Parcial 1 PyE
Sofía Moreno
2025-05-22
Lectura de datos
Como paso inicial realizamos la lectura de los datos a tratar que en este caso son sobre como la adicción por las Redes Sociales de los jovenes afecta su rendimiento académico.
library(readxl)
Datos <- read_excel("Students Social Media Addiction.xlsx",
col_types = c("numeric", "text", "text",
"text", "numeric", "text", "text",
"numeric", "numeric", "text", "numeric",
"numeric"))
summary(Datos)## Age Gender Academic_Level Country
## Min. :18.00 Length:705 Length:705 Length:705
## 1st Qu.:19.00 Class :character Class :character Class :character
## Median :21.00 Mode :character Mode :character Mode :character
## Mean :20.66
## 3rd Qu.:22.00
## Max. :24.00
## Daily Usage Hours Most Used Platform Affects Academic Performance
## Min. :1.500 Length:705 Length:705
## 1st Qu.:4.100 Class :character Class :character
## Median :4.800 Mode :character Mode :character
## Mean :4.919
## 3rd Qu.:5.800
## Max. :8.500
## Sleep Hours Per Night Mental Health Score Relationship Status Conflicts
## Min. :3.800 Min. :4.000 Length:705 Min. :0.00
## 1st Qu.:6.000 1st Qu.:5.000 Class :character 1st Qu.:2.00
## Median :6.900 Median :6.000 Mode :character Median :3.00
## Mean :6.869 Mean :6.227 Mean :2.85
## 3rd Qu.:7.700 3rd Qu.:7.000 3rd Qu.:4.00
## Max. :9.600 Max. :9.000 Max. :5.00
## Addicted Score
## Min. :2.000
## 1st Qu.:5.000
## Median :7.000
## Mean :6.437
## 3rd Qu.:8.000
## Max. :9.000colnames(Datos)=c("Age","Gender","Academic_Level","Country","Daily Usage Hours","Most Used Platform","Affects Academic Performance","Sleep Hours Per Night","Mental Health Score","Conflicts Over Social Media","Addicted Score")
head(Datos) ## # A tibble: 6 × 12
## Age Gender Academic_Level Country `Daily Usage Hours` `Most Used Platform`
## <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 19 Female Undergraduate Banglade… 5.2 Instagram
## 2 22 Male Graduate India 2.1 Twitter
## 3 20 Female Undergraduate USA 6 TikTok
## 4 18 Male High School UK 3 YouTube
## 5 21 Male Graduate Canada 4.5 Facebook
## 6 19 Female Undergraduate Australia 7.2 Instagram
## # ℹ 6 more variables: `Affects Academic Performance` <chr>,
## # `Sleep Hours Per Night` <dbl>, `Mental Health Score` <dbl>,
## # `Conflicts Over Social Media` <chr>, `Addicted Score` <dbl>, `` <dbl>tail(Datos)## # A tibble: 6 × 12
## Age Gender Academic_Level Country `Daily Usage Hours` `Most Used Platform`
## <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 22 Male Graduate UK 6.2 Twitter
## 2 20 Female Undergraduate Italy 4.7 TikTok
## 3 23 Male Graduate Russia 6.8 Instagram
## 4 21 Female Undergraduate China 5.6 WeChat
## 5 24 Male Graduate Japan 4.3 Twitter
## 6 19 Female Undergraduate Poland 6.2 Facebook
## # ℹ 6 more variables: `Affects Academic Performance` <chr>,
## # `Sleep Hours Per Night` <dbl>, `Mental Health Score` <dbl>,
## # `Conflicts Over Social Media` <chr>, `Addicted Score` <dbl>, `` <dbl>str(Datos)## tibble [705 × 12] (S3: tbl_df/tbl/data.frame)
