Research Methods in International Health: An introduction to R and the environment RStudio

Content:

• Learning the language R
• Handling datasets of different types
• Apply basic functions for data elaboration
• Data plotting
• Elaborate the data-analysis for participant’s own Master’s or even PhD Thesis (if applicable)
• Produce a complete data report in pdf file using R

Learning objectives:

During the course the students will learn to...

• Install R and the environment Rstudio on the personal computer
• Read-in .csv and .xlsx files in RStudio
• Reproduce the idea of the R-package dplyr
• Define the following data-operations: filter, arrange, select mutate, %>%, group_by, pivot and merge
• Create a basic data-description with the function summarise()
• Apply the data-operations to arrange the data for analysis
• Reproduce the idea of the R-package ggplot2
• Generate plots like: scatterplot, line chart, histogram, density distribution, boxplot
• Applying test like: Kruskal Wallis and Dunn’s Post Test
• Depict the p-value in the graph
• Generate a linear regression
• Plot the result of the linear regression in the graph
• Generate a report of the data analysis
• Summarize the data-analysis results
• Generate a pdf file using R
• Report the summary of the data-analysis on a pdf

At the end of the course students will be able to…

• depict data-analysis on real data
• elaborate a reproducible code
• outline the data-analysis for their own Master’s or even PhD Thesis
• elaborate the data-analysis for their own Master’s or even PhD Thesis

Course details:

1. Lecture 1: Description of the lecture; Discussion on learning objectives (Menti-tool); Explanation of organisational tasks

2. Lectures 2-7:

• receiving part: lectures will be pre-recorded and made available for student via Moodle one week before the lecture
• active/constructive part: students are expected to give feedback in the Moodle-platform for each session prior to lecture day, either by giving feedback to the video, or answering to specific questions given in the Moodle. For each video a specific Moodle forum side will be opened
• active/interacting/practical part (on Zoom):
• based on the video recording there will be a question round (the Menti-tool will be applied)
• group-workshops, prepared by the lecturers using tools like Padlet and Miro; students will either revise the produced analysis-code or elaborate new code for data analysis
• final wrap-up with lecture evaluation and Q&A session (18:45 -20:00 CET)

3. Lecture: Closing remarks; Lecture evaluation; Final exam: 1h

Please find here the final course agenda.

Contact:

For more information, please contact Noemi.Castellitti@med.uni-muenchen.de