practica
Geneidys de la hoz
2026-08-13
library(lsm) # Para descargar una base de datos
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(moments) # Para hallar las medidas de forma
library(e1071)##
## Adjuntando el paquete: 'e1071'## The following objects are masked from 'package:moments':
##
## kurtosis, moment, skewnesslibrary(ggplot2)##
## Adjuntando el paquete: 'ggplot2'## The following object is masked from 'package:e1071':
##
## elementdatosCompleto <- lsm::surveyMuestra <- datosCompleto[1:100,]
Sexo <- as.factor(Muestra$Gender)
Fuma <- as.factor(Muestra$Smoke) Tablas
ggplot(Muestra, aes(x = Sexo)) + #1
#geom_bar() + #2
geom_bar(width=0.5, colour="blue", fill="purple") + #2
labs(x="Sexo",y= "Frecuencia") + #3
ylim(c(0,60)) + #4
#xlim(c(0,300)) + #4
ggtitle("Diagrama de barras") + #5
# theme_bw() + #6
theme_bw(base_size = 12) + #6
#coord_flip() + #7
geom_text(aes(label=..count..), stat='count', #8
position=position_dodge(0.9),
vjust=-0.5,
size=5.0
) +
facet_wrap(~"Variable Sexo") #9
En la gráfica podemos observar que la cantidad de hombres y mujeres es muy similar, porque se trata de 51 hombres (51%) y 49 mujeres (49%). Los hombres por lo tanto, constituyen una proporción ligeramente mayor de la muestra con una diferencia de 2 personas.
Tabla doble.
ggplot(Muestra, aes(Fuma, fill=Sexo)) +
geom_bar(position="dodge",colour="green") +
labs(x= "Fuma", y="Frecuencias", fill="Sexo") +
ylim(c(0,30)) +
#xlim(c(0,300)) +
ggtitle("Diagrama de barras") +
#theme_bw() +
theme_bw(base_size = 12) +
#coord_flip() +
#guides(fill=FALSE)+ #8
scale_fill_manual(values = c("purple","pink")) + #9
geom_text(aes(label=..count..), stat='count', #10
position=position_dodge(0.9),
vjust=-0.5,
size=5.0
)+
facet_wrap(~"Sexo por fumadores y no fumadores") #11
El gráfico nos indica que de las 100 personas estudiadas, 55 son fumadoras y 45 no lo son, de los fumadores, 28 son mujeres y 27 son hombres, de los no fumadores, 21 son mujeres y 24 son hombres. Se observa que el número de mujeres fumadoras es ligeramente superior al de hombres fumadores.
Tabla doble intercambio
ggplot(Muestra, aes(Sexo, fill=Fuma)) +
geom_bar(position="dodge",colour="black") +
labs(x= "Sexo", y="Frecuencias", fill="Fuma") +
ylim(c(0,30)) +
#xlim(c(0,300)) +
ggtitle("Diagrama de barras") +
#theme_bw() +
theme_bw(base_size = 12) +
#coord_flip() +
#guides(fill=FALSE)+ #8
scale_fill_manual(values = c("blue","yellow")) + #9
geom_text(aes(label=..count..), stat='count', #10
position=position_dodge(0.9),
vjust=-0.5,
size=5.0
)+
facet_wrap(~"Fuma por genero") #11
Las gráficas nos muestran que 55 personas son fumadoras y 45 no lo son de ellos, 28 son mujeres fumadoras y 27 son hombres fumadores; mientras que 21 son mujeres no fumadoras y 24 son hombres no fumadores, los datos no cambiaron, sólo la forma de presentar la información, se modificaron las variables del eje y los colores.
Medidas estadisticas
str(Muestra)## tibble [100 × 66] (S3: tbl_df/tbl/data.frame)