## $ Age : num [1:705] 19 22 20 18 21 19 23 20 18 21 ...
## $ Gender : chr [1:705] "Female" "Male" "Female" "Male" ...
## $ Academic_Level : chr [1:705] "Undergraduate" "Graduate" "Undergraduate" "High School" ...
## $ Country : chr [1:705] "Bangladesh" "India" "USA" "UK" ...
## $ Daily Usage Hours : num [1:705] 5.2 2.1 6 3 4.5 7.2 1.5 5.8 4 3.3 ...
## $ Most Used Platform : chr [1:705] "Instagram" "Twitter" "TikTok" "YouTube" ...
## $ Affects Academic Performance: chr [1:705] "Yes" "No" "Yes" "No" ...
## $ Sleep Hours Per Night : num [1:705] 6.5 7.5 5 7 6 4.5 8 6 6.5 7 ...
## $ Mental Health Score : num [1:705] 6 8 5 7 6 4 9 6 7 7 ...
## $ Conflicts Over Social Media : chr [1:705] "In Relationship" "Single" "Complicated" "Single" ...
## $ Addicted Score : num [1:705] 3 0 4 1 2 5 0 2 1 1 ...
## $ NA : num [1:705] 8 3 9 4 7 9 2 8 5 4 ...Análisis Estadístico Descriptivo Univariado
Edad
media_Edad=mean(Datos$Age)
varianza_Edad=var(Datos$Age)
sd_Edad=sd(Datos$Age)
#calcular cve
cve_media_Edad=100*sd_Edad/media_Edad
medianaEdad=median(Datos$Age)
MAD_Edad=mad(Datos$Age)
cve_mediana_Edad=100*MAD_Edad/medianaEdad
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Edad=c(mean(Datos$Age),var(Datos$Age),sd(Datos$Age),100*sd(Datos$Age)/mean(Datos$Age),median(Datos$Age),100*mad(Datos$Age),mad(Datos$Age)/median(Datos$Age)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Edad
## Promedio 20.659574
## Varianza 1.957809
## Desv_estandar 1.399217
## cve_media 6.772731
## Mediana 21.000000
## MAD 148.260000
## cve_mediana 0.070600summary(Datos$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 19.00 21.00 20.66 22.00 24.00min_Datos <- min(Datos$Age)
min_Datos## [1] 18max_Datos <- max(Datos$Age)
max_Datos## [1] 24range(Datos$Age)## [1] 18 24rango_Datos=max(Datos$Age)-min(Datos$Age)
cuartiles_Datos <- quantile(Datos$Age,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 18 19 21 22 24rango_intercuartil_Datos<- IQR(Datos$Age)
rango_intercuartil_Datos## [1] 3Medidas_Posic=data.frame("Medidas de posición Edad"=c(min(Datos$Age),max(Datos$Age),max(Datos$Age)-min(Datos$Age),quantile(Datos$Age,probs = seq(0,1,by=0.25)),IQR(Datos$Age)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Edad
## Mínimo 18
## Máximo 24
## Rango 6
## Cuartil0_min 18
## Cuartil_1 19
## Cuartil_2 21
## Cuartil_3 22
## Cuartil_4_max 24
## IQR 3Horas Diarias de uso
media_Horas=mean(Datos$`Daily Usage Hours`)
varianza_Horas=var(Datos$`Daily Usage Hours`)
sd_Horas=sd(Datos$`Daily Usage Hours`)
#calcular cve
cve_media_Horas=100*sd_Horas/media_Horas
medianaHoras=median(Datos$`Daily Usage Hours`)
MAD_Horas=mad(Datos$`Daily Usage Hours`)
cve_mediana_Horas=100*MAD_Horas/medianaHoras
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Horas=c(mean(Datos$`Daily Usage Hours`),var(Datos$`Daily Usage Hours`),sd(Datos$`Daily Usage Hours`),100*sd(Datos$`Daily Usage Hours`)/mean(Datos$`Daily Usage Hours`),median(Datos$`Daily Usage Hours`),100*mad(Datos$`Daily Usage Hours`),mad(Datos$`Daily Usage Hours`)/median(Datos$`Daily Usage Hours`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Horas
## Promedio 4.9187234
## Varianza 1.5810410
## Desv_estandar 1.2573945
## cve_media 25.5634322
## Mediana 4.8000000
## MAD 133.4340000
## cve_mediana 0.2779875summary(Datos$`Daily Usage Hours`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.500 4.100 4.800 4.919 5.800 8.500min_Datos <- min(Datos$`Daily Usage Hours`)
min_Datos## [1] 1.5max_Datos <- max(Datos$`Daily Usage Hours`)
max_Datos## [1] 8.5range(Datos$`Daily Usage Hours`)## [1] 1.5 8.5rango_Datos=max(Datos$`Daily Usage Hours`)-min(Datos$`Daily Usage Hours`)
cuartiles_Datos <- quantile(Datos$`Daily Usage Hours`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 1.5 4.1 4.8 5.8 8.5rango_intercuartil_Datos<- IQR(Datos$`Daily Usage Hours`)
rango_intercuartil_Datos## [1] 1.7Medidas_Posic=data.frame("Medidas de posición Horas"=c(min(Datos$`Daily Usage Hours`),max(Datos$`Daily Usage Hours`),max(Datos$`Daily Usage Hours`)-min(Datos$`Daily Usage Hours`),quantile(Datos$`Daily Usage Hours`,probs = seq(0,1,by=0.25)),IQR(Datos$`Daily Usage Hours`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Horas
## Mínimo 1.5
## Máximo 8.5
## Rango 7.0
## Cuartil0_min 1.5
## Cuartil_1 4.1
## Cuartil_2 4.8
## Cuartil_3 5.8
## Cuartil_4_max 8.5
## IQR 1.7Horas que duermen a diario
media_Sueño=mean(Datos$`Sleep Hours Per Night`)
varianza_Sueño=var(Datos$`Sleep Hours Per Night`)
sd_Sueño=sd(Datos$`Sleep Hours Per Night`)
#calcular cve
cve_media_Sueño=100*sd_Sueño/media_Sueño
mediana_Sueño=median(Datos$`Sleep Hours Per Night`)
MAD_Sueño=mad(Datos$`Sleep Hours Per Night`)
cve_mediana_Sueño=100*MAD_Sueño/mediana_Sueño
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Horas_de_Sueño=c(mean(Datos$`Sleep Hours Per Night`),var(Datos$`Sleep Hours Per Night`),sd(Datos$`Sleep Hours Per Night`),100*sd(Datos$`Sleep Hours Per Night`)/mean(Datos$`Sleep Hours Per Night`),median(Datos$`Sleep Hours Per Night`),100*mad(Datos$`Sleep Hours Per Night`),mad(Datos$`Sleep Hours Per Night`)/median(Datos$`Sleep Hours Per Night`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Horas_de_Sueño
## Promedio 6.8689362
## Varianza 1.2697865
## Desv_estandar 1.1268480
## cve_media 16.4049864
## Mediana 6.9000000
## MAD 133.4340000
## cve_mediana 0.1933826summary(Datos$`Sleep Hours Per Night`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.800 6.000 6.900 6.869 7.700 9.600min_Datos <- min(Datos$`Sleep Hours Per Night`)
min_Datos## [1] 3.8max_Datos <- max(Datos$`Sleep Hours Per Night`)
max_Datos## [1] 9.6range(Datos$`Sleep Hours Per Night`)## [1] 3.8 9.6rango_Datos=max(Datos$`Sleep Hours Per Night`)-min(Datos$`Sleep Hours Per Night`)
cuartiles_Datos <- quantile(Datos$`Sleep Hours Per Night`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 3.8 6.0 6.9 7.7 9.6rango_intercuartil_Datos<- IQR(Datos$`Sleep Hours Per Night`)
rango_intercuartil_Datos## [1] 1.7Medidas_Posic=data.frame("Medidas de posición Horas de Sueño"=c(min(Datos$`Sleep Hours Per Night`),max(Datos$`Sleep Hours Per Night`),max(Datos$`Sleep Hours Per Night`)-min(Datos$`Sleep Hours Per Night`),quantile(Datos$`Sleep Hours Per Night`,probs = seq(0,1,by=0.25)),IQR(Datos$`Sleep Hours Per Night`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Horas.de.Sueño
## Mínimo 3.8
## Máximo 9.6
## Rango 5.8
## Cuartil0_min 3.8
## Cuartil_1 6.0
## Cuartil_2 6.9
## Cuartil_3 7.7
## Cuartil_4_max 9.6
## IQR 1.7Escala de Salud Mental
media_Mental=mean(Datos$`Mental Health Score`)
varianza_Mental=var(Datos$`Mental Health Score`)
sd_Mental=sd(Datos$`Mental Health Score`)
#calcular cve
cve_media_Mental=100*sd_Mental/media_Mental
mediana_Mental=median(Datos$`Mental Health Score`)
MAD_Mental=mad(Datos$`Mental Health Score`)
cve_mediana_Mental=100*MAD_Mental/mediana_Mental
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Salud_Mental=c(mean(Datos$`Mental Health Score`),var(Datos$`Mental Health Score`),sd(Datos$`Mental Health Score`),100*sd(Datos$`Mental Health Score`)/mean(Datos$`Mental Health Score`),median(Datos$`Mental Health Score`),100*mad(Datos$`Mental Health Score`),mad(Datos$`Mental Health Score`)/median(Datos$`Mental Health Score`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Salud_Mental
