Course: "Getting started with R"

18-22 June 2018, Berlin (Germany)

Application deadline is: May 20th, 2018.


Dr. Alexandre Courtiol (Leibniz Institute for Zoo and Wildlife Research,
Berlin (Germany))

Session content

Monday 18th – Classes from 09:30 to 17:30

Monday is DATA day

This first day will be dedicated to the data. R is software dedicated to
data analysis, so mastering the basics of data manipulation in R is
essential for further steps. It  will be explained how to import data into R
and how to manipulate them (e.g. from adding or removing rows or columns, to
merging tables and using pivot tables). This will be good practice for
students to learn the basics of the R language. We will illustrate how to do
everything using R base (that is R out of the box), but we will also
introduce that allow users to perform some operations on large datasets a
little faster.

Tuesday 19th – Classes from 09:30 to 17:30

Tuesday is PLOTTING day

Plotting is a crucial part of any data analysis no matter how advanced you
are in statistics. It is important to visualise the data before the analysis
(e.g. to visually check the presence of potential errors and for getting a
sense of the distribution of the data), during the analysis (e.g. to check
the distribution of model residuals), and after the analysis (to communicate
findings in the most efficient way). Therefore, knowing how to plot various
kind of data matters a lot. We will thus show how to plot different types of
data in R (points, distributions, rasters...) using different graphic
environments (e.g graphics, lattice, ggplot2).

Wednesday 20th – Classes from 09:30 to 17:30

Wednesday is FUNCTIONS day

As John Chambers -- the grandfather of R -- put it "Everything that happens
in R is a function call". That R allows for so-called functional programming
is one of its great benefits: it allows the implementation of any workflow
of statistical analysis as the succession of simple clearly identified
steps. Each step is described by a function that takes an input and
generates an output. The output of one function is often the input of the
next. On this third day we will show that creating one's own function is
very very easy (yes, even YOU can program!) and can be very very useful. As
a first application we will create our own functions to implement a
randomisation test. As a second application we will show how to create
functions to perform a power analysis (the estimation of the probability of
getting true positives when applying a test) for any statistical test.

Thursday 21st – Classes from 09:30 to 17:30

Thursday is LM day

Many widely used statistical methods (t-tests, anova, ancova, linear
regressions...) are just different type of Linear Models (LM), which is why
LM represent the most useful statistical toolbox to be familiar with if you
are in natural or social sciences. Fitting a LM in R is easy -- but building
models, checking model assumptions, interpreting the outputs, and plotting
predictions correctly requires some know how. After refreshing the most
important concepts surrounding LM, we will go through each of these steps in
detail, working on real datasets. It is important to understand LM quite
well before jumping into the more complex methods which we will see on the
next day.

Friday 22nd – Classes from 09:30 to 17:30

Friday is GLMM day

On this last day we will continue with LM and explore what to do when
everything goes wrong; starting with when the process generating the data is
not gaussian, when the observations are not independent. We will see that
the solution is often to use Generalised Linear Models (GLM) which can
handle binary and count data and to use Linear Mixed-effects Models (LMM)
which can handle non-independence in the data. Both can also be combined
under  Generalised Linear Mixed-effects Models (GLMM). We will show some
specific application of such models (e.g. how LMM can be used to study
heredity). We will then see how to tackle spatial and temporal sources of
independence in the data, non constant residual variance, and other
LM-related pathologies. We will illustrate (G)LM(M) mainly using two R
packages: lme4 and spaMM.

For more information about the course, please visit our website:

Here is the full list of our courses and Workshops:

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