accommodation/visitors&lt;/a&gt;&lt;/p&gt;

&lt;h2 id=&#34;twitter&#34;&gt;TWITTER&lt;/h2&gt;

&lt;p&gt;#BESQuantMove2019&lt;/p&gt;

&lt;h2 id=&#34;deadline&#34;&gt;DEADLINE&lt;/h2&gt;

&lt;p&gt;The deadline for registration is &lt;strong&gt;Friday, 7th June 2019&lt;/strong&gt; (11:59 PM UK time). We will contact attendees after this to confirm talk slots.&lt;/p&gt;

&lt;h2 id=&#34;register&#34;&gt;REGISTER&lt;/h2&gt;

&lt;p&gt;You can register on our &lt;a href=&#34;https://www.eventbrite.co.uk/e/bes-quantitative-movement-ecology-2019-tickets-59447350626&#34; target=&#34;_blank&#34;&gt;EventBrite&lt;/a&gt; page.&lt;/p&gt;

&lt;h2 id=&#34;contact&#34;&gt;CONTACT&lt;/h2&gt;

&lt;p&gt;Feel free to contact us on twitter &lt;a href=&#34;https://twitter.com/BES_QE_SIG&#34; target=&#34;_blank&#34;&gt;@bes_qe_sig&lt;/a&gt; and &lt;a href=&#34;https://twitter.com/BES_Move_SIG&#34; target=&#34;_blank&#34;&gt;@bes_move_sig&lt;/a&gt; or by emailing &lt;a href=&#34;mailto:quantitative@britishecologicalsociety.org&#34; target=&#34;_blank&#34;&gt;quantitative@britishecologicalsociety.org&lt;/a&gt; or &lt;a href=&#34;mailto:movement@britishecologicalsociety.org&#34; target=&#34;_blank&#34;&gt;movement@britishecologicalsociety.org&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Quantitative Ecology Special Interest Group QE-SIG</title>
      <link>https://BES-QSIG.github.io/about/qesig/</link>
      <pubDate>Sun, 24 Feb 2019 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/about/qesig/</guid>
      <description>&lt;p&gt;We have migrated from our old &lt;a href=&#34;https://besquantitativeecology.wordpress.com/&#34; target=&#34;_blank&#34;&gt;Wordpress site&lt;/a&gt; in order to keep everything in one place. Our Wordpress site will function as an archive, but new material will be updated here.&lt;/p&gt;

&lt;p&gt;Our aim as a group is to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Support quantitative skills development for all ecologists&lt;/li&gt;
&lt;li&gt;Improve dissemination of new quantitative methods to their users&lt;/li&gt;
&lt;li&gt;Provide a forum to advance quantitative ecology in its own right&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you have ideas for events, workshops or courses that you would like to see us organise (or would like to organise yourself with our support), please &lt;a href=&#34;https://BES-QSIG.github.io/#contact&#34;&gt;contact us&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Welcome to the new Quantitative Ecology Site</title>
      <link>https://BES-QSIG.github.io/post/welcome-to-new-site/</link>
      <pubDate>Tue, 27 Nov 2018 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/post/welcome-to-new-site/</guid>
      <description>&lt;p&gt;We have migrated from our old &lt;a href=&#34;https://besquantitativeecology.wordpress.com/&#34; target=&#34;_blank&#34;&gt;Wordpress site&lt;/a&gt; in order to keep everything in one place. Our Wordpress site will function as an archive, but new material will be updated here.&lt;/p&gt;

&lt;p&gt;Our aim as a group is to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Support quantitative skills development for all ecologists&lt;/li&gt;
&lt;li&gt;Improve dissemination of new quantitative methods to their users&lt;/li&gt;
&lt;li&gt;Provide a forum to advance quantitative ecology in its own right&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you have ideas for events, workshops or courses that you would like to see us organise (or would like to organise yourself with our support), please &lt;a href=&#34;https://BES-QSIG.github.io/#contact&#34;&gt;contact us&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Advancing Your R</title>
      <link>https://BES-QSIG.github.io/courses/advancingr/</link>
      <pubDate>Tue, 20 Nov 2018 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/courses/advancingr/</guid>
      <description>

&lt;h2 id=&#34;pre-course-instructions&#34;&gt;Pre-course Instructions&lt;/h2&gt;

&lt;p&gt;Below see instructions on the software and R packages to install &lt;strong&gt;before&lt;/strong&gt; you arrive. Note that you will receive the greatest benefit of the course if you have already installed the software and packages.&lt;/p&gt;

&lt;p&gt;You will need to have an up-to-date installation of &lt;a href=&#34;https://cran.r-project.org/mirrors.html&#34; target=&#34;_blank&#34;&gt;R&lt;/a&gt; and &lt;a href=&#34;https://www.rstudio.com/products/rstudio/download/#download&#34; target=&#34;_blank&#34;&gt;RStudio&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You will need to install &lt;a href=&#34;https://www.postgresql.org/download/&#34; target=&#34;_blank&#34;&gt;PostgreSQL&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You will also need to install some R packages using the below code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;install.packages(&amp;quot;rmarkdown&amp;quot;)
install.packages(&amp;quot;tidyverse&amp;quot;)
install.packages(&amp;quot;broom&amp;quot;)
install.packages(&amp;quot;ggExtra&amp;quot;)
install.packages(&amp;quot;maps&amp;quot;)
install.packages(&amp;quot;RColorBrewer&amp;quot;)
install.packages(&amp;quot;gridExtra&amp;quot;)
install.packages(&amp;quot;rgbif&amp;quot;)
install.packages(&amp;quot;CoordinateCleaner&amp;quot;)
install.packages(&amp;quot;ggrepel&amp;quot;)
install.packages(&amp;quot;png&amp;quot;)
install.packages(&amp;quot;mapdata&amp;quot;)
install.packages(&amp;quot;ggthemes&amp;quot;)
install.packages(&amp;quot;sf&amp;quot;)
install.packages(&amp;quot;RPostgreSQL&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;getting-here&#34;&gt;Getting here&lt;/h2&gt;

&lt;p&gt;The course is taking place in the Waterhouse Room at the &lt;a href=&#34;https://www.liverpool.ac.uk/conferences-and-events/venue-finder/foresight-centre/&#34; target=&#34;_blank&#34;&gt;Foresight Centre&lt;/a&gt;, University of Liverpool.&lt;/p&gt;

&lt;iframe src=&#34;https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d1681.7435965583238!2d-2.9694946677972656!3d53.40866296464316!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x487b211782d9e8e3%3A0x8524f47e6bc7433d!2sForesight+Centre+(Block+J)%2C+Liverpool+L3+5QA!5e0!3m2!1sen!2suk!4v1543347942213&#34; width=&#34;600&#34; height=&#34;450&#34; frameborder=&#34;0&#34; style=&#34;border:0&#34; allowfullscreen&gt;&lt;/iframe&gt;

&lt;h3 id=&#34;road&#34;&gt;Road&lt;/h3&gt;

&lt;p&gt;From the M62 (end of motorway 3 miles)&lt;/p&gt;

&lt;p&gt;From the M53( Wirral/Mersey Tunnels &lt;sup&gt;3&lt;/sup&gt;&amp;frasl;&lt;sub&gt;4&lt;/sub&gt; mile)&lt;/p&gt;

&lt;p&gt;Please use L3 5DA for sat nav&lt;/p&gt;

&lt;h3 id=&#34;rail&#34;&gt;Rail&lt;/h3&gt;

&lt;p&gt;Liverpool Lime St Station is a 10 minute walk.&lt;/p&gt;

&lt;p&gt;For local public transport please contact Merseytravel&lt;/p&gt;

&lt;h3 id=&#34;air&#34;&gt;Air&lt;/h3&gt;

&lt;p&gt;Liverpool John Lennon  Airport is 7 Miles away from the Foresight Centre.&lt;/p&gt;

&lt;p&gt;The Airlink 500 loops to the city centre every 30 minutes.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Day One: Data manipulation and Visualisation using the tidyverse</title>
      <link>https://BES-QSIG.github.io/courses/advancingr-tidyverse/</link>
      <pubDate>Tue, 20 Nov 2018 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/courses/advancingr-tidyverse/</guid>
      <description>

&lt;h1 id=&#34;part-i-create-a-reproducible-report-using-markdown&#34;&gt;PART I: Create a reproducible report using Markdown&lt;/h1&gt;

&lt;h2 id=&#34;1-what-is-r-markdown&#34;&gt;1. What is R Markdown?&lt;/h2&gt;

&lt;p&gt;R Markdown allows you to create documents that serve as a neat record of your analysis. In the world of reproducible research, we want other researchers to easily understand what we did in our analysis. You might choose to create an R markdown document as an appendix to a paper or project assignment that you are doing, upload it to an online repository such as Github, or simply to keep as a personal record so you can quickly look back at your code and see what you did. R Markdown presents your code alongside its output (graphs, tables, etc.) with conventional text to explain it, a bit like a notebook. Your report can also be what you base your future methods and results sections in your manuscripts, thesis chapters, etc.&lt;/p&gt;

&lt;p&gt;R Markdown uses &lt;a href=&#34;http://www.markdowntutorial.com&#34; target=&#34;_blank&#34;&gt;markdown syntax&lt;/a&gt;. Markdown is a very simple &amp;lsquo;markup&amp;rsquo; language which provides methods for creating documents with headers, images, links etc. from plain text files, while keeping the original plain text file easy to read. You can convert Markdown documents to other file types like &lt;code&gt;.html&lt;/code&gt; or &lt;code&gt;.pdf&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/md_script.png&#34; alt=&#34;Img&#34; style=&#34;width: 900px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;h2 id=&#34;2-download-r-markdown&#34;&gt;2. Download R Markdown&lt;/h2&gt;

&lt;p&gt;To get R Markdown working in RStudio, the first thing you need is the &lt;code&gt;rmarkdown&lt;/code&gt; package, which you can get from &lt;a href=&#34;https://cran.r-project.org/web/packages/rmarkdown/index.html&#34; target=&#34;_blank&#34;&gt;CRAN&lt;/a&gt; by running the following commands in R or RStudio:&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;install.packages(&amp;quot;rmarkdown&amp;quot;)
library(rmarkdown)
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;3-the-different-parts-of-an-r-markdown-file&#34;&gt;3. The different parts of an R Markdown file&lt;/h2&gt;

&lt;h3 id=&#34;the-yaml-header&#34;&gt;The YAML Header&lt;/h3&gt;

&lt;p&gt;At the top of any R Markdown script is a &lt;code&gt;YAML&lt;/code&gt; header section enclosed by &lt;code&gt;---&lt;/code&gt;. By default this includes a title, author, date and the file type you want to output to. Many other options are available for different functions and formatting, see &lt;a href=&#34;http://rmarkdown.rstudio.com/html_document_format.html&#34; target=&#34;_blank&#34;&gt;here for &lt;code&gt;.html&lt;/code&gt; options&lt;/a&gt; and &lt;a href=&#34;http://rmarkdown.rstudio.com/pdf_document_format.html&#34; target=&#34;_blank&#34;&gt;here for &lt;code&gt;.pdf&lt;/code&gt; options&lt;/a&gt;. Rules in the header section will alter the whole document.&lt;/p&gt;

&lt;p&gt;Add your own details at the top of your&lt;code&gt;.Rmd&lt;/code&gt; script, e.g.:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;---
title: &amp;quot;The tidyverse in action - population change in forests&amp;quot;
author: Gergana Daskalova
date: 22/Oct/2016
output: html_document
---
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;By default, the &lt;code&gt;title&lt;/code&gt;, &lt;code&gt;author&lt;/code&gt;, &lt;code&gt;date&lt;/code&gt; and &lt;code&gt;output&lt;/code&gt; format are printed at the top of your &lt;code&gt;.html&lt;/code&gt; document.&lt;/p&gt;

&lt;p&gt;Now that we have our first piece of content, we can test the &lt;code&gt;.Rmd&lt;/code&gt; file by compiling it to &lt;code&gt;.html&lt;/code&gt;. To compile your &lt;code&gt;.Rmd&lt;/code&gt; file into a &lt;code&gt;.html&lt;/code&gt; document, you should press the &lt;code&gt;Knit&lt;/code&gt; button in the taskbar:&lt;/p&gt;

&lt;p&gt;&lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/Knit_HTML_Screenshot.jpg&#34; alt=&#34;Img&#34;&gt;&lt;/p&gt;

&lt;p&gt;Not only does a preview appear in the &lt;code&gt;Viewer&lt;/code&gt; window in RStudio, but it also saves a &lt;code&gt;.html&lt;/code&gt; file to the same folder where you saved your &lt;code&gt;.Rmd&lt;/code&gt; file.&lt;/p&gt;

&lt;p&gt;&lt;a name=&#34;insert&#34;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h3 id=&#34;code-chunks&#34;&gt;Code Chunks&lt;/h3&gt;

&lt;p&gt;&lt;strong&gt;Have a read through the text below to learn a bit more about how Markdown works and then you can start compiling the rest of your &lt;code&gt;.Md&lt;/code&gt; file.&lt;/strong&gt;&lt;/p&gt;

&lt;h4 id=&#34;the-setup-chunk&#34;&gt;The setup chunk&lt;/h4&gt;

&lt;p&gt;&lt;strong&gt;This code chunk appears in &lt;code&gt;.Md&lt;/code&gt; files in R by default, it won&amp;rsquo;t appear in your html or pdf document, it just sets up the document.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;h4 id=&#34;the-rest-of-the-code-chunks&#34;&gt;The rest of the code chunks&lt;/h4&gt;

&lt;p&gt;This is where you can add your own code, accompanying explanation and any outputs. Code that is included in your &lt;code&gt;.Rmd&lt;/code&gt; document should be enclosed by three backwards apostrophes &lt;code&gt;```&lt;/code&gt; (grave accents!). These are known as code chunks and look like this (no need to copy this, just an example):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
norm &amp;lt;- rnorm(100, mean = 0, sd = 1)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Inside the curly brackets is a space where you can assign rules for that code chunk. The code chunk above says that the code is R code.&lt;/p&gt;

&lt;p&gt;It&amp;rsquo;s important to remember when you are creating an R Markdown file that if you want to run code that refers to an object, for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
plot(dataframe)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You have to include the code that defines what &lt;code&gt;dataframe&lt;/code&gt; is, just like in a normal R script. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
A &amp;lt;- c(&amp;quot;a&amp;quot;, &amp;quot;a&amp;quot;, &amp;quot;b&amp;quot;, &amp;quot;b&amp;quot;)
B &amp;lt;- c(5, 10, 15, 20)
dataframe &amp;lt;- data.frame(A, B)
plot(dataframe)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or if you are loading a dataframe from a &lt;code&gt;.csv&lt;/code&gt; file, you must include the code in the &lt;code&gt;.Rmd&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
dataframe &amp;lt;- read.csv(&amp;quot;~/Desktop/Code/dataframe.csv&amp;quot;)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Similarly, if you are using any packages in your analysis, you have to load them in the &lt;code&gt;.Rmd&lt;/code&gt; file using &lt;code&gt;library()&lt;/code&gt; like in a normal R script.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
library(dplyr)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;h4 id=&#34;hiding-code-chunks&#34;&gt;Hiding code chunks&lt;/h4&gt;