## $ Observation : num [1:100] 1 2 3 4 5 6 7 8 9 10 ...
## $ ID : chr [1:100] "SB11201910010435" "SB11201910004475" "SB11201910011427" "SB11201910041975" ...
## $ Gender : chr [1:100] "Female" "Male" "Male" "Male" ...
## $ Like : chr [1:100] "TV" "Network" "Network" "TV" ...
## $ Age : num [1:100] 21.4 21.1 20.9 18.4 16.6 ...
## $ Smoke : chr [1:100] "No" "Yes" "Yes" "Yes" ...
## $ Height : num [1:100] 1.58 1.6 1.5 1.53 1.78 1.65 1.73 1.53 1.64 1.52 ...
## $ Weight : num [1:100] 75 80 64 49 82 80 90 55 50 78 ...
## $ BMI : num [1:100] 30 31.2 28.4 20.9 25.9 ...
## $ School : chr [1:100] "Private" "Public" "Private" "Public" ...
## $ SES : chr [1:100] "Medium" "High" "High" "Low" ...
## $ Enrollment : chr [1:100] "Credit" "Scholarship" "Scholarship" "Credit" ...
## $ Score : num [1:100] 81 78 77 70 68 65 54 50 36 35 ...
## $ MotherHeight: chr [1:100] "Short_M" "Normal_M" "Normal_M" "Tall_M" ...
## $ MotherAge : num [1:100] 41 45 45 45 46 46 47 48 48 48 ...
## $ MotherCHD : num [1:100] 0 0 0 0 1 0 0 0 0 1 ...
## $ FatherHeight: chr [1:100] "Normal_F" "Short_F" "Tall_F" "Short_F" ...
## $ FatherAge : num [1:100] 40 43 44 45 45 46 46 48 48 49 ...
## $ FatherCHD : num [1:100] 1 1 1 2 1 1 1 1 1 1 ...
## $ Status : chr [1:100] "Distinguished" "Distinguished" "Distinguished" "Regular" ...
## $ SemAcum : num [1:100] 4.25 2.8 4.15 3.2 3.45 2.75 2.7 4.35 4.3 2.8 ...
## $ Exam1 : num [1:100] 1.5 2.3 3.4 2.5 3.1 3.8 5 4 2.5 2.4 ...
## $ Exam2 : num [1:100] 5 4.9 3.6 4.2 3.5 4.4 3 2.3 3.3 2.6 ...
## $ Exam3 : num [1:100] 5 3.7 2 5 5 4.2 3.5 4.6 3.8 4.3 ...
## $ Exam4 : num [1:100] 4.5 3.3 1.9 2.5 3 5 3.6 4.3 1.9 5 ...
## $ ExamAcum : num [1:100] 16 14.2 10.9 14.2 14.6 17.4 15.1 15.2 11.5 14.3 ...
## $ Definitive : num [1:100] 4 3.55 2.73 3.55 3.65 ...
## $ Expense : num [1:100] 48.9 72.1 85.2 56.6 64.6 63 40.8 65.4 37.3 63 ...
## $ Income : num [1:100] 1.61 2.07 2.84 1.55 2.32 2.1 1.69 2.18 1.71 2.1 ...
## $ Gas : num [1:100] 27.4 24.2 22.3 23.1 27.3 ...
## $ Course : chr [1:100] "Face-to-Face" "Virtual" "Face-to-Face" "Virtual" ...
## $ Law : chr [1:100] "Agree" "Agree" "Agree" "Agree" ...
## $ Economic : chr [1:100] "Regular" "Good" "Regular" "Bad" ...
## $ Race : chr [1:100] "Ethnic" "Ethnic" "Ethnic" "Ethnic" ...
## $ Region : chr [1:100] "North" "Center" "North" "Center" ...
## $ EMO1 : num [1:100] 1 4 3 4 2 3 2 3 4 2 ...
## $ EMO2 : num [1:100] 2 4 1 2 1 1 4 1 2 2 ...
## $ EMO3 : num [1:100] 2 1 3 3 2 4 2 4 3 3 ...
## $ EMO4 : num [1:100] 1 2 3 1 4 2 3 2 1 1 ...
## $ EMO5 : num [1:100] 4 1 2 2 2 2 1 1 2 2 ...
## $ GOAL1 : chr [1:100] "Strongly agree" "Undecided" "Agree" "Agree" ...
## $ GOAL2 : chr [1:100] "Agree" "Disagree" "Disagree" "Undecided" ...
## $ GOAL3 : chr [1:100] "Strongly agree" "Disagree" "Agree" "Strongly agree" ...
## $ Pre_STAT1 : num [1:100] 2 1 5 4 1 4 4 2 2 2 ...
## $ Pre_STAT2 : num [1:100] 4 1 1 3 4 1 2 3 3 5 ...
## $ Pre_STAT3 : num [1:100] 2 1 3 1 1 5 4 3 3 2 ...