## Promedio 6.226950
## Varianza 1.221148
## Desv_estandar 1.105055
## cve_media 17.746335
## Mediana 6.000000
## MAD 148.260000
## cve_mediana 0.247100summary(Datos$`Mental Health Score`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.000 5.000 6.000 6.227 7.000 9.000min_Datos <- min(Datos$`Mental Health Score`)
min_Datos## [1] 4max_Datos <- max(Datos$`Mental Health Score`)
max_Datos## [1] 9range(Datos$`Mental Health Score`)## [1] 4 9rango_Datos=max(Datos$`Mental Health Score`)-min(Datos$`Mental Health Score`)
cuartiles_Datos <- quantile(Datos$`Mental Health Score`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 4 5 6 7 9rango_intercuartil_Datos<- IQR(Datos$`Mental Health Score`)
rango_intercuartil_Datos## [1] 2Medidas_Posic=data.frame("Medidas de posición Salud Mental"=c(min(Datos$`Mental Health Score`),max(Datos$`Mental Health Score`),max(Datos$`Mental Health Score`)-min(Datos$`Mental Health Score`),quantile(Datos$`Mental Health Score`,probs = seq(0,1,by=0.25)),IQR(Datos$`Mental Health Score`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Salud.Mental
## Mínimo 4
## Máximo 9
## Rango 5
## Cuartil0_min 4
## Cuartil_1 5
## Cuartil_2 6
## Cuartil_3 7
## Cuartil_4_max 9
## IQR 2Escala de adicción por las Redes Sociales
media_Adicción=mean(Datos$`Addicted Score`)
varianza_Adicción=var(Datos$`Addicted Score`)
sd_Adicción=sd(Datos$`Addicted Score`)
#calcular cve
cve_media_Adicción=100*sd_Adicción/media_Adicción
mediana_Adicción=median(Datos$`Addicted Score`)
MAD_Adicción=mad(Datos$`Addicted Score`)
cve_mediana_Adicción=100*MAD_Adicción/mediana_Adicción
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Adicción_por_las_redes_sociales=c(mean(Datos$`Addicted Score`),var(Datos$`Addicted Score`),sd(Datos$`Addicted Score`),100*sd(Datos$`Addicted Score`)/mean(Datos$`Addicted Score`),median(Datos$`Addicted Score`),100*mad(Datos$`Addicted Score`),mad(Datos$`Addicted Score`)/median(Datos$`Addicted Score`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Adicción_por_las_redes_sociales
## Promedio 2.8496454
## Varianza 0.9177023
## Desv_estandar 0.9579678
## cve_media 33.6170877
## Mediana 3.0000000
## MAD 148.2600000
## cve_mediana 0.4942000summary(Datos$`Addicted Score`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 2.00 3.00 2.85 4.00 5.00min_Datos <- min(Datos$`Addicted Score`)
min_Datos## [1] 0max_Datos <- max(Datos$`Addicted Score`)
max_Datos## [1] 5range(Datos$`Addicted Score`)## [1] 0 5rango_Datos=max(Datos$`Addicted Score`)-min(Datos$`Addicted Score`)
cuartiles_Datos <- quantile(Datos$`Addicted Score`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 0 2 3 4 5rango_intercuartil_Datos<- IQR(Datos$`Addicted Score`)
rango_intercuartil_Datos## [1] 2Medidas_Posic=data.frame("Medidas de posición de Adicción por las redes sociales"=c(min(Datos$`Addicted Score`),max(Datos$`Addicted Score`),max(Datos$`Addicted Score`)-min(Datos$`Addicted Score`),quantile(Datos$`Addicted Score`,probs = seq(0,1,by=0.25)),IQR(Datos$`Addicted Score`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.de.Adicción.por.las.redes.sociales
## Mínimo 0
## Máximo 5
## Rango 5
## Cuartil0_min 0
## Cuartil_1 2
## Cuartil_2 3
## Cuartil_3 4
## Cuartil_4_max 5
## IQR 2Diagramas de Barras, circulares y Tablas de Contingencia
Género
library(plotrix)
pie3D(table(Datos$Gender), radius=0.9,main="Número de personas de cada género",labels=c("Hombres","Mujeres"),explode=0.1,col = hcl.colors(length(table(Datos$Gender)),"Zissou"), border = "black")
barplot(table(Datos$Gender),col="aquamarine2",main="Frecuencias de la variable Género")
Tablas Género y Edad
TablaCruzada <- table(Datos$Gender,Datos$Age)
TablaCruzada##
## 18 19 20 21 22 23 24
## Female 5 135 146 31 13 15 8
## Male 9 28 19 125 134 19 18TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores2 <- c("#A020F0","darkslategray4")
barplot(TablaCruzada, col = colores2)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores2, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores2)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores2)
mosaicplot(TablaCruzada_propFilas, col = colores2)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores2)
Tablas Género y Horas diarias de uso de redes sociales
TablaCruzada <- table(Datos$Gender,Datos$`Daily Usage Hours`)
TablaCruzada##
## 1.5 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6
## Female 0 0 0 1 1 1 3 3 4 5 4 3 3 6 6 6 6 7
## Male 1 1 1 1 3 3 2 2 1 3 3 3 4 3 3 2 4 6
##
## 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4
## Female 6 7 4 6 6 13 9 16 12 16 12 17 8 8 5 6 4 6
## Male 9 12 11 9 11 10 14 11 18 12 19 12 12 6 8 9 10 8
##
## 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2
## Female 9 10 10 8 8 3 9 7 5 6 3 4 7 7 6 8 5 3
## Male 10 9 9 10 5 4 4 6 6 4 7 4 3 6 4 2 3 5
##
## 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2 8.3 8.4 8.5
## Female 2 2 1 1 1 1 1 1 1 1 1 1 1
## Male 2 1 1 0 0 0 0 0 0 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores3 <- c("khaki","lightsalmon2")
barplot(TablaCruzada, col = colores3)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores3, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores3)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores3)
mosaicplot(TablaCruzada_propFilas, col = colores3)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores3)
Tablas de Género y Horas de sueño diarias
TablaCruzada <- table(Datos$Gender,Datos$`Sleep Hours Per Night`)
TablaCruzada##
## 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5
## Female 1 1 1 1 2 1 2 2 3 1 3 1 5 3 9 4 10 8
## Male 0 0 0 1 0 1 0 1 0 2 1 4 2 4 4 7 4 7
##
## 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3
## Female 7 10 17 10 9 6 7 6 11 7 7 10 11 5 10 9 14 14
## Male 2 8 6 11 9 6 13 13 6 16 13 15 13 9 8 11 14 14
##
## 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9 9.1
## Female 15 12 8 6 7 7 7 6 7 7 7 6 6 6 4 4 2 2
## Male 11 13 8 9 13 12 11 10 8 8 7 4 3 1 2 1 1 1
##
## 9.2 9.3 9.4 9.5 9.6
## Female 2 1 1 1 1
## Male 1 1 1 1 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores4 <- c("maroon2","mediumpurple3")
barplot(TablaCruzada, col = colores4)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores4, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores4)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores4)
mosaicplot(TablaCruzada_propFilas, col = colores4)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores4)
Tablas de género y Salud Mental
TablaCruzada <- table(Datos$Gender,Datos$`Mental Health Score`)
TablaCruzada##
## 4 5 6 7 8 9
## Female 21 94 98 82 58 0
## Male 8 79 121 96 47 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores5 <- c("slateblue2","tomato2")
barplot(TablaCruzada, col = colores5)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores5, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores5)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores5)
mosaicplot(TablaCruzada_propFilas, col = colores5)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores5)
Tablas de Género y Adicción a las Redes Sociales
TablaCruzada <- table(Datos$Gender,Datos$`Addicted Score`)
TablaCruzada##
## 0 1 2 3 4 5
## Female 0 21 103 115 106 8
## Male 4 26 101 146 68 7TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores7 <- c("forestgreen","lightgoldenrodyellow")
barplot(TablaCruzada, col = colores7)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores7, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores7)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores7)
