R is rapidly becoming the standard platform for data manipulation, visualization and analysis and has a number of advantages over other statistical software packages. A wide community of users contribute to R, resulting in an enormous coverage of statistical procedures, including many that are not available in any other statistical program. Furthermore, it is highly flexible for programming and scripting purposes, for example when manipulating data or creating professional plots. However, R lacks standard GUI menus, as in SPSS for example, from which to choose what statistical test to perform or which graph to create. As a consequence, R is more challenging to master. Therefore, this course offers an elaborate introduction into statistical programming in R. Students learn to operate R, make plots, fit, assess and interpret a variety of basic statistical models and do advanced statistical programming and data manipulation. The topics in this course include regression models for linear, dichotomous, ordinal and multivariate data, statistical inference, statistical learning, bootstrapping and Monte Carlo simulation techniques.
The course deals with the following topics:
The course starts at a very basic level and builds up gradually. At the end of the week, participants will master advanced programming skills with R. No previous experience with R is required.
Prerequisites: Participants are requested to bring their own laptop for lab meetings.
Participants will receive a certificate at the end of the course.
When? | What? | |
---|---|---|
09.00 | 09.30 | Lecture |
09:30 | 10.15 | Practical |
10.15 | 10.45 | Discussion |
10.45 | 11:00 | break |
11.00 | 11.45 | Lecture |
11:45 | 13:00 | Lunch |
13:00 | 14.00 | Practical |
14:00 | 14.30 | Discussion |
14:30 | 14:45 | break |
14:45 | 15:45 | Lecture |
15:45 | 16.30 | Practical |
16:30 | 17:00 | Discussion |
When? | Where |
---|---|
Monday | Viktor J. Koningsberger building, Room Pangea |
Tuesday | Viktor J. Koningsberger building, Room Pangea |
Wednesday | Viktor J. Koningsberger building, Room Pangea |
Thursday | Viktor J. Koningsberger building, Room Pangea |
Friday | Viktor J. Koningsberger building, Room Pangea |
Dear all,
This summer you will participate in the USS24:
Statistical programming with R course in Utrecht, the
Netherlands. To realize a steeper learning curve, we will use some
functionality that is not part of the base installation for
R
. The below steps guide you through installing both
R
as well as the necessary additions.
We look forward to see you all in Utrecht,
The Statistical Programming with R Team
Bring a laptop computer to the course and make sure that you have full write access and administrator rights to the machine. We will explore programming and compiling in this course. This means that you need full access to your machine. Some corporate laptops come with limited access for their users, we therefore advice you to bring a personal laptop computer, if you have one.
R
R
can be obtained here. We won’t use R
directly in the course, but rather call R
through
RStudio
. Therefore it needs to be installed.
RStudio
DesktopRstudio is an Integrated Development Environment (IDE). It can be
obtained as stand-alone software here.
The free and open source RStudio Desktop
version is
sufficient.
Execute the following lines of code in the console window:
install.packages(c("ggplot2", "tidyverse", "magrittr", "knitr", "rmarkdown",
"plotly", "ggplot2", "shiny", "devtools", "boot", "class",
"car", "MASS", "ggplot2movies", "ISLR", "DAAG", "mice",
"purrr", "furrr", "future"), dependencies = TRUE)
If you are not sure where to execute code, use the following figure to identify the console - ignore the outdated version in the example:
Just copy and paste the installation command and press the return key. When asked
Do you want to install from sources the package which needs /no/cancel) compilation? (Yes
type Yes
in the console and press the return key.
If all fails and you have insufficient rights to your machine, the following web-based service will offer a solution.
R
and RStudio
there. You may need to
install packages for new sessions during the course.RStudio
environment there.Naturally, you will need internet access for these services to be accessed. Wireless internet access will be available at the course location.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.
R
organized?
R
Functionality
All lectures are in html
format and have two versions:
the slides and the handout version. Practical template files are
R Markdown
files with the exercises of the practicals. Fill
in the the code (the answers) in these files. Practical solutions files
give the answers to the exercises of the practicals.
The above links are useful references that connect to today’s materials.
rmarkdown
What is R Markdown? from RStudio, Inc. on Vimeo.
See also this
rmarkdown
cheat sheet.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.
All lectures are in html
format and have two versions:
the slides and the handout version. Practical template files are
R Markdown
files with the exercises of the practicals. Fill
in the the code (the answers) in these files. Practical solutions files
give the answers to the exercises of the practicals.
The above links are useful references that connect to today’s materials.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.
All lectures are in html
format and have two versions:
the slides and the handout version. Practical template files are
R Markdown
files with the exercises of the practicals. Fill
in the the code (the answers) in these files. Practical solutions files
give the answers to the exercises of the practicals.
The R Graph Gallery: an overview of plots you can make with R code provided
These papers are a nice reference for editors:
library(mice)
library(magrittr)
%$% lm(age ~ reg) %>% coef() boys
(Intercept) regeast regwest regsouth regcity
11.898420 -2.656786 -2.900792 -3.307388 -3.626625
$reg <- relevel(boys$reg, ref = "south")
boys%$% lm(age ~ reg) %>% coef() boys
(Intercept) regnorth regeast regwest regcity
8.5910314 3.3073883 0.6506021 0.4065962 -0.3192369
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.
All lectures are in html
format and have two versions:
the slides and the handout version. Practical template files are
R Markdown
files with the exercises of the practicals. Fill
in the the code (the answers) in these files. Practical solutions files
give the answers to the exercises of the practicals.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online.
All lectures are in html
format and have two versions:
the slides and the handout version. Practical template files are
R Markdown
files with the exercises of the practicals. Fill
in the the code (the answers) in these files. Practical solutions files
give the answers to the exercises of the practicals.
glm
s in
R
.The above references are (currently) available for free in these links. I deem them very useful and I would highly recommend them.