&lt;p&gt;If you don&amp;rsquo;t want the code of a particular code chunk to appear in the final document, but still want to show the output (e.g. a plot), then you can include &lt;code&gt;echo = FALSE&lt;/code&gt; in the code chunk instructions.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r, echo = FALSE}
A &amp;lt;- c(&amp;quot;a&amp;quot;, &amp;quot;a&amp;quot;, &amp;quot;b&amp;quot;, &amp;quot;b&amp;quot;)
B &amp;lt;- c(5, 10, 15, 20)
dataframe &amp;lt;- data.frame(A, B)
plot(dataframe)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sometimes, you might want to create an object, but not include both the code and its output in the final &lt;code&gt;.html&lt;/code&gt; file. To do this you can use, &lt;code&gt;include = FALSE&lt;/code&gt;. Be aware though, when making reproducible research it&amp;rsquo;s often not a good idea to completely hide some part of your analysis:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;REMEMBER: R Markdown doesn&amp;rsquo;t pay attention to anything you have loaded in other R scripts, you have to load all objects and packages in the R Markdown script.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Now you can start copying across the code from your tidyverse script and insert it into a code chunk in your &lt;code&gt;.Rmd&lt;/code&gt; document. Better not to do it all at once, you can start with the first parts of the tidyverse script and gradually add on more after you&amp;rsquo;ve seen what the &lt;code&gt;.Rmd&lt;/code&gt; output looks like.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You can run an individual chunk of code at any time by placing your cursor inside the code chunk and selecting &lt;code&gt;Run -&amp;gt; Run Current Chunk&lt;/code&gt;:&lt;/p&gt;

&lt;p&gt;&lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/run_sel.png&#34; alt=&#34;Img&#34; style=&#34;width: 900px;&#34;/&gt;&lt;/p&gt;

&lt;h3 id=&#34;summary-of-code-chunk-instructions&#34;&gt;Summary of  code chunk instructions&lt;/h3&gt;

&lt;p&gt;&lt;style type=&#34;text/css&#34;&gt;
.tg  {border-collapse:collapse;border-spacing:0;}
.tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;}
.tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;}
.tg .tg-yw4l{vertical-align:top}
&lt;/style&gt;
&lt;table class=&#34;tg&#34;&gt;
  &lt;tr&gt;
    &lt;th class=&#34;tg-yw4l&#34;&gt;&lt;b&gt;Rule&lt;/b&gt;&lt;/th&gt;
    &lt;th class=&#34;tg-yw4l&#34;&gt;&lt;b&gt;Example&lt;/b&gt;&lt;br&gt;(default)&lt;/th&gt;
    &lt;th class=&#34;tg-yw4l&#34;&gt;&lt;b&gt;Function&lt;/b&gt;&lt;/th&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;eval&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;eval=TRUE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Is the code run and the results included in the output?&lt;/td&gt;
  &lt;/tr&gt;
    &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;include&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;include=TRUE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Are the code and the results included in the output?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;echo&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;echo=TRUE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Is the code displayed alongside the results?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;warning&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;warning=TRUE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Are warning messages displayed?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;error&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;error=FALSE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Are error messages displayed?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;message&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;message=TRUE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Are messages displayed?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;tidy&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;tidy=FALSE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Is the code reformatted to make it look “tidy”?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;results&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;results=&amp;ldquo;markup&amp;rdquo;&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;&lt;b&gt; How are results treated? &lt;/b&gt; &lt;br&gt; &amp;ldquo;hide&amp;rdquo; = no results &lt;br&gt;&amp;ldquo;asis&amp;rdquo; = results without formatting &lt;br&gt;&amp;ldquo;hold&amp;rdquo; = results only compiled at end of chunk (use if many commands act on one object)&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;cache&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;cache=FALSE&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;Are the results cached for future renders?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;comment&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;comment=&amp;ldquo;##&amp;rdquo;&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;What character are comments prefaced with?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;fig.width, fig.height&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;fig.width=7&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;What width/height (in inches) are the plots?&lt;/td&gt;
  &lt;/tr&gt;
  &lt;tr&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;fig.align&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;fig.align=&amp;ldquo;left&amp;rdquo;&lt;/td&gt;
    &lt;td class=&#34;tg-yw4l&#34;&gt;&amp;ldquo;left&amp;rdquo; &amp;ldquo;right&amp;rdquo; &amp;ldquo;center&amp;rdquo;&lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;&lt;/p&gt;

&lt;h2 id=&#34;4-inserting-figures&#34;&gt;4. Inserting Figures&lt;/h2&gt;

&lt;p&gt;By default, RMarkdown will place graphs by maximising their height, while keeping them within the margins of the page and maintaining aspect ratio. If you have a particularly tall figure, this can mean a really huge graph. To manually set the figure dimensions, you can insert an instruction into the curly braces:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r, fig.width = 2.5, fig.height = 7.5}
ggplot(df, aes(x = x, y = y) + geom_point()
```
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;5-inserting-tables&#34;&gt;5. Inserting Tables&lt;/h2&gt;

&lt;p&gt;R Markdown can print the contents of a data frame easily by enclosing the name of the data frame in a code chunk:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
dataframe
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This can look a bit messy, especially with data frames with a lot of columns. You can also use a table formatting function, e.g. &lt;code&gt;kable()&lt;/code&gt; from the &lt;code&gt;knitr&lt;/code&gt; package. The first argument tells kable to make a table out of the object &lt;code&gt;dataframe&lt;/code&gt; and that numbers should have two significant figures. Remember to load the &lt;code&gt;knitr&lt;/code&gt; package in your &lt;code&gt;.Rmd&lt;/code&gt; file, if you are using the &lt;code&gt;kable()&lt;/code&gt; function.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
kable(dataframe, digits = 2)
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you want a bit more control over the content of your table you can use &lt;code&gt;pander()&lt;/code&gt; from the &lt;code&gt;pander&lt;/code&gt; package. Imagine I want the 3rd column to appear in italics:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;```{r}
emphasize.italics.cols(3)  # Make the 3rd column italics
pander(richness_abund)  # Create the table
```
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Now that you have started your &lt;code&gt;Markdown&lt;/code&gt; document, you can use that when completing the next part of the tutorial, i.e., inserting the code that follows into code chunks and then generating a report at the end of this tutorial.&lt;/strong&gt;&lt;/p&gt;

&lt;h1 id=&#34;part-ii-analyse-and-visualise-data-using-the-tidyverse&#34;&gt;PART II: Analyse and visualise data using the &lt;code&gt;tidyverse&lt;/code&gt;&lt;/h1&gt;

&lt;h2 id=&#34;learning-objectives&#34;&gt;Learning Objectives&lt;/h2&gt;

&lt;h2 id=&#34;1-intro-to-the-tidyverse&#34;&gt;1. Intro to the &lt;code&gt;tidyverse&lt;/code&gt;&lt;/h2&gt;

&lt;h3 id=&#34;how-to-analyse-population-change-of-forest-vertebrates&#34;&gt;How to analyse population change of forest vertebrates&lt;/h3&gt;

&lt;ol&gt;
&lt;li&gt;How to write a custom &lt;code&gt;ggplot2&lt;/code&gt; function&lt;/li&gt;
&lt;li&gt;How to use &lt;code&gt;gather()&lt;/code&gt; and &lt;code&gt;spread()&lt;/code&gt; from the &lt;code&gt;tidyr&lt;/code&gt; package&lt;/li&gt;
&lt;li&gt;How to parse numbers using &lt;code&gt;parse_number()&lt;/code&gt; from the &lt;code&gt;readr&lt;/code&gt; package&lt;/li&gt;
&lt;li&gt;How to use the &lt;code&gt;distinct()&lt;/code&gt; function from &lt;code&gt;dplyr&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;How to use the &lt;code&gt;filter()&lt;/code&gt; function from &lt;code&gt;dplyr&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;How to use the &lt;code&gt;mutate()&lt;/code&gt; function from &lt;code&gt;dplyr&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;How to use the &lt;code&gt;summarise()&lt;/code&gt;/&lt;code&gt;summarize()&lt;/code&gt; function from &lt;code&gt;dplyr&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;How to use the &lt;code&gt;tidy()&lt;/code&gt; function from the &lt;code&gt;broom&lt;/code&gt; package to summarise model results&lt;/li&gt;
&lt;li&gt;How to use the &lt;code&gt;select()&lt;/code&gt; function from &lt;code&gt;dplyr&lt;/code&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;b&gt;In this tutorial, we will focus on how to efficiently format, manipulate and visualise large datasets. We will use the &lt;code&gt;tidyr&lt;/code&gt; and &lt;code&gt;dplyr&lt;/code&gt; packages to clean up data frames and calculate new variables. We will use the &lt;code&gt;broom&lt;/code&gt; and &lt;code&gt;purr&lt;/code&gt; packages to make the modelling of thousands of population trends more efficient. We will use the &lt;code&gt;ggplot2&lt;/code&gt; package to make graphs, maps of occurrence records, and to visualise ppulation trends and then we will arrange all of our graphs together using the &lt;code&gt;gridExtra&lt;/code&gt; package.&lt;/b&gt;&lt;/p&gt;

&lt;p&gt;We will be working with population data from the &lt;a href=&#34;http://www.livingplanetindex.org/home/index&#34; target=&#34;_blank&#34;&gt;Living Planet Database&lt;/a&gt; and red deer occurrence data from the &lt;a href=&#34;http://www.gbif.org/&#34; target=&#34;_blank&#34;&gt;Global Biodiversity Information Facility&lt;/a&gt;, both of which are publicly available datasets.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;First, we will model population change for vertebrate forest species to see whether greater population change is found for longer duration studies.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Because we have created a version-controlled &lt;code&gt;R&lt;/code&gt; project using the repository for the workshop, we are already in the right working directory, i.e. the folder that contains all the data and other files, thus there is no need for us to set a working directory at the start of the script, unless we explicitly want to change it for some reason.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Here are the packages we need. Note that not all &lt;code&gt;tidyverse&lt;/code&gt; packages load automatically with &lt;code&gt;library(tidyverse)&lt;/code&gt; -  only the core ones do, so you need to load &lt;code&gt;broom&lt;/code&gt; separately. If you don&amp;rsquo;t have some of the packages installed, you can install them using &lt;code&gt;ìnstall.packages(&amp;quot;package-name&amp;quot;)&lt;/code&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Packages ----
library(tidyverse)  # Hadley Wickham&#39;s tidyverse - the theme of this tutorial
library(broom)  # To summarise model outputs
library(ggExtra)  # To make pretty graphs - addon package to ggplot2
library(maps)  # To make pretty maps - warning: maps masks map from purr!
library(RColorBrewer)  # To make pretty colours
library(gridExtra)  # To arrange multi-plot panels
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you&amp;rsquo;ve ever tried to perfect your &lt;code&gt;ggplot2&lt;/code&gt; graphs, you might have noticed that the lines starting with &lt;code&gt;theme()&lt;/code&gt; quickly pile up: you adjust the font size of the axes and the labels, the position of the title, the background colour of the plot, you remove the grid lines in the background, etc. And then you have to do the same for the next plot, which really increases the amount of code you use. Here is a simple solution: create a customised theme that combines all the &lt;code&gt;theme()&lt;/code&gt; elements you want and apply it to your graphs to make things easier and increase consistency. You can include as many elements in your theme as you want, as long as they don&amp;rsquo;t contradict one another and then when you apply your theme to a graph, only the relevant elements will be considered - e.g. for our graphs we won&amp;rsquo;t need to use &lt;code&gt;legend.position&lt;/code&gt;, but it&amp;rsquo;s fine to keep it in the theme in case any future graphs we apply it to do have the need for legends.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Setting a custom ggplot2 function ---
# *** Functional Programming ***
# This function makes a pretty ggplot theme
# This function takes no arguments!
theme_LPD &amp;lt;- function(){
  theme_bw()+
    theme(axis.text.x = element_text(size = 12, vjust = 1, hjust = 1),
          axis.text.y = element_text(size = 12),
          axis.title.x = element_text(size = 14, face = &amp;quot;plain&amp;quot;),
          axis.title.y = element_text(size = 14, face = &amp;quot;plain&amp;quot;),
          panel.grid.major.x = element_blank(),
          panel.grid.minor.x = element_blank(),
          panel.grid.minor.y = element_blank(),
          panel.grid.major.y = element_blank(),
          plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), units = , &amp;quot;cm&amp;quot;),
          plot.title = element_text(size = 20, vjust = 1, hjust = 0.5),
          legend.text = element_text(size = 12, face = &amp;quot;italic&amp;quot;),
          legend.title = element_blank(),
          legend.position = c(0.5, 0.8))
}
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;load-population-trend-data&#34;&gt;Load population trend data&lt;/h3&gt;

&lt;p&gt;&lt;strong&gt;The data are in a &lt;code&gt;.RData&lt;/code&gt; format, as those are quicker to use, since &lt;code&gt;.Rdata&lt;/code&gt; files are more compressed. Of course, a drawback is that &lt;code&gt;.RData&lt;/code&gt; files can only be used within R, whereas &lt;code&gt;.csv&lt;/code&gt; files are more transferable.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Load data ----
load(&amp;quot;LPDdata_Feb2016.RData&amp;quot;)

# Inspect data ----
head(LPDdata_Feb2016)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/wide.png&#34; alt=&#34;Img&#34; style=&#34;width: 600px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;p&gt;At the moment, each row contains a population that has been monitored over time and towards the right of the data frame there are lots of columns with population estimates for each year. To make this data &amp;ldquo;tidy&amp;rdquo; (one column per variable) we can use &lt;code&gt;gather()&lt;/code&gt; to transform the data so there is a new column containing all the years for each population and an adjacent column containing all the population estimates for those years.&lt;/p&gt;

&lt;p&gt;This takes our original dataset &lt;code&gt;LPIdata_Feb2016&lt;/code&gt; and creates a new column called &lt;code&gt;year&lt;/code&gt;, fills it with column names from columns &lt;code&gt;26:70&lt;/code&gt; and then uses the data from these columns to make another column called &lt;code&gt;pop&lt;/code&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Format data for analysis ----

# Transform from wide to long format usign gather (opposite is spread)
# *** gather() function from the dplyr package in the tidyverse ***
LPD_long &amp;lt;- gather(data = LPDdata_Feb2016, key = &amp;quot;year&amp;quot;, value = &amp;quot;pop&amp;quot;, select = 26:70)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Because column names are coded in as characters, when we turned the column names (&lt;code&gt;1970&lt;/code&gt;, &lt;code&gt;1971&lt;/code&gt;, &lt;code&gt;1972&lt;/code&gt;, etc.) into rows, R automatically put an &lt;code&gt;X&lt;/code&gt; in front of the numbers to force them to remain characters. We don&amp;rsquo;t want that, so to turn &lt;code&gt;year&lt;/code&gt; into a numeric variable, use the &lt;code&gt;parse_number()&lt;/code&gt; function from the &lt;code&gt;readr&lt;/code&gt; package. We can also make all the column names lowercase and remove some of the funky characters in the country column - strange characters mess up things in general, e.g. when you want to save files, push them to GitHub, etc.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Get rid of the X in front of years
# *** parse_number() from the readr package in the tidyverse ***
LPD_long$year &amp;lt;- parse_number(LPD_long$year)

