Intro to R Course for Field Epidemiology Training

ECDC EPIET & EUPHEM Program

Welcome to the Intro to R Course module webpage of the ECDC EPIET & EUPHEM Program.

This is the first edition to the course. It will take place during 6 half-days session from September 8th to 17th, spanning Tuesday-Wednesday-Thursday of the two weeks:

Tuesday-Thursday 8th, 9th and 10th 13-16pm CET [Week 1]
Tuesday-Thursday 15th, 16th and 17th 13-16pm CET [Week 2]

Additionally, we will have two troubleshooting sessions on Tuesday 8th and 15th, 11-12am in the morning, where you are invited to attend if you need technical assistance, have pending questions, or would like to have an individual follow-up session before and during the course.

Module description summary

The Intro to R Course provides a ground-level introduction to the R Programming software and environment, assuming no prior experience with this or any other programming language. The module is organized in six fully online half-day sessions consisting of brief presentations and plenty of practical exercises where fellows learn and consolidate their skills. Facilitators oversee small-size groups, to make sure everyone is on track, solve questions and problems and explain code if needed, but exercises are mainly self-guided.

Target audience

All fellows of every starting cohort, including EPIET, EUPHEM, EAP and any MediPIET/External participants considered eligible.

Training of Trainers is possible, depending on availability of spots in groups.

Intended learning outcomes (ILOs)

By the end of this course, participants will be able to…

Navigate RStudio and organise an analysis project reproducibly: work within the RStudio panes, use RStudio Projects together with here() for file paths, create and inspect objects, and import surveillance data with {rio}.
Verify results and interpret R’s feedback rather than guessing: check outputs manually against the data, read error and warning messages as information, and locate the source of a problem in a pipeline.
Transform a dataset using a small, reusable vocabulary of tidyverse verbs: combine select(), filter(), mutate(), arrange(), group_by() and summarise() in pipelines built with the pipe %>% to answer specific epidemiological questions.
Clean and prepare real-world surveillance data for analysis: standardise variable names, convert and derive date variables with {lubridate}, recode categorical variables with case_match() and case_when(), and control factor levels explicitly with factor().
·Produce descriptive tables suitable for a surveillance report: summarise counts, proportions and distributions by person, place and time, and format the result for presentation with {janitor} and {gtsummary}.
Build and export epidemiological graphics with {ggplot2}: construct bar charts, line graphs and epicurves by ISO week using isoweek() and isoyear(), choose appropriate scales, colour palettes and themes, and save publication-ready output with ggsave().
Use AI assistants critically as a programming aid: formulate an effective prompt for an R task, and evaluate, test and correct the code returned before trusting it

How to prepare for the module

Fellows must ensure that they have a working computer with R, RStudio and RTools (Windows users only) installed before the start of the module, and download the data folder for the course [link]. Given the module’s early start – when working computers may not be ready or configured by IT, it is highly recommended that fellows have their own computer for the course.

It is highly recommended that fellows go through the freely available Applied Epi R tutorials online.

Course organizers

Main organizers: Javier Del Águila Mejía (ISCIII, Spain) & Kostas Danis (ECDC, Sweden), EPIET Scientific Coordinators

--- title: "Intro to R Course for Field Epidemiology Training" subtitle: "ECDC EPIET & EUPHEM Program" --- Welcome to the **Intro to R Course** module webpage of the *ECDC EPIET & EUPHEM* Program. This is the first edition to the course. It will take place during 6 half-days session from September 8th to 17th, spanning Tuesday-Wednesday-Thursday of the two weeks: 1. Tuesday-Thursday 8th, 9th and 10th 13-16pm CET \[Week 1\] 2. Tuesday-Thursday 15th, 16th and 17th 13-16pm CET \[Week 2\] Additionally, we will have two troubleshooting sessions on Tuesday 8th and 15th, 11-12am in the morning, where you are invited to attend if you need technical assistance, have pending questions, or would like to have an individual follow-up session before and during the course. ### Module description summary The Intro to R Course provides a ground-level introduction to the R Programming software and environment, assuming no prior experience with this or any other programming language. The module is organized in six fully online half-day sessions consisting of brief presentations and plenty of practical exercises where fellows learn and consolidate their skills. Facilitators oversee small-size groups, to make sure everyone is on track, solve questions and problems and explain code if needed, but exercises are mainly self-guided. ### Target audience All fellows of every starting cohort, including EPIET, EUPHEM, EAP and any MediPIET/External participants considered eligible. Training of Trainers is possible, depending on availability of spots in groups. ### Intended learning outcomes (ILOs) By the end of this course, participants will be able to… 1. ***Navigate RStudio and organise an analysis project reproducibly***: work within the RStudio panes, use RStudio Projects together with here() for file paths, create and inspect objects, and import surveillance data with {rio}. 2. ***Verify results and interpret R's feedback rather than guessing***: check outputs manually against the data, read error and warning messages as information, and locate the source of a problem in a pipeline. 3. ***Transform a dataset using a small, reusable vocabulary of tidyverse verbs***: combine select(), filter(), mutate(), arrange(), group_by() and summarise() in pipelines built with the pipe %\>% to answer specific epidemiological questions. 4. ***Clean and prepare real-world surveillance data for analysis***: standardise variable names, convert and derive date variables with {lubridate}, recode categorical variables with case_match() and case_when(), and control factor levels explicitly with factor(). 5. ·***Produce descriptive tables suitable for a surveillance report***: summarise counts, proportions and distributions by person, place and time, and format the result for presentation with {janitor} and {gtsummary}. 6. ***Build and export epidemiological graphics with {ggplot2}***: construct bar charts, line graphs and epicurves by ISO week using isoweek() and isoyear(), choose appropriate scales, colour palettes and themes, and save publication-ready output with ggsave(). 7. ***Use AI assistants critically as a programming aid***: formulate an effective prompt for an R task, and evaluate, test and correct the code returned before trusting it ### How to prepare for the module Fellows must ensure that they have a working computer with R, RStudio and RTools (Windows users only) installed before the start of the module, and download the data folder for the course \[[link](scripts/preparation.qmd)\]. Given the module’s early start – when working computers may not be ready or configured by IT, it is highly recommended that fellows ***have their own computer for the course***. It is highly recommended that fellows go through the freely available Applied Epi R tutorials online. ### Course organizers *Main organizers:* Javier Del Águila Mejía (ISCIII, Spain) & Kostas Danis (ECDC, Sweden), EPIET Scientific Coordinators