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1 MI CURSO DE ESTADÍSTICA Y PROBABILIDAD - 2026

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2 SEMANA 7. Mi página Web en Estadistica

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3 Clase N° 28 LABORATORIO 7 - ANALISIS DE DATOS EN R

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##(i)Datos

library(readxl)
DATOS2026 <- read_excel("00. DATOS202460ULTIMOS25.xlsx")
DATOS2026

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#####<-========== PASO 4. =========->

3.1 (2i)Usando la aplicación para hacer la tabla

table_sexo<-table(DATOS2026$SEXO)
table_sexo

## 
##  Femenino Masculino 
##        42        32

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#####<-========== PASO 5.=========->

3.2 (3i)Gráfico de torta para SEXO

pie_1<-pie(table_sexo, col=c("lightblue","pink"),
        main="Estudio de Pastel.\n Distribución por sexos-kalollero torres.", labels = table_sexo)

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3.3 (4i)Construimos el diagrama de barras y el diagrama de Pastel para esta variable cualitativa

barp<-barplot(table_sexo, col = rainbow(5), border = "darkred",main = "Gráfico de Barras-kalollero torres",sub = "UTB",xlab = "SEXO", ylab = "Conteo")
text(barp, table_sexo-30, labels = table_sexo)

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#####<-========== PASO 7. =========->

3.4 (5i)Usando la aplicación para hacer la tabla porcentual redondeando al entero mas cercano

table_sexo2<-round(table(DATOS2026$SEXO)/74*100)
table_sexo2

## 
##  Femenino Masculino 
##        57        43

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#####<-========== PASO 8. =========->

3.5 (6i)Construimos el diagrama de barras % y da pastel para esta variable cualitativa

barp2<-barplot(table_sexo2, col = rainbow(5), border = "darkred",main = "Gráfico de Barras-kalollero torres",sub = "UTB",xlab = "SEXO", ylab = "Porcentaje")
text(barp2, table_sexo2-30, labels = table_sexo2)

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#####<-========== PASO 9. =========->

3.5.1 (7i)Las tablas de frecuencias y las representaciones gráficas son dos maneras equivalentes de presentar la información. Las dos exponen ordenadamente la información recogida en una muestra

pie_1<-pie(table_sexo2, col=c("lightblue","pink"),
        main="Estudio de Pastel.\n Distribución por sexos.", labels = table_sexo2)

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#####<-========== PASO 10. =========->

3.5.2 (8i)Usando la aplicación para hacer la tabla con dos varibles SEXO y CURSO

table_3<-table(DATOS2026$SEXO, DATOS2026$CURSO)
table_3

##            
##             ESTADISTICAI PROBABILIDAD
##   Femenino            16           26
##   Masculino           10           22

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#####<-========== PASO 11. =========->

3.5.3 (9i)Usando la aplicación para hacer el gráfico con dos varibles SEXO y CURSO

barp3<-barplot(table_3,
        main = "Gráfico de barras CURSO vs SEXO",
        xlab = "CURSO", ylab = "Frecuencia",
        col = c("pink", "blue"),
        legend.text = rownames(table_3),
        beside = TRUE) # Barras agrupadas
text(barp3, table_3-5, labels = table_3)

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#####<-========== PASO 12. =========->

3.5.4 (10i)Usando la aplicación para hacer la tabla con dos varibles SEXO y CURSO pero usando las frecuencias relativas aproximadas

table_4<-round(table(DATOS2026$SEXO, DATOS2026$CURSO)/74*100)
table_4

##            
##             ESTADISTICAI PROBABILIDAD
##   Femenino            22           35
##   Masculino           14           30

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#####<-========== PASO 13. =========->

3.5.5 (11i)Usando la aplicación para hacer el gráfico con dos varibles SEXO y CURSO pero usando las frecuencias relativas aproximadas

barp4<-barplot(table_4,
        main = "Gráfico de barras CURSO vs SEXO en porcentajes",
        xlab = "CURSO", ylab = "Frecuencia",
        col = c("pink", "blue"),
        legend.text = rownames(table_4),
        beside = TRUE) # Barras agrupadas
text(barp4, table_4-5, labels = table_4)

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#####<-========== PASO 14. =========->

3.5.6 (12i)Usando la aplicación para hacer la tabla con dos varibles SEXO y CURSO

table_5<-table(DATOS2026$ESTRATO, DATOS2026$CURSO)
table_5

##      
##       ESTADISTICAI PROBABILIDAD
##   I              5           10
##   II             7           18
##   III            9            9
##   IV             5            5
##   V              0            5