# Rename variable names for consistency
names(LPD_long)
names(LPD_long) &amp;lt;- tolower(names(LPD_long))
names(LPD_long)

# Create new column with genus and species together
LPD_long$species.name &amp;lt;- paste(LPD_long$genus, LPD_long$species, sep = &amp;quot; &amp;quot;)

# Get rid of strange characters like &amp;quot; / &amp;quot;
LPD_long$country.list &amp;lt;- gsub(&amp;quot;,&amp;quot;, &amp;quot;&amp;quot;, LPD_long$country.list, fixed = TRUE)
LPD_long$biome &amp;lt;- gsub(&amp;quot;/&amp;quot;, &amp;quot;&amp;quot;, LPD_long$biome, fixed = TRUE)

# Examine the tidy data frame
head(LPD_long)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/long.png&#34; alt=&#34;Img&#34; style=&#34;width: 600px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;p&gt;Now that our dataset is &lt;em&gt;tidy&lt;/em&gt; we can get it ready for our analysis. We want to only use populations that have more than 5 years of data to make sure our analysis has enough data to capture population change. We should also scale the population data, because since the data come from many species, the units and magnitude of the data are very different - imagine tiny fish whose abundance is in the millions, and large carnivores whose abundance is much smaller. Scaling also normalises the data, as later on we will be using linear models assuming a normal distribution. To do all of this in one go, we can use pipes.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Pipes (&lt;code&gt;%&amp;gt;%&lt;/code&gt;) are a way of streamlining data manipulation - imagine all of your data coming in one end of the pipe, while they are in there, they are manipulated, summarised, etc., then the output (e.g. your new data frame or summary statistics) comes out the other end of the pipe. At each step of the pipe processing, the pipe is using the ouput of the previous step.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Data manipulation ----

# *** piping from from dplyr
LPD_long2 &amp;lt;- LPD_long %&amp;gt;%
  # Remove duplicate rows
  # *** distinct() function from dplyr
  distinct(LPD_long) %&amp;gt;%
  # remove NAs in the population column
  # *** filter() function from dplyr
  filter(is.finite(pop)) %&amp;gt;%
  # Group rows so that each group is one population
  # *** group_by() function from dplyr
  group_by(id) %&amp;gt;%
  # Make some calculations
  # *** mutate() function from dplyr
  mutate(maxyear = max(year), minyear = min(year),
         # Calculate duration
         duration = maxyear - minyear,
         # Scale population trend data
         scalepop = (pop - min(pop))/(max(pop) - min(pop))) %&amp;gt;%
  # Keep populations with &amp;gt;5 years worth of data and calculate length of monitoring
  filter(is.finite(scalepop),
         length(unique(year)) &amp;gt; 5) %&amp;gt;%
  # Remove any groupings you&#39;ve greated in the pipe
  ungroup()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we can explore our data a bit. Let&amp;rsquo;s create a few basic summary statistics for each biome and store them in a new data frame:&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Calculate summary statistics for each biome
LPD_biome_sum &amp;lt;- LPD_long2 %&amp;gt;%
  # Group by biome
  group_by(biome) %&amp;gt;%
  # Create columns, number of populations
  #  *** summarise()/summarize() function from dplyr in the tidyverse ***
  summarise(populations = n(),
            # Calculate the mean study length
            mean_study_length_years = mean(duration),
            # Model sampling method
            dominant_sampling_method = names(which.max(table(sampling.method))),
            # Model unit type
            dominant_units = names(which.max(table(units)))) %&amp;gt;%
  # Remove any groupings you&#39;ve greated in the pipe
  ungroup()

# Take a look at some of the records
head(LPD_biome_sum)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Next we will explore how populations from two of these biomes, temperate coniferous and temperate broadleaf forests, have changed over the monitoring duration. We will make the &lt;code&gt;biome&lt;/code&gt; variable a factor (before it is just a character variable), so that later on we can make graphs based on the two categories (coniferous and broadleaf forests). We&amp;rsquo;ll use the &lt;code&gt;filter()&lt;/code&gt; function from &lt;code&gt;dplyr&lt;/code&gt; to subset the data to just the forest species.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Subset to just temperate forest species -----
  # Notice the difference between | and &amp;amp; when filtering
  # | is an &amp;quot;or&amp;quot; whereas &amp;amp; is &amp;quot;and&amp;quot;, i.e. both conditions have to be met
  # at the same time
LPD_long2$biome &amp;lt;- as.factor(LPD_long2$biome)
LPD.forest &amp;lt;- filter(LPD_long2, biome == &amp;quot;Temperate broadleaf and mixed forests&amp;quot; |
                                      biome == &amp;quot;Temperate coniferous forests&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Before running models, it&amp;rsquo;s a good idea to visualise our data to explore what kind of distribution we are working with.&lt;/p&gt;

&lt;p&gt;The &lt;code&gt;gg&lt;/code&gt; in &lt;code&gt;ggplot2&lt;/code&gt; stands for grammar of graphics. Writing the code for your graph is like constructing a sentence made up of different parts that logically follow from one another. In a data visualisation context, the different elements of the code represent layers - first you make an empty plot, then you add a layer with your data points, then your measure of uncertainty, the axis labels and so on.&lt;/p&gt;

&lt;p&gt;&lt;b&gt; When using &lt;code&gt;ggplot2&lt;/code&gt;, you usually start your code with &lt;code&gt;ggplot(your_data, aes(x = independent_variable, y = dependent_variable))&lt;/code&gt;, then you add the type of plot you want to make using &lt;code&gt;+ geom_boxplot()&lt;/code&gt;, &lt;code&gt;+ geom_histogram()&lt;/code&gt;, etc. &lt;code&gt;aes&lt;/code&gt; stands for aesthetics, hinting to the fact that using &lt;code&gt;ggplot2&lt;/code&gt; you can make aesthetically pleasing graphs - there are many &lt;code&gt;ggplot2&lt;/code&gt; functions to help you clearly communicate your results, and we will now go through some of them.&lt;/b&gt;&lt;/p&gt;

&lt;p&gt;&lt;b&gt;When we want to change the colour, shape or fill of a variable based on another variable, e.g. colour-code by species, we include &lt;code&gt;colour = species&lt;/code&gt; inside the &lt;code&gt;aes()&lt;/code&gt; function. When we want to set a specific colour, shape or fill, e.g. &lt;code&gt;colour = &amp;quot;black&amp;quot;&lt;/code&gt;, we put that outside of the &lt;code&gt;aes()&lt;/code&gt; function.&lt;/b&gt;&lt;/p&gt;

&lt;p&gt;We will see our custom theme &lt;code&gt;theme_LPD()&lt;/code&gt; in action as well!&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Data visualisation ----
# Data distribution - a histogram
(forest.hist &amp;lt;- ggplot(LPD.forest, aes(x = scalepop)) +
   geom_histogram(aes(fill = biome),
                  position = &amp;quot;identity&amp;quot;, colour = &amp;quot;grey40&amp;quot;) +
   geom_vline(aes(xintercept = mean(scalepop)),  # Adding a line for mean abundance
              colour = &amp;quot;darkred&amp;quot;, linetype = &amp;quot;dashed&amp;quot;, size = 1) +
   scale_fill_manual(values = c(&amp;quot;#66CD00&amp;quot;, &amp;quot;#53868B&amp;quot;)) +
   theme_LPD() +
   labs(title = &amp;quot;a) Data distribution\n&amp;quot;, x = &amp;quot;\nScaled population size&amp;quot;,
        y = &amp;quot;Count\n&amp;quot;) +
   # \n adds a blank line
   guides(fill = F)) # Hiding the legend - this will be a two plot panel
# thus we don&#39;t need the same legend twice
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Next up we can explore for how long populations have been monitored in the two biomes using a density histogram.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Density histogram of duration of studies in the two biomes
(duration.forests &amp;lt;- ggplot(LPD.forest, aes(duration, colour = biome)) +
   stat_density(geom = &amp;quot;line&amp;quot;, size = 2, position = &amp;quot;identity&amp;quot;) +
   theme_LPD() +
   scale_colour_manual(values = c(&amp;quot;#66CD00&amp;quot;, &amp;quot;#53868B&amp;quot;)) +
   labs(x = &amp;quot;\nYears&amp;quot;, y = &amp;quot;Density\n&amp;quot;, title = &amp;quot;b) Monitoring duration\n&amp;quot;))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We&amp;rsquo;ll use the &lt;code&gt;grid.arrange&lt;/code&gt; function from the &lt;code&gt;gridExtra&lt;/code&gt; package to combine the two plots in a panel. You can specify the number of columns using &lt;code&gt;ncol =&lt;/code&gt; and the number of rows using &lt;code&gt;nrow =&lt;/code&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Arrange in a panel and save
forest.panel &amp;lt;- grid.arrange(forest.hist, duration.forests, ncol = 2)
ggsave(forest.panel, file = &amp;quot;forest_panel.png&amp;quot;, height = 5, width = 10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/forest_panel.png&#34; alt=&#34;Img&#34; style=&#34;width: 900px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;p&gt;We are now ready to model how each population has changed over time. There are 1785 populations, so with this one code chunk, we will run 1785 models and tidy up their outputs. You can read through the line-by-line comments to get a feel for what each line of code is doing.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;One specific thing to note is that when you add the &lt;code&gt;lm()&lt;/code&gt; function in a pipe, you have to add &lt;code&gt;data = .&lt;/code&gt;, which means use the outcome of the previous step in the pipe for the model.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Calculate population change for each forest population
# 1785 models in one go!
# Using a pipe
forest.slopes &amp;lt;- LPD.forest %&amp;gt;%
  # Group by the key variables that we want to interate over
  group_by(decimal.latitude, decimal.longitude, class, species.name, id, duration, location.of.population) %&amp;gt;%
  # Create a linear model for each group
  do(mod = lm(scalepop ~ year, data = .)) %&amp;gt;%
  # Extract model coefficients using tidy() from the
  # *** tidy() function from the broom package ***
  tidy(mod) %&amp;gt;%
  # Filter out slopes and remove intercept values
  filter(term == &amp;quot;year&amp;quot;) %&amp;gt;%
  # Get rid of the column term as we don&#39;t need it any more
  #  *** select() function from dplyr in the tidyverse ***
  dplyr::select(-term) %&amp;gt;%
  # Remove any groupings you&#39;ve greated in the pipe
  ungroup()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We are ungrouping at the end of our pipe just because otherwise the object remains grouped and later on that might cause problems, if we forget about it.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Now we can visualise the outputs of all our models and see how they vary based on study duration. We will add density histograms along the margins of the graph which makes for a more informative graph using the &lt;code&gt;ggMarginal()&lt;/code&gt; function from the &lt;code&gt;ggExtra&lt;/code&gt; package. Note that &lt;code&gt;ggExtra&lt;/code&gt; is also an addin in &lt;code&gt;RStudio&lt;/code&gt;, so for future reference, if you select some &lt;code&gt;ggplot2&lt;/code&gt; code, then click on &lt;code&gt;Addins/ggplot2 Marginal plots&lt;/code&gt; (the menu is in the middle top part of the screen), you can customise marginal histograms and the code gets automatically generated.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Visualising model outputs ----

# Plotting slope estimates and standard errors for all populations and adding histograms along the margins
(all.slopes &amp;lt;- ggplot(forest.slopes, aes(x = duration, y = estimate)) +
         geom_pointrange(aes(ymin = estimate - std.error,
                             ymax = estimate + std.error),
                         alpha = 0.3, size = 0.3) +
         geom_hline(yintercept = 0, linetype = &amp;quot;dashed&amp;quot;) +
         theme_LPD() +
         ylab(&amp;quot;Population change\n&amp;quot;) +
         xlab(&amp;quot;\nDuration (years)&amp;quot;))

(density.slopes &amp;lt;- ggExtra::ggMarginal(
  p = all.slopes,
  type = &#39;density&#39;,
  margins = &#39;both&#39;,
  size = 5,
  col = &#39;gray40&#39;,
  fill = &#39;gray&#39;
))

# Save the plot
ggsave(density.slopes, filename = &amp;quot;slopes_duration.png&amp;quot;, height = 6, width = 6)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/slopes_duration-2.png&#34; alt=&#34;Img&#34; style=&#34;width: 600px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;p&gt;&lt;a name=&#34;pipes&#34;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h2 id=&#34;2-using-pipes-to-make-figures-with-large-datasets&#34;&gt;2. Using pipes to make figures with large datasets&lt;/h2&gt;

&lt;p&gt;How to print plots of population change for multiple taxa&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How to set up file paths and folders in R&lt;/li&gt;
&lt;li&gt;How to use a pipe to plot many plots by taxa&lt;/li&gt;
&lt;li&gt;How to use the purrr package and functional programming&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;In the next part of the tutorial, we will focus on automating iterative actions, for example when we want to create the same type of graph for different subsets of our data. In our case, we will make histograms of the population change experienced by different vertebrate taxa in forests. When making multiple graphs at once, we have to specify the folder where they will be saved first:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# PART 2: Using pipes to make figures with large datasets ----

# Make histograms of slope estimates for each taxa -----
# Set up new folder for figures
# Set path to relevant path on your computer/in your repository
path1 &amp;lt;- &amp;quot;Taxa_Forest_LPD/&amp;quot;
# Create new folder
dir.create(path1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There isn&amp;rsquo;t a right answer here, there are different ways to achieve the same result and you can decide which one works best for your workflow. First we will use &lt;code&gt;dplyr&lt;/code&gt; and pipes &lt;code&gt;%&amp;gt;%&lt;/code&gt;. Since we want one graph per taxa, we are going to group by the &lt;code&gt;class&lt;/code&gt; variable. You can add functions that are not part of the &lt;code&gt;dplyr&lt;/code&gt; package to pipes using &lt;code&gt;do&lt;/code&gt; - in our case, we are saying that we want &lt;code&gt;R&lt;/code&gt; to do our requested action (making and saving the histograms) for each taxa.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# First we will do this using dplyr and a pipe

forest.slopes %&amp;gt;%
# Select the relevant data
dplyr::select(id, class, species.name, estimate) %&amp;gt;%
# Group by taxa
group_by(class) %&amp;gt;%
# Save all plots in new folder
do(ggsave(ggplot(., aes(x = estimate)) +
            # Add histograms
            geom_histogram(colour = &amp;quot;darkgreen&amp;quot;, fill = &amp;quot;darkgreen&amp;quot;, binwidth = 0.02) +
            # Use custom theme
            theme_LPD() +
            # Add axis lables
            xlab(&amp;quot;Rate of population change (slopes)&amp;quot;),
            # Set up file names to print to
            filename = gsub(&amp;quot;&amp;quot;, &amp;quot;&amp;quot;, paste0(path1, unique(as.character(.$class)),
                                          &amp;quot;.pdf&amp;quot;)), device = &amp;quot;pdf&amp;quot;))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A warning message pops up: &lt;code&gt;Error: Results 1, 2, 3, 4 must be data frames, not NULL&lt;/code&gt; - you can ignore this, it&amp;rsquo;s because the &lt;code&gt;do()&lt;/code&gt; function expects a data frame as an output, but in our case we are making graphs, not data frames.&lt;/p&gt;

