# [∇][@]Introduction à R et RStudio

## Licence cc-by-sa ModLibre.info sauf autres indications

### Dates1

‘format(Sys.Date(), “%B %d, %Y”)’    ‘avril 05, 2022’

‘format(Sys.Date(), “%Y-%B-%d”)’    ‘2022-avril-05’

‘format(Sys.Date(), “%Y-%m-%d”)’    ‘2022-04-05’

‘format(Sys.Date(), “%y-%m-%d”)’    ‘22-04-05’

### Statistiques simples avec R

summary((0:9)^2)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##    0.00    5.25   20.50   28.50   45.75   81.00

# [∇][Δ]Graphes simples avec R2

# Define cars vector with 5 values
cars <- c(1, 3, 6, 4, 9)

# Define some colors ideal for black & white print
colors <- c("white","grey70","grey90","grey50","black")

# Calculate the percentage for each day, rounded to one
# decimal place
car_labels <- round(cars/sum(cars) * 100, 1)

# Concatenate a '%' char after each value
car_labels <- paste(car_labels, "%", sep="")

# Create a pie chart with defined heading and custom colors
# and labels
pie(cars, main="Cars", col=colors, labels=car_labels,
cex=0.8)

# Create a legend at the right
legend(1.5, 0.5, c("Mon","Tue","Wed","Thu","Fri"), cex=0.8,
fill=colors)

# Get a random log-normal distribution
r <- rlnorm(1000)

# Get the distribution without plotting it using tighter breaks
h <- hist(r, plot=F, breaks=c(seq(0,max(r)+1, .1)))

# Plot the distribution using log scale on both axes, and use
# blue points
plot(h\$counts, log="xy", pch=20, col="blue",
main="Log-normal distribution",
xlab="Value", ylab="Frequency")
## Warning in xy.coords(x, y, xlabel, ylabel, log): 261 y values <= 0 omitted from
## logarithmic plot

# [∇][Δ]Quelques examples d’utilisation de $$\LaTeX$$ dans des documents R

R Markdown allows you to mix text, R code, R output, R graphics, and mathematics in a single document. The mathematics is done using a version of $$\LaTeX$$, the premiere mathematics typesetting program. (Our textbook was done entirely in $$\LaTeX$$…) (à compléter).

$$A = \pi*r^{2}$$

$E = mc^{2}$

# [∇][Δ]Tableau des lettres grecques en $$\LaTeX$$ 3

For those of you who don’t know your Greek alphabet, it’s time to learn it : name symbol $$\LaTeX$$

Prononciation et lettre Prononciation et lettre Prononciation et lettre Prononciation et lettre
alpha $$\alpha \ A$$ A beta $$\beta \ B$$ B gamma $$\gamma \ \Gamma$$ delta $$\delta \ \Delta$$
epsilon $$\epsilon \ E$$ E (epsilon) $$\varepsilon \ E$$ zeta $$\zeta \ Z$$ Z eta $$\eta \ H$$
theta $$\theta \ \Theta$$ iota $$\iota \ I$$ I kappa $$\kappa \ K$$ K lambda $$\lambda \ \Lambda$$
mu $$\mu \ M$$ M nu $$\nu \ N$$ N xi $$\xi \ \Xi$$ omicron $$\omicron \ O$$ O
pi $$\pi \ \Pi$$ rho $$\rho \ P$$ P sigma $$\sigma \ \Sigma$$ tau $$\tau \ T$$
upsilon $$\upsilon \ Y$$ Y phi $$\phi \ \Phi$$ (phi) $$\varphi$$ chi $$\chi \ X$$ X
psi $$\psi \ \Psi$$ omega $$\omega \ \Omega$$ - -

# [∇][Δ]Modèles d’épidémies avec R4

#### Exemple : Modèle SIR Model avec une dynamique vitale (Un Groupe)

param <- param.dcm(inf.prob = 0.2, act.rate = 5,
rec.rate = 1/3, a.rate = 1/90, ds.rate = 1/100,
di.rate = 1/35, dr.rate = 1/100)
init <- init.dcm(s.num = 500, i.num = 1, r.num = 0)
control <- control.dcm(type = "SIR", nsteps = 500)
mod2 <- dcm(param, init, control)
mod2
plot(mod2)

# [@][Δ]Fin

##### 2021-03-13     © 2020-2021 ModLibre.infolicence cc-by-saSourceW3CHTML5CSS

1. Producing Simple Graphs with R © 2006-16 by Frank McCown

2. Modèle SIR (Licence GPL-3)