Formation R débutant
Jimmy Lopez
28 novembre, 2018
0) Les variables et l’affectation
x <- 1
x## [1] 1y = 1
y## [1] 1x <- y <- z <- 23Remarque : porté des variables ! différents des autres languages
VAR <<- 421) Les types de base
1.1) numeric
a <- 3.14
b <- 23
class(a)## [1] "numeric"1.2) character
fname <- "Jimmy"; lname <- "Lopez"
name <- paste(fname, lname, sep = " ")
name## [1] "Jimmy Lopez"s1 <- paste0("Eco", "tron")
s1## [1] "Ecotron"1.3) logical
u <- TRUE; v <- FALSE
u2 <- T; v2 <- F
u## [1] TRUE1.4) other
p1 <- NULL
p2 <- NA
p3 <- NaN
p1## NULLp2## [1] NAp3## [1] NaN2) Les autres structures des données
2.1) vector
Un seul type mais : TRUE < 3 < “s” et NA NULL NaN neutre
v1 <- c(2, 3, 5)
v1## [1] 2 3 5v2 <- c(1:10)
v2## [1] 1 2 3 4 5 6 7 8 9 10v3 <- c(rep(1, 10))
v3## [1] 1 1 1 1 1 1 1 1 1 1v4 <- c(a, b)
v4## [1] 3.14 23.00v5 <- c(TRUE, FALSE)
v5## [1] TRUE FALSEv6 <- c(v1, v5) # ! convertion de type
v6## [1] 2 3 5 1 0v7 <- c("toto", "tutu")
v7## [1] "toto" "tutu"v8 <- c(v6, v7) # ! convertion de type
v8## [1] "2" "3" "5" "1" "0" "toto" "tutu"v9 <- c(c(3,4), "3")
v9## [1] "3" "4" "3"! l’indice des tableaux ou autre commence à 1
v1 <- c(2, 3, 5)
v1[1]## [1] 2v1[2] <- 42
v1## [1] 2 42 5v1 <- c(v1, 13)
v1## [1] 2 42 5 13v1[-4] # ! supprime l'indice 4## [1] 2 42 5v1## [1] 2 42 5 13v1[1:2]## [1] 2 42Pour avoir la taille de notre vecteur
v1 <- c(2, 3, 5)
length(v1)## [1] 32.2) matrix
Un seul type de donnée
m1 <- matrix(0, nrow = 5, ncol = 4)
m1## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 0 0 0 0
## [5,] 0 0 0 0m2 <- matrix(1:20, nrow = 5, ncol = 4)
m2## [,1] [,2] [,3] [,4]
## [1,] 1 6 11 16
## [2,] 2 7 12 17
## [3,] 3 8 13 18
## [4,] 4 9 14 19
## [5,] 5 10 15 20length(m1)## [1] 20nrow(m1)## [1] 5ncol(m1)## [1] 4m2[3,2]## [1] 8m2[3,]## [1] 3 8 13 18m2[,2]## [1] 6 7 8 9 10m2[c(2,4), c(1,3)]## [,1] [,2]
## [1,] 2 12
## [2,] 4 14Translation
m2 <- matrix(1:20, nrow = 5, ncol = 4)
m2## [,1] [,2] [,3] [,4]
## [1,] 1 6 11 16
## [2,] 2 7 12 17
## [3,] 3 8 13 18
## [4,] 4 9 14 19
## [5,] 5 10 15 20t(m2)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 6 7 8 9 10
## [3,] 11 12 13 14 15
## [4,] 16 17 18 19 20Diagonale
m2 <- matrix(1:16, nrow = 4, ncol = 4)
diag(m2)## [1] 1 6 11 162.3) array
Un seul type de donnée
ar1 <- array(1:60, dim=c(3,4,5))
ar1## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 13 16 19 22
## [2,] 14 17 20 23
## [3,] 15 18 21 24
##
## , , 3
##
## [,1] [,2] [,3] [,4]
## [1,] 25 28 31 34
## [2,] 26 29 32 35
## [3,] 27 30 33 36
##
## , , 4
##
## [,1] [,2] [,3] [,4]
## [1,] 37 40 43 46
## [2,] 38 41 44 47
## [3,] 39 42 45 48
##
## , , 5
##
## [,1] [,2] [,3] [,4]
## [1,] 49 52 55 58
## [2,] 50 53 56 59
## [3,] 51 54 57 602.4) list
Plusieurs type de données
l1 <- list(e1=c(2, 3, 5), e2=c("aa", "bb"), e3=3)
l1## $e1
## [1] 2 3 5
##
## $e2
## [1] "aa" "bb"
##
## $e3
## [1] 3l1$e1 ## [1] 2 3 5l1["e1"]## $e1
## [1] 2 3 5l1[["e1"]]## [1] 2 3 5l1$e3 = c(FALSE)
#pas obligé de nommer
l2 <- list(1,2,3)