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#####<-========== PASO 15. =========->

3.5.7 (13i)Usando la aplicación para hacer el gráfico con dos varibles SEXO y CURSO

barp3<-barplot(table_5,
        main = "Gráfico de barras CURSO vs ESTRATO",
        xlab = "CURSO", ylab = "Frecuencia",
        col = rainbow(5),
        legend.text = rownames(table_5),
        beside = TRUE) # Barras agrupadas
text(barp3, table_5-1, labels = table_3)

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#####<-========== PASO 16. =========->

3.5.8 (14i)Usando la aplicación para hacer la tabla con dos varibles SEXO y CURSO

table_6<-table(DATOS2026$ESTRATO, DATOS2026$SEXO)
table_6

##      
##       Femenino Masculino
##   I          8         7
##   II        17         8
##   III       10         8
##   IV         4         6
##   V          3         2

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#####<-========== PASO 17. =========->

3.5.9 (15i)Usando la aplicación para hacer el gráfico con dos varibles SEXO y CURSO

barp3<-barplot(table_6,
        main = "Gráfico de barras CURSO vs ESTRATO",
        xlab = "CURSO", ylab = "Frecuencia",
        col = rainbow(5),
        legend.text = rownames(table_6),
        beside = TRUE) # Barras agrupadas
text(barp3, table_6-1, labels = table_6)

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#####<-========== PASO 18. =========->

3.5.10 (16i)Tabla de Frecuencias Usando el paquete summarytools: observamos ya una tabla mas completa

library(summarytools)

## Warning in fun(libname, pkgname): couldn't connect to display ":0"

## system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)

tabla_8 <- freq(DATOS2026$EDAD)
tabla_8

## Frequencies  
## DATOS2026$EDAD  
## Type: Numeric  
## 
##               Freq   % Valid   % Valid Cum.   % Total   % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
##          17      7      9.46           9.46      9.46           9.46
##          18     32     43.24          52.70     43.24          52.70
##          19     21     28.38          81.08     28.38          81.08
##          20      7      9.46          90.54      9.46          90.54
##          21      4      5.41          95.95      5.41          95.95
##          22      3      4.05         100.00      4.05         100.00
##        <NA>      0                               0.00         100.00
##       Total     74    100.00         100.00    100.00         100.00

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#####<-========== PASO 19. =========->

3.5.11 (17i)Tabla de Frecuencias Usando el paquete summarytools: observamos ya una tabla mas completa

#install.packages("summarytools")

library(summarytools)
summary(DATOS2026$EDAD)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    17.0    18.0    18.0    18.7    19.0    22.0

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#####<-========== PASO 20. =========->

3.5.12 (18i)Una primera vista del diagrama de caja para la variable Edad

boxplot(DATOS2026$EDAD, horizontal = TRUE, col = rainbow(3))

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#####<-========== PASO 21. =========->

3.5.13 (19i)Identificamos donde queda la mediana

x = DATOS2026$EDAD
boxplot(x, notch = TRUE, horizontal = TRUE, col = rainbow(3))

## Warning in (function (z, notch = FALSE, width = NULL, varwidth = FALSE, : some
## notches went outside hinges ('box'): maybe set notch=FALSE

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#####<-========== PASO 22. =========->

3.5.14 (20i)EDAD vs SEXO

x = DATOS2026$EDAD
y = DATOS2026$SEXO
boxplot(x~y, horizontal = TRUE, col = rainbow(3))

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#####<-========== PASO 23. =========->

3.5.15 (21i)EDAD vs ESTRATO

#install.packages("ggplot2")

library(ggplot2)
x = DATOS2026$EDAD
z = DATOS2026$ESTRATO
boxplot(x~z, horizontal = TRUE, col = rainbow(3))

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####<-========== PASO 24. =========->

3.5.16 (22i)EDAD vs ESTRATO vs SEXO

library(ggplot2)
ggplot(data= DATOS2026,mapping= aes(y=EDAD,x = ESTRATO, fill=SEXO))+geom_boxplot()+
  scale_y_continuous(name = "EDAD") +
  scale_x_discrete(labels = abbreviate, name = "ESTRATO")

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####<-========== PASO 25. =========->

