# ONLINE COURSE – Data visualization using GG plot 2 (R and Rstudio) (DVGG01) This course will be delivered live

## 20 August 2020 - 21 August 2020

£300.00### Event Navigation

## This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.

This 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.

TIME ZONE – Western European Time +1 – 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@psstatistics.com for full details or to discuss how we can accommodate you).

## Course Overview:

In this two day course, we provide a comprehensive introduction to data visualization in R using ggplot. On the first day, 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. On Day 2, we begin by covering some additional plot types that are often related but not identical to those major types covered on Day 1: 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.

This is one module of a seven module series – you do not need to attend them all but they are designed to complement each other. Please see the links below

“1” June 23rd – 24th Introduction to R for ecologists and evolutionary biologists

“2”July 23rd – 24th Introduction to generalised linear models using R and Rstudio

“3” August 6th – 7th Introduction to mixed models using R an d Rstudio

“4” August 20th – 21st Data visualization using GG plot 2 (R and R studio)

“5” September 3rd – 4th Data wrangling using R and Rstudio

“6” October 1st – 2nd Introduction to Nonlinear Regression using Generalized Additive Models

https://www.psstatistics.com/course/introduction to nonlinear-regression-using-generalized-additive-models-gamr01

“7” Date to be confirmed Introduction to time series analysis using R

### Intended Audience

This course is aimed at anyone who is interested in doing data visualization using R. Data visualization is a major part of data science and statistical data analysis, and R is the most widely used program for data science and statistics. Data visualization using R is widely used throughout academic scientific research, as well as widely throughout the public and private sectors.

Venue – Delivered remotely

Time zone – Western European Time +1

Availability – 20 places

Duration – 5 days

Contact hours – Approx. 15 hours

ECT’s – Equal to 2 ECT’s

Language – English

PLEASE READ – CANCELLATION POLICY: Cancellations 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@psstatistics.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 (and accommodation fees if booked through PS statistics) will be credited.