LS 2026 NPFL112
RStudio on Jupyter lab: https://aic.ufal.mff.cuni.cz/jlab/
DataCamp: https://www.datacamp.com/users/sign_in
- Starting Feb 19, 2026, you have premium access for six months (i.e. until Aug 18, 2026). Then your account falls back to the free tier.
This website: https://ufal.github.io/NPFL112/
1. Feb 20
Goals
- access computer network in the lab (save your credentials)
- access the Jupyter Hub RStudio (save your credentials)
- access DataCamp (create your account with the same e-mail address with which you enrolled)
- Overview of
- the course scope
- grading requirements
- Know where to find presentations online:
- https://github.com/ufal/NPFL112?tab=readme-ov-file or
- Press s to display speaker view with ample fluent notes
- Know how to download presentations in other formats (pdf, html pages) from RStudio on Jupyter Hub
SCRIPTS.NPFL112/pagesSCRIPTS.NPFL112/pdf
Presentations
Activities
In R Studio
Log in at RStudio
In the Files tab (right bottom pane), create a new directory (folder) and call it - exactly! -
SUBMISSIONS.NPFL112.Explore the folders
SCRIPTS.NPFL112,DATA.NPFL112Download to your computer
~/SCRIPTS.NPFL112/pdf/04_NavigatingRStudioForProgramming.pdf.Log in at https://www.datacamp.com/users/sign_in (you should have received an invite from the DataCamp system Feb 19, around 9:30 PM). Sign up with the same e-mail address with which you have enrolled this course. If there is time left, start with your first home assignment.
Assignments
In RStudio, make sure that you have created your folder called SUBMISSIONS.NPFL112.
Open the folder HOMEWORK_ASSIGNMENTS. Select (tick) the file HW_001.R and copy it into your SUBMISSIONS.NPFL112 folder.
Open this file, read the instructions and proceed accordingly. Go through the entire file but do not spend more than 30 minutes with it. If you use AI, only adopt responses that you have fully understood. The goal is to make you notice, learn, and internalize something - or make your initial self-assessment.
2. Feb 27
Goals
Internalize the following concepts:
Data types/classes (numeric, character, boolean)
Data type coercion (what happens to your numeric vector when you blend in a non-digit, etc. )
Data structures (vectors, data frames/tibbles)
Functions and their arguments
Working directory (R)
Learn to invoke and read the built-in R Help
Presentations
Activities
Group work: exchange about HW_001.R