graebnerc

#' Normalize a numeric vector into zero-one-range
#' 
#' This simple function covers simple cases: when you have a vector of numbers
#'  larger or equal to zero, then this function normalizes these numbers into
#'  the range between zero and one.
#' @param input_vector A vector of numeric values greater or equal to zero
#' @return A vector of the same length as `input_vector`, the elements are 
#'  normalized into the range between zero and one
normalize_vector <- function(input_vector){
  min_max_diff <- max(input_vector) - min(input_vector)

graebnerc

---
title: "What a desaster!"
author: "Claudius"
date: '2022-04-06'
output: pdf_document
---

# Packages used
```{r}
library(tidyverse)

graebnerc

# T4: Intermediate exercises and possible solutions

# I. Create a vector with the numbers from -2 to 19 (step size: 0.75)
ex_1 <- seq(from=-2, to=19, by=0.75)
ex_1

# II. Create an index vector for this first vector (note: an index vector is a 
#       vector with all possible indices of the original vector)
ex_1_index <- seq(1, length(ex_1))
ex_1_index

graebnerc

# Session Script T5, Advanced object types (April 6, 2022)

# Factors - Slide 10

f_1 <- factor(c(rep("F", 2), rep("M", 3), rep("D", 3)), 
              levels = c("D", "F", "M"))
f_1

f_2 <- factor(c(rep("F", 2), rep("M", 3), rep("D", 3)))
f_2

graebnerc

---
title: "A possible solution"
author: "Claudius"
date: "4/6/2022"
output:
  html_document:
    toc: yes
    toc_float: yes
    code_download: yes
    theme: "spacelab"

graebnerc

library(ggplot2)
library(tidyr)
library(dplyr)
library(DataScienceExercises) # https://github.com/graebnerc/DataScienceExercises/

beer_data <- DataScienceExercises::beer
# Original source: http://www.principlesofeconometrics.com/poe4/poe4stata.htm
beer_data_plot <- beer_data %>%
  pivot_longer(
    cols = everything(), 

graebnerc

Lecture notes and solutions to the exercises of session 8 on data wrangling

graebnerc

Solutions to the exercises that are mentioned in the tutorial on Monte Carlo Simulations.

graebnerc

Code zur Replikation der Abbildung im Blogbeitrag "Zur ökonomischen Bedeutung des Genugs: Warum Suffizienz ein größere Rolle in den Wirtschaftswissenschaften spielen sollte"
Die Rohdaten (`Datensatz Daten_Wohnflaeche_Rebound.xlsx`) sind aus den im Blog verlinkten Quellen entnommen und können von Frauke Wiese angefordert werden.

graebnerc

Lecture notes and solutions to the exercises of session 11 and 12 on linear models