Taller 2 Informatica Medica Joseph Alejandro Valdes
Actividad 2.1
"A"## [1] "A"x= c(1,3,5,7,9)
x## [1] 1 3 5 7 9"B"## [1] "B"y=c(2,4,6,7,11,12)
y## [1] 2 4 6 7 11 12"C"## [1] "C"x + 1## [1] 2 4 6 8 10"D"## [1] "D"y*2## [1] 4 8 12 14 22 24"E"## [1] "E"length(x) ## [1] 5length(y)## [1] 6"F"## [1] "F"x+y## Warning in x + y: longitud de objeto mayor no es múltiplo de la longitud de uno
## menor## [1] 3 7 11 14 20 13"G"## [1] "G"sum(x>5) ## [1] 2sum(x[x>5])## [1] 16"H"## [1] "H"sum(x>5 | x< 3)## [1] 3"I"## [1] "I"y[2]## [1] 4"J"## [1] "J"y[-2]## [1] 2 6 7 11 12"K"## [1] "K"y[x]## [1] 2 6 11 NA NA"L"## [1] "L""NA es la representacion de un dato perdido o a una operacion que no tuvo exito en su solucion"## [1] "NA es la representacion de un dato perdido o a una operacion que no tuvo exito en su solucion""M"## [1] "M"y[y>=8]## [1] 11 12Actividad 2.2
millas= c(65241,65665,65998,66014,66547,66857,67025,67447,66958,67002)
"B"## [1] "B"mks= millas*1609/1
mks## [1] 104972769 105654985 106190782 106216526 107074123 107572913 107843225
## [8] 108522223 107735422 107806218"C"## [1] "C"diff(mks)## [1] 682216 535797 25744 857597 498790 270312 678998 -786801 70796diff(millas)## [1] 424 333 16 533 310 168 422 -489 44"D"## [1] "D"Actividad 2.3
facturas = c(47,32,40,36,49,31,49,30,49,35,48,32)
"A"## [1] "A"sum(facturas)## [1] 478"B"## [1] "B"mean(facturas)## [1] 39.83333"C"## [1] "C"min(facturas)## [1] 30max(facturas)## [1] 49"D"## [1] "D"facturas ==30## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSEfacturas ==49## [1] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE"E"## [1] "E"sum(facturas>40)## [1] 5pago_40E= facturas>40
pago_40E## [1] TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE"F"## [1] "F"pago_40E*5/100## [1] 0.05 0.00 0.00 0.00 0.05 0.00 0.05 0.00 0.05 0.00 0.05 0.00Actividad 2.4
datos =c(61,88,73,49,41,72,99,07,12,13,87,91,05,17,97)
names(datos)=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)
"A"## [1] "A""Hacer un diagrama porcentual"## [1] "Hacer un diagrama porcentual"barplot(datos)
"B"## [1] "B"head(datos)## 1 2 3 4 5 6
## 61 88 73 49 41 72summary(datos)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 15.00 61.00 54.13 87.50 99.00fivenum(datos)## 13 10 1 11 7
## 5.0 15.0 61.0 87.5 99.0"C"## [1] "C""¿Qué diferencias hay entre summary(x) y fivenum(x)?"## [1] "¿Qué diferencias hay entre summary(x) y fivenum(x)?""summary nos da un resumen general de las variables que escojamos, como minimo, maximo, media, mediana y algunos cuartiles"## [1] "summary nos da un resumen general de las variables que escojamos, como minimo, maximo, media, mediana y algunos cuartiles""en cambio la función fivenum solo nos da un reusmen de los cuartiles"## [1] "en cambio la función fivenum solo nos da un reusmen de los cuartiles"Actividad 2.5
"A"## [1] "A"arreglo = rnorm(100)
arreglo## [1] 0.758567763 0.970237460 -1.554872639 0.915579974 -2.304084261