3.5.17 (23i)Estudiemos la variable ESTATURA y obtengamos sus seis medidas representativas

summary(DATOS2026$ESTATURA)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   153.0   163.0   168.0   168.4   174.0   192.0

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####<-========== PASO 26. =========->

3.5.17.1 (24i)Una primera vista del diagrama de caja para la variable ESTATURA

boxplot(DATOS2026$ESTATURA, horizontal = TRUE, col = rainbow(3))

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####<-========== PASO 27. =========->

3.5.17.2 (25i)Identificamos donde queda la mediana

x = DATOS2026$ESTATURA
boxplot(x, notch = TRUE, horizontal = TRUE, col = rainbow(3))

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####<-========== PASO 28. =========->

3.5.17.3 (26i)EDAD vs SEXO

x = DATOS2026$ESTATURA
y = DATOS2026$SEXO
boxplot(x~y, horizontal = TRUE, col = rainbow(3))

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####<-========== PASO 29. =========->

3.5.17.4 (27)EDAD vs ESTRATO

library(ggplot2)
x = DATOS2026$ESTATURA
z = DATOS2026$ESTRATO
boxplot(x~z, horizontal = TRUE, col = rainbow(3))

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####<-========== PASO 30. =========->

3.5.18 (28i)ESTATURA vs ESTRATO vs SEXO

library(ggplot2)
ggplot(data= DATOS2026,mapping= aes(y=ESTATURA,x = ESTRATO, fill=SEXO))+geom_boxplot()+
  scale_y_continuous(name = "ESTATURA") +
  scale_x_discrete(labels = abbreviate, name = "ESTRATO")

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####<-========== PASO 31. =========->

3.5.19 (29i)Histograma y tabla de frecuencias usando Regla de Sturges

3.5.19.0.1 Usando la libreria “agricolae”

#install.packages("agricolae")

library(agricolae)
h2<-graph.freq(DATOS2026$EDAD, col=colors()[75]) #[86]

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####<-========== PASO 32. =========->

3.5.20 (30i)Tabla de fecuencias agrupadas Regla de Sturges

3.5.20.0.1 Usando la libreria “agricolae”

summary(h2)

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####<-========== PASO 33. =========->

3.5.20.1 (31i)Polígono de frecuencia absolutas

3.5.20.1.0.1 frequency : counts (1) and relative (2)

plot(h2,  col=colors()[70], frequency = 1)
polygon.freq(h2, col = "red", frequency = 1, lwd = 2)

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####<-========== PASO 34. =========->

3.5.21 (32i)Polígono de frecuencia relativas

3.5.21.0.0.1 frequency : counts (1) and relative (2)

plot(h2,  col=colors()[70], frequency = 2)
polygon.freq(h2, col = "red", frequency = 2, lwd = 2)

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####<-========== PASO 35. =========->

3.5.22 (33i)Ojivas - usando R

fr_por_clase2<-h2$counts
fr_por_clase2

## [1]  7 32 21  0  7  4  0  3

total_n2<-sum(h2$counts)
total_n2

## [1] 74

fr_relativos2<-fr_por_clase2/total_n2
fr_porcentuales2<-100*fr_relativos2
fr_porcentuales2

## [1]  9.459459 43.243243 28.378378  0.000000  9.459459  5.405405  0.000000
## [8]  4.054054

cumsum(fr_por_clase2)

## [1]  7 39 60 60 67 71 71 74

cumsum(fr_relativos2)

## [1] 0.09459459 0.52702703 0.81081081 0.81081081 0.90540541 0.95945946 0.95945946
## [8] 1.00000000

cumsum(fr_porcentuales2)

## [1]   9.459459  52.702703  81.081081  81.081081  90.540541  95.945946  95.945946
## [8] 100.000000

🌈🧪🌟💻✨🧠📈🔬⚗️📚💻🌈🧪🌟💻✨🧠📈🌈🧪🌟💻✨🧠📈🔬⚗️📚💻🌈🧪🌟💻✨🧠📈

####<-========== PASO 36. =========->

3.6 (34i)Ojivas - frecuencias porcentuales

p4<-cumsum(fr_porcentuales2)
plot(p4, col = "red")
lines(p4, col = "red")

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####<-========== AQUI FINALIZA EL LABORATORIO 7. =========-> 🌈🧪🌟💻✨🧠📈🔬⚗️📚💻🌈🧪🌟💻✨🧠📈🌈🧪🌟💻✨🧠📈🔬⚗️📚💻🌈🧪🌟💻✨🧠📈