l2## [[1]]
## [1] 1
##
## [[2]]
## [1] 2
##
## [[3]]
## [1] 3length(l2) # la taille de notre liste## [1] 3Attention : ne permet pas de fusionner
l3 <- list(l1, l2) # list of list
l3## [[1]]
## [[1]]$e1
## [1] 2 3 5
##
## [[1]]$e2
## [1] "aa" "bb"
##
## [[1]]$e3
## [1] FALSE
##
##
## [[2]]
## [[2]][[1]]
## [1] 1
##
## [[2]][[2]]
## [1] 2
##
## [[2]][[3]]
## [1] 3Pour la fusion de list
l4 <- append(l1, l2) # l2 dans l1
l4 ## $e1
## [1] 2 3 5
##
## $e2
## [1] "aa" "bb"
##
## $e3
## [1] FALSE
##
## [[4]]
## [1] 1
##
## [[5]]
## [1] 2
##
## [[6]]
## [1] 32.5) data.frame
prenom <- c("jimmy", "khalid", "joana")
nom <- c("lopez", "belkhir", "sauze")
team <- c("MBB", "MBB", "ecotron")
data <- data.frame(prenom, nom, team)
data## prenom nom team
## 1 jimmy lopez MBB
## 2 khalid belkhir MBB
## 3 joana sauze ecotronrownames(data) <- c("p1", "p2", "p3")
colnames(data) <- c("nom", "prenom", "plateforme")
data## nom prenom plateforme
## p1 jimmy lopez MBB
## p2 khalid belkhir MBB
## p3 joana sauze ecotrondata["p1","nom"] ## [1] jimmy
## Levels: jimmy joana khaliddata["nom"]## nom
## p1 jimmy
## p2 khalid
## p3 joanadata["p1",]## nom prenom plateforme
## p1 jimmy lopez MBBdata[c("nom", "plateforme")] ## nom plateforme
## p1 jimmy MBB
## p2 khalid MBB
## p3 joana ecotrondata[1,2]## [1] lopez
## Levels: belkhir lopez sauzedata$prenom## [1] lopez belkhir sauze
## Levels: belkhir lopez sauzedata$new_c <- c(4,3,2)
data## nom prenom plateforme new_c
## p1 jimmy lopez MBB 4
## p2 khalid belkhir MBB 3
## p3 joana sauze ecotron 2data[3, 2] # factor ## [1] sauze
## Levels: belkhir lopez sauzenrow(data)## [1] 3ncol(data)## [1] 4Fusion de data.frame : doit avoir même nom de colonne
fred <- data.frame(nom="fred",prenom="snow",plateforme="NEW")
bob <- data.frame(nom="bob",prenom="daul",plateforme="NEW2")
data <- rbind(fred, bob)
data## nom prenom plateforme
## 1 fred snow NEW
## 2 bob daul NEW2Les noms des colonnes seront choisie pour vous
d1 <- data.frame(1,2,3)
d1## X1 X2 X3
## 1 1 2 33) Les operateurs arithmétiques
- + : addition
- - : Soustraction
- * : multiplication
- / : division
- ^ : exposant
- x %% y : modulo
- x %/% y : Partie entière de la division
- x %*% x : multiplication de matrice/vecteur
o1 <- 3 + 2.5
o1## [1] 5.5o2 <- TRUE + 3
o2## [1] 4o3 <- c(1:10) * 3
o3## [1] 3 6 9 12 15 18 21 24 27 30! Possible mais la taille d’un objet plus long n’est pas multiple de la taille d’un objet plus court
o4 <- c(1:10) * c(2, 3, 5) # v2 * c(2,3,4)## Warning in c(1:10) * c(2, 3, 5): la taille d'un objet plus long n'est pas
## multiple de la taille d'un objet plus courto4## [1] 2 6 15 8 15 30 14 24 45 20o5 <- m2 * 2
o5## [,1] [,2] [,3] [,4]
## [1,] 2 10 18 26
## [2,] 4 12 20 28
## [3,] 6 14 22 30
## [4,] 8 16 24 32o6 <- m2 * v2 # mais la aussi attention à la taille r * c * n = l ## Warning in m2 * v2: la taille d'un objet plus long n'est pas multiple de la
## taille d'un objet plus courto6## [,1] [,2] [,3] [,4]
## [1,] 1 25 81 39
## [2,] 4 36 100 56
## [3,] 9 49 11 75
## [4,] 16 64 24 965 / 2 ## [1] 2.55 %% 2 ## [1] 15 %/% 2 ## [1] 22 ^ 3## [1] 84) Les opérateurs logiques & les structures conditionelles
- < : inférieur à