## [6] -0.298948685 0.581336928 -0.501864292 -0.098407593 0.343662740
## [11] -0.128744379 0.470450932 0.826334853 -0.921381182 0.007928847
## [16] -0.801549258 -2.020405467 0.261386124 1.294014620 0.506199358
## [21] -0.358993993 0.616114256 -0.173604231 1.118117694 -0.027557239
## [26] -0.109109270 0.570346934 0.497445815 -2.028914369 -0.219271670
## [31] 0.378626113 0.355202548 -0.421972711 1.548915324 1.444969748
## [36] -0.511468109 -0.737270238 -1.732185698 0.566258769 -1.770082901
## [41] -0.224538569 -0.714611309 2.571756246 -1.266709431 0.549420870
## [46] -0.975378091 -0.710920616 1.807723921 0.158128764 0.880739969
## [51] 0.777962348 -1.161412391 0.520697383 -0.619949094 -1.533272352
## [56] -1.035107516 -0.155033311 -0.860194790 -0.374062393 0.062736461
## [61] 0.683896451 -0.084686746 1.132835732 -0.752352465 0.712006673
## [66] 0.023087241 0.047337016 0.316358795 -0.990073782 -1.122308246
## [71] 0.083171346 -0.583365738 -0.387704299 -0.903534424 1.015860354
## [76] 0.800818627 -0.183176402 0.289930898 -0.577210091 0.195073620
## [81] -0.535613937 -1.999151298 0.655478284 0.732089120 1.325979214
## [86] -0.013506015 0.591535200 -1.250082571 -0.570003642 1.248133950
## [91] 0.716426396 0.455689787 0.532200604 0.352238695 -1.121897606
## [96] 1.469631416 0.448391558 0.251758878 1.642412333 0.454895402hist(arreglo)
"B"## [1] "B""Se observa que los valores que arroja la función rnorm son diferentes cada vez que se ejecuta, por lo tanto, la grafica y resumen de datos son diferentes de igual manera"## [1] "Se observa que los valores que arroja la función rnorm son diferentes cada vez que se ejecuta, por lo tanto, la grafica y resumen de datos son diferentes de igual manera""C"## [1] "C"summary(arreglo)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.304084 -0.592512 0.055037 0.001116 0.597680 2.571756Actividad 2.6
Act_6 = rbinom(30,5,0.9)
Act_6## [1] 4 5 5 5 5 5 4 5 4 5 5 3 5 3 5 4 4 3 4 4 2 4 4 5 5 5 3 5 5 4"A"## [1] "A"barplot(Act_6)
"B"## [1] "B"head(Act_6)## [1] 4 5 5 5 5 5summary(Act_6)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 4.0 4.5 4.3 5.0 5.0Actividad 2.7
Num_fallos =c(0,1,0,NA,0,0,0,0,0,1,1,1,0,0,3,0,0,0,0,0,2,0,1)
"A"## [1] "A"barplot(Num_fallos)
"el diagrama de cajas no nos proporciona la informacion de forma precisa, habría que suministrar otros datos. Es mas adecuado un diagrama de barras"## [1] "el diagrama de cajas no nos proporciona la informacion de forma precisa, habría que suministrar otros datos. Es mas adecuado un diagrama de barras""B"## [1] "B"Num_med_errores = mean(Num_fallos,na.rm = TRUE)
Num_med_errores## [1] 0.4545455Actividad 2.8
"A"## [1] "A"Estudiantes = c(1,2,3,4,5,6,7,8,9,10)
Estudiantes## [1] 1 2 3 4 5 6 7 8 9 10P1 = c(3,3,3,4,3,4,3,4,4,3)
P1## [1] 3 3 3 4 3 4 3 4 4 3P2 = c(5,5,2,2,5,2,2,5,5,2)
P2## [1] 5 5 2 2 5 2 2 5 5 2P3 = c(1,3,1,3,3,3,1,3,1,1)
"B"## [1] "B"tabla = c(P1,P2,P3)
matriz = matrix(tabla,3,10,byrow = TRUE)
rownames(matriz)= c("P1","P2","P3")
colnames(matriz)=paste("Est",1:10)
matriz## Est 1 Est 2 Est 3 Est 4 Est 5 Est 6 Est 7 Est 8 Est 9 Est 10
## P1 3 3 3 4 3 4 3 4 4 3
## P2 5 5 2 2 5 2 2 5 5 2
## P3 1 3 1 3 3 3 1 3 1 1Estudiantes=data.entry(matriz,Names = Estudiantes)
Estudiantes## [1] "matriz""C"## [1] "C"matriz[2,]## 1 2 3 4 5 6 7 8 9 10
## 5 5 2 2 5 2 2 5 5 2matriz[3,]## 1 2 3 4 5 6 7 8 9 10
## 1 3 1 3 3 3 1 3 1 1"D"## [1] "D"Datos_2 = c(P2,P3)
Datos_3 = matrix(Datos_2,2,10,byrow = TRUE)
Datos_3## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 5 5 2 2 5 2 2 5 5 2
## [2,] 1 3 1 3 3 3 1 3 1 1barplot(Datos_3,beside=F,xlab = "Estudiantes")
"C"## [1] "C"barplot(matriz,beside = T,xlab = "Estudiantes")
