graebnerc

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()

graebnerc

# 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

graebnerc

# 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`.
# 

graebnerc

# Task 1: Intermediate exercises I===============

# Create a vector containing the numbers 2, 5, 2.4 and 11.
vec_task_1 <- c(2, 5, 2.4, 11)

# What is the type of this vector?
typeof(vec_task_1)

# Transform this vector into the type integer. What happens?
typeof(vec_task_1)

graebnerc

# Session Script on Advanced object types (Spring Semester 2024)

# Digression
ff <- factor(c("F", "M", "M"), levels = c("F", "M", "D"))
attributes(ff) # See the class attribute 'factor'
typeof(ff) # It still remains an integer type...
class(ff) # but the class was changed

# Factors--------------------

graebnerc

# This is the script that we developed during the session on March 21, 2024

# 1. Basic commands-----------
# This is an addition:
2 + 4
4 - 9 # This is substraction
4/9
3*9
2**3

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.