graebnerc

here::i_am("inclass-regression-solution.R")
library(here)
library(dplyr)
library(tidyr)
library(data.table)
library(ggplot2)

# Step 1: import data----------
reg_data <- data.table::fread(here("data/reg_data_1.csv")) %>%
  as_tibble()

graebnerc

Contains solutions to the exercises as well as the code used to create the relevant figures in the slides.
The code assumes a standard folder structure and might need to be adjusted to your case.

graebnerc

This is the script we used during the lecture. For a version that  is better documented but essentially covers the same issues, see the tutorial on Monte Carlo Simulations:
https://euf-datascience-spring23.netlify.app/tutorial/mcs/pubdir/onlinecontent.html

graebnerc

This contains the script developed during the recap session.

graebnerc

The script using during session 10. For more precise solutions see:
https://gist.github.com/graebnerc/5d5ec7591a45d6cbad3a58ddf06fff6b

graebnerc

Example solutions for the recap session on exploratory data analysis (spring semester 2023).

graebnerc

Scrips and exercise solutionat accompanying the videos for sessions 8 and 9 on data preparation.

The data is available via the course homepage.

graebnerc

Scripts containing the code developed during the 7th lecture on project management and data import, as well as solutions to the exercises.

The code assumes that you created a folder structure as discussed during the lecture:

YourProject/
|─ YourProject.Rproj
├─ data/
│  ├─ raw/
|        |─fread_expls-1.csv
|        |─fread_expls-2.csv

graebnerc

Lecture notes and solutions to the in-class exercises of session 4 in the spring semester 2023.

graebnerc

 Lecture notes and solutions to the exercises of session 2 in the spring semester 2023