&lt;p&gt;If you go check out your folder now, you should see four histograms, one per taxa:
&lt;center&gt;
  &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/folder.png&#34; alt=&#34;Img&#34; style=&#34;width: 500px;&#34;/&gt;
  &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/mamm.png&#34; alt=&#34;Img&#34; style=&#34;width: 500px;&#34;/&gt;
&lt;/center&gt;&lt;/p&gt;

&lt;p&gt;Another way to make all those histograms in one go is by creating a function for it. In general, whenever you find yourself copying and pasting lots of code only to change the object name, you&amp;rsquo;re probably in a position to swap all the code with a function - you can then apply the function using the &lt;code&gt;purrr&lt;/code&gt; package.&lt;/p&gt;

&lt;p&gt;But what is &lt;code&gt;purrr&lt;/code&gt;? &lt;strong&gt;It is a way to &amp;ldquo;map&amp;rdquo; or &amp;ldquo;apply&amp;rdquo; functions to data. Note that there are functions from other packages also called &lt;code&gt;map()&lt;/code&gt;, which is why we are specifying we want the &lt;code&gt;map()&lt;/code&gt; function from the &lt;code&gt;purrr&lt;/code&gt; package. Here we will first format the data &lt;code&gt;taxa.slopes&lt;/code&gt; and then we will map it to the mean fuction:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;We have to change the format of the data, in our case we will split the data using &lt;code&gt;spread()&lt;/code&gt; from the &lt;code&gt;tidyr&lt;/code&gt; package.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Here we select the relevant data
# Let&#39;s get rid of the other levels of &#39;class&#39;
forest.slopes$class &amp;lt;- as.character(forest.slopes$class)
# Selecting the relevant data and splitting it into a list
taxa.slopes &amp;lt;- forest.slopes %&amp;gt;%
  dplyr::select(id, class, estimate) %&amp;gt;%
  spread(class, estimate) %&amp;gt;%
  dplyr::select(-id)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can apply the &lt;code&gt;mean&lt;/code&gt; function using &lt;code&gt;purrr::map()&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;taxa.mean &amp;lt;- purrr::map(taxa.slopes, ~mean(., na.rm = TRUE))
# This plots the mean population change per taxa
taxa.mean
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we can write our own function to make histograms and use the &lt;code&gt;purrr&lt;/code&gt; package to apply it to each taxa.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;### Intro to the purrr package ----

# First let&#39;s write a function to make the plots
# *** Functional Programming ***
# This function takes one argument x, the data vector that we want to make a histogram
plot.hist &amp;lt;- function(x) {
  ggplot() +
  geom_histogram(aes(x), colour = &amp;quot;darkgreen&amp;quot;, fill = &amp;quot;darkgreen&amp;quot;, binwidth = 0.02) +
  theme_LPD() +
  xlab(&amp;quot;Rate of population change (slopes)&amp;quot;)
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Now we can use purr to &amp;ldquo;map&amp;rdquo; our figure making function. The first input is your data that you want to iterate over and the second input is the function.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;taxa.plots &amp;lt;- purrr::map(taxa.slopes, ~plot.hist(.))
# We need to make a new folder to put these figures in
path2 &amp;lt;- &amp;quot;Taxa_Forest_LPD_purrr/&amp;quot;
dir.create(path2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;First we learned about &lt;code&gt;map()&lt;/code&gt; when there is one dataset, but there are other &lt;code&gt;purrr&lt;/code&gt; functions,too.
&lt;code&gt;walk2()&lt;/code&gt; takes two arguments and returns nothing. In our case we just want to print the graphs, so we don&amp;rsquo;t need anything returned. The first argument is our file path, the second is our data and ggsave is our function.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# *** walk2() function in purrr from the tidyverse ***
walk2(paste0(path2, names(taxa.slopes), &amp;quot;.pdf&amp;quot;), taxa.plots, ggsave)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a name=&#34;mapping&#34;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h2 id=&#34;3-downloading-and-mapping-data-from-large-datasets&#34;&gt;3. Downloading and mapping data from large datasets&lt;/h2&gt;

&lt;h3 id=&#34;map-the-distribution-of-a-forest-vertebrate-species-and-the-location-of-monitored-populations&#34;&gt;Map the distribution of a forest vertebrate species and the location of monitored populations&lt;/h3&gt;

&lt;ol&gt;
&lt;li&gt;How to download GBIF records&lt;/li&gt;
&lt;li&gt;How to map occurence data and populations&lt;/li&gt;
&lt;li&gt;How to make a custom function for plotting figures&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;In this part of the tutorial, we will focus on one particular species, red deer (&lt;em&gt;Cervus elaphus&lt;/em&gt;), where it has been recorded around the world, and where it&amp;rsquo;s populations are being monitored. We will use occurrence data from the &lt;a href=&#34;http://www.gbif.org/&#34; target=&#34;_blank&#34;&gt;Global Biodiversity Information Facility&lt;/a&gt; which we will download in &lt;code&gt;R&lt;/code&gt; using the &lt;code&gt;rgbif&lt;/code&gt; package.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Occurrence data can be messy and when you are working with thousands of records, not all of them might be valid records. If you are keen to find out how to test the validity of geographic coordinates using the &lt;code&gt;CoordinateCleaner&lt;/code&gt; package, check out our tutorial &lt;a href=&#34;https://ourcodingclub.github.io/2018/01/06/occurrence.html&#34; target = &#34;_blank&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;### PART 3: Downloading and mapping data from large datasets ----
#### How to map distributions and monitoring locations for one or more taxa

# Packages ----
library(rgbif)  # To extract GBIF data
# library(CoordinateCleaner)  # To clean coordinates if you want to explore that later
library(gridExtra)  # To make pretty graphs
library(ggrepel)  # To add labels with rounded edges
library(png)  # To add icons
library(mapdata)  # To plot maps
library(ggthemes)  # To make maps extra pretty
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We are limiting the number of records to 5000 for the sake of time - in the future you can ask for more records as well, there&amp;rsquo;s just a bit of waiting involved. The records come with a lot of metadata. For our purposes, we will select just the columns we need. Similar to how before we had to specify that we want the &lt;code&gt;map()&lt;/code&gt; function from the &lt;code&gt;purrr&lt;/code&gt; package, there are often other &lt;code&gt;select()&lt;/code&gt; functions, so we are saying that we want the one from &lt;code&gt;dplyr&lt;/code&gt; using &lt;code&gt;dplyr::select()&lt;/code&gt;. Otherwise, the &lt;code&gt;select()&lt;/code&gt; function might not work because of a conflict with another &lt;code&gt;select()&lt;/code&gt; function from a different package, e.g. the &lt;code&gt;raster&lt;/code&gt; packge.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Download species occurrence records from the Global Biodiversity Information Facility
# *** rgbif package and the occ_search() function ***
# You can increase the limit to get more records - 5000 takes a couple of minutes
deer.locations &amp;lt;- occ_search(scientificName = &amp;quot;Cervus elaphus&amp;quot;, limit = 5000,
                             hasCoordinate = TRUE, return = &amp;quot;data&amp;quot;) %&amp;gt;%
                  # Simplify occurrence data frame
                  dplyr::select(key, name, decimalLongitude,
		                decimalLatitude, year,
				individualCount, country)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Next we will extract the red deer population data - the raw time series and the slopes of population change from the two data frames.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Data formatting &amp;amp; manipulation ----

# Filter out population data for chosen species - red deer
deer.data &amp;lt;- LPD_long2 %&amp;gt;%
  filter(species.name == &amp;quot;Cervus elaphus&amp;quot;) %&amp;gt;%
  dplyr::select(id, species.name, location.of.population, year, pop)

# Filter out population estimates for chosen species - red deer
deer.slopes &amp;lt;- forest.slopes %&amp;gt;%
  filter(species.name == &amp;quot;Cervus elaphus&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;In addition to making histograms, scatterplots and such, you can use &lt;code&gt;ggplot2&lt;/code&gt; to make maps as well - the maps come from the &lt;code&gt;mapdata&lt;/code&gt; package we loaded earlier. In this map, we want to visualise information from two separate data frames - where the species occurs (&lt;code&gt;deer.locations&lt;/code&gt;, the GBIF data) and where it is monitored (&lt;code&gt;deer.slopes&lt;/code&gt;, the Living Planet Database data). We can combine this information in the same &lt;code&gt;ggplot2&lt;/code&gt; code chunk using the &lt;code&gt;geom_point()&lt;/code&gt; function twice - the first time it will plot the occurrences since that the data frame associated with the plot in the very first line, and the second time we&amp;rsquo;ve told the function to specifically use the &lt;code&gt;deer.slopes&lt;/code&gt; object using &lt;code&gt;data = deer.slopes&lt;/code&gt;.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As you start making your maps, you may get this warning message:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Warning message:
In drawGrob(x) : reached elapsed time limit
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We are working with thousands of records, so depending on your computer, making the map might take a while. This message doesn&amp;rsquo;t mean something is wrong, just lets you know that generating the map took a bit longer than what &lt;code&gt;RStudio&lt;/code&gt; expected.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Make an occurrence map and include the locations of the populations part of the Living Planet Database
(deer.map.LPD &amp;lt;- ggplot(deer.locations, aes(x = decimalLongitude, y = decimalLatitude)) +
    # Add map data
    borders(&amp;quot;world&amp;quot;, colour = &amp;quot;gray80&amp;quot;, fill = &amp;quot;gray80&amp;quot;, size = 0.3) +
    # Use custom map theme from ggthemes package
    theme_map() +
    # Add the points from the population change data
    geom_point(alpha = 0.3, size = 2, colour = &amp;quot;aquamarine3&amp;quot;) +
    # Specify where the data come from when plotting from more than one data frame using data = &amp;quot;&amp;quot;
    geom_point(data = deer.slopes, aes(x = decimal.longitude, y = decimal.latitude),
               size = 2, colour = &amp;quot;darkgreen&amp;quot;))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/deer_map.png&#34; alt=&#34;Img&#34; style=&#34;width: 900px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;p&gt;The map already looks fine, but we can customise it further to add more information. For example, we can add labels for the locations of some of the monitored populations and we can add plots of population change next to our map.&lt;/p&gt;

&lt;p&gt;First we will rename some of the populations, just so that our labels are not crazy long, using the &lt;code&gt;recode()&lt;/code&gt; function from the &lt;code&gt;dplyr&lt;/code&gt; package.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Customising map to make it more beautiful ----

# Check site names
print(deer.slopes$location.of.population)

# Beautify site names
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Northern Yellowstone National Park&amp;quot;
					     = &amp;quot;Yellowstone National Park&amp;quot;)
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Mount Rainier National Park, USA&amp;quot;
					     = &amp;quot;Mount Rainier National Park&amp;quot;)
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Bow Valley - eastern zone, Banff National Park, Alberta&amp;quot; =
					     &amp;quot;Banff National Park, Alberta&amp;quot;)
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Bow Valley - western zone, Banff National Park, Alberta&amp;quot; =
					     &amp;quot;Banff National Park, Alberta&amp;quot;)
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Bow Valley - central zone, Banff National Park, Alberta&amp;quot; =
					     &amp;quot;Banff National Park, Alberta&amp;quot;)
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Study area within Bow Valley, Banff National Park, Alberta&amp;quot; =
					     &amp;quot;Banff National Park, Alberta&amp;quot;)
deer.slopes$location.of.population &amp;lt;- recode(deer.slopes$location.of.population,
                                             &amp;quot;Bow Valley watershed of Banff National Park, Alberta&amp;quot; =
					     &amp;quot;Banff National Park, Alberta&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can also use &lt;code&gt;ggplot2&lt;/code&gt; to add images to your graphs, so here we will add a deer icon.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Load packages for adding images
packs &amp;lt;- c(&amp;quot;png&amp;quot;,&amp;quot;grid&amp;quot;)
lapply(packs, require, character.only = TRUE)

# Load red deer icon
icon &amp;lt;- readPNG(&amp;quot;reddeer.png&amp;quot;)
icon &amp;lt;- rasterGrob(icon, interpolate = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can update our map by adding labels and our icon - this looks like a gigantic chunk of code, but we&amp;rsquo;ve added line by line comments so that you can see what&amp;rsquo;s happening at each step. The &lt;code&gt;ggrepel&lt;/code&gt; package adds labels whilst also aiming to avoid overlap and as a bonus, the labels have rounded edges.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Update map
# Note - this takes a while depending on your computer
(deer.map.final &amp;lt;- ggplot(deer.locations, aes(x = decimalLongitude, y = decimalLatitude)) +
    # For more localized maps use &amp;quot;worldHires&amp;quot; instead of &amp;quot;world&amp;quot;
    borders(&amp;quot;world&amp;quot;, colour = &amp;quot;gray80&amp;quot;, fill = &amp;quot;gray80&amp;quot;, size = 0.3) +
    theme_map() +
    geom_point(alpha = 0.3, size = 2, colour = &amp;quot;aquamarine3&amp;quot;) +
    # We are specifying the data frame for the labels - one site has three monitored populations
    # but we only want to label it once so we are subsetting using data = deer.slopes[c(2, 4, 5, 9),]
    # to get only the first rows number 2, 4, 5 and 9
    geom_label_repel(data = deer.slopes[c(2, 4, 5, 9),], aes(x = decimal.longitude, y = decimal.latitude,
                                                     label = location.of.population),
                     box.padding = 1, size = 5, nudge_x = 1,
                     # We are specifying the size of the labels and nudging the points so that they
                     # don&#39;t hide data points, along the x axis we are nudging by one
                     min.segment.length = 0, inherit.aes = FALSE) +
    # We can recreate the shape of a dropped pin by overlaying a circle and a triangle
    geom_point(data = deer.slopes, aes(x = decimal.longitude, y = decimal.latitude + 0.6),
               size = 4, colour = &amp;quot;darkgreen&amp;quot;) +
    geom_point(data = deer.slopes, aes(x = decimal.longitude, y = decimal.latitude - 0.3),
               size = 3, fill = &amp;quot;darkgreen&amp;quot;, colour = &amp;quot;darkgreen&amp;quot;, shape = 25) +
    # Adding the icon using the coordinates on the x and y axis
    annotation_custom(icon, xmin = -210, xmax = -100, ymin = -60 , ymax = -30) +
    # Adding a title
    labs(title = &amp;quot;a. Red Deer GBIF occurrences&amp;quot;, size = 12))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Let&amp;rsquo;s add some additional plots to our figure, for example how many occurrences there are for each year.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Visualise the number of occurrence records through time ----
# This plot is more impressive if you have downloaded more records
# as GBIF downloads the most recent records first
yearly.obs &amp;lt;- deer.locations %&amp;gt;% group_by(year) %&amp;gt;% tally() %&amp;gt;% ungroup() %&amp;gt;% filter(is.na(year) == FALSE)

