**Course Description: This will be an introduction course intended to give student
s and doctoral researchers an overview and introduction to R applied to an
alysing environmental data\, with specific emphasis on chemical datasets.
The course will begin with a brief introduction to the R environment and b
asic function and then include an overview of statistical analyses that ar
e valuable in environmental datasets. The doctoral researchers and master
students will be able to use R efficiently and they will be able to improv
e their scientific work with the possibilities of R.**

**Tenta
tive schedule with course content:**

1. \; \;
\;Introduction to the R environment (R-Studio) \;

- \;Projects handling
- \;Package management
- &nb sp\;Data: Input / Output
- \;Workspace
- \;Insta lling and loading R packages\, importing\, manipulating\, and exporting da ta.

2. \; \; \;Pre-Processing and Data composi tion: Looking at the R data frame\, cleaning up and compose data\, dealing with missing data\, data normalization\, basic functions (mean and weight ed mean\, standard error\, etc.)

\n3. \; \; \;Visualizi ng Data in R.

\n- ggplot
- plotly
- shiny \ ; \; \;Scatter plots\, bar plots\, violin plots\, etc. Interactiv e possibilities. \;

4. \; \; \;Data Report
s and publishing with R markdown and Shiny.

\; \; \;
\; Reporting with markdown and Shiny.

5. \; \; \;B
asic Statistics and Statistical Analyses.

\; \; \; &n
bsp\; Calculating and visualizing a PCA\, PCoA\, NMDS\, ANOVA\, linear mod
els.

6. \; \; \;Handling largescale server side data se
ts using the HPC-Cluster.

\; \; \; \;Analyze and
correlate large chemical data (e.g FT-ICR-MS).

Target group: Grad uate level course\, with specific aim at doctoral researchers and new mast er’s students who want to use R to analyse their data.

\n

~~Requirements: ~~~~All levels are welcome. Students should be fa
miliar with basic concepts of data analysis. Students and doctoral researc
hers will need to bring a laptop with the current version of R and RStudio
installed. It would be nice if the participant have already started to ge
t familiar with RStudio.~~

**Course instructors / Lecturers: André El-Ama: andre.el-ama@uni-oldenburg.de Priv.-Doz
.Dr. Jan Freund: jan.freund@uni-oldenburg.de Dr. Hannelore Waska: ha
nnelore.waska@uni-oldenburg.de Matthias Schröder: matthias.schroede
r@uni-oldenburg.de Dr. Ferdinand Esser: ferdinand.esser@uni-oldenbur
g.de**

**Time / Date / Format: \;**

Seminar i
s organised block wise at the following dates: **25.07.\, 26.07.\, a
nd 27.07.2023 and 26.09.\, 27.09. and 28.09.2023.**

Meeting t
ime is 5 hours with 1 hours break. 10:00 – 15:00

**Registr
ation for the workshop via Stud.IP is required!**<
/p>\n

Room: W15 1-146 (ICBM building)