mosaicplot(TablaCruzada_propFilas, col = colores7)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores7)
Nivel académico
barplot(table(Datos$Academic_Level),col="plum1",main="Frecuencias de la variable Nivel académico")
library(plotrix)
pie3D(table(Datos$Academic_Level), radius=0.9,main="Nivel Académico",labels=c("Graduados","Bachillerato","No graduados"),explode=0.1,col = hcl.colors(length(table(Datos$Academic_Level)),"Temps"), border = "black")
Tablas de Nivel Académico y Edad
TablaCruzada <- table(Datos$Academic_Level,Datos$Age)
TablaCruzada##
## 18 19 20 21 22 23 24
## Graduate 0 0 0 118 147 34 26
## High School 14 11 2 0 0 0 0
## Undergraduate 0 152 163 38 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores2 <- c("#A020F0","darkslategray4","mediumvioletred")
barplot(TablaCruzada, col = colores2)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores2, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores2)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores2)
mosaicplot(TablaCruzada_propFilas, col = colores2)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores2)
Tablas de Nivel académico y Horas diarias de uso de las redes sociales
TablaCruzada <- table(Datos$Academic_Level,Datos$`Daily Usage Hours`)
TablaCruzada##
## 1.5 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4
## Graduate 1 1 1 1 3 3 4 4 3 5 4 2 4 4 4 3
## High School 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## Undergraduate 0 0 0 1 1 1 1 1 2 3 3 3 3 5 5 5
##
## 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5
## Graduate 2 4 6 9 8 8 10 11 11 12 13 12 14 14 8 6
## High School 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## Undergraduate 8 9 9 10 7 6 7 12 12 15 17 16 17 15 12 7
##
## 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6
## Graduate 9 10 7 7 9 8 7 8 6 4 5 6 6 5 5 3
## High School 0 0 2 2 4 3 3 3 2 0 2 1 1 0 1 0
## Undergraduate 4 5 5 5 6 8 9 7 5 3 6 6 4 5 4 5
##
## 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2
## Graduate 3 6 3 3 2 4 2 1 1 0 0 0 0 0 0 0
## High School 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Undergraduate 7 7 7 7 6 4 2 2 1 1 1 1 1 1 1 1
##
## 8.3 8.4 8.5
## Graduate 0 0 0
## High School 0 0 0
## Undergraduate 1 1 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores3 <- c("khaki","lightsalmon2","red3")
barplot(TablaCruzada, col = colores3)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores3, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores3)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores3)
mosaicplot(TablaCruzada_propFilas, col = colores3)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores3)
Tablas de contingencias Nivel académico y Horas de sueño Diarias
TablaCruzada <- table(Datos$Academic_Level,Datos$`Sleep Hours Per Night`)
TablaCruzada##
## 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3
## Graduate 0 0 0 1 0 1 0 1 0 2 2 3 2 4 3 3
## High School 0 0 0 0 0 0 0 0 0 0 0 0 0 1 7 4
## Undergraduate 1 1 1 1 2 1 2 2 3 1 2 2 5 2 3 4
##
## 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9
## Graduate 2 4 3 4 5 6 6 6 10 10 9 12 9 12 14 10
## High School 6 6 0 1 0 0 0 0 0 0 0 1 0 0 0 0
## Undergraduate 6 5 6 13 18 15 12 6 10 9 8 10 11 13 10 4
##
## 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2 8.3 8.4 8.5
## Graduate 10 13 15 18 12 11 7 8 14 12 11 9 9 8 7 3
## High School 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Undergraduate 7 7 13 10 14 14 9 7 6 7 7 7 6 7 7 7
##
## 8.6 8.7 8.8 8.9 9 9.1 9.2 9.3 9.4 9.5 9.6
## Graduate 4 1 2 1 1 1 1 1 1 1 0
## High School 0 0 0 0 0 0 0 0 0 0 0
## Undergraduate 5 6 4 4 2 2 2 1 1 1 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores4 <- c("maroon2","mediumpurple3","paleturquoise2")
barplot(TablaCruzada, col = colores4)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores4, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores4)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores4)
mosaicplot(TablaCruzada_propFilas, col = colores4)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores4)
Tablas de contingencias Nivel Académico y Salud Mental
TablaCruzada <- table(Datos$Academic_Level,Datos$`Mental Health Score`)
TablaCruzada##
## 4 5 6 7 8 9
## Graduate 6 67 112 81 58 1
## High School 1 24 0 2 0 0
## Undergraduate 22 82 107 95 47 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores5 <- c("maroon","tomato2","darkslateblue")
barplot(TablaCruzada, col = colores5)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores5, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores5)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores5)
mosaicplot(TablaCruzada_propFilas, col = colores5)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores5)
Tablas de contingencias Nivel Académico y Adicción
TablaCruzada <- table(Datos$Academic_Level,Datos$`Addicted Score`)
TablaCruzada##
## 0 1 2 3 4 5
## Graduate 4 30 93 136 56 6
## High School 0 2 0 1 24 0
## Undergraduate 0 15 111 124 94 9TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores7 <- c("forestgreen","lightgoldenrodyellow","sienna")
barplot(TablaCruzada, col = colores7)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores7, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores7)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores7)
mosaicplot(TablaCruzada_propFilas, col = colores7)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores7)
Países
barplot(table(Datos$Country),col="palegreen3",main="Frecuencias de la variable Países")
Aplicaciones más usadas
barplot(table(Datos$`Most Used Platform`),col="salmon2",main="Frecuencias de la variable Aplicaciones")
library(plotrix)
pie3D(table(Datos$`Most Used Platform`), radius=0.9,main="Aplicaciones más utilizadas",labels=c(),explode=0.1,col = hcl.colors(length(table(Datos$`Most Used Platform`)),"Temps"), border = "black")
Tablas de contingencias Aplicaciones más usadas y edad
TablaCruzada <- table(Datos$`Most Used Platform`,Datos$Age)
TablaCruzada##
## 18 19 20 21 22 23 24
## Facebook 0 8 21 35 47 6 6
## Instagram 4 88 64 62 20 8 3
## KakaoTalk 0 0 12 0 0 0 0
## LINE 0 12 0 0 0 0 0
## LinkedIn 0 0 0 1 5 11 4
## Snapchat 4 1 6 2 0 0 0
## TikTok 5 38 52 20 30 4 5
## Twitter 0 5 4 4 6 5 6
## VKontakte 0 0 0 0 12 0 0
## WeChat 0 2 0 5 6 0 2
## WhatsApp 0 8 3 22 21 0 0
## YouTube 1 1 3 5 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores2 <- c("#A020F0","darkslategray4","mediumvioletred")
barplot(TablaCruzada, col = colores2)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores2, beside = TRUE)
legend("topleft", legend = c(""), fill = colores2)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores2)
mosaicplot(TablaCruzada_propFilas, col = colores2)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores2)
Tablas Aplicaciones más usadas y Horas diarias de uso
TablaCruzada <- table(Datos$`Most Used Platform`,Datos$`Daily Usage Hours`)
TablaCruzada##