- <= : inférieur ou égal à
- > : supérieur
- >= : supérieur ou égal à
- == : exactement égale à
- != : différent de
- |y : x ou y
- & y : x et ou
- isTRUE(x)
! : négation
En R pas de ++ -- += -=
x <- 6
if( x == 3 ) {
print("OK")
} else if ( x < 5 ) {
print (" < 5 ")
} else if ( x > 7 ) {
print (" > 7 ")
} else {
print("else")
}## [1] "else"5) Les boucles
For par élément
v2 <- c(1:10)
for (e in v2) {
print(e)
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10For par indice
v2 <- c(1:10)
for(x in 1:length(v2)) {
print(v2[x])
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10Next et Break
v2 <- c(1:10)
for (e in v2) {
if (!(e %% 2)){ # 0 = FALSE ; other numeric = TRUE
next
}
if(e == 9) {
break
}
print(e)
}## [1] 1
## [1] 3
## [1] 5
## [1] 7m3 <- matrix(0, 4, 5)
m3## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0
## [4,] 0 0 0 0 0for (i in 1:nrow(m3)) {
for (j in 1:ncol(m3)) {
m3[i, j] <- i + j
}
}
m3## [,1] [,2] [,3] [,4] [,5]
## [1,] 2 3 4 5 6
## [2,] 3 4 5 6 7
## [3,] 4 5 6 7 8
## [4,] 5 6 7 8 9While
i <- 1
while (i < 6) {
print(i)
i = i+1
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5Repeat
x <- 1
repeat {
print(x)
x = x+1
if (x == 6){
break
}
}## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 56) Les fonctions
increment <- function(x) {
res <- x + 1
return(res)
}
increment(22)## [1] 23mult <- function(x = 1, y = 1) {
return(x*y)
}
mult()## [1] 1mult(3)## [1] 3mult(y = 2) ## [1] 2mult(3,2)## [1] 6mult(y <- 2) # attention car init la var y et en plus x = 2 et y = 1## [1] 2getPI <- function() {
return(3.14159265359)
}
getPI()## [1] 3.141593Quelques fonction de base :
x <- c(9,5,6,8,4,11,36,59,12)
sum(x) # somme des éléments de x## [1] 150prod(x) # produit des éléments de x## [1] 2422379520max(x) # maximum des éléments de x## [1] 59min(x) # minimum des éléments de x## [1] 4which.max(x) # retourne l'indice du maximum des éléments de x## [1] 8which.min(x) # retourne l'indice du minimum des éléments de x## [1] 5range(x) # idem que c(min(x), max(x)); vector## [1] 4 59length(x) # nombre d'éléments dans x## [1] 9mean(x) # moyenne des éléments de x## [1] 16.66667median(x) # médiane des éléments de x## [1] 9class(x)## [1] "numeric"as.numeric("56") # else NA## [1] 56as.character(123) # as. ...## [1] "123"is.null(x) #is. ...## [1] FALSEis.numeric(x)## [1] TRUEis.vector(x)## [1] TRUEAttention ne pas nommer des fonctions de base ou de package
sum <- function(x) {
return(42)
}
x <- c(9,5,6,8,4,11,36,59,12)
sum(x)## [1] 42base::sum(x)## [1] 150Fonction dans une fonction
foo <- function() { # function in function
bar <- function() { # private function, possible mais peut utile
return(4)
}
return(3+bar())
}
foo()## [1] 78) Lire / Ecrire un fichier csv
The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).
