Vanessa 24/6/2021
Primera Práctica calificada
Vanessa Nina 14160173
#1)
a<-10000%%3
a #es lo que sobra al repartir 10000$ entre 3 personas## [1] 1
#2)
4560%%3 #si es divisible por 3 porque su residuo es cero## [1] 0
#3)
b<- c(2:87)
b[b%%7==0]## [1] 7 14 21 28 35 42 49 56 63 70 77 84
#4)
c<- c(7:3)
d<-c(seq(from=5, to=25, by=5))
condiA <- ifelse(c%%2==0, "TRUE", "FALSE")
condiB <- ifelse(d>10, "TRUE", "FALSE")
data.frame(condiA,condiB)#en la cuarta posicion se cumplen ambas condiciones## condiA condiB
## 1 FALSE FALSE
## 2 TRUE FALSE
## 3 FALSE TRUE
## 4 TRUE TRUE
## 5 FALSE TRUE
#5)
S <- (1:100)
sum(1:100)## [1] 5050
sum_s<- (100*101)/2
sum_s## [1] 5050
## la suma es 5050, usando sumatoria y la fórmula#6)
e<-c(1, -4, 5, 9, -4)
min(e)## [1] -4
nivel<-c(1, -4, 5, 9,-4)
nivel[2]## [1] -4
nivel[5]## [1] -4
ifelse(e==min(e), "ValorMinimo", "***")## [1] "***" "ValorMinimo" "***" "***" "ValorMinimo"
#7)
factorial(8)## [1] 40320
#8)
x<- 3:7
sum(exp(x))## [1] 1723.159
#9)
y<-1:10
prod(log(sqrt(y)))## [1] 0
#10)
R=25
teta= 10 #grados
d=R/2
ar1 <- function(R,teta){
result <- (((pi*(R^2))*teta)/360)
return(result)
}
area1 <- ar1(R,teta)
area1## [1] 54.54154
ar2 <- function(d,R){
result2 <- (sqrt(R^2 - d^2))
return(result2)
}
area2 <-ar2(d,R)
area2## [1] 21.65064
ar_final <- area1 - area2
ar_final## [1] 32.8909
#11)
n<- c(5:15)
n## [1] 5 6 7 8 9 10 11 12 13 14 15
ninvert<- c(15:5)
ninvert## [1] 15 14 13 12 11 10 9 8 7 6 5
n<- c(5:15)
rev(n)#resulta lo mismo que al invertirlo usar la función rev## [1] 15 14 13 12 11 10 9 8 7 6 5
#12)
w<- 10:100
sum(w^3+ 4*w^2)## [1] 26852735
#13)
z<- 1:25
sum(2^z/z +3^z/z^2)## [1] 2129170437
Ahora crearemos gráficos aprovechando el data frame de la pregunta N°14
#14)
df<-read.csv("https://raw.githubusercontent.com/fhernanb/datos/master/Paises.txt",sep="",dec=".")
View(df)
plot(df)
alta<-subset(df, alfabetizacion >=70)
plot(alta)
x<- alta$Pais
y<- alta$alfabetizacion
hist(df$alfabetizacion, freq = T, col = "skyblue", labels = T)
#primer
length(df)## [1] 5
#segundo
length(df[,1])## [1] 107
#tercero
df[df$poblacion==max(df[,2]),]## Pais poblacion alfabetizacion tasamortinf PIB
## 25 China_ 1205200 78 52 377
#cuarto
df[df$alfabetizacion==min(df[,3]),]## Pais poblacion alfabetizacion tasamortinf PIB
## 19 Burkina_Faso 10000 18 118 357
#15
df<-mtcars
df[df$mpg<18.0,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 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
## 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
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
df[df$cyl==4,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
df[df$wt>2.500&df$am==1,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Ford Pantera L 15.8 8 351 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121 109 4.11 2.780 18.60 1 1 4 2
#16
x<-0:365
y<-pi*2*(x-81)/365
funcion<-9.87*sin(2*y)-7.35*cos(y)-1.5*sin(y)
n<-month.abb
n<-x/33.27
m<-month.abb
plot(n, funcion, xaxt = "n")
axis(1, at = seq(round(min(n)), round(max(n)), by = 1), labels =m)
Gracias