## 1.5 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5
## Facebook 0 0 0 0 1 1 1 1 1 1 3 1 4 3 3 1 3
## Instagram 0 0 0 1 1 1 1 1 1 2 2 2 2 5 4 6 3
## KakaoTalk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LINE 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
## LinkedIn 1 1 0 1 2 2 3 3 2 4 1 1 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TikTok 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3
## Twitter 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## VKontakte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WeChat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WhatsApp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
##
## 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2
## Facebook 4 5 3 2 3 4 5 4 4 9 5 8 7 4 3 4 2
## Instagram 7 3 8 6 7 8 9 9 14 9 12 10 10 6 6 3 6
## KakaoTalk 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 1 1
## LINE 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Snapchat 0 1 0 0 0 0 0 0 0 0 2 1 2 1 0 0 0
## TikTok 1 3 2 3 3 3 4 4 3 6 4 8 6 8 3 4 6
## Twitter 0 0 1 0 0 0 2 3 2 3 3 1 2 0 0 1 0
## VKontakte 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
## WeChat 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0
## WhatsApp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## YouTube 0 1 3 2 0 0 0 0 1 0 0 1 0 0 0 0 0
##
## 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9
## Facebook 0 1 1 1 0 0 1 1 1 4 1 4 5 0 0 0 0
## Instagram 5 5 5 5 5 7 6 0 4 2 1 1 3 2 8 7 5
## KakaoTalk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LINE 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Snapchat 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0
## TikTok 7 6 7 6 8 4 2 2 3 2 6 3 0 4 0 3 2
## Twitter 2 1 2 0 1 0 1 0 1 2 1 0 0 0 0 0 0
## VKontakte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WeChat 0 0 2 3 2 1 0 0 0 0 0 0 0 0 0 0 0
## WhatsApp 0 0 1 3 2 4 3 4 3 3 2 2 2 2 2 3 3
## YouTube 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
##
## 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2 8.3 8.4 8.5
## Facebook 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## Instagram 2 3 1 1 0 1 0 1 0 1 0 1 0 1 0 1
## KakaoTalk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LINE 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TikTok 5 1 2 0 1 0 1 0 1 0 1 0 1 0 1 0
## Twitter 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## VKontakte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WeChat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WhatsApp 3 3 3 3 2 1 0 0 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores3 <- c("khaki","lightsalmon2","red3")
barplot(TablaCruzada, col = colores3)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores3, beside = TRUE)
legend("topleft", legend = c(""), fill = colores3)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores3)
mosaicplot(TablaCruzada_propFilas, col = colores3)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores3)
Tablas Aplicaciones más usadas y Horas de sueño Diarias
TablaCruzada <- table(Datos$`Most Used Platform`,Datos$`Sleep Hours Per Night`)
TablaCruzada##
## 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4
## Facebook 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
## Instagram 1 0 1 0 1 0 1 1 2 0 2 0 3 1 7 2 6
## KakaoTalk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LINE 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0
## TikTok 0 1 0 2 1 2 1 2 1 2 1 3 2 3 2 5 5
## Twitter 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## VKontakte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WeChat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WhatsApp 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 3
## YouTube 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
##
## 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1
## Facebook 1 0 2 2 2 1 4 5 5 2 3 4 3 3 2 4 5
## Instagram 2 2 2 8 11 9 5 3 3 6 7 5 10 5 8 5 7
## KakaoTalk 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
## LINE 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3
## Snapchat 2 1 2 1 1 1 0 1 0 0 1 0 0 0 0 0 0
## TikTok 7 3 9 9 3 3 1 7 5 4 4 5 4 5 2 5 2
## Twitter 0 0 0 0 0 0 0 1 2 1 0 1 1 5 0 0 2
## VKontakte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## WeChat 0 0 0 0 0 0 0 0 0 0 1 0 4 3 0 0 0
## WhatsApp 3 3 3 3 3 3 2 3 4 3 4 2 2 1 1 0 0
## YouTube 0 0 0 0 1 1 0 0 0 1 2 2 0 1 0 1 0
##
## 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8
## Facebook 6 6 4 4 3 3 6 4 5 3 4 4 4 3 2 2 2
## Instagram 7 7 10 12 5 9 5 11 5 9 4 7 4 5 4 4 3
## KakaoTalk 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## LINE 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
## LinkedIn 6 5 3 0 0 0 1 0 1 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TikTok 7 2 4 2 4 2 4 1 4 1 4 1 4 1 2 0 0
## Twitter 0 6 3 5 2 0 1 0 0 0 0 0 0 0 0 0 0
## VKontakte 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
## WeChat 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0
## WhatsApp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 8.9 9 9.1 9.2 9.3 9.4 9.5 9.6
## Facebook 2 1 1 1 1 1 1 0
## Instagram 2 2 2 2 1 1 1 1
## KakaoTalk 0 0 0 0 0 0 0 0
## LINE 1 0 0 0 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0
## TikTok 0 0 0 0 0 0 0 0
## Twitter 0 0 0 0 0 0 0 0
## VKontakte 0 0 0 0 0 0 0 0
## WeChat 0 0 0 0 0 0 0 0
## WhatsApp 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores4 <- c("maroon2","mediumpurple3","paleturquoise2")
barplot(TablaCruzada, col = colores4)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores4, beside = TRUE)
legend("topleft", legend = c(""), fill = colores4)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores4)
mosaicplot(TablaCruzada_propFilas, col = colores4)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores4)
Tablas de Aplicaciones más usadas y Salud Mental
TablaCruzada <- table(Datos$`Most Used Platform`,Datos$`Mental Health Score`)
TablaCruzada##
## 4 5 6 7 8 9
## Facebook 3 19 12 65 24 0
## Instagram 15 73 66 56 39 0
## KakaoTalk 0 0 12 0 0 0
## LINE 0 0 0 0 12 0
## LinkedIn 0 0 0 1 19 1
## Snapchat 1 5 6 1 0 0
## TikTok 10 46 77 20 1 0
## Twitter 0 4 7 9 10 0
## VKontakte 0 0 0 12 0 0
## WeChat 0 0 8 7 0 0
## WhatsApp 0 25 29 0 0 0
## YouTube 0 1 2 7 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores5 <- c("maroon","tomato2","darkslateblue")
barplot(TablaCruzada, col = colores5)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores5, beside = TRUE)
legend("topleft", legend = c(""), fill = colores5)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores5)
mosaicplot(TablaCruzada_propFilas, col = colores5)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores5)
Tablas Aplicaciones más usadas y Adicción
TablaCruzada <- table(Datos$`Most Used Platform`,Datos$`Addicted Score`)
TablaCruzada##
## 0 1 2 3 4 5
## Facebook 0 8 79 14 19 3
## Instagram 0 7 70 111 53 8
## KakaoTalk 0 0 0 12 0 0
## LINE 0 12 0 0 0 0
## LinkedIn 3 17 1 0 0 0
## Snapchat 0 0 2 6 5 0
## TikTok 0 1 10 72 67 4
## Twitter 1 0 18 7 4 0
## VKontakte 0 0 12 0 0 0
## WeChat 0 0 7 8 0 0
## WhatsApp 0 0 0 29 25 0
## YouTube 0 2 5 2 1 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores7 <- c("forestgreen","lightgoldenrodyellow","sienna")
barplot(TablaCruzada, col = colores7)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores7, beside = TRUE)
legend("topleft", legend = c(""), fill = colores7)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores7)
mosaicplot(TablaCruzada_propFilas, col = colores7)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores7)
¿El desempeño académico se ve afectado por el ujso de redes sociales?