Actividad 2.9
VEC_1= rnorm(50)
VEC_1## [1] -0.347245192 0.662616790 1.481524495 1.506145631 1.270172090
## [6] 0.704499120 -0.514572430 -0.818626543 -0.502027867 -1.262105288
## [11] -0.730995685 -0.472622274 1.648046363 -1.318294010 -0.392958418
## [16] -1.097597080 0.503324375 1.241896063 0.598202738 -1.596422551
## [21] 0.927001180 0.417191055 -0.629422385 -0.057872138 -0.301736294
## [26] 0.838273754 0.299370916 0.240879453 0.775890459 0.187276838
## [31] -0.126095363 0.242901865 0.250886501 0.526232095 0.261035402
## [36] -0.001049428 0.123640700 1.192964103 -0.090073811 -0.889549691
## [41] -0.496040147 1.229005009 1.444616606 -0.265857679 0.353085354
## [46] 0.284343407 -0.731706001 -0.693649183 0.155718499 1.920311395VEC_2 = rnorm(50)
VEC_2## [1] 0.722571006 1.430384129 -0.282245780 -1.209216200 1.274945941
## [6] 0.883275307 1.642667301 1.092660200 1.329698366 0.774136571
## [11] 0.388473130 -0.758900369 -0.490144911 0.209933232 0.525619465
## [16] -0.011253807 -0.680268548 1.098904737 0.028134090 -0.367071314
## [21] -0.007675149 -1.150322447 -1.230913239 -0.519658725 1.547687900
## [26] 0.018872129 1.417112795 -0.894794694 -1.467448825 -0.362061670
## [31] 0.107984443 2.125335901 -0.203560530 1.089361446 -0.649451685
## [36] 0.903121403 -0.238822256 -0.472596363 -0.218036010 0.876312687
## [41] 0.602804828 -0.599747998 -0.955964979 0.235519398 0.340314815
## [46] 0.363291482 -0.958545395 -0.624678638 1.928447336 -0.446758466"A"## [1] "A"shapiro.test(VEC_1)##
## Shapiro-Wilk normality test
##
## data: VEC_1
## W = 0.9855, p-value = 0.7927shapiro.test(VEC_2)##
## Shapiro-Wilk normality test
##
## data: VEC_2
## W = 0.97276, p-value = 0.2987"B"## [1] "B"TEST = t.test(VEC_1,VEC_2)
TEST##
## Welch Two Sample t-test
##
## data: VEC_1 and VEC_2
## t = -0.023667, df = 97.397, p-value = 0.9812
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3511303 0.3428544
## sample estimates:
## mean of x mean of y
## 0.1590107 0.1631486Actividad 2.10
VEC_3 = rnorm(50)
VEC_3## [1] 0.58667605 -0.71808428 -1.33029935 -0.70260832 -0.56658162 0.42617821
## [7] -0.15022596 -0.26867749 1.17811527 0.77125353 0.61093253 0.83207171
## [13] 0.07030169 1.02730674 1.32202827 -0.14232699 1.36849427 0.44446842
## [19] 1.04411607 -1.74610773 0.77256657 -0.51639450 0.25120732 -0.71985032
## [25] -0.67504599 0.30703642 -0.02416357 -0.30644442 0.26502998 1.01133152
## [31] -0.22406580 -0.39647257 -0.37557126 0.17025622 0.42009694 1.05024639
## [37] 1.27680081 -0.95152699 -0.05434760 1.08878850 0.54703702 -2.69031196
## [43] -0.75181438 0.62594090 -0.29472433 -0.53657741 2.05503019 1.45879789
## [49] -0.25536684 -0.65284979VEC_4 = rbinom(50,3,0.9)
VEC_4## [1] 2 3 3 2 3 3 2 2 2 3 2 3 3 3 2 3 3 3 3 3 2 2 2 3 3 2 3 3 3 3 2 3 3 3 3 3 3 2
## [39] 3 3 3 3 1 3 3 2 3 3 3 3"A"## [1] "A"shapiro.test(VEC_3)##
## Shapiro-Wilk normality test
##
## data: VEC_3
## W = 0.97355, p-value = 0.3209shapiro.test(VEC_4)##
## Shapiro-Wilk normality test
##
## data: VEC_4
## W = 0.61292, p-value = 3.044e-10"B"## [1] "B"TEST_2 = t.test(VEC_3,VEC_4)
TEST_2##
## Welch Two Sample t-test
##
## data: VEC_3 and VEC_4
## t = -17.538, df = 77.957, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.852126 -2.270607
## sample estimates:
## mean of x mean of y
## 0.1186334 2.6800000