(occurrences &amp;lt;- ggplot(yearly.obs, aes(x = year, y = n)) +
    geom_smooth(colour = &amp;quot;aquamarine3&amp;quot;, method = &#39;loess&#39;, size = 1) +
    labs(x = NULL, y = &amp;quot;Number of occurrences\n&amp;quot;,
         title = &amp;quot;b. GBIF occurrences\n&amp;quot;, size = 12) +
    # Use our customised theme, saves many lines of code!
    theme_LPD() +
    # if you want to change things about your theme, you need to include the changes after adding the theme
    theme(plot.title = element_text(size = 12), axis.title.y = element_text(size = 10)))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can add plots that show the population trends for those populations we&amp;rsquo;ve labelled. Given that we will be doing the same thing for multiple objects (the same type of plot for each population), we can practice functional programming and using &lt;code&gt;purrr&lt;/code&gt; again here. The function looks very similar to a normal &lt;code&gt;ggplot2&lt;/code&gt; code chunk, except we&amp;rsquo;ve wrapped it up in a function and we are not using any specific objects, just &lt;code&gt;x&lt;/code&gt;, &lt;code&gt;y&lt;/code&gt; and &lt;code&gt;z&lt;/code&gt; as the three arguments the function needs.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Visualise population trends ----
# Visualising the population trends of four deer populations

# Let&#39;s practice functional programming here
# *** Functional Programming ***
# Let&#39;s make a function to make the population trend plots
# First we need to decide what values the function needs to take
# x - The population data
# y - the slope value
# z - the location of the monitoring
# This function needs to take three arguments

# Let&#39;s make the ggplot function
pop.graph &amp;lt;- function(x, y, z) {
  # Make a ggplot graph with the &#39;x&#39;
  ggplot(x, aes(x = year, y = pop)) +
  # Shape 21 chooses a point with a black outline filled with aquamarine
  geom_point(shape = 21, fill = &amp;quot;aquamarine3&amp;quot;, size = 2) +
  # Adds a linear model fit, alpha controls the transparency of the confidence intervals
  geom_smooth(method = &amp;quot;lm&amp;quot;, colour = &amp;quot;aquamarine3&amp;quot;, fill = &amp;quot;aquamarine3&amp;quot;, alpha = 0.4) +
  # Add the monitoring location &#39;y&#39; into the plot
  labs(x = &amp;quot;&amp;quot;, y = &amp;quot;Individuals\n&amp;quot;, title = paste(&amp;quot;c. &amp;quot;, y, &amp;quot;\n&amp;quot;), size = 7) +
  # Set the y limit to the maximum population for each &#39;x&#39;
  ylim(0, max(x$pop)) +
  # Set the x limit to the range of years of data
  xlim(1970, 2010) +
  # Add the slope &#39;y&#39; into the plot
  annotate(&amp;quot;text&amp;quot;, x = 1972, y = 0,  hjust = 0, vjust = -2, label = paste(&amp;quot;Slope =&amp;quot;, z), size = 3) +
  theme_LPD() +
  theme(plot.title = element_text(size=12), axis.title.y = element_text(size=10))
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We will focus on four populations in the USA, Switzerland and Canada. We will make three objects for each population, which represent the three arguments the function takes - the population data, the slope value and the location of the population. Then we will run our function &lt;code&gt;pop.graph()&lt;/code&gt; using those objects.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Find all unique ids for red deer populations
unique(deer.slopes$id)

# Create an object each of the unique populations
# Deer population 1 - Northern Yellowstone National Park
deer1 &amp;lt;- filter(deer.data, id == &amp;quot;6395&amp;quot;)
slope_deer1 &amp;lt;- round(deer.slopes$estimate[deer.slopes$id == &amp;quot;6395&amp;quot;],2)
location_deer1 &amp;lt;- deer.slopes$location.of.population[deer.slopes$id == &amp;quot;6395&amp;quot;]
yellowstone &amp;lt;- pop.graph(deer1, location_deer1, slope_deer1)

# Deer population 2 - Mount Rainier National Park, USA
deer2 &amp;lt;- filter(deer.data, id == &amp;quot;3425&amp;quot;)
slope_deer2 &amp;lt;- round(deer.slopes$estimate[deer.slopes$id == &amp;quot;3425&amp;quot;],2)
location_deer2 &amp;lt;- deer.slopes$location.of.population[deer.slopes$id == &amp;quot;3425&amp;quot;]
rainier &amp;lt;- pop.graph(deer2, location_deer2, slope_deer2)

# Deer population 3 - Switzerland
deer3 &amp;lt;- filter(deer.data, id == &amp;quot;11170&amp;quot;)
slope_deer3 &amp;lt;- round(deer.slopes$estimate[deer.slopes$id == &amp;quot;11170&amp;quot;],2)
location_deer3 &amp;lt;- deer.slopes$location.of.population[deer.slopes$id == &amp;quot;11170&amp;quot;]
switzerland &amp;lt;- pop.graph(deer3, location_deer3, slope_deer3)

# Deer population 4 - Banff National Park, Alberta (there are more populations here)
deer4 &amp;lt;- filter(deer.data, id == &amp;quot;4383&amp;quot;)
slope_deer4 &amp;lt;- round(deer.slopes$estimate[deer.slopes$id == &amp;quot;4383&amp;quot;],2)
location_deer4 &amp;lt;- deer.slopes$location.of.population[deer.slopes$id == &amp;quot;4383&amp;quot;]
banff &amp;lt;- pop.graph(deer4, location_deer4, slope_deer4)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We are now ready to combine all of our graphs into one panel. When using &lt;code&gt;grid.arrange()&lt;/code&gt;, you can add an additional argument &lt;code&gt;widths = c()&lt;/code&gt; or &lt;code&gt;heights = c()&lt;/code&gt;, which controls the ratios between the different plots. By default, &lt;code&gt;grid.arrange()&lt;/code&gt; will give equal space to each graph, but sometimes you might want one graph to be wider than others, like here we want the map to have more space.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Create panel of all graphs
# Makes a panel of the map and occurrence plot and specifies the ratio
# i.e., we want the map to be wider than the other plots
# suppressWarnings() suppresses warnings in the ggplot call here
row1 &amp;lt;- suppressWarnings(grid.arrange(deer.map.final, occurrences, ncol = 2, widths = c(1.96, 1.04)))
# Makes a panel of the four population plots
row2 &amp;lt;- grid.arrange(yellowstone, rainier, switzerland, banff, ncol = 4, widths = c(1, 1, 1, 1))

# Makes a panel of all the population plots and sets the ratio
# Stich all of your plots together
deer.panel &amp;lt;- grid.arrange(row1, row2, nrow = 2, heights = c(1.2, 0.8))

ggsave(deer.panel, filename = &amp;quot;deer_panel2.png&amp;quot;, height = 10, width = 15)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;center&gt; &lt;img src=&#34;https://BES-QSIG.github.io/courses/advancingr/deer_panel2-3.png&#34; alt=&#34;Img&#34; style=&#34;width: 900px;&#34;/&gt; &lt;/center&gt;&lt;/p&gt;

&lt;h2 id=&#34;challenges&#34;&gt;Challenges&lt;/h2&gt;

&lt;p&gt;&lt;strong&gt;Take what you have learned about pipes and make a map of the five most well-sampled populations in the LPD database (the ones with the most replicate populations) and colour code the points by the population trend (derived from the models we did) and the size by the duration of the time series. You can try incorporating a handwritten function to make the map and using purr to implement that function, or you can go straight into &lt;code&gt;ggplot2&lt;/code&gt;.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Pick a country and species of your choice. Download the GBIF records for that species from your selected country (or you can do the world if you don&amp;rsquo;t mind waiting a few more minutes for the GBIF data to download). Plot where the species occurs. Then, add the locations of the Living Planet Database populations of the same species - do we have long-term records from the whole range of the species? Where are the gaps? You can have a go at combining the LPD and GBIF databases in a meaningful way - hint: look up the different joining functions from &lt;code&gt;dplyr&lt;/code&gt; - &lt;code&gt;left_join()&lt;/code&gt;, &lt;code&gt;inner_join()&lt;/code&gt;, etc.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Use another projection for the map - the default is Mercator, but that&amp;rsquo;s not the best way to represent the world. Hint - you can still use &lt;code&gt;ggplot2&lt;/code&gt; - look up the &lt;code&gt;proj4&lt;/code&gt; package and how to combine it with &lt;code&gt;ggplot2&lt;/code&gt;.&lt;/strong&gt;&lt;/p&gt;

&lt;h2 id=&#34;extra-resources&#34;&gt;Extra resources&lt;/h2&gt;

&lt;p&gt;You can find more info on &lt;code&gt;pander&lt;/code&gt; &lt;a href=&#34;https://cran.r-project.org/web/packages/pander/pander.pdf&#34; target=&#34;_blank&#34;&gt; here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;To learn more about the power of pipes check out:
&lt;a href = &#34;http://dplyr.tidyverse.org/&#34; target =&#34;_blank&#34;&gt; the tidyverse website&lt;/a&gt; and &lt;a href=&#34;http://r4ds.had.co.nz/pipes.html&#34; target=&#34;_blank&#34;&gt; the R for Data Science book&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;To learn more about &lt;code&gt;purrr&lt;/code&gt; check out the &lt;a href=&#34;http://purrr.tidyverse.org/reference/map2.html&#34; target=&#34;_blank&#34;&gt;tidyverse website&lt;/a&gt; and the &lt;a href=&#34;http://r4ds.had.co.nz/iteration.html&#34; target=&#34;_blank&#34;&gt; R for Data Science book&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For more information on functional programming see the &lt;a href=&#34;http://r4ds.had.co.nz/functions.html&#34; target=&#34;_blank&#34;&gt;R for Data Science book chapter here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;To learn more about the &lt;code&gt;tidyverse&lt;/code&gt; in general, check out Charlotte Wickham&amp;rsquo;s slides &lt;a href=&#34;https://github.com/cwickham/data-science-in-tidyverse/tree/master/slides&#34; target=&#34;_blank&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;

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      <title>Day Two: Spatial data manipulation and visualisation in R</title>
      <link>https://BES-QSIG.github.io/courses/advancingr-spatial/</link>
      <pubDate>Tue, 20 Nov 2018 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/courses/advancingr-spatial/</guid>
      <description>

&lt;h1 id=&#34;part-i-preliminaries&#34;&gt;Part I: Preliminaries&lt;/h1&gt;

&lt;h2 id=&#34;required-packages&#34;&gt;Required packages&lt;/h2&gt;

&lt;p&gt;For this &amp;ldquo;tutorial&amp;rdquo;, you will need some packages and their dependencies&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;if(!requireNamespace(&amp;quot;raster&amp;quot;)) install.packages(&amp;quot;raster&amp;quot;)
if(!requireNamespace(&amp;quot;sf&amp;quot;)) install.packages(&amp;quot;sf&amp;quot;)
if(!requireNamespace(&amp;quot;ggplot2&amp;quot;)) install.packages(&amp;quot;ggplot2&amp;quot;)
if(!requireNamespace(&amp;quot;ggspatial&amp;quot;)) install.packages(&amp;quot;ggspatial&amp;quot;)
if(!requireNamespace(&amp;quot;rgdal&amp;quot;)) install.packages(&amp;quot;rgdal&amp;quot;)
if(!requireNamespace(&amp;quot;RPostgreSQL&amp;quot;)) install.packages(&amp;quot;RPostgreSQL&amp;quot;)
if(!requireNamespace(&amp;quot;rgbif&amp;quot;)) install.packages(&amp;quot;rgbif&amp;quot;)

library(raster)
library(sf)
library(ggplot2)
library(ggspatial)
library(RPostgreSQL)
library(rgdal)
library(rgbif)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The data for this tutorial are available from &lt;a href=&#34;needs link&#34; target=&#34;_blank&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;h2 id=&#34;what-is-a-spatial-object&#34;&gt;What is a spatial object?&lt;/h2&gt;

&lt;p&gt;A spatial object is an entity with coordinates in a geographical space (x, y) or (x, y, z) with a specific projection.&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;Coordinates are not enough!&lt;/strong&gt; To illustrate this affirmation, just have a look at this example.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# create two ojbects with their long/lat coordinates
point_liverpool &amp;lt;- data.frame(name = &#39;Liverpool&#39;, longitude = -2.98, latitude = 53.41)
point_edinburgh &amp;lt;- data.frame(name = &#39;Edinburgh&#39;, longitude = -3.19, latitude = 55.95)
city_points &amp;lt;- rbind(point_liverpool, point_edinburgh)

# Now have a look at them in a 2D space.
plot(city_points[,c(&amp;quot;longitude&amp;quot;, &amp;quot;latitude&amp;quot;)], pch = 19, col = &#39;magenta&#39;)
text(city_points[,c(&amp;quot;longitude&amp;quot;, &amp;quot;latitude&amp;quot;)], labels = city_points$name, pos = c(2, 4))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With these two cities, although the coordinates are right, you have no information about the context of their coordinates.&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;em&gt;Spatial projection&lt;/em&gt;
The Coordinate Reference System or CRS of a spatial object tells R where the spatial object is located in geographic space (&lt;em&gt;see&lt;/em&gt; &lt;a href=&#34;http://spatialreference.org/&#34; target=&#34;_blank&#34;&gt;http://spatialreference.org/&lt;/a&gt;).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;For example, you might have seen the expression WGS84, the common longitude-latitude (degree decimal), or maybe EPSG:4326.
In other words, spatial objects should come with some information relative to their reference system (CRS), no matter if it&amp;rsquo;s a raster, a point, a line of a complex polygon. To locate an object in space, you need to know the reference system and the units in which the coordinates are expressed.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Adding a projection to our cities, using WGS84
proj_city_points &amp;lt;- sf::st_as_sf(city_points, coords = c(&amp;quot;longitude&amp;quot;, &amp;quot;latitude&amp;quot;), crs = 4326)
plot(proj_city_points, pch = 19, col = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;), cex = 1.5)
legend(&amp;quot;topleft&amp;quot;, legend = proj_city_points$name, col = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;), pch = 19, cex = 1.5, bty=&amp;quot;n&amp;quot;)

# get some base layer to see this in perspective
country_sf_gbr &amp;lt;- sf::st_as_sf(raster::getData(name = &amp;quot;GADM&amp;quot;, country = &#39;GBR&#39;, level = 1))

