Projet 1

Authors

Magatte

Imane

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Running Code

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1 + 10

[1] 11

You can add options to executable code like this

[1] 4

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Ecrire en latex dans quatro

il y’a des formules en ligne et des formules en display En ligne \(\int_0^{\infty}e^{-x^2}dx\) En display \[ \int_0^{\infty}e^{-x^2}dx=\frac{\sqrt{\pi}}{2} \] on suppose que \(X\sim \mathcal{N}(100, 20)\) et on veut calculer \(\Pr(X > 120)\)

pnorm(120,100,20,lower.tail = FALSE)

[1] 0.1586553

Comment faire pour que le résutat de ce calcul soit transparant et s’affiche directement dans mon document?

La probabilité que \(( X > 120 )\) est 0.16.

Exercie : reproduire le même graphique

Tidyverse

library(tidyverse)

── 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 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── 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

Les pipes

120|>pnorm(100,20,lower.tail = FALSE)|>round(2)

[1] 0.16

Manipuler des données

mtcars|>data()
df<-mtcars
df$mpg #Méthode à l'ancienne

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

df|>select(mpg) # Méthde moderne

                     mpg
Mazda RX4           21.0
Mazda RX4 Wag       21.0
Datsun 710          22.8
Hornet 4 Drive      21.4
Hornet Sportabout   18.7
Valiant             18.1
Duster 360          14.3
Merc 240D           24.4
Merc 230            22.8
Merc 280            19.2
Merc 280C           17.8
Merc 450SE          16.4
Merc 450SL          17.3
Merc 450SLC         15.2
Cadillac Fleetwood  10.4
Lincoln Continental 10.4
Chrysler Imperial   14.7
Fiat 128            32.4
Honda Civic         30.4
Toyota Corolla      33.9
Toyota Corona       21.5
Dodge Challenger    15.5
AMC Javelin         15.2
Camaro Z28          13.3
Pontiac Firebird    19.2
Fiat X1-9           27.3
Porsche 914-2       26.0
Lotus Europa        30.4
Ford Pantera L      15.8
Ferrari Dino        19.7
Maserati Bora       15.0
Volvo 142E          21.4

Création de nouvelles variables

df<-df |> 
  mutate(lcentKM = 100 * 3.78541 / (mpg * 1.60934)) |> 
  relocate(lcentKM, .before = everything())

Faire des graphiques avec ggplot

df|>ggplot(aes(x=lcentKM))+
  geom_histogram(binwidth = 5, fill = "#E54AE0", color = "black")+ labs(title = "Histogramme de la consommation (L/100 km)",
       x = "Consommation (L/100 km)",
       y = "Nombre de modèles")