This data package contains the data that powers the chart "Domestic material consumption per gross domestic product" on the Our World in Data website. It was downloaded on April 30, 2025.
A filtered subset of the full data was downloaded. The following filters were applied:
| # dyplyr is used for such cases: | |
| library(dplyr) | |
| # Create a small dataset with missing values: | |
| students <- data.frame( | |
| name = c("Alice", "Bob", "Charlie", "Diana", "Eve"), | |
| age = c(20, NA, 22, 19, NA), | |
| grade = c(85, 92, NA, 78, 88), | |
| city = c("Boston", "Chicago", NA, "Denver", "Austin") | |
| ) |
| This is the code from which you can start working on the exercises of the session on "AI for coding". |
| The prompts used as examples in the slides from the lab on "AI for Coding". | |
| Results were created by Claude Sonnet 3.7 in May 2025. |
| This contains a clean version of the scripts used during the lab. |
| The notes made on the questions that you posed during day 3 on the content from day 2. |
| Hier findet sich das Skript zur Entwicklung der beiden Beispielabbildung, die wir im Kurs am 15. Juli 2024 entwickelt haben, sowie den weiteren Visualisiserungsnotizen. |
| Hier sind alle Notizen und Aufgabenlösungen zu Tag 2 (8. Juli 2024). |
| Hier sind alle Notizen und Aufgabenlösungen zu Tag 1 (1. Juli 2024). |
| library(tibble) | |
| library(ggplot2) | |
| library(moderndive) | |
| # 1. Implement linear regression----------------- | |
| # Make a shortcut to the data: | |
| beer_data <- as_tibble(DataScienceExercises::beer) | |
| head(beer_data) | |
| # Conduct the linear regression: |