# visualize the points on a map
plot(country_sf_gbr$geometry, graticule = TRUE, axes = TRUE, col = &amp;quot;wheat1&amp;quot;, xlim = c(-15, 5), ylim = c(50, 61))
plot(proj_city_points, pch = 19, col = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;), cex = 1.5, add = TRUE)
legend(&amp;quot;topleft&amp;quot;, legend = proj_city_points$name, col = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;), pch = 19, cex = 1.5, bty=&amp;quot;n&amp;quot;)
text(x = -15, y = 50.2, &amp;quot;EPSG:4326 - WGS84&amp;quot;, pos = 4, cex = 0.7)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The advantage of having a CRS is that you can transform your coordinates, so it is expressed to other reference system (i.e. on other origin and units).&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;em&gt;Changing Projection&lt;/em&gt; (st_tranform)
Some projections more appropriate for representing specific geographic context. Why is their so many CRS?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# Reproject your city points in OSGB 1936 / British National Grid (EPSG:27700)
proj_city_points_osgb &amp;lt;- sf::st_transform(proj_city_points, crs = 27700)
country_sf_gbr_osgb &amp;lt;- sf::st_transform(country_sf_gbr, crs = 27700)

# visualize the points on a map
dev.new()
plot(country_sf_gbr_osgb$geometry, graticule = TRUE, axes = TRUE, col = &amp;quot;wheat1&amp;quot;, xlim = c(-333585, 713000), ylim = c(20000, 1290000))
plot(proj_city_points_osgb, pch = 19, col = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;), cex = 1.5, add = TRUE)
legend(&amp;quot;topleft&amp;quot;, legend = proj_city_points_osgb$name, col = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;), pch = 19, cex = 1.5, bty=&amp;quot;n&amp;quot;)
text(x = -453585, y = 25000, &amp;quot;EPSG:27700 - OSGB 1936 &amp;quot;, pos = 4, cex = 0.7)
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
&lt;p&gt;to get coordinates on a plot, you can use the function drawExtent() from the raster package that allow you to clic on a map to get the coordinates of an extent.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;plot(country_sf_gbr_osgb$geometry, graticule = TRUE, axes = TRUE, col = &amp;quot;wheat1&amp;quot;)
raster::drawExtent()
&lt;/code&gt;&lt;/pre&gt;

&lt;h1 id=&#34;part-ii-building-and-working-with-spatial-objects-using-sf-in-r&#34;&gt;PART II: Building and working with spatial objects using sf in R&lt;/h1&gt;

&lt;p&gt;This is a revolution, providing a modern, stronger and cleaner workflow to deal with spatial object in R, at least vector data. The &amp;ldquo;sf&amp;rdquo; is developed by some of the same people that provide us with &amp;ldquo;sp&amp;rdquo;, offering an ecosystem that open new opportunities to do GIS in R. The firs place to look for resource is the sf package website &lt;a href=&#34;https://r-spatial.github.io/sf/index.html&#34; target=&#34;_blank&#34;&gt;https://r-spatial.github.io/sf/index.html&lt;/a&gt;, this a first stop to learn how it work and develop new skills in R.&lt;/p&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(sf)

p1 &amp;lt;- sf::st_point(c(1, 2))
p2 &amp;lt;- sf::st_point(c(3, 5))
p3 &amp;lt;- sf::st_multipoint(matrix(2 * c(1, 2, 4, 2, 3, 5, 7, 3), ncol = 2, byrow = TRUE))
p4 &amp;lt;- sf::st_as_sf(data.frame(X = c(1, 4, 3, 7), Y = c(2, 2, 5, 3) ), coords = c(&amp;quot;X&amp;quot;, &amp;quot;Y&amp;quot;))
p5 &amp;lt;- sf::st_sfc(p1, p2, p3)

plot(p1)
plot(p3, col = &amp;quot;blue&amp;quot;, pch = 19)
plot(p2, col = &amp;quot;magenta&amp;quot;, pch = 19, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;modify-sf-sfc-objects&#34;&gt;Modify sf - sfc objects&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;p6 &amp;lt;- sf::st_cast(x = st_sfc(p3), to = &amp;quot;POINT&amp;quot;)
p_multi &amp;lt;- sf::st_cast(p6, ids = c(1, 2, 1, 2), group_or_plist = TRUE, to = &amp;quot;MULTIPOINT&amp;quot;)

plot(p_multi[1], ylim = c(0, 20), xlim = c(0, 20), col = &amp;quot;tomato3&amp;quot;, pch = 19)
plot(p_multi[2], col = &amp;quot;magenta&amp;quot;, pch = 19, add = TRUE)

p7 &amp;lt;- st_cast(x = p4, to = &amp;quot;POINT&amp;quot;)
p8 &amp;lt;- rbind(st_cast(x = p4[1:3,], to = &amp;quot;MULTIPOINT&amp;quot;), p4[4,])
p8
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;lines&#34;&gt;Lines&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;l1 &amp;lt;- sf::st_linestring(matrix(c(1, 1, 2, 2, 3, 3, 4, 4, 4, 2), ncol = 2, byrow = TRUE))
lp &amp;lt;- sf::st_cast(x = sf::st_sfc(l1), to = &amp;quot;MULTIPOINT&amp;quot;)
bl1 &amp;lt;- sf::st_buffer(l1, 2)
blp &amp;lt;- sf::st_cast(x = sf::st_sfc(bl1), to = &amp;quot;MULTIPOINT&amp;quot;)

plot(lp, col = &amp;quot;blue&amp;quot;, pch = 19, cex = 2, ylim = c(-5, 10), xlim = c(-5, 10))
plot(blp, col = &amp;quot;magenta&amp;quot;, pch = 19, cex = 1, add = TRUE)
plot(l1, col = &amp;quot;tomato3&amp;quot;, lwd = 1.5, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;polygon&#34;&gt;Polygon&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;bl1 &amp;lt;- sf::st_buffer(l1, 2)
blp &amp;lt;- sf::st_cast(x = sf::st_sfc(bl1), to = &amp;quot;MULTIPOINT&amp;quot;)

plot(bl1, col = &amp;quot;lightblue&amp;quot;, border = NA)
plot(lp, col = &amp;quot;blue&amp;quot;, pch = 19, cex = 2, ylim = c(-5, 10), xlim = c(-5, 10), add = TRUE)
plot(blp, col = &amp;quot;magenta&amp;quot;, pch = 19, cex = 1, add = TRUE)
plot(l1, col = &amp;quot;tomato3&amp;quot;, lwd = 1.5, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;intersects-and-intesections&#34;&gt;Intersects and intesections&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;p1 &amp;lt;- sf::st_as_sf(data.frame(X = c(1, 4, 3, 7), Y = c(2, 2, 5, 3) ), coords = c(&amp;quot;X&amp;quot;, &amp;quot;Y&amp;quot;), crs = 4326)
poly1 &amp;lt;- st_as_sfc(st_bbox(st_buffer(p1[2,], 2)))
poly2 &amp;lt;- st_as_sfc(st_bbox(st_buffer(p1[3,], 1.5)))

plot(st_geometry(poly1), col = &amp;quot;goldenrod&amp;quot;, xlim = c(-5, 5), ylim = c(0, 10))
plot(st_geometry(poly2), col = rgb(1,1,0,0.3), add = TRUE)
plot(st_geometry(p1), col = &amp;quot;magenta&amp;quot;, pch = 19, cex= 1.5, add = TRUE)

## INTERSECTION
poly3 &amp;lt;- sf::st_intersection(poly1, poly2)
plot(st_geometry(poly3), col = &amp;quot;lightblue&amp;quot;, add = TRUE)

## INTERSECT
p1_poly1 &amp;lt;- sf::st_intersects(p1, poly1, sparse = FALSE)
plot(st_geometry(p1[p1_poly1,]), col = &amp;quot;turquoise&amp;quot;, pch = 19, cex = 2, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;circle-buffer-intersection&#34;&gt;circle buffer intersection&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;poly1 &amp;lt;- sf::st_buffer(p1[2,], 2)
poly2 &amp;lt;- sf::st_buffer(p1[3,], 1.5)
int_b2_b1 &amp;lt;- sf::st_intersection(poly2, poly1)

plot(st_geometry(poly1), col = NA, border = &amp;quot;red&amp;quot;, ylim = c(0, 7), axes = TRUE)
plot(st_geometry(poly2), add = TRUE)
plot(st_geometry(p1[2,]), col = &amp;quot;black&amp;quot;, pch = 19, add = TRUE)
plot(st_geometry(p1[3,]), col = &amp;quot;blue&amp;quot;, pch = 19, add = TRUE)
plot(st_geometry(int_b2_b1), col = &amp;quot;lightblue&amp;quot;, boder = NA, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;difference-between-objects&#34;&gt;Difference between objects&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;poly1 &amp;lt;- sf::st_buffer(p1[2,], 2)
poly2 &amp;lt;- sf::st_buffer(p1[2,], 4)
dif_poly2_poly1 &amp;lt;- sf::st_difference(poly2, poly1)

plot(st_geometry(dif_poly2_poly1), col = &amp;quot;orange&amp;quot;, axes = TRUE)
plot(st_geometry(poly1), col = &amp;quot;blue&amp;quot;, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;union-merge-and-melt-objects&#34;&gt;Union (merge and melt) objects&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;poly1 &amp;lt;- sf::st_buffer(p1[2,], 2)
poly2 &amp;lt;- sf::st_buffer(p1[2,], 4)
uni_poly1_2 &amp;lt;- sf::st_union(dif_poly2_poly1, poly1)

plot(st_geometry(uni_poly1_2), col = &amp;quot;lightblue&amp;quot;, axes = TRUE)
plot(st_geometry(poly1), col = &amp;quot;tomato3&amp;quot;, add = TRUE)
plot(st_geometry(st_buffer(poly1,-1)), col = &amp;quot;white&amp;quot;, add = TRUE)
plot(st_geometry(st_centroid(poly1)), col = &amp;quot;black&amp;quot;, pch = 19, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you like it tidy and the dplyr way? Since sf is essentally a data.frame with  a list of spatial attribute attached, it works well in the tidy univers. Have a look at this blog to get a first feel &lt;a href=&#34;http://strimas.com/r/tidy-sf/&#34; target=&#34;_blank&#34;&gt;http://strimas.com/r/tidy-sf/&lt;/a&gt;.&lt;/p&gt;

&lt;h1 id=&#34;part-iii-load-and-manipulate-spatial-objects&#34;&gt;PART III: Load and manipulate spatial objects&lt;/h1&gt;

&lt;p&gt;Spatial data are increasingly available from the Web, from species occurrence to natural and cultural features data, accessing spatial data is now relatively easy. For base layers, you can find many freely available data sets such as the ones provided by the Natural Earth [&lt;a href=&#34;http://www.naturalearthdata.com&#34; target=&#34;_blank&#34;&gt;http://www.naturalearthdata.com&lt;/a&gt;], the IUCN Protected Planet database [www.protectedplanet.net], the GADM project [&lt;a href=&#34;https://gadm.org&#34; target=&#34;_blank&#34;&gt;https://gadm.org&lt;/a&gt;], worldclim [&lt;a href=&#34;http://worldclim.org/version2&#34; target=&#34;_blank&#34;&gt;http://worldclim.org/version2&lt;/a&gt;] the CHELSA climate data sets [&lt;a href=&#34;http://chelsa-climate.org&#34; target=&#34;_blank&#34;&gt;http://chelsa-climate.org&lt;/a&gt;] or the European Environmental Agency [&lt;a href=&#34;https://www.eea.europa.eu/data-and-maps/data#c0=5&amp;amp;c11=&amp;amp;c5=all&amp;amp;b_start=0&#34; target=&#34;_blank&#34;&gt;https://www.eea.europa.eu/data-and-maps/data#c0=5&amp;amp;c11=&amp;amp;c5=all&amp;amp;b_start=0&lt;/a&gt;]&lt;/p&gt;

&lt;h2 id=&#34;raster-object&#34;&gt;Raster object&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(raster)
# annual mean temperature
chelsa_amt &amp;lt;- raster::raster(&amp;quot;data/CHELSA_bio10_1.tif&amp;quot;)
raster::plot(chelsa_amt)
chelsa_amt

# crop climate data for a specific bounding box
chelsa_amt_gbr &amp;lt;- raster::crop(chelsa_amt, raster::extent(country_sf_gbr)) # cut the worldwide raster according to
raster::plot(chelsa_amt_gbr)

# convert temperature data in usual unit
chelsa_amt_gbr[] &amp;lt;- chelsa_amt_gbr[]*0.1 # convert temperature data in usual unit
raster::res(chelsa_amt_gbr)

# change resolution aggregate() or disaggregate()
chelsa_amt_gbr_2 &amp;lt;- raster::disaggregate(chelsa_amt_gbr, fact=4)
raster::res(chelsa_amt_gbr_2)

chelsa_amt_gbr_3 &amp;lt;- raster::aggregate(chelsa_amt_gbr, fact=4)
raster::res(chelsa_amt_gbr_3)
raster::plot(chelsa_amt_gbr_3)
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;em&gt;Other format for gridded data&lt;/em&gt;
netCDF format is commonly used for gridded time series (temperature)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;rast_1 &amp;lt;- raster::raster(&amp;quot;data/tg_0.25deg_reg_2011-2017_v17.0.nc&amp;quot;, band = 1)
rast_2 &amp;lt;- raster::raster(&amp;quot;data/tg_0.25deg_reg_2011-2017_v17.0.nc&amp;quot;, band = 2)
rast_stack &amp;lt;- raster::stack(rast_1, rast_2)
plot(rast_stack$mean.temperature.1)
plot(rast_stack)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;land-cover&#34;&gt;Land cover&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;eunis_1km &amp;lt;- raster::raster(&amp;quot;data/es_l1_1km.tif&amp;quot;)
raster::plot(eunis_1km)
raster::projection(eunis_1km)
&lt;/code&gt;&lt;/pre&gt;

&lt;h3 id=&#34;spatial-extraction-raster-value-for-a-vector-object&#34;&gt;Spatial extraction (raster value for a vector object)&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;proj_city_points_laea &amp;lt;- sf::st_transform(proj_city_points, raster::projection(eunis_1km))
eunis_city &amp;lt;- raster::extract(eunis_1km, as(proj_city_points_laea, &amp;quot;Spatial&amp;quot;))
eunis_city &amp;lt;- raster::extract(eunis_1km, as(sf::st_buffer(proj_city_points_laea, 10000), &amp;quot;Spatial&amp;quot;))

str(eunis_city)
table(eunis_city[[1]])

proportion &amp;lt;- as.numeric(table(eunis_city[[1]]))[which(names(table(eunis_city[[1]]))!=&amp;quot;10&amp;quot;)] / sum(as.numeric(table(eunis_city[[1]]))) * 100
proportion
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;vector-object&#34;&gt;Vector object&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(sf)
st_prov &amp;lt;- sf::st_read(&amp;quot;data/GADM_2.8_GBR_adm2.shp&amp;quot;)
plot(st_prov[,&amp;quot;HASC_2&amp;quot;], graticule = TRUE, axes = TRUE)

