BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Statistics X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20231029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20250217 DTEND;VALUE=DATE:20250222 DTSTAMP:20260419T103920 CREATED:20240530T130225Z LAST-MODIFIED:20240926T113018Z UID:10000458-1739750400-1740182399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live DESCRIPTION:Prof. David Warton\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 17th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n Course Details\n Course overview: \nEcological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. \nThis course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles\, 2) remotely sensed data gathering and analysis\, 3) monitoring ecosystem change in space and time by remote sensing data. \nThe course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. \nBy the end of the course\, participants will:• be able to create their own projects on monitoring of spatial and temporal changes of ecosystems with remote sensing data• be able to report in LaTeX and R Markdown the achieved results \n \n \n \n \n \n Intended Audiences\n Intended Audience• Practitioners\, students\, academics• People new to R \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central European Time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Theoretical presentations will introduce coding sessions. The whole course is intended to be practical. \n \n \n \n \n \n Assumed quantitative knowledge\n No previous knowledge of R is needed. \n \n \n \n \n \n Assumed computer background\n A basic computer background is needed. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\nPackage needed for the course:– imageRy– overlap– spatstat– terra– vegan \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n\nMonday 17th – Classes from 09:30 to 17:30 \n– R (intro) \n[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!] \n[Spatial R] \n[Reference systems: introduction to the main coordinate systems] \n– Visualizing data \n[Visualizing multi- e hyper-spectral data] \n  \nTuesday 18th – Classes from 09:30 to 17:30 \n– Spectral indices extracted from satellite imagery \n[Main spectral indices extracted from remote sensing data] \n– Remote sensing data classification \n[Generating land cover maps from remotely sensed data] \n  \nWednesday 19th – Classes from 09:30 to 17:30 \n– Land use change in space and time \n[Analysis ecosystem change in space and time: the case of Mato Grosso] \n[Time series: ice melt in Greenland] \n  \nThursday 20th – Classes from 09:30 to 17:30 \n– External remote sensing data \n[Download and use remote sensing data from internet sources] \n[Downloading and visualising Copernicus data] \n– Image data processing \n[Ecosystem variability] \n[Multivariate analysis on remotely sensed data] \n  \nFriday 21st – Classes from 09:30 to 17:30 \n– Reporting \n[LaTeX for scientific reporting via articles] \n[LaTeX/Beamer for scientific reporting via presentations] \n[R Markdown for scientific reporting via internet pages] \n  \n\n  \n  \n \n \n \n \n \n Course Instructor\n \nDr. Duccio Rocchini\nComing soon… URL:https://prstats.preprodw.com/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01-1.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240422 DTEND;VALUE=DATE:20240426 DTSTAMP:20260419T103920 CREATED:20200327T044645Z LAST-MODIFIED:20240403T125252Z UID:10000304-1713744000-1714089599@prstats.preprodw.com SUMMARY:ONLINE COURSE – Spatio-Ecological Data Analysis using R and Rstudio (SEAR01) This course will be delivered live DESCRIPTION:Prof. David Warton\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, April 22nd\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n Course Details\n Spatial ecology is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. \nThis course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time\, due to both human impact and global change. Particular emphasis will be given to: 1) population ecology: how organisms spread in space and how to study it by point pattern analysis\, 2) community ecology: how communities are structured and how to study such structure by multivariate analysis; 3) monitoring species distributions and their change in space and time by species distribution modelling; 4) monitoring ecosystem change in space and time by remote sensing data. \nThe course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. \nBy the end of the course\, participants will:• be able to create their own projects on monitoring of spatial and temporal changes of species and ecosystems at different spatial scales• be able to report in LaTeX and R Markdown the achieved results \n \n \n \n \n \n Intended Audiences\n This course is aimed at academics and post-graduate students working in spatial ecology \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central European Time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Theoretical presentations will introduce coding sessions. The whole course is intended to be practical. \n \n \n \n \n \n Assumed quantitative knowledge\n No previous knowledge of R is needed. \n \n \n \n \n \n Assumed computer background\n A basic computer background is needed. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\nCourse packages:– imageRy– overlap– spatstat– terra– vegan \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n\nMonday 25th – Classes from 09:30 to 17:30 \n– R (intro) \n[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!] \n[Spatial R] \n– Population Ecology \n[Point Patterns Analysis – Spatial statistics: deriving continuous maps from in-situ data\, principles of autocorrelation and spatial interpolation] \nTuesday 26th – Classes from 09:30 to 17:30 \n– Community ecology[Multivariate analysis in R] \n[Community niche overlap] \n– Remote sensing in R \n[Remotely sensed data visualisation] \nWednesday 27th – Classes from 09:30 to 17:30 \n– Remote sensing in R \n[Spectral indices] \n[Time series] \nThursday 28th – Classes from 09:30 to 17:30 \n– External remote sensing data \n[Download and use remote sensing data from internet sources] \n[Downloading and visualising Copernicus data] \n– Image data processing \n[Remotely sensed data classification: land cover maps] \n[Ecosystem variability] \n[Multivariate analysis on remotely sensed data] \nFriday 29th – Classes from 09:30 to 17:30 \n– Reporting \n[LaTeX for scientific reporting via articles] \n[LaTeX/Beamer for scientific reporting via presentations] \n[R Markdown for scientific reporting via internet pages] \n  \n\n  \n  \n \n \n \n \n \n Course Instructor\n  \nDr. Duccio Rocchini\nComing soon… URL:https://prstats.preprodw.com/course/spatio-ecological-data-analysis-using-r-and-rstudio-sear01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01-1.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240220 DTEND;VALUE=DATE:20240223 DTSTAMP:20260419T103920 CREATED:20200804T125230Z LAST-MODIFIED:20240222T142952Z UID:10000313-1708387200-1708646399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Data visualization with ggplot2 using R and Rstudio (DVGG04) This course will be delivered live DESCRIPTION:Prof. David Warton\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, March 26th\, 2023\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n Course Details\n During this course we provide a comprehensive introduction to data visualization in R using ggplot. We begin by providing a brief overview of the general principles data visualization\, and an overview of the general principles behind ggplot. We then proceed to cover the major types of plots for visualizing distributions of univariate data: histograms\, density plots\, barplots\, and Tukey boxplots. In all of these cases\, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here\, we will explore how to apply linear and nonlinear smoothing functions to the data\, how to add marginal histograms to the scatterplot\, add labels to points\, and scale each point by the value of a third variable. We then cover some additional plot types that are often related but not identical to those major types covered during the beginning of the course: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. We then consider more fine grained control of the plot by changing axis scales\, axis labels\, axis tick points\, colour palettes\, and ggplot “themes”. Finally\, we consider how to make plots for presentations and publications. Here\, we will introduce how to insert plots into documents using RMarkdown\, and also how to create labelled grids of subplots of the kind seen in many published articles. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n  \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n None needed. \n \n \n \n \n \n Assumed computer background\n Some familiarity with R. \n \n \n \n \n \n Equipment and software requirements\n \n\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).  \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n\nTuesday 26th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: What is data visualization. Data visualization is a means to explore and understand our data and should be a major part of any data analysis. Here\, we briefly discuss why data visualization is so important and what the major principles behind it are. \nTopic 2: Introducing ggplot. Though there are many options for visualization in R\, ggplot is simply the best. Here\, we briefly introduce the major principles behind how ggplot works\, namely how it is a layered grammar ofgraphics. \nWednesday 27th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \n\nTopic 3: Visualizing univariate data. Here\, we cover a set of major tools for visualizing distributions over single variables: histograms\, density plots\, barplots\, Tukey boxplots. In each case\, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots. \nTopic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data. Here\, in addition to covering how to visualize multiple groups using colours and facets\, we will also cover how to provide marginal plots on the scatterplots\, labels to points\, and how to obtain linear and nonlinear smoothing of the plots. \nTopic 5: More plot types. Having already covered the most widely used general purpose plots on Day 1\, we now turn to cover a range of other major plot types: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. Each of these are important and widely used types of plots\, and knowing them will expand your repertoire. \n\nThursday 