R
https:\/\/www.r-project.org\/
Bases
Fonctions
sum(1,3,5)
rep("Yo ho!", times = 3)
sqrt(16)
Help
help(functionname)
Fichiers
list.files()
Exécuter un script
source("bottle1.R")
Vector
v <- c(1,2,3)
seq(1,10,0.5)
names(v)<-c("un","deux","trois")
Visualisation
barplot(vector)
plot(vector)
Matrices
matrix(init,nb lignes, nb de colonnes)
ou matrix(vector,nb lignes, nb de colonnes)
matrix(0,3,4)
dim: assigner le nb de lignes et de colonnes
p<-1:8
dim(p)<-c(2,4)
p[2,3]
ligne entiere
p[2,]
plusieurs colonnes
p[,2:4]
Visualisation
contour(matrix)
Vue 3D
persp(matrix)
persp(matrix, expand=0.2)
Carte de chaleur
image(matrix)
Statistics
mean(vector)
barplot(vector)
If we draw a line on the plot representing the mean, we can easily compare the various values to the average. The abline function can take an h parameter with a value at which to draw a horizontal line, or a v parameter for a vertical line.
abline(h = mean(vector))
median(vector)
Statisticians use the concept of "standard deviation" from the mean to describe the range of typical values for a data set. For a group of numbers, it shows how much they typically vary from the average value. To calculate the standard deviation, you calculate the mean of the values, then subtract the mean from each number and square the result, then average those squares, and take the square root of that average.
sd(vector)
Factors
Pour grouper par catégorie
factor(vector)
levels(vector)
Utiliser différents items pour les points avec pch:
types<-factor(vector)
plot(x,y, pch=as.integer(types))
La légende:
legend(position, vector, pch)
legend("topright", c("gems", "gold", "silver"), pch=1:3)
Data Frames
Pour structurer les données (comme dans une BD ou une feuille excel)
treasure <- data.frame(weights, prices, types)
Pour accéder aux données d'une colonne
treasure[["weights"]] ou treasure$weights
I\/O
Charger des fichiers
read.csv("targets.csv")
read.table("infantry.txt", sep="\t", header=TRUE)
Merger des fichiers
piracy <- read.csv("piracy.csv")
gdp <- read.table("gdp.txt", sep="\t", header=TRUE)
countries<-merge(x = gdp, y = piracy)
Statistiques
Tester la corrélation
Tester la corrélation entre 2 vecteurs:
cor.test(countries$GDP, countries$Piracy)
Prédiction
We have more countries represented in our GDP data than we do our piracy rate data. If we know a country's GDP, can we use that to estimate its piracy rate?
We can, if we calculate the linear model that best represents all our data points (with a certain degree of error). The lm function takes a model formula, which is represented by a response variable (piracy rate), a tilde character (~), and a predictor variable (GDP). (Note that the response variable comes first.)
line <- lm(countries$Piracy ~ countries$GDP)
Packages
Installer un package
install.packages("ggplot2")
help(package = "ggplot2")
library("ggplot2")