# Older option
library(rgdal)
st_prov_sp &amp;lt;- rgdal::readOGR(&amp;quot;data&amp;quot;, &amp;quot;GADM_2.8_GBR_adm2&amp;quot;)
class(st_prov_sp)
plot(st_prov_sp, axes = TRUE)

# Change projection of an &amp;quot;sp&amp;quot; object
crs.osgb = CRS(&amp;quot;+init=epsg:27700&amp;quot;)
st_prov_sp.osgb = sp::spTransform(st_prov_sp, crs.osgb)
plot(st_prov_sp.osgb, axes = TRUE)

spplot(st_prov_sp.osgb, &amp;quot;HASC_2&amp;quot;, colorkey = FALSE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;read-and-write-your-spatial-object-in-shapefile&#34;&gt;Read and write your spatial object in Shapefile&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;# read with &amp;quot;sf&amp;quot;
st_prov &amp;lt;- sf::st_read(&amp;quot;data/GADM_2.8_GBR_adm2.shp&amp;quot;)

# write with &amp;quot;sf&amp;quot;
sf::st_write(st_prov, dsn = &amp;quot;st_prov.shp&amp;quot;, delete_layer = TRUE)

# write with OGR (rgdal)
st_prov_sp &amp;lt;- sf::as(st_prov, &amp;quot;Spatial&amp;quot;)
rgdal::writeOGR(st_prov_sp, &amp;quot;st_prov_sp&amp;quot;, driver = &amp;quot;ESRI Shapefile&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;subseting-and-filtering&#34;&gt;Subseting and filtering&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;wdpa_gbr &amp;lt;- sf::st_read(&amp;quot;data/wdpa_gbr.shp&amp;quot;)
wdpa_gbr &amp;lt;- wdpa_gbr[wdpa_gbr$MARINE == 0,]
plot(wdpa_gbr$geometry)

## bounding box
bb &amp;lt;- sf::st_bbox(country_sf_gbr)
bb

## faster solution
bb_poly &amp;lt;- sf::st_make_grid(country_sf_gbr, n = 1)
wdpa_gbr_2 &amp;lt;- sf::st_intersects(wdpa_gbr, bb_poly, sparse = FALSE)
wdpa_gbr &amp;lt;- wdpa_gbr[wdpa_gbr_2,]
plot(wdpa_gbr$geometry[wdpa_gbr$IUCN_CAT == &amp;quot;V&amp;quot;])

wdpa_cntr &amp;lt;- sf::st_centroid(wdpa_gbr)
plot(wdpa_cntr$geometry)

## building a box from the st_bbox output
g.bbox &amp;lt;- raster::extent(as.numeric(sf::st_bbox(country_sf_gbr))[c(1,3,2,4)])
g.bbox_sf &amp;lt;- sf::st_set_crs(sf::st_as_sfc(as(g.bbox, &#39;SpatialPolygons&#39;)), 3035)
plot(g.bbox_sf, add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;em&gt;Try it your self&lt;/em&gt;
Get some river data from
&lt;a href=&#34;http://land.copernicus.eu/pan-european/satellite-derived-products/eu-hydro/eu-hydro-public-beta/eu-hydro-river-network/view&#34; target=&#34;_blank&#34;&gt;http://land.copernicus.eu/pan-european/satellite-derived-products/eu-hydro/eu-hydro-public-beta/eu-hydro-river-network/view&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h1 id=&#34;part-iv-interfacing-r-and-posgresql-postgis&#34;&gt;PART IV: Interfacing R and PosgreSQL/PostGIS&lt;/h1&gt;

&lt;ol&gt;
&lt;li&gt;Install PostgreSQL, with the PostGIS extension&lt;/li&gt;
&lt;li&gt;Create a database&lt;/li&gt;
&lt;li&gt;Populate your database&lt;/li&gt;
&lt;li&gt;Extract from your database&lt;/li&gt;
&lt;/ol&gt;

&lt;h2 id=&#34;create-postgresql-database&#34;&gt;Create PostgreSQL database&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(&#39;RPostgreSQL&#39;)

# create a new database in on your local PostgreSQL server
sql_createdb &amp;lt;- paste0(&amp;quot;-h localhost -U &amp;quot;, &amp;quot;postgres&amp;quot;, &amp;quot; -T &amp;quot;, &amp;quot;postgis_22_sample&amp;quot;, &amp;quot; -E UTF8 -O postgres &amp;quot;, &amp;quot;RGIS_workshop&amp;quot;)
system2(&amp;quot;createdb&amp;quot;, sql_createdb, invisible = FALSE)

# connect R to your server and newly created database
drv &amp;lt;- dbDriver(&amp;quot;PostgreSQL&amp;quot;)
dbcon &amp;lt;- dbConnect(drv, dbname = &amp;quot;RGIS_workshop&amp;quot;,
                 host = &amp;quot;localhost&amp;quot;, port = 5432,
                 user = &amp;quot;postgres&amp;quot;, password = &amp;quot;****&amp;quot;)

# create a schema, send your first SQL statement (query)
sql_createschema &amp;lt;- paste0(&amp;quot;CREATE SCHEMA IF NOT EXISTS my_shemas AUTHORIZATION postgres;&amp;quot;)
dbSendStatement(dbcon, sql_createschema)

# have a look at your PostgreSQL database, using pgAdmin
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;send-spatial-data-to-your-newly-created-database-located-on-your-local-server&#34;&gt;Send spatial data to your newly created database located on your local server&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(sf)
dbcon &amp;lt;- dbConnect(drv, dbname = &amp;quot;RGIS_workshop&amp;quot;,
                 host = &amp;quot;localhost&amp;quot;, port = 5432,
                 user = &amp;quot;postgres&amp;quot;, password = &amp;quot;*****&amp;quot;)

wdpa_gbr &amp;lt;- sf::st_read(&amp;quot;data/wdpa_gbr.shp&amp;quot;)
sf::st_write(wdpa_gbr, dbcon, overwrite = TRUE)

# check if you succeeded
dbExistsTable(dbcon, &amp;quot;wdpa_gbr&amp;quot;)

# extract some data with a SQL query and a condition
psql_extract &amp;lt;- dbGetQuery(dbcon, &amp;quot;SELECT \&amp;quot;IUCN_CAT\&amp;quot;, ST_AsText(geometry) as geom FROM wdpa_gbr WHERE \&amp;quot;IUCN_CAT\&amp;quot; = \&#39;V\&#39; AND \&amp;quot;MARINE\&amp;quot; = \&#39;0\&#39;&amp;quot;)

str(psql_extract)
new_extract &amp;lt;- sf::st_as_sf(psql_extract, wkt = &amp;quot;geom&amp;quot;)
new_extract &amp;lt;- sf::st_set_crs(new_extract, 4326)
plot(new_extract)

new_area &amp;lt;- sf::st_area(sf::st_transform(new_extract, 27700))
head(new_area)
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;interacting-with-postgresql-through-your-terminal&#34;&gt;Interacting with PostgreSQL through your terminal&lt;/h2&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;em&gt;In your terminal, type&lt;/em&gt;
ogr2ogr -f &amp;ldquo;PostgreSQL&amp;rdquo; -t_srs EPSG:27700 PG:&amp;ldquo;host=localhost port=5432 dbname=RGIS_workshop user=postgres password=****&amp;rdquo; &amp;lsquo;C:\Users\retoschm\OneDrive - Natural Environment Research Council\Rgis_workshop\data\GADM_2.8_GBR_adm2.shp&amp;rsquo; -nln public.wdpa_gbr2 -nlt MULTIPOLYGON -overwrite -progress -unsetFid &amp;ndash;config PG_USE_COPY YES&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h2 id=&#34;some-more-raster-operations&#34;&gt;Some more raster operations&lt;/h2&gt;

&lt;h3 id=&#34;hillshade-and-terrain-map&#34;&gt;Hillshade and Terrain map&lt;/h3&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(raster)
alt &amp;lt;- raster::getData(&amp;quot;alt&amp;quot;, country = &amp;quot;GBR&amp;quot;)
slope &amp;lt;- raster::terrain(alt, opt = &amp;quot;slope&amp;quot;)
aspect &amp;lt;- raster::terrain(alt, opt = &amp;quot;aspect&amp;quot;)
hill &amp;lt;- raster::hillShade(slope, aspect, angle = 40, direction = 270)

# plot your raster and newly extracted polygons on a map
raster::plot(hill, col = grey(0:100/100), legend = FALSE)
plot(sf::st_transform(new_extract, 4326), add = TRUE)
raster::plot(alt, col = terrain.colors(25, alpha = 0.5), add = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;h1 id=&#34;part-v-plot-your-spatial-object-data-with-ggplot2&#34;&gt;PART V: Plot your spatial object data with ggplot2&lt;/h1&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(ggplot2)
library(ggspatial)

country_sf_gbr &amp;lt;- sf::st_as_sf(raster::getData(name = &amp;quot;GADM&amp;quot;, country = &#39;GBR&#39;, level = 1))
country_sf_gbr_osgb &amp;lt;- sf::st_transform(country_sf_gbr, crs = 27700)

point_liverpool &amp;lt;- data.frame(name = &#39;Liverpool&#39;, longitude = -2.98, latitude = 53.41)
point_edinburgh &amp;lt;- data.frame(name = &#39;Edinburgh&#39;, longitude = -3.19, latitude = 55.95)
city_points &amp;lt;- rbind(point_liverpool, point_edinburgh)
proj_city_points_osgb &amp;lt;- sf::st_transform(sf::st_as_sf(city_points, coords = c(&amp;quot;longitude&amp;quot;, &amp;quot;latitude&amp;quot;), crs = 4326), crs = 27700)

## simple plot with ggplot
ggplot(data = country_sf_gbr_osgb) +
    geom_sf() +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;))

## some colors
ggplot(data = country_sf_gbr_osgb) +
    geom_sf(color = &amp;quot;black&amp;quot;, fill = &amp;quot;wheat1&amp;quot; ) +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;))

## color with some qualitative meaning
ggplot(data = country_sf_gbr_osgb) +
    geom_sf(aes(fill = as.numeric(sf::st_area(country_sf_gbr_osgb)))) +
    scale_fill_viridis_c(option = &amp;quot;plasma&amp;quot;, name = &amp;quot;Area&amp;quot;) +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;))

# add a scale bar and a north arrow
ggplot(data = country_sf_gbr_osgb) +
    geom_sf() +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    annotation_scale(location = &amp;quot;bl&amp;quot;, width_hint = 0.5) +
    annotation_north_arrow(location = &amp;quot;bl&amp;quot;, which_north = &amp;quot;true&amp;quot;,
          pad_x = unit(0.75, &amp;quot;in&amp;quot;), pad_y = unit(0.5, &amp;quot;in&amp;quot;),
          style = north_arrow_fancy_orienteering) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;))

# add more spatial object with geom_sf
ggplot(data = country_sf_gbr_osgb) +
    geom_sf() +
    geom_sf(data = proj_city_points_osgb, size = 4, color = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;)) +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    annotate(geom = &amp;quot;text&amp;quot;,
            x = sf::st_coordinates(sf::st_centroid(country_sf_gbr_osgb))[,1],
            y = sf::st_coordinates(sf::st_centroid(country_sf_gbr_osgb))[,2],
            label = country_sf_gbr_osgb$NAME_1,
        fontface = &amp;quot;italic&amp;quot;, color = &amp;quot;grey22&amp;quot;, size = 3)  +
    annotation_scale(location = &amp;quot;bl&amp;quot;, width_hint = 0.5) +
    annotation_north_arrow(location = &amp;quot;bl&amp;quot;, which_north = &amp;quot;true&amp;quot;,
          pad_x = unit(0.75, &amp;quot;in&amp;quot;), pad_y = unit(0.5, &amp;quot;in&amp;quot;),
          style = north_arrow_fancy_orienteering) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;))
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;adding-a-raster-to-a-ggplot&#34;&gt;Adding a raster to a ggplot&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(raster)
alt &amp;lt;- raster::getData(&amp;quot;alt&amp;quot;, country = &amp;quot;GBR&amp;quot;)
# need to reproject your raster to match the other layer and get a regular resolution
alt_prj &amp;lt;- raster::projectRaster(alt, crs = sp::CRS(&amp;quot;+init=epsg:27700&amp;quot;), res = 1000)

# first method
alt_prj_df &amp;lt;- raster::as.data.frame(alt_prj, xy = TRUE)
colnames(alt_prj_df)  &amp;lt;- c(&amp;quot;x&amp;quot;, &amp;quot;y&amp;quot;, &amp;quot;Altitude&amp;quot;)

ggplot() +
    geom_tile(data = alt_prj_df, aes(x = x, y = y, fill = Altitude)) +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    annotate(geom = &amp;quot;text&amp;quot;,
            x = sf::st_coordinates(sf::st_centroid(country_sf_gbr_osgb))[,1],
            y = sf::st_coordinates(sf::st_centroid(country_sf_gbr_osgb))[,2],
            label = country_sf_gbr_osgb$NAME_1,
        fontface = &amp;quot;italic&amp;quot;, color = &amp;quot;white&amp;quot;, size = 3) +
    annotation_scale(location = &amp;quot;bl&amp;quot;, width_hint = 0.5) +
    annotation_north_arrow(location = &amp;quot;bl&amp;quot;, which_north = &amp;quot;true&amp;quot;,
          pad_x = unit(0.75, &amp;quot;in&amp;quot;), pad_y = unit(0.5, &amp;quot;in&amp;quot;),
          style = north_arrow_fancy_orienteering) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;)) +
    theme(panel.background = element_rect(fill = &amp;quot;aliceblue&amp;quot;))

## second method (better)
alt_prj_spdf &amp;lt;- as(alt_prj, &amp;quot;SpatialPixelsDataFrame&amp;quot;)
alt_prj_df2 &amp;lt;- as.data.frame(alt_prj_spdf)
colnames(alt_prj_df2) &amp;lt;- c(&amp;quot;Altitude&amp;quot;, &amp;quot;x&amp;quot;, &amp;quot;y&amp;quot;)

ggplot() +
    geom_sf(data = country_sf_gbr_osgb) +
    geom_tile(data = alt_prj_df2, aes(x = x, y = y, fill = Altitude)) +
    geom_sf(data = proj_city_points_osgb, size = 4, color = c(&amp;quot;magenta&amp;quot;, &amp;quot;blue&amp;quot;)) +
    xlab(&amp;quot;Longitude&amp;quot;) + ylab(&amp;quot;Latitude&amp;quot;) +
    annotate(geom = &amp;quot;text&amp;quot;,
            x = sf::st_coordinates(sf::st_centroid(country_sf_gbr_osgb))[,1],
            y = sf::st_coordinates(sf::st_centroid(country_sf_gbr_osgb))[,2],
            label = country_sf_gbr_osgb$NAME_1,
        fontface = &amp;quot;italic&amp;quot;, color = &amp;quot;white&amp;quot;, size = 3) +
    annotation_scale(location = &amp;quot;bl&amp;quot;, width_hint = 0.5) +
    annotation_north_arrow(location = &amp;quot;bl&amp;quot;,which_north = &amp;quot;true&amp;quot;,
          pad_x = unit(0.75, &amp;quot;in&amp;quot;), pad_y = unit(0.5, &amp;quot;in&amp;quot;),
          style = north_arrow_fancy_orienteering) +
    ggtitle(&amp;quot;GBR map&amp;quot;, subtitle = paste0(&amp;quot;(&amp;quot;, length(unique(country_sf_gbr_osgb$NAME_1)), &amp;quot; countries)&amp;quot;)) +
    theme(panel.background = element_rect(fill = &amp;quot;aliceblue&amp;quot;))
&lt;/code&gt;&lt;/pre&gt;