## $ Pre_STAT4 : num [1:100] 5 1 1 2 2 3 2 3 2 4 ...
## $ Post_STAT1 : num [1:100] 4 5 5 3 5 2 3 3 2 5 ...
## $ Post_STAT2 : num [1:100] 5 1 2 2 3 3 2 3 2 3 ...
## $ Post_STAT3 : num [1:100] 2 3 3 4 3 5 5 4 5 4 ...
## $ Post_STAT4 : num [1:100] 2 3 3 5 4 4 3 5 5 1 ...
## $ Pre_IDARE1 : chr [1:100] "Quite a bit" "Quite a bit" "Quite a bit" "Little" ...
## $ Pre_IDARE2 : chr [1:100] "Little" "Little" "Little" "Nothing" ...
## $ Pre_IDARE3 : chr [1:100] "Quite a bit" "A lot" "Quite a bit" "Quite a bit" ...
## $ Pre_IDARE4 : chr [1:100] "Quite a bit" "Nothing" "Quite a bit" "Quite a bit" ...
## $ Pre_IDARE5 : chr [1:100] "Little" "Quite a bit" "Little" "Nothing" ...
## $ Post_IDARE1 : chr [1:100] "A lot" "A little" "Nothing" "Quite a bit" ...
## $ Post_IDARE2 : chr [1:100] "A lot" "Nothing" "Quite a bit" "A little" ...
## $ Post_IDARE3 : chr [1:100] "A little" "Quite a bit" "Nothing" "A lot" ...
## $ Post_IDARE4 : chr [1:100] "Quite a bit" "A lot" "Nothing" "Quite a bit" ...
## $ Post_IDARE5 : chr [1:100] "A lot" "Quite a bit" "Nothing" "A lot" ...
## $ PSICO1 : chr [1:100] "Frequently" "Frequently" "Sometimes" "Almost always" ...
## $ PSICO2 : chr [1:100] "Almost always" "Sometimes" "Sometimes" "Frequently" ...
## $ PSICO3 : chr [1:100] "Frequently" "Sometimes" "Sometimes" "Frequently" ...
## $ PSICO4 : chr [1:100] "Almost always" "Frequently" "Frequently" "Almost never" ...
## $ PSICO5 : chr [1:100] "Almost always" "Frequently" "Sometimes" "Sometimes" ...Muestra2 <- datosCompleto[1:100,]
x <- as.numeric(Muestra2$Exam3) # A) Convirtiendo la variable a numérica
x## [1] 5.0 3.7 2.0 5.0 5.0 4.2 3.5 4.6 3.8 4.3 3.0 3.8 3.4 3.3 3.5 4.5 3.6 4.0
## [19] 3.4 4.0 4.2 3.5 3.7 4.0 4.0 3.2 2.9 2.9 3.0 3.3 2.8 2.4 3.8 3.3 3.2 2.2
## [37] 2.6 3.2 3.3 1.2 4.2 2.4 5.0 2.8 3.0 3.8 3.2 1.5 2.6 3.8 3.2 3.3 1.4 3.8
## [55] 1.4 3.6 3.6 2.4 2.8 3.1 2.4 1.8 1.6 3.3 4.4 1.0 4.5 2.0 4.2 4.2 3.1 2.3
## [73] 2.6 2.7 2.4 2.2 2.8 2.4 1.9 2.4 1.7 2.9 2.4 2.2 2.8 3.2 3.1 2.7 2.5 3.5
## [91] 3.3 2.1 3.3 2.1 3.7 5.0 3.7 2.0 5.0 5.0min(x) #B) Mínimo## [1] 1max(x) #C) Máximo## [1] 5range(x) #D) Obtenemos (min, max)## [1] 1 5length(x) #E) Tamaño## [1] 100sum(x) #F) Suma los valores de los datos## [1] 317.6mean(x) #G) Media aritmética## [1] 3.176median(x) #H) Mediana## [1] 3.2var(x) #I) Varianza muestral## [1] 0.8885091sqrt(var(x)) #J) Desviación estándar muestral (una forma)## [1] 0.9426076sd(x) #K) Desviación estándar muestral (otra forma)## [1] 0.9426076skewness(x) #L) Sesgo## [1] 0.01846742quantile(x, probs=0.80) #M) 80-ésimo percentil o percentil 85## 80%
## 4quantile(x, probs=0.25) #N) Primer cuartil o 25-ésimo percentil## 25%
## 2.4quantile(x, probs=0.50) #O) Segundo cuartil o 50-ésimo percentil o mediana## 50%
## 3.2quantile(x, probs=0.75) #P) Tercer cuartil o 75-ésimo percentil## 75%
## 3.8quantile(x, probs=0.85) #M) 80-ésimo percentil o percentil 85## 85%
## 4.2