library(plotrix)
pie3D(table(Datos$`Affects Academic Performance`), radius=0.9,main="El uso de aplicaciones afecta el desempeño académico",labels=c("No","Si"),explode=0.1,col = hcl.colors(length(table(Datos$`Affects Academic Performance`)),"Temps"), border = "black")
barplot(table(Datos$`Affects Academic Performance`),col="rosybrown1",main="Afecta o no el uso de Redes sociales")
Lectura datos muestra de 50
library(readxl)
Datos2 <- read_excel("Datos2.0.xlsx", col_types = c("numeric",
"text", "text", "text", "numeric", "text",
"text", "numeric", "numeric", "text",
"numeric", "numeric"))
summary(Datos2)## Age Gender Academic Level Country
## Min. :18.00 Length:50 Length:50 Length:50
## 1st Qu.:19.00 Class :character Class :character Class :character
## Median :20.00 Mode :character Mode :character Mode :character
## Mean :20.36
## 3rd Qu.:21.00
## Max. :24.00
## Daily Usage Hours Most Used Platform Affects Academic Performance
## Min. :1.500 Length:50 Length:50
## 1st Qu.:3.300 Class :character Class :character
## Median :4.550 Mode :character Mode :character
## Mean :4.364
## 3rd Qu.:5.500
## Max. :7.200
## Sleep Hours Per Night Mental Health Score Relationship Status Conflicts
## Min. :4.500 Min. :4.00 Length:50 Min. :0.00
## 1st Qu.:5.525 1st Qu.:5.00 Class :character 1st Qu.:1.00
## Median :6.000 Median :6.00 Mode :character Median :3.00
## Mean :6.204 Mean :6.28 Mean :2.42
## 3rd Qu.:6.800 3rd Qu.:7.00 3rd Qu.:4.00
## Max. :8.000 Max. :9.00 Max. :5.00
## Addicted Score
## Min. :2.00
## 1st Qu.:5.00
## Median :7.00
## Mean :6.34
## 3rd Qu.:8.00
## Max. :9.00colnames(Datos2)=c("Age","Gender","Academic_Level","Country","Daily Usage Hours","Most Used Platform","Affects Academic Performance","Sleep Hours Per Night","Mental Health Score","Conflicts Over Social Media","Addicted Score")
head(Datos2) ## # A tibble: 6 × 12
## Age Gender Academic_Level Country `Daily Usage Hours` `Most Used Platform`
## <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 19 Female Undergraduate Banglade… 5.2 Instagram
## 2 22 Male Graduate India 2.1 Twitter
## 3 20 Female Undergraduate USA 6 TikTok
## 4 18 Male High School UK 3 YouTube
## 5 21 Male Graduate Canada 4.5 Facebook
## 6 19 Female Undergraduate Australia 7.2 Instagram
## # ℹ 6 more variables: `Affects Academic Performance` <chr>,
## # `Sleep Hours Per Night` <dbl>, `Mental Health Score` <dbl>,
## # `Conflicts Over Social Media` <chr>, `Addicted Score` <dbl>, `` <dbl>tail(Datos2)## # A tibble: 6 × 12
## Age Gender Academic_Level Country `Daily Usage Hours` `Most Used Platform`
## <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 18 Male High School Argentina 5.5 TikTok
## 2 23 Female Graduate Chile 2.7 LinkedIn
## 3 19 Male Undergraduate Colombia 4.8 Instagram
## 4 20 Female Undergraduate Peru 5.5 TikTok
## 5 22 Male Graduate Venezuela 3.3 Facebook
## 6 19 Female High School Ecuador 6.3 Instagram
## # ℹ 6 more variables: `Affects Academic Performance` <chr>,
## # `Sleep Hours Per Night` <dbl>, `Mental Health Score` <dbl>,
## # `Conflicts Over Social Media` <chr>, `Addicted Score` <dbl>, `` <dbl>str(Datos2)## tibble [50 × 12] (S3: tbl_df/tbl/data.frame)