&lt;h2 id=&#34;can-you-plot-the-deer-occurrence-extracted-from-gbif-on-a-uk-map&#34;&gt;Can you plot the deer occurrence extracted from GBIF on a UK map?&lt;/h2&gt;

&lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(rgbif)
deer_locations &amp;lt;- occ_search(scientificName = &amp;quot;Cervus elaphus&amp;quot;, limit = 5000,
                             hasCoordinate = TRUE, country = &amp;quot;GB&amp;quot;,
                             return = &amp;quot;data&amp;quot;) %&amp;gt;% dplyr::select(key, name, decimalLongitude,
		                         decimalLatitude, year, individualCount, datasetKey, country)

deer_locations_sf &amp;lt;- sf::st_as_sf(deer_locations, coords = c(&amp;quot;decimalLongitude&amp;quot;, &amp;quot;decimalLatitude&amp;quot;), crs = 4326)
plot(deer_locations_sf[, &amp;quot;datasetKey&amp;quot;])
# ============
# YOUR TURN!
# ===========
&lt;/code&gt;&lt;/pre&gt;

&lt;h1 id=&#34;your-challenge-build-some-wider-map-for-europe&#34;&gt;Your challenge, build some wider map for Europe&lt;/h1&gt;

&lt;p&gt;Have a look at tutorial 1, 2 and 3 on how to draw beautiful map with sf.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html&#34; target=&#34;_blank&#34;&gt;https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-2.html&#34; target=&#34;_blank&#34;&gt;https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-2.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-3.html&#34; target=&#34;_blank&#34;&gt;https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-3.html&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Look at the exellent sf vignettes (&lt;em&gt;see list here&lt;/em&gt;: &lt;a href=&#34;https://cran.r-project.org/web/packages/sf/index.html&#34; target=&#34;_blank&#34;&gt;https://cran.r-project.org/web/packages/sf/index.html&lt;/a&gt;)&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Advancing your R</title>
      <link>https://BES-QSIG.github.io/events/2018-12-3-advancing-your-r/</link>
      <pubDate>Tue, 13 Nov 2018 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/events/2018-12-3-advancing-your-r/</guid>
      <description>

&lt;p&gt;The British Ecological Society Quantitative Ecology Group are hosting a two-day R workshop which will cover reproducible data manipulation and visualisation workflows in R. Day One will be focussed on the tidyverse, while Day Two will move into working with spatial data.&lt;/p&gt;

&lt;h1 id=&#34;day-one-data-manipulation-and-visualisation-in-r-using-the-tidyverse&#34;&gt;Day One: Data manipulation and visualisation in R using the tidyverse&lt;/h1&gt;

&lt;h2 id=&#34;workshop-leader-gergana-daskalova-university-of-edinburgh-coding-club&#34;&gt;Workshop leader: Gergana Daskalova, University of Edinburgh, Coding Club&lt;/h2&gt;

&lt;p&gt;The tidyverse collection of packages (tidyr, dplyr, ggplot2, broom and more) is a great tool for efficient data manipulation, visualisation and analysis. In this workshop, you’ll learn how to manipulate data using pipes, build functions and apply them in a pipe, summarise model output and visualise data analysis results. We’ll then delve into functional programming using purr. Along the way, we’ll be using large ecological datasets, so you will get hands-on experience with how you can tackle big data in ecology and achieve a lot in not that many lines of code. You’ll gain skills in customising different types of graphs to maximise the impact of your data visualisation and finally, we will be doing all of this under the Markdown framework for reproducible report to promote transparency in research.&lt;/p&gt;

&lt;h1 id=&#34;day-two-spatial-data-manipulation-and-visualisation-in-r&#34;&gt;Day Two: Spatial data manipulation and visualisation in R&lt;/h1&gt;

&lt;h2 id=&#34;workshop-leader-reto-schmucki-centre-for-ecology-and-hydrology&#34;&gt;Workshop leader: Reto Schmucki, Centre for Ecology and Hydrology&lt;/h2&gt;

&lt;p&gt;Geocomputing and manipulating geographic data are important skills for modelling spatial phenomena and have become increasingly important in data science. Over the last decade, there has been important progress in open-source GIS. In this workshop, you&amp;rsquo;ll learn how to create, import and manipulate spatial data, using the rich spatial ecosystem available in R (sf, sp, raster, tmap, leaflet). With emphasis on Simple Features for R available through the sf package. You will learn how to extract spatial information, merge complex data sets, visualise geographic data and produce interactive maps. Along the way, we&amp;rsquo;ll work on real-word data and explore how to harness the power of R and how it links to other open-source GIS solutions (QGIS, GRASS, PostGIS). At the end of this workshop, you will be able to implement a workflow to manipulate data, run analysis and visualise your results, using open-source solutions with a transparent and fully reproducible workflow.&lt;/p&gt;

&lt;p&gt;The option is available to come to both days, or just one of the two. Lunch and refreshments will be provided.&lt;/p&gt;

&lt;p&gt;&lt;a href=&#34;https://www.eventbrite.co.uk/e/advancing-your-r-tickets-49210051589&#34; target=&#34;_blank&#34;&gt;SOLD OUT&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Commitee Members</title>
      <link>https://BES-QSIG.github.io/about/committee/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>https://BES-QSIG.github.io/about/committee/</guid>
      <description>&lt;!-- Laura Graham --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/LauraGraham.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Laura Graham&lt;/b&gt; (Secretary)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Birmingham&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/laurajanegraham?lang=en&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/profile/Laura_Graham13&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;http://laurajanegraham.github.io/&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Laura is a spatial/computational ecologist who is interested in what impacts environmental changes have on species’ distributions and how we can use this information in landscape and conservation planning to reduce detrimental effects. In a previous career, Laura was a mathematician and database developer, and applies skills learned there to answer ecological questions.&lt;/p&gt;

&lt;!-- Chris Sutherland --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/ChrisSutherland.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Chris Sutherland&lt;/b&gt; (Deputy Secretary)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of St Andrews&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/chrissuthy?lang=en&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
             &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.com/citations?user=PZhqA9UAAAAJ&amp;hl=en&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/profile/Chris_Sutherland2&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.sutherlandecology.com/&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Chris is primarily interested in spatial population ecology, with a specific focus on space use, dispersal, and demography. The overarching theme of his research, and the research of those in his group, is increasing the biological realism of modern eco-statistical models to best test spatial ecological theory.&lt;/p&gt;

&lt;!-- Gergana Daskalova --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/GerganaDaskalova.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Gergana Daskalova&lt;/b&gt; (Training and Education Rep)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Edinburgh&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/gndaskalova?lang=en&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.com/citations?user=i5lOn9sAAAAJ&amp;hl=en&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://gndaskalova.com&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;far fa-envelope&#34;&gt;&lt;/i&gt; &lt;a href=&#34;mailto:gndaskalova@gmail.com&#34;&gt; Email&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Gergana is a global change ecologist with a passion for biodiversity change, agro-ecology and quantitative syntheses. The overarching aim of her research is to determine the sources of the complex patterns of population and biodiversity change observed over time around the world. From forest cover change around the world to climate warming in the Arctic and more, she is investigating how and why Earth’s biota is changing across the Anthropocene.&lt;/p&gt;

&lt;!-- Fiona Burns --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/FionaBurns.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Fiona Burns&lt;/b&gt; (Policy Rep)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;Royal Society for the Protection of Birds&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/f_e_burns&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/profile/Fiona_Burns2&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.rspb.org.uk/our-work/conservation/centre-for-conservation-science/our-team/fiona-burns/&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Fiona is a scientist in the Centre for Conservation Science at the RSPB. Her work aims to understand the status and trends of a broad range of species and habitats in the UK, and the underlying environmental variables which drive change in these, including conservation action. She works work with other NGOs, academics and statutory agencies to produce syntheses of these data for a range of audiences.&lt;/p&gt;

&lt;!-- Phil Bouchet --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/PhilBouchet.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Phil Bouchet&lt;/b&gt; (Website Coordinator)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of St Andrews&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/pjbouchet&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
         &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.com/citations?user=X5SMt_8AAAAJ&amp;hl=en&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
        &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
        &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/profile/Phil_Bouchet&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://pjbouchet.github.io/&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Phil is a quantitative marine ecologist with an active interest in the use of GIS and statistical methods to address pressing wildlife conservation problems in the ocean. Phil’s background is in marine mammal science, although his work encompasses a range of other species including sharks, pelagic fishes, turtles and seabirds. Phil’s interests lie in the estimation of animal abundance and distribution, using frequentist and Bayesian methods. He  is currently a Research Associate at the Centre for Research into Ecological &amp;amp; Environmental Modelling (CREEM) at the University of St Andrews, where he focuses on extrapolation in cetacean density models.&lt;/p&gt;

&lt;!-- Sarah Marley --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/SarahMarley.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Sarah Marley&lt;/b&gt; (Ordinary Member)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Portsmouth&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/sarahmarley86?lang=en&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
         &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.com.au/citations?user=BjaSV0sAAAAJ&amp;hl=en&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
        &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
        &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/lab/Marley-Lab-Sarah-Anne-Marley&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://sarahmarley.com/&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Sarah is a bioacoustician specialising in vertebrate ecology, animal behaviour, and underwater soundscapes. Her research focuses primarily on marine megafauna, including cetaceans (whales, dolphins, porpoises), pinnipeds (seals, sea lions), birds, and fish. Sarah is a prize-winning science communicator and tertiary educator with a strong interest in both public engagement and citizen science.&lt;/p&gt;

&lt;!-- Tom August --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/TomAugust.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Tom August&lt;/b&gt; (Social Media Rep)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;Centre for Ecology &amp; Hydrology&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/TomAugust85&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
         &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.co.uk/citations?user=X1G0mv0AAAAJ&amp;hl=en&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
        &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
        &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/profile/Tom_August/&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Tom is a computational ecologist at the Centre for Ecology &amp;amp; Hydrology. Tom’s work focuses on the nexus between citizen science, technology, and data science. Tom works with statistical experts to develop methods for analysing species occurrence data and works to make these methods available to other academics and practitioners. Tom’s work spans a range of tools and technologies including; AI, computer vision, drones, mobile phone applications and web platforms.&lt;/p&gt;

&lt;!-- Simon Kapitza --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/SimonKapitza.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Simon Kapitza&lt;/b&gt; (Ordinary Member)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Melbourne&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/simonecology&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
        &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://kapitzas.github.io&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;far fa-envelope&#34;&gt;&lt;/i&gt; &lt;a href=&#34;mailto:simon.statecology@gmail.com&#34;&gt; Email&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Simon is a global change ecologist at the Quantitative &amp;amp; Applied Ecology Group in Melbourne. He is interested in developing methods for integrated assessments of biodiversity change to better forecast the fate of biodiversity under different future scenarios. His particular focus is on understanding the importance of socioeconomic drivers and capturing impacts of global transitions by coupling models across scientific disciplines.&lt;/p&gt;

&lt;!-- Jon Yearsley --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/JonYearsley.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Jon Yearsley&lt;/b&gt; (Ordinary Member)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University College Dublin&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/jonyearsley?lang=en&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
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          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.ucd.ie/ecomodel&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
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          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.com/citations?user=vdwNKNYAAAAJ&amp;hl=en&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
           &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-researchgate&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://www.researchgate.net/profile/Jon_Yearsley&#34; target=&#34;_blank&#34;&gt; ResearchGate&lt;/a&gt;&lt;/li&gt;
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          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;far fa-envelope&#34;&gt;&lt;/i&gt; &lt;a href=&#34;mailto:Jon.Yearsley@ucd.ie&#34;&gt; Email&lt;/a&gt;&lt;/li&gt;
        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Jon is a mathematical biologist with research interests that combine ecological theory with ecological data. His active research areas are in ecosystem resilience (primarily grasslands), phenology, analysis of biological records, and modelling of dispersal for genetic diversity and population spread.  Jon is also enthusiastic about teaching quantitative methods to life science students. A sample of teaching resources can be found on his website. His is secretary of the Irish Ecological Association (&lt;a href=&#34;https://www.irishecologicalassociation.org&#34; target=&#34;_blank&#34;&gt;https://www.irishecologicalassociation.org&lt;/a&gt;)&lt;/p&gt;

&lt;!-- Shaun Keegan --&gt;

&lt;table&gt;
  &lt;tr&gt;
    &lt;td class=&#34;leftcol&#34;&gt;&lt;img src=&#34;https://bes-qsig.github.io/img/ShaunKeegan.png&#34;/&gt;&lt;/td&gt;
    &lt;td class=&#34;rightcol&#34;&gt;&lt;table&gt;
      &lt;b&gt;Shaun Keegan&lt;/b&gt; (Early Career Representative)&lt;br&gt;
      &lt;span class=&#34;member_affiliation&#34;&gt;University of Glasgow&lt;/span&gt;&lt;/table&gt;
      &lt;table&gt;
        &lt;ul&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-twitter&#34;&gt; &lt;/i&gt; &lt;a href=&#34;https://twitter.com/DrShaunKeegan&#34; target=&#34;_blank&#34;&gt; Twitter&lt;/a&gt;&lt;/li&gt;
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          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fas fa-globe-americas&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://keegan.science/&#34; target=&#34;_blank&#34;&gt; Website&lt;/a&gt;&lt;/li&gt;
          &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
          &lt;li class=&#34;member_web&#34;&gt;&lt;i class=&#34;fab fa-google&#34;&gt;&lt;/i&gt; &lt;a href=&#34;https://scholar.google.com/citations?user=88-24B0AAAAJ&amp;hl=en&amp;oi=ao&#34; target=&#34;_blank&#34;&gt; Scholar&lt;/a&gt;&lt;/li&gt;
           &lt;li class=&#34;list_separator&#34;&gt; | &lt;/li&gt;
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        &lt;/ul&gt;
      &lt;/table&gt;
    &lt;/td&gt;
  &lt;/tr&gt;
&lt;/table&gt;

&lt;p&gt;Shaun is an epidemiologist and quantitative ecologist. His core research currently focuses on mathematically modelling antimicrobial resistance (AMR) emergence and transmission in Trypanosoma congolense, one of the causes of Nagana, a wasting disease of African cattle. He is interested in AMR in a broader sense, and keen to explore the utility of mathematical models to produce actionable suggestions for AMR stewardship.&lt;/p&gt;
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