library(plotrix)
# PUNTO 1, RESULTADO DE LAS SIGUIENTES INSTRUCCIONES:
# PUNTO 1A)
x = c(1,3,5,7,9)
x
## [1] 1 3 5 7 9
# PUNTO 1B)
y = c(2,4,6,7,11,12)
y
## [1] 2 4 6 7 11 12
# PUNTO 1C)
x+1
## [1] 2 4 6 8 10
# PUNTO 1D)
y*2
## [1] 4 8 12 14 22 24
# PUNTO 1E)
length(x)
## [1] 5
length(y)
## [1] 6
# PUNTO 1F)
x + y
## Warning in x + y: longer object length is not a multiple of shorter object
## length
## [1] 3 7 11 14 20 13
# PUNTO 1G)
sum(x>5)
## [1] 2
sum(x[x>5])
## [1] 16
# PUNTO 1H)
sum(x>5 | x< 3)
## [1] 3
# PUNTO 1I)
y[2]
## [1] 4
# PUNTO 1J)
y[-2]
## [1] 2 6 7 11 12
# PUNTO 1K)
y[x]
## [1] 2 6 11 NA NA
# PUNTO 1M)
y[y>=8]
## [1] 11 12
# PUNTO 2, MILLAS RECORRIDAS POR EL CARRO DEL CONDUCTOR EUROPEO CADA VEZ QUE LLENA EL TANQUE DE GASOLINA:
# PUNTO 2A) VARIABLE "MILLAS"
millas = c(65241,65665,65998,66014, 66547, 66857, 67025, 67447, 66958, 67002)
millas
## [1] 65241 65665 65998 66014 66547 66857 67025 67447 66958 67002
# PUNTO 2B) VARIABLE "KMS" Y VALOR DE "MILLAS"
kms = 1.609*(millas)
kms
## [1] 104972.8 105655.0 106190.8 106216.5 107074.1 107572.9 107843.2 108522.2
## [9] 107735.4 107806.2
# PUNTO 2C) FUNCION DIFF:
diff(millas)
## [1] 424 333 16 533 310 168 422 -489 44
diff(kms)
## [1] 682.216 535.797 25.744 857.597 498.790 270.312 678.998 -786.801
## [9] 70.796
# PUNTO 2D) FUNCIONES ADECUADAS QUE RESUMEN LOS DATOS:
mean (millas)
## [1] 66475.4
# PUNTO 3, CONTRATO DE PAGO MINIMO TELEFONICO:
telefono=c(47,32,40,36,31,49,30,49,35,48,32)
telefono
## [1] 47 32 40 36 31 49 30 49 35 48 32
# PUNTO 3A) FACTURA MAS CARA DEL ULTIMO AÑO:
a2.5=sum(telefono)
a2.5
## [1] 429
# PUNTO 3B) PAGO PROMEDIO DE CADA MES:
b2.5=a2.5/12
b2.5
## [1] 35.75
# PUNTO 3C) CANTIDADES MINIMAS Y MAXIMAS PAGADAS:
range(telefono, na.rm=T)
## [1] 30 49
# PUNTO 3D) MES EN QUE SE REALIZO CADA PAGO:
d2.5=ts(telefono, frequency=12, start=c(2020,1), end=c(2020,12))
d2.5
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 2020 47 32 40 36 31 49 30 49 35 48 32 47
# PUNTO 3E) MESES QUE PAGO MAS DE 40 EUROS:
e2.5=sum(telefono>40)
e2.5
## [1] 4
# PUNTO 3F) PORCENTAJE DEL GASTO TOTAL:
(sum(telefono>40)/length(telefono))*100
## [1] 36.36364
# PUNTO 4, CON LOS SIGUIENTES DATOS:
x=c(61, 88, 73, 49, 41, 72, 99, 07, 12, 13, 87, 91, 05, 17, 97)
x
## [1] 61 88 73 49 41 72 99 7 12 13 87 91 5 17 97
# PUNTO 4A) DIAGRAMA PORCENTUAL:
pie(x, main = 'REPRESENTACION GRAFICA DE DATOS ALEATORIOS')
# PUNTO 4B) RESUMENES NUMERICOS:
summary(x)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 15.00 61.00 54.13 87.50 99.00
# PUNTO 4C) DIFERENCIA ENTRE SUMMARY (X) Y FIVENUM (X):
summary(x)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 15.00 61.00 54.13 87.50 99.00
fivenum(x)