## $ Age : num [1:50] 19 22 20 18 21 19 23 20 18 21 ...
## $ Gender : chr [1:50] "Female" "Male" "Female" "Male" ...
## $ Academic_Level : chr [1:50] "Undergraduate" "Graduate" "Undergraduate" "High School" ...
## $ Country : chr [1:50] "Bangladesh" "India" "USA" "UK" ...
## $ Daily Usage Hours : num [1:50] 5.2 2.1 6 3 4.5 7.2 1.5 5.8 4 3.3 ...
## $ Most Used Platform : chr [1:50] "Instagram" "Twitter" "TikTok" "YouTube" ...
## $ Affects Academic Performance: chr [1:50] "Yes" "No" "Yes" "No" ...
## $ Sleep Hours Per Night : num [1:50] 6.5 7.5 5 7 6 4.5 8 6 6.5 7 ...
## $ Mental Health Score : num [1:50] 6 8 5 7 6 4 9 6 7 7 ...
## $ Conflicts Over Social Media : chr [1:50] "In Relationship" "Single" "Complicated" "Single" ...
## $ Addicted Score : num [1:50] 3 0 4 1 2 5 0 2 1 1 ...
## $ NA : num [1:50] 8 3 9 4 7 9 2 8 5 4 ...Análisis Estadístico Descriptivo Univariado muestra de 50
Edad
media_Edad=mean(Datos2$Age)
varianza_Edad=var(Datos2$Age)
sd_Edad=sd(Datos2$Age)
#calcular cve
cve_media_Edad=100*sd_Edad/media_Edad
medianaEdad=median(Datos2$Age)
MAD_Edad=mad(Datos2$Age)
cve_mediana_Edad=100*MAD_Edad/medianaEdad
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Edad=c(mean(Datos2$Age),var(Datos2$Age),sd(Datos2$Age),100*sd(Datos2$Age)/mean(Datos2$Age),median(Datos2$Age),100*mad(Datos2$Age),mad(Datos2$Age)/median(Datos2$Age)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Edad
## Promedio 20.360000
## Varianza 2.888163
## Desv_estandar 1.699460
## cve_media 8.347052
## Mediana 20.000000
## MAD 148.260000
## cve_mediana 0.074130summary(Datos2$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 19.00 20.00 20.36 21.00 24.00min_Datos <- min(Datos2$Age)
min_Datos## [1] 18max_Datos <- max(Datos2$Age)
max_Datos## [1] 24range(Datos2$Age)## [1] 18 24rango_Datos=max(Datos2$Age)-min(Datos2$Age)
cuartiles_Datos <- quantile(Datos2$Age,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 18 19 20 21 24rango_intercuartil_Datos<- IQR(Datos2$Age)
rango_intercuartil_Datos## [1] 2Medidas_Posic=data.frame("Medidas de posición Edad"=c(min(Datos2$Age),max(Datos2$Age),max(Datos2$Age)-min(Datos2$Age),quantile(Datos2$Age,probs = seq(0,1,by=0.25)),IQR(Datos2$Age)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Edad
## Mínimo 18
## Máximo 24
## Rango 6
## Cuartil0_min 18
## Cuartil_1 19
## Cuartil_2 20
## Cuartil_3 21
## Cuartil_4_max 24
## IQR 2Horas Diarias de uso
media_Horas=mean(Datos2$`Daily Usage Hours`)
varianza_Horas=var(Datos2$`Daily Usage Hours`)
sd_Horas=sd(Datos2$`Daily Usage Hours`)
#calcular cve
cve_media_Horas=100*sd_Horas/media_Horas
medianaHoras=median(Datos2$`Daily Usage Hours`)
MAD_Horas=mad(Datos2$`Daily Usage Hours`)
cve_mediana_Horas=100*MAD_Horas/medianaHoras
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Horas=c(mean(Datos2$`Daily Usage Hours`),var(Datos2$`Daily Usage Hours`),sd(Datos2$`Daily Usage Hours`),100*sd(Datos2$`Daily Usage Hours`)/mean(Datos2$`Daily Usage Hours`),median(Datos2$`Daily Usage Hours`),100*mad(Datos2$`Daily Usage Hours`),mad(Datos2$`Daily Usage Hours`)/median(Datos2$`Daily Usage Hours`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Horas
## Promedio 4.3640000
## Varianza 1.8970449
## Desv_estandar 1.3773325
## cve_media 31.5612404
## Mediana 4.5500000
## MAD 155.6730000
## cve_mediana 0.3421385summary(Datos2$`Daily Usage Hours`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.500 3.300 4.550 4.364 5.500 7.200min_Datos <- min(Datos2$`Daily Usage Hours`)
min_Datos## [1] 1.5max_Datos <- max(Datos2$`Daily Usage Hours`)
max_Datos## [1] 7.2range(Datos2$`Daily Usage Hours`)## [1] 1.5 7.2rango_Datos=max(Datos2$`Daily Usage Hours`)-min(Datos2$`Daily Usage Hours`)
cuartiles_Datos <- quantile(Datos2$`Daily Usage Hours`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 1.50 3.30 4.55 5.50 7.20rango_intercuartil_Datos<- IQR(Datos2$`Daily Usage Hours`)
rango_intercuartil_Datos## [1] 2.2Medidas_Posic=data.frame("Medidas de posición Horas"=c(min(Datos2$`Daily Usage Hours`),max(Datos2$`Daily Usage Hours`),max(Datos2$`Daily Usage Hours`)-min(Datos2$`Daily Usage Hours`),quantile(Datos2$`Daily Usage Hours`,probs = seq(0,1,by=0.25)),IQR(Datos2$`Daily Usage Hours`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Horas
## Mínimo 1.50
## Máximo 7.20
## Rango 5.70
## Cuartil0_min 1.50
## Cuartil_1 3.30
## Cuartil_2 4.55
## Cuartil_3 5.50
## Cuartil_4_max 7.20
## IQR 2.20Horas que duermen a diario
media_Sueño=mean(Datos2$`Sleep Hours Per Night`)
varianza_Sueño=var(Datos2$`Sleep Hours Per Night`)
sd_Sueño=sd(Datos2$`Sleep Hours Per Night`)
#calcular cve
cve_media_Sueño=100*sd_Sueño/media_Sueño
mediana_Sueño=median(Datos2$`Sleep Hours Per Night`)
MAD_Sueño=mad(Datos2$`Sleep Hours Per Night`)
cve_mediana_Sueño=100*MAD_Sueño/mediana_Sueño
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Horas_de_Sueño=c(mean(Datos2$`Sleep Hours Per Night`),var(Datos2$`Sleep Hours Per Night`),sd(Datos2$`Sleep Hours Per Night`),100*sd(Datos2$`Sleep Hours Per Night`)/mean(Datos2$`Sleep Hours Per Night`),median(Datos2$`Sleep Hours Per Night`),100*mad(Datos2$`Sleep Hours Per Night`),mad(Datos2$`Sleep Hours Per Night`)/median(Datos2$`Sleep Hours Per Night`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Horas_de_Sueño
## Promedio 6.2040000
## Varianza 0.6669224
## Desv_estandar 0.8166532
## cve_media 13.1633333
## Mediana 6.0000000
## MAD 88.9560000
## cve_mediana 0.1482600summary(Datos2$`Sleep Hours Per Night`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.500 5.525 6.000 6.204 6.800 8.000min_Datos <- min(Datos2$`Sleep Hours Per Night`)
min_Datos## [1] 4.5max_Datos <- max(Datos2$`Sleep Hours Per Night`)
