Claudius Graebner-Radkowitsch graebnerc

## # Skript zu Tag 3 - Wiederholungsfragen Data Prep
The notes made on the questions that you posed during day 3 on the content from day 2.

## # Skript zu Tag 3 - Visualisierung
Hier findet sich das Skript zur Entwicklung der beiden Beispielabbildung, die wir im Kurs am 15. Juli 2024 entwickelt haben, sowie den weiteren Visualisiserungsnotizen.

## # Skript zu Tag 2
Hier sind alle Notizen und Aufgabenlösungen zu Tag 2 (8. Juli 2024).

## # Skript zu Tag 1
Hier sind alle Notizen und Aufgabenlösungen zu Tag 1 (1. Juli 2024).

## LinearRegression.R
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:

## # Sampling
Code used during the lecture on sampling.

## # Data Preparation: solutions
Solutions for the exercises in the session on data preparation.

## VisualizationScript.R
library(DataScienceExercises)
library(ggplot2)
library(scales)

# Bubble plot------
gdp_data <- DataScienceExercises::gdplifexp2007
# In the following, lines with changes are marked with '# <---'

# 1st step: empty list
gdp_plot <- ggplot()

## FunctionDefinition.R
# A step-by-step solution for the first function task of the "Basics" tutorial

# Goal: define a function that computes the sample variance of a vector

# Note: there are many strategies to develop functions; here we start by first
#  writing the code that solves our problem for one particular case outside the
#  function, and then generalize this code in a function.

# First step: think about the starting point for your function. In our case:
#  we start with a vector containing some numbers, because this is from what

## Recap1-Exercises-Template.R
# remotes::install_github("graebnerc/DataScienceExercises")
library(DataScienceExercises)
library(tibble)

# # Exercise 1: Basic object types I---------
#
# 1. Create a vector containing the numbers `2`, `5`, `2.4` and `11`.
#
# 2. Replace the second element with `5.9`.
#
	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:
	library(DataScienceExercises)
	library(ggplot2)
	library(scales)

	# Bubble plot------
	gdp_data <- DataScienceExercises::gdplifexp2007
	# In the following, lines with changes are marked with '# <---'

	# 1st step: empty list
	gdp_plot <- ggplot()
	# A step-by-step solution for the first function task of the "Basics" tutorial

	# Goal: define a function that computes the sample variance of a vector

	# Note: there are many strategies to develop functions; here we start by first
	# writing the code that solves our problem for one particular case outside the
	# function, and then generalize this code in a function.

	# First step: think about the starting point for your function. In our case:
	# we start with a vector containing some numbers, because this is from what
	# remotes::install_github("graebnerc/DataScienceExercises")
	library(DataScienceExercises)
	library(tibble)

	# # Exercise 1: Basic object types I---------
	#
	# 1. Create a vector containing the numbers `2`, `5`, `2.4` and `11`.
	#
	# 2. Replace the second element with `5.9`.
	#