## [1] 5.0 15.0 61.0 87.5 99.0
# PUNTO 5, 100 DATOS ALEATORIOS:
# PUNTO 5A) VALORES CON RNORM (100):
datos=rnorm(100)
datos
## [1] -1.788267e+00 -1.440957e+00 -1.055295e+00 -1.243241e+00 -7.229097e-01
## [6] 7.044180e-02 -9.518319e-01 2.363417e-01 1.849747e-01 1.890476e+00
## [11] -5.252796e-01 5.361532e-01 -1.147435e+00 1.441789e-01 -4.807018e-01
## [16] -1.417936e+00 7.827628e-01 -9.187232e-01 -3.141989e-02 3.912989e-01
## [21] -1.019215e+00 1.581353e+00 -5.225821e-01 6.474442e-01 -1.286910e+00
## [26] -1.038990e-01 -6.303228e-02 5.229182e-01 1.148266e+00 -1.445632e+00
## [31] 1.653792e-01 9.748363e-01 -6.841252e-01 -1.355400e+00 -2.360963e-01
## [36] 3.829308e-01 3.999699e-02 8.635039e-01 -7.545955e-01 8.360835e-01
## [41] 3.245848e+00 -2.552946e+00 -1.437560e+00 -2.390200e+00 1.628244e-01
## [46] 3.972152e-01 4.772153e-01 1.240476e-02 1.653099e-01 4.541218e-02
## [51] 2.550990e+00 4.286915e-01 -7.317718e-02 -3.921163e-05 -2.283504e-01
## [56] 1.344200e+00 -1.258649e+00 9.821894e-01 1.089515e+00 1.918868e+00
## [61] -5.866294e-02 -4.111336e-02 9.827419e-01 -1.046236e+00 1.927750e+00
## [66] -9.888116e-01 -7.456931e-01 1.761954e+00 -8.253739e-01 8.217496e-01
## [71] -1.493164e-02 1.028383e+00 -6.792063e-01 1.044234e+00 7.357956e-01
## [76] 1.836552e+00 5.756929e-01 -9.024525e-01 -9.034727e-01 -1.829726e+00
## [81] -6.378582e-01 -1.225442e+00 8.244903e-01 -2.113304e-01 1.732050e+00
## [86] -1.574714e+00 -1.231505e-01 8.015896e-02 1.494992e-02 -5.769705e-01
## [91] 1.642794e-01 2.693557e-01 3.909897e-02 -2.477118e-01 -3.293633e-01
## [96] 3.644648e-01 -1.457609e+00 1.442004e+00 3.529000e-01 7.711953e-02
# PUNTO 5B) HISTOGRAMA:
hist(datos, main = 'REPRESENTACION GRAFICA DE DATOS ALEATORIOS', col = "pink")
# PUNTO 5C) RESUMEN NUMERICO:
summary(datos)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.55295 -0.77229 0.01368 -0.01260 0.59363 3.24585
# PUNTO 6, 30 VALORES DE DISTRIBUCION BINOMIAL:
binomial=rbinom(5, 30, 0.9)
binomial
## [1] 25 30 23 24 28
binomial=rbinom(30, 5, 0.9)
binomial
## [1] 5 4 4 4 5 4 5 4 5 4 4 4 4 4 5 4 3 5 4 4 5 5 5 5 4 4 5 5 4 5
# PUNTO 6A) DIAGRAMA DE BARRAS O PASTEL:
pie(binomial,col = rainbow(length(binomial)), main="REPRESENTACION DISTRIBUCION BINOMIAL")
pie3D(binomial, explode=0.1, main="REPRESENTACION DISTRIBUCION BINOMIAL")
# PUNTO 6B) RESUMEN NUMERICO Y COMPARACION CON EL PUNTO ANTERIOR:
summary(binomial)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.0 4.0 4.0 4.4 5.0 5.0
# PUNTO 7, NUMERO DE FALLOS:
fallas=c(0,1,0,NA,0,0,0,0,0,1,1,1,0,0,3,0,0,0,0,0,2,0,1)
fallas
## [1] 0 1 0 NA 0 0 0 0 0 1 1 1 0 0 3 0 0 0 0 0 2 0 1
# PUNTO 7A) REPRESENTACION GRAFICA:
boxplot(fallas, main = 'REPRESENTACION GRAFICA DE NUMERO DE FALLOS', col = "yellow")