max_Datos## [1] 8range(Datos2$`Sleep Hours Per Night`)## [1] 4.5 8.0rango_Datos=max(Datos2$`Sleep Hours Per Night`)-min(Datos2$`Sleep Hours Per Night`)
cuartiles_Datos <- quantile(Datos2$`Sleep Hours Per Night`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 4.500 5.525 6.000 6.800 8.000rango_intercuartil_Datos<- IQR(Datos2$`Sleep Hours Per Night`)
rango_intercuartil_Datos## [1] 1.275Medidas_Posic=data.frame("Medidas de posición Horas de Sueño"=c(min(Datos2$`Sleep Hours Per Night`),max(Datos2$`Sleep Hours Per Night`),max(Datos2$`Sleep Hours Per Night`)-min(Datos2$`Sleep Hours Per Night`),quantile(Datos2$`Sleep Hours Per Night`,probs = seq(0,1,by=0.25)),IQR(Datos2$`Sleep Hours Per Night`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Horas.de.Sueño
## Mínimo 4.500
## Máximo 8.000
## Rango 3.500
## Cuartil0_min 4.500
## Cuartil_1 5.525
## Cuartil_2 6.000
## Cuartil_3 6.800
## Cuartil_4_max 8.000
## IQR 1.275Escala de Salud Mental
media_Mental=mean(Datos2$`Mental Health Score`)
varianza_Mental=var(Datos2$`Mental Health Score`)
sd_Mental=sd(Datos2$`Mental Health Score`)
#calcular cve
cve_media_Mental=100*sd_Mental/media_Mental
mediana_Mental=median(Datos2$`Mental Health Score`)
MAD_Mental=mad(Datos2$`Mental Health Score`)
cve_mediana_Mental=100*MAD_Mental/mediana_Mental
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Salud_Mental=c(mean(Datos2$`Mental Health Score`),var(Datos2$`Mental Health Score`),sd(Datos2$`Mental Health Score`),100*sd(Datos2$`Mental Health Score`)/mean(Datos2$`Mental Health Score`),median(Datos2$`Mental Health Score`),100*mad(Datos2$`Mental Health Score`),mad(Datos2$`Mental Health Score`)/median(Datos2$`Mental Health Score`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Salud_Mental
## Promedio 6.280000
## Varianza 1.389388
## Desv_estandar 1.178723
## cve_media 18.769473
## Mediana 6.000000
## MAD 148.260000
## cve_mediana 0.247100summary(Datos2$`Mental Health Score`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 5.00 6.00 6.28 7.00 9.00min_Datos <- min(Datos2$`Mental Health Score`)
min_Datos## [1] 4max_Datos <- max(Datos2$`Mental Health Score`)
max_Datos## [1] 9range(Datos2$`Mental Health Score`)## [1] 4 9rango_Datos=max(Datos2$`Mental Health Score`)-min(Datos2$`Mental Health Score`)
cuartiles_Datos <- quantile(Datos2$`Mental Health Score`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 4 5 6 7 9rango_intercuartil_Datos<- IQR(Datos2$`Mental Health Score`)
rango_intercuartil_Datos## [1] 2Medidas_Posic=data.frame("Medidas de posición Salud Mental"=c(min(Datos2$`Mental Health Score`),max(Datos2$`Mental Health Score`),max(Datos2$`Mental Health Score`)-min(Datos2$`Mental Health Score`),quantile(Datos2$`Mental Health Score`,probs = seq(0,1,by=0.25)),IQR(Datos2$`Mental Health Score`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.Salud.Mental
## Mínimo 4
## Máximo 9
## Rango 5
## Cuartil0_min 4
## Cuartil_1 5
## Cuartil_2 6
## Cuartil_3 7
## Cuartil_4_max 9
## IQR 2Escala de adicción por las Redes Sociales
media_Adicción=mean(Datos2$`Addicted Score`)
varianza_Adicción=var(Datos2$`Addicted Score`)
sd_Adicción=sd(Datos2$`Addicted Score`)
#calcular cve
cve_media_Adicción=100*sd_Adicción/media_Adicción
mediana_Adicción=median(Datos2$`Addicted Score`)
MAD_Adicción=mad(Datos2$`Addicted Score`)
cve_mediana_Adicción=100*MAD_Adicción/mediana_Adicción
#min,max,cuartiles,media y mediana
resumen=data.frame(Estadísticos_Descriptivos_univariados_Adicción_por_las_redes_sociales=c(mean(Datos2$`Addicted Score`),var(Datos2$`Addicted Score`),sd(Datos2$`Addicted Score`),100*sd(Datos2$`Addicted Score`)/mean(Datos2$`Addicted Score`),median(Datos2$`Addicted Score`),100*mad(Datos2$`Addicted Score`),mad(Datos2$`Addicted Score`)/median(Datos2$`Addicted Score`)))
rownames(resumen)=c("Promedio","Varianza","Desv_estandar","cve_media","Mediana","MAD","cve_mediana")
resumen## Estadísticos_Descriptivos_univariados_Adicción_por_las_redes_sociales
## Promedio 2.420000
## Varianza 1.881224
## Desv_estandar 1.371577
## cve_media 56.676751
## Mediana 3.000000
## MAD 148.260000
## cve_mediana 0.494200summary(Datos2$`Addicted Score`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 1.00 3.00 2.42 4.00 5.00min_Datos <- min(Datos2$`Addicted Score`)
min_Datos## [1] 0max_Datos <- max(Datos2$`Addicted Score`)
max_Datos## [1] 5range(Datos2$`Addicted Score`)## [1] 0 5rango_Datos=max(Datos2$`Addicted Score`)-min(Datos2$`Addicted Score`)
cuartiles_Datos <- quantile(Datos2$`Addicted Score`,probs = seq(0,1,by=0.25))
cuartiles_Datos## 0% 25% 50% 75% 100%
## 0 1 3 4 5rango_intercuartil_Datos<- IQR(Datos2$`Addicted Score`)
rango_intercuartil_Datos## [1] 3Medidas_Posic=data.frame("Medidas de posición de Adicción por las redes sociales"=c(min(Datos2$`Addicted Score`),max(Datos2$`Addicted Score`),max(Datos2$`Addicted Score`)-min(Datos2$`Addicted Score`),quantile(Datos2$`Addicted Score`,probs = seq(0,1,by=0.25)),IQR(Datos2$`Addicted Score`)))
rownames(Medidas_Posic)=c("Mínimo","Máximo","Rango","Cuartil0_min","Cuartil_1","Cuartil_2","Cuartil_3","Cuartil_4_max","IQR")
Medidas_Posic## Medidas.de.posición.de.Adicción.por.las.redes.sociales
## Mínimo 0
## Máximo 5
## Rango 5
## Cuartil0_min 0
## Cuartil_1 1
## Cuartil_2 3
## Cuartil_3 4
## Cuartil_4_max 5
## IQR 3Diagramas de Barras, circulares y Tablas de Contingencia
Género
library(plotrix)
pie3D(table(Datos2$Gender), radius=0.9,main="Número de personas de cada género",labels=c("Hombres","Mujeres"),explode=0.1,col = hcl.colors(length(table(Datos2$Gender)),"Zissou"), border = "black")
barplot(table(Datos2$Gender),col="aquamarine2",main="Frecuencias de la variable Género")
Tablas Género y Edad
TablaCruzada <- table(Datos2$