28th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 6: Fine control of plots. Thus far\, we will have mostly used the default for the plot styles and layouts. Here\, we will introduce how to modify things like the limits and scales on the axes\, the positions and nature of the axis ticks\, the colour palettes that are used\, and the different types of ggplot themes that are available. \nTopic 7: Plots for publications and presentations: Thus far\, we have primarily focused on data visualization as a means of interactively exploring data. Often\, however\, we also want to present our plots in\, for example\, published articles or in slide presentations. It is simple to save a plot in different file formats\, and then insert them into a document. However\, a much more efficient way of doing this is to use RMarkdown to run the R code and automatically insert the resulting figure into a\, for example\, Word document\, pdf document\, html page\, etc. In addition\, here we will also cover how to make labelled grids of subplots like those found in many scientific articles. \n\n  \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here\n\nResearchGateGoogleScholarORCIDGitHub \n  URL:https://prstats.preprodw.com/course/data-visualization-with-ggplot2-using-r-and-rstudio-dvgg04/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/DVGG02.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20231211 DTEND;VALUE=DATE:20231215 DTSTAMP:20260419T103920 CREATED:20231121T142647Z LAST-MODIFIED:20231204T170316Z UID:10000334-1702252800-1702598399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Data wrangling using R and Rstudio (DWRS03) This course will be delivered live DESCRIPTION:Prof. David Warton\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 11th\, 2023\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n During this course we provide a comprehensive practical introduction to data wrangling using R. In particular\, we focus on tools provided by R’s tidyverse\, including dplyr\, tidyr\, purrr\, etc. Data wrangling is the art of taking raw and messy data and formatting and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly\, it can be time consuming\, laborious\, and error-prone. Fortunately\, the tools provided by R’s tidyverse allow us to do data wrangling in a fast\, efficient\, and high-level manner\, which can have dramatic consequences for ease and speed with which we analyse data. We start with how to read data of different types into R\, we then cover in detail all the dplyr tools such as select\, filter\, mutate\, etc. Here\, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. We then cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here\, we will consider how to concatenate data frames\, including concatenating all data files in a folder\, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n \n \n \n \n \n Assumed quantitative knowledge\n Coming soon.. \n \n \n \n \n \n Assumed computer background\n Minimal prior experience with R and RStudio is required. Attendees should be familiar with some basic R syntax and commands\, how to write code in the RStudio console and script editor\, how to load up data from files\, etc. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n\n\nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: Reading in data. We will begin by reading in data into R using tools such as readr and readxl. Almost all types of data can be read into R\, and here we will consider many of the main types\, such as csv\, xlsx\, sav\, etc. Here\, we will also consider how to contol how data are parsed\, e.g.\, so that they are read as dates\, numbers\, strings\, etc. \nTopic 2: Wrangling with dplyr. For the remainder of Day 1\, we will next cover the very powerful dplyr R package. This package supplies a number of so-called “verbs” — select\, rename\, slice\, filter\, mutate\, arrange\, etc. — each of which focuses on a key data manipulation tools\, such as selecting or changing variables. All of these verbs can be chained together using “pipes” (represented by %>%). Together\, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here\, we will also learn about the key concept of “tidy data”\, which is roughly where each row of a data frame is an observation and each column is a variable. \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nTopic 2 continued: \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with great effect to summarize data using descriptive statistics. \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 4: Merging and joining data frames. There are multiple ways to combine data frames\, with the simplest being “bind” operations\, which are effectively horizontal or vertical concatenations. Much more powerful are the SQL like “join” operations. Here\, we will consider the inner_join\, left_join\, right_join\, full_join operations. In this section\, we will also consider how to use purrr to read in and automatically merge large sets of files. \nTopic 5: Pivoting data. Sometimes we need to change data frames from “long” to “wide” formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this. \n\n\n\n \n \n \n \n \n Course Instructor\n \n\n\n\n\nDr. Rafael De Andrade Moral \n\n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \n\n\n  \nResearchGateGoogleScholarORCIDGitHub \n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/data-wrangling-using-r-and-rstudio-dwrs03-2/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/DWRS02R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210201 DTEND;VALUE=DATE:20210213 DTSTAMP:20260419T103920 CREATED:20180924T194838Z LAST-MODIFIED:20220222T023842Z UID:10000283-1612137600-1613174399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Model-based multivariate analysis of abundance data using R (MBMV03) DESCRIPTION:Prof. David Warton\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 1st\, 2021\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n Course Details\n This course will provide an introduction to modern multivariate techniques\, with a special focus on the analysis of abundance or presence/absence data. Multivariate analysis in ecology has been changing rapidly in recent years\, with a focus now on formulating a statistical model to capture key properties of the observed data\, rather than transformation of data using a dissimilarity-based framework. In recent years\, model-based techniques have been developed for hypothesis testing\, identifying indicator species\, ordination\, clustering\, predictive modelling\, and use of species traits as predictors to explain interspecific variation in environmental response.  These techniques are more interpretable than alternatives\, have better statistical properties\, and can be used to address new problems\, such as the prediction of a species’ spatial distribution from its traits alone. \n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – Australian Eastern Daylight Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n Intended Audiences\n PhD students\, research postgraduates\, and practicing academics as well as persons in industry working with multivariate data\, especially when recorded as presence/absences or some measure of abundance (counts\, biomass\, % cover\, etc). \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Availability – 40 places \nDuration – 10 daysContact hours – Approx. 30 hoursECT’s – Equal to 3 ECT’sLanguage – English \nOther payment options are available please email oliverhooker@prstatistics.com \n \n \n \n \n \n Teaching Format\n \n\n\nA mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \nAssumed quantitative knowledge \nAn understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \nAssumed computer background \nPrevious experience with data analysis using R is required. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \nEquipment and software requirements \nA laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/ \n\nDownload RStudio \n\n \nIt is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\n \n \n \n \n \n Assumed quantitative knowledge\n Coming soon.. \n \n \n \n \n \n Assumed computer background\n Coming soon.. \n \n \n \n \n \n Equipment and software requirements\n Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n\n\n\nThere will additional Q and A support for people who can follow during real time – this will be from 21:30 to 22:00 EDT \nWEEK 1 \nMonday 1st – Classes from 10:00 to 13:00 EDTRevision of key “Stat 101” messages \nTuesday 2nd – Classes from 10:00 to 13:00 EDTRevision of (univariate) regression analysis: the linear model\, generalised linear model.Main packages: lme4. \nWednesday 3rd – Classes from 10:00 to 13:00 EDTLinear mixed models\, the parametric bootstrap\, permutation tests and the bootstrap.Main packages: lme4\, mvabund. \nThursday 4th – Classes from 10:00 to 13:00 EDTModel selection\, classical multivariate analysis.Main packages: glmnet. \nFriday 5th – Classes from 10:00 to 13:00 EDTMultivariate abundance data: hierarchical models\, key properties\, hypothesis testing.Main packages: mvabund. \nWEEK 2 \nMonday 8th – Classes from 10:00 to 13:00 EDTMultivariate abundance data: design-based inference for dependent data\, indicator species.Main packages: mvabund. \nTuesday 9th – Classes from 10:00 to 13:00 EDTCompositional data\, explaining cross-species patterns using traits.Main packages: mvabund. \nWednesday 10th – Classes from 10:00 to 13:00 EDTClassifying species based on environmental response\, predictive modelsMain packages: Speciesmix\, mvabund\, lme4. \nThursday 11th – Classes from 10:00 to 13:00 EDTModel-based ordination and inferenceMain packages: gllvm. \nFriday 12th – Classes from 10:00 to 13:00 EDTInferring interactions form co-occurrence dataMain packages: gllvm\, ecoCopula. \n\n\n\n \n \n \n \n \n Course Instructor\n\n  \nDr. Antoine Becker-Scarpitta\nWorks at – University of Helsink\nTeaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)\nAntoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay\, and from the Natural History Museum of Paris\, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology\, plant ecology and evolution\, linear and multivariate statistics assisted on R.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/model-based-multivariate-analysis-of-abundance-data-using-r-mbmv03/ LOCATION:Delivered remotely (Australia)\, Australia CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/09/16-Model-base-multivaraite-analysis-of-abundance-data-using-R-MBMV.jpg GEO:-25.274398;133.775136 END:VEVENT END:VCALENDAR