barplot(fallas, main = 'REPRESENTACION GRAFICA DE NUMERO DE FALLOS', col = "purple")
# PUNTO 7B) TABULACION DE DATOS Y NUMERO MEDIO DE ERRORES:
mean(fallas,na.rm=TRUE)
## [1] 0.4545455
fallas[!is.na(fallas)]
## [1] 0 1 0 0 0 0 0 0 1 1 1 0 0 3 0 0 0 0 0 2 0 1
# PUNTO 8, ENCUESTA DEL CURSO:
# PUNTO 8A) DATOS C(), SCAN(), READ.TABLE(), DATA.ENTRY():
estudiante=c(1,2,3,4,5,6,7,8,9,10)
estudiante
## [1] 1 2 3 4 5 6 7 8 9 10
P1=c(3,3,3,4,3,4,3,4,4,3)
P1
## [1] 3 3 3 4 3 4 3 4 4 3
P2= c(5,5,2,2,5,2,2,5,5,2)
P2
## [1] 5 5 2 2 5 2 2 5 5 2
P3=c(1,3,1,3,3,3,1,3,1,1)
P3
## [1] 1 3 1 3 3 3 1 3 1 1
# PUNTO 8B) RESULTADOS DE CADA PREGUNTA:
table(P1)
## P1
## 3 4
## 6 4
table(P2)
## P2
## 2 5
## 5 5
table(P3)
## P3
## 1 3
## 5 5
# PUNTO 8C) TABLA DE CONTINGUENCIA CRUZADA:
table(P1,P2)
## P2
## P1 2 5
## 3 3 3
## 4 2 2
table(P1,P3)
## P3
## P1 1 3
## 3 4 2
## 4 1 3
table(P2,P3)
## P3
## P2 1 3
## 2 3 2
## 5 2 3
table(P1,P2,P3)
## , , P3 = 1
##
## P2
## P1 2 5
## 3 3 1
## 4 0 1
##
## , , P3 = 3
##
## P2
## P1 2 5
## 3 0 2
## 4 2 1
# PUNTO 8D) DIAGRAMA DE BARRAS:
estudiante <- sample(1:10, size= 50, replace=TRUE)
resultados <- sample(c("P1", "P2", "P3"), size=50, replace= TRUE)
tabla <- table(estudiante, resultados)
tabla
## resultados
## estudiante P1 P2 P3
## 1 2 0 2
## 2 2 2 1
## 3 1 1 1
## 4 0 3 1
## 5 1 0 4
## 6 0 2 3
## 7 3 3 3
## 8 1 4 4
## 9 0 1 4
## 10 0 1 0
# PUNTO 8E) DIAGRAMA DE BARRAS SIMULTANEAS:
matrix(c(P1,P2,P3),nrow=3,byrow=1)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 3 3 3 4 3 4 3 4 4 3
## [2,] 5 5 2 2 5 2 2 5 5 2
## [3,] 1 3 1 3 3 3 1 3 1 1
barplot(tabla, main = "REPRESENTACION GRAFICA DE RESULTADOS DE LAS PREGUNTAS", sub = "encuestas", xlab = "ESTUDIANTES", ylab = "RESULTADOS", horiz = TRUE, col = c("thistle1", "pink1", "violet", "aquamarine1", "turquoise1", "steelblue1", "lightsteelblue1", "slateblue1", "salmon1", "lightgoldenrod1"), border = NA)
# PUNTO 9, VECTORES DE 50 VALORES:
vectorA=rnorm(50)
vectorA
## [1] -0.75056161 -0.33285794 -1.23345765 0.79457731 -0.89051613 -0.99884047
## [7] 0.32083242 -0.21178720 -0.42869207 -1.90536421 0.84861388 0.28430675
## [13] -1.52584403 0.24090573 0.56737789 0.11938610 1.78733799 0.90527581
## [19] 1.04045860 -0.79499924 0.87309097 0.26469885 -0.04561253 1.27524215
## [25] 1.01998269 0.28322524 0.18920028 -1.46458619 3.00560028 -1.35715341
## [31] -0.18920472 -0.61817747 -1.41482144 -0.32416193 0.75638669 -2.26104277
## [37] 0.64043218 -0.61799402 -0.15460085 0.65472618 0.64169910 0.78378459
## [43] -1.27431197 -0.61521987 -0.28274939 1.71751203 1.09903762 -2.13980792
## [49] 1.05207133 0.90295168
vectorB=rnorm(50)
vectorB
