Template file for working on the exercises for the first recap session.

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`.
# 
# 3. Add the elements `3` and `1` to the beginning, and the elements 
# `"8.0"` and `"9.2"` to the end of the vector.
# 
# 4. Create a vector with the numbers from -8 to 9 (step size: 0.5)
# 
# 5. Compute the square root of each element of the first vector 
# using vectorisation.
# 
# 6. Create a character vector containing then strings `"Number_1"` to 
# `"Number_5"`. Use suitable helper functions to create this vector quickly.


# # Exercise 2: Basic object types II---------
#   
#   Consider the following vector:
#   
ex_2_vec <- c(1, "2", FALSE)
# 
# 1. What is the type of this vector? Why?
#   
# 2. What happens if you coerce this vector into type integer? Why?
#   
# 3. What does `sum(is.na(x))` tell you about a vector `x`? What is happening here?
#   
# 4. Is it a good idea to use `as.integer()` on double characters to round them to 
#    the next integer? Why (not)? What other ways are there to do the rounding?


# # Exercise 3: Define a function---------
#   
# Create functions that take a vector as input and returns:
#   
# 1. The last value.
# 
# 2. Every element except the last value and any missing values.
# 
# 3. Only even numbers.
# 
# > Hint: Use the operation `x %% y` to get the remainder from diving `x` by `y`, the so called 'modulo y'. For even numbers, the modulo 2 is zero.
# 
# Apply your function to the following example vector:
#   

ex_3_vec <- c(1, -8, 99, 3, NA, 3, -0.5)


# # Exercise 4: Lists---------
# 
# 1. Create a list that contains three elements called `'a'`, `'b'` and `'c'`.
# The first element should correspond to a double vector with the elements
# `1.5`, `-2.9` and `99`.
# The second element should correspond to a character vector with the elments
# `'Hello'`, `'3'`, and `'EUF'`.
# The third element should contain three times the entry `FALSE`.
# 
# 2. Transform this list into a `data.frame` and a `tibble`. Then apply `str()` to
# get information about the respective structure. How do the results differ?



# # Exercise 5: Data frames and the study semester distribution at EUF---------
#   
# The package `DataScienceExercises` contains a data set called `EUFstudentsemesters`,
# which contains information about the distribution of study semesters of 
# enrolled students at the EUF in 2021. You can shortcut the data set as follows:
#   

euf_semesters <- DataScienceExercises::EUFstudentsemesters

# 1. What happens if you extract the column with study semesters as a vector
# and transform it into a `double`? 
#   
# 2. What is the average study semester of those students being in their 8th or 
# earlier semester?
#   
# 3. How many students are in their 9th or higher study semester?
#   
# 4. What does `typeof(euf_semesters)` return and why?