Basics

Variables and Data types

If a golf ball and a club together cost 2,100 PLN, and the price difference between the club and the ball is 2,000 PLN, then how much costs the ball?

Consider the data-frame mtcars (it should be loaded when you load R, if not, it is provided by the library datasets). Calculate

  1. the mean consumption of the cars in the data base
  2. the mean consumption of the cars that have four cylinders

Then create a function to convert miles per gallon to liter per 100 km, rounded up to 2 digits. Then add a column lp100km where you put all the consumption in liter per 100 km and store the results in a variable d

Now execute the same with a for-loop.

Lexical Scoping

Create a function to demonstrate the lexical scoping in R with the following content

a <- 1
b <- 2
c <- 3
f <- function(a, b) {
  print(a) 
  print(b)
  # C is used from the higher environment
  print(c)
  b <- 666
  a <<- 111
  d <<- 222
  print(a) 
  print(b)
}
f(a, b)

Try to predict what f(a,b) does, then write the function and exectute it. Can you explain what happens?

Obiect Oriented (OO) aspects of R

Create an object type cardata, that is your personal soution for car databases. create a specific print function that plots mpg in function of hp when called by plot(x)

Tidyverse

Consider the dataset mtcars and sumarise the fuel consumption in function of the number of cylinders.

## ── Attaching core tidyverse packages ──────────────────────────────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Now for both cyl and vs

## `summarise()` has grouped output by 'dispCats'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'dispCats'. You can override using the `.groups` argument.

Create a dataset d as a copy of mtcars with one column extra for the fuel consumption in SI.

Descriptive statitstics

Visualisations

Plot the mpg in function of all other variables for mtcars.

Plot the fuel consumption in function of the horse power of the motor with blue crosses

Time series

use the data of the Standards and Poors index, it is in R as SP500, convert it to a time series with start date january 1990, and produce moving average forecast.