## [1] 0.12977388 0.23306532 -0.72569751 0.02040637 0.38672839 1.01590037
## [7] 2.35053498 0.37070843 -1.28896686 -0.57026099 2.25542691 -0.09644457
## [13] -0.92296086 -0.22917857 1.26044764 0.14769578 -1.77375766 -1.35325021
## [19] -0.24183310 0.30585324 -1.75440691 -1.25771754 0.35305540 0.87100751
## [25] 1.55815054 -0.32925833 0.37480032 0.00311389 -0.49533257 -0.43488775
## [31] -0.47629096 0.42888635 -0.89196954 1.37664651 -0.33715125 -1.95919929
## [37] -0.66756867 -0.23070015 1.83101487 1.01077571 0.13440340 -0.05229164
## [43] 0.44408753 0.43093869 -0.25809064 0.10721083 -0.55647232 -0.48972954
## [49] -1.32772218 1.10210085
# PUNTO 9A) PRUEBA DE NORMALIDAD:
shapiro.test(vectorA)
##
## Shapiro-Wilk normality test
##
## data: vectorA
## W = 0.98072, p-value = 0.5825
shapiro.test(vectorB)
##
## Shapiro-Wilk normality test
##
## data: vectorB
## W = 0.97813, p-value = 0.4761
# PUNTO 9B) DIFERENCIAS ENTRE LOS VECTORES:
t.test(vectorA)
##
## One Sample t-test
##
## data: vectorA
## t = 0.030805, df = 49, p-value = 0.9756
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.3036399 0.3130939
## sample estimates:
## mean of x
## 0.004726986
t.test(vectorB)
##
## One Sample t-test
##
## data: vectorB
## t = -0.031619, df = 49, p-value = 0.9749
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.2819889 0.2732527
## sample estimates:
## mean of x
## -0.004368118
# PUNTO 10, DOS VECTORES DE 50 VALORES:
ran=rbinom(50,6,0.6)
ran
## [1] 4 3 4 4 4 5 5 1 3 4 4 1 3 5 3 3 5 4 5 5 6 6 6 3 3 1 5 6 4 4 4 3 4 5 3 3 2 4
## [39] 3 2 5 4 4 2 2 3 3 6 5 4
ran2=rnorm(50)
ran2
## [1] -0.1621290144 1.3406134268 0.2796914695 -0.7808568690 -0.2775350959
## [6] 0.2482405000 -0.1190571812 0.6335923983 -0.5087041642 1.3843863785
## [11] 0.9012023654 1.1071596604 -0.8526611567 1.4366296209 -1.4934937715
## [16] 0.2177862619 0.6302322014 -0.2665680854 -0.5473265895 0.3968792303
## [21] 0.3462265631 -0.1155529947 -0.2754346045 -0.5776074862 -1.0812798394
## [26] -1.0536087378 1.3032690169 0.1331236304 0.6676992050 0.5594234890
## [31] -2.4056341861 1.2279403028 -0.6503644555 0.9955349963 0.5796158558
## [36] -0.2152599063 -1.0980772636 -0.0884283882 -1.0195027603 -0.0001077023
## [41] 2.0164383174 -0.1576414743 0.0633727016 0.4045203910 -1.3033366729
## [46] -1.7882101369 -1.0210407555 1.5068568327 -1.3987552820 0.1748660545
# PUNTO 10A) PRUEBA DE NORMALIDAD:
shapiro.test(ran)
##
## Shapiro-Wilk normality test
##
## data: ran
## W = 0.93423, p-value = 0.008016
# PUNTO 10B) DIFERENCIAS ENTRE LOS VECTORES:
t.test(ran)
##
## One Sample t-test
##
## data: ran
## t = 20.522, df = 49, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 3.427899 4.172101
## sample estimates:
## mean of x
## 3.8