mtcars ## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2write.csv(mtcars, "exemple_1.csv")
write.csv(mtcars, "exemple_2.csv", row.names = F)
write.csv2(mtcars, "exemple_3.csv")
cars <- read.csv("exemple_1.csv")
cars## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2cars3 <- read.csv("exemple_3.csv", sep = ";")
cars3## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21 6 160 110 3,9 2,62 16,46 0 1 4 4
## 2 Mazda RX4 Wag 21 6 160 110 3,9 2,875 17,02 0 1 4 4
## 3 Datsun 710 22,8 4 108 93 3,85 2,32 18,61 1 1 4 1
## 4 Hornet 4 Drive 21,4 6 258 110 3,08 3,215 19,44 1 0 3 1
## 5 Hornet Sportabout 18,7 8 360 175 3,15 3,44 17,02 0 0 3 2
## 6 Valiant 18,1 6 225 105 2,76 3,46 20,22 1 0 3 1
## 7 Duster 360 14,3 8 360 245 3,21 3,57 15,84 0 0 3 4
## 8 Merc 240D 24,4 4 146,7 62 3,69 3,19 20 1 0 4 2
## 9 Merc 230 22,8 4 140,8 95 3,92 3,15 22,9 1 0 4 2
## 10 Merc 280 19,2 6 167,6 123 3,92 3,44 18,3 1 0 4 4
## 11 Merc 280C 17,8 6 167,6 123 3,92 3,44 18,9 1 0 4 4
## 12 Merc 450SE 16,4 8 275,8 180 3,07 4,07 17,4 0 0 3 3
## 13 Merc 450SL 17,3 8 275,8 180 3,07 3,73 17,6 0 0 3 3
## 14 Merc 450SLC 15,2 8 275,8 180 3,07 3,78 18 0 0 3 3
## 15 Cadillac Fleetwood 10,4 8 472 205 2,93 5,25 17,98 0 0 3 4
## 16 Lincoln Continental 10,4 8 460 215 3 5,424 17,82 0 0 3 4
## 17 Chrysler Imperial 14,7 8 440 230 3,23 5,345 17,42 0 0 3 4
## 18 Fiat 128 32,4 4 78,7 66 4,08 2,2 19,47 1 1 4 1
## 19 Honda Civic 30,4 4 75,7 52 4,93 1,615 18,52 1 1 4 2
## 20 Toyota Corolla 33,9 4 71,1 65 4,22 1,835 19,9 1 1 4 1
## 21 Toyota Corona 21,5 4 120,1 97 3,7 2,465 20,01 1 0 3 1
## 22 Dodge Challenger 15,5 8 318 150 2,76 3,52 16,87 0 0 3 2
## 23 AMC Javelin 15,2 8 304 150 3,15 3,435 17,3 0 0 3 2
## 24 Camaro Z28 13,3 8 350 245 3,73 3,84 15,41 0 0 3 4
## 25 Pontiac Firebird 19,2 8 400 175 3,08 3,845 17,05 0 0 3 2
## 26 Fiat X1-9 27,3 4 79 66 4,08 1,935 18,9 1 1 4 1
## 27 Porsche 914-2 26 4 120,3 91 4,43 2,14 16,7 0 1 5 2
## 28 Lotus Europa 30,4 4 95,1 113 3,77 1,513 16,9 1 1 5 2
## 29 Ford Pantera L 15,8 8 351 264 4,22 3,17 14,5 0 1 5 4
## 30 Ferrari Dino 19,7 6 145 175 3,62 2,77 15,5 0 1 5 6
## 31 Maserati Bora 15 8 301 335 3,54 3,57 14,6 0 1 5 8
## 32 Volvo 142E 21,4 4 121 109 4,11 2,78 18,6 1 1 4 2# other write si pas un csv9) Charger et utiliser un autre script R
source("./tools.R") # on charge un autre script R
jimmy_sub1()## [1] "call sub function"10) Utiliser un package R
#install.packages('ggplot2')
library(ggplot2)
data <- data.frame(a=1:7, b=c(2, 5, 6, 12, 20, 3, 7))
p <- ggplot(data=data, aes(x=a, y=b,colour="Cond"))
p <- p + geom_point(size=4)
p <- p + geom_line(size=2)
p
?ggplot2 # info sur le package
?ggplot # info sur la fonction11) La manipulation des données avec dplyr
library (dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2select(mtcars, drat) # on sélectionne une colonne## drat
## Mazda RX4 3.90
## Mazda RX4 Wag 3.90
## Datsun 710 3.85
## Hornet 4 Drive 3.08
## Hornet Sportabout 3.15
## Valiant 2.76
## Duster 360 3.21
## Merc 240D 3.69
## Merc 230 3.92
## Merc 280 3.92
## Merc 280C 3.92
## Merc 450SE 3.07
## Merc 450SL 3.07
## Merc 450SLC 3.07
## Cadillac Fleetwood 2.93
## Lincoln Continental 3.00
## Chrysler Imperial 3.23
## Fiat 128 4.08
## Honda Civic 4.93
## Toyota Corolla 4.22
## Toyota Corona 3.70
## Dodge Challenger 2.76
## AMC Javelin 3.15
## Camaro Z28 3.73
## Pontiac Firebird 3.08
## Fiat X1-9 4.08
## Porsche 914-2 4.43
## Lotus Europa 3.77
## Ford Pantera L 4.22
## Ferrari Dino 3.62
## Maserati Bora 3.54
## Volvo 142E 4.11mtcars["drat"] # équivalent classique## drat
## Mazda RX4 3.90
## Mazda RX4 Wag 3.90
## Datsun 710 3.85
## Hornet 4 Drive 3.08
## Hornet Sportabout 3.15
## Valiant 2.76
## Duster 360 3.21
## Merc 240D 3.69
## Merc 230 3.92
## Merc 280 3.92
## Merc 280C 3.92
## Merc 450SE 3.07
## Merc 450SL 3.07
## Merc 450SLC 3.07
## Cadillac Fleetwood 2.93
## Lincoln Continental 3.00
## Chrysler Imperial 3.23
## Fiat 128 4.08
## Honda Civic 4.93
## Toyota Corolla 4.22
## Toyota Corona 3.70
## Dodge Challenger 2.76
## AMC Javelin 3.15
## Camaro Z28 3.73
## Pontiac Firebird 3.08
## Fiat X1-9 4.08
## Porsche 914-2 4.43
## Lotus Europa 3.77
## Ford Pantera L 4.22
## Ferrari Dino 3.62
## Maserati Bora 3.54
## Volvo 142E 4.11select(mtcars, -drat) # on élimine une colonne## mpg cyl disp hp wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 2.780 18.60 1 1 4 2mtcars[,! names(data) %in% c("drat")] # équivalent classique## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2filter(mtcars, disp > 160, drat < 3) # pour filtrer ## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
## 2 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
## 3 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2f1 <- mtcars[mtcars$disp>160,]
f1[f1$drat < 3, ] # équivalent classique## mpg cyl disp hp drat wt qsec vs am gear carb
## Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
## Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2arrange(mtcars, disp) # on range dans l'ordre croissant## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 3 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 4 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 5 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 6 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 8 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 9 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 10 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 11 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 12 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 13 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 14 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 15 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 16 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 17 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 18 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 19 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 20 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 21 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 22 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 25 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 26 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 27 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 28 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 29 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 30 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 31 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 32 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4mtcars[order(mtcars$disp),] # équivalent classique## mpg cyl disp hp drat wt qsec vs am gear carb
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4arrange(mtcars, desc(disp)) # on range dans l'ordre croissant## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 2 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 3 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 4 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 7 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 8 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 9 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 10 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 11 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 16 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 17 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 18 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 19 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 20 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 21 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 22 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 23 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 24 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 25 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 26 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 27 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 30 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 31 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 32 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1mtcars[order(-mtcars$disp),] # équivalent classique## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1Utilisation de l’operateur ‘%>%’ pour plus de souplesse
mtcars %>% select(drat, wt, am) %>% filter(drat < 3) %>% arrange(desc(wt)) ## drat wt am
## 1 2.93 5.25 0
## 2 2.76 3.52 0
## 3 2.76 3.46 0f1 <- mtcars[c("drat", "wt", "am")]
f2 <- f1[f1$drat < 3,]
f3 <- f2[order(-f2$wt),]
f3## drat wt am
## Cadillac Fleetwood 2.93 5.25 0
## Dodge Challenger 2.76 3.52 0
## Valiant 2.76 3.46 012) Comment avoir une interactions avec une base de données
library(RMySQL)## Loading required package: DBIlibrary(pool)
connectDB <- function() {
pool <- dbPool(
drv = RMySQL::MySQL(),
dbname = "earthworms",
host = "localhost",
username = "root",
password = "mbb"
)
return(pool)
}
#' Get the data in range from data base
#' @param name a character : the name of table in DB
#' @param start a character : the start Timestamp
#' @param end a character : the end Timestamp
#' @return a data.frame
get_data_in_range <- function(name, start, end) {
pool = connectDB()
sql <- paste0("select `Timestamp`, `Value` from `", name, "` WHERE `Timestamp` between '", start, "' AND '", end, "';")
#print(sql)
query <- suppressWarnings(sqlInterpolate(pool, sql))
data <- suppressWarnings(dbGetQuery(pool, query))
poolClose(pool) #important
return(data)
}
# TAIR m1 consigne
data <- get_data_in_range("cfp_mac1_vir_cons_t_regul", "2018-03-03 07:19:38", "2018-03-03 18:36:14")
mean(data$Value)## [1] 3.838089max(data$Value)## [1] 5.5315min(data$Value)## [1] 1.6365max(data$Timestamp)## [1] "2018-03-03 18:36:03"min(data$Timestamp)## [1] "2018-03-03 07:19:51"
7) Comment optimiser les boucles
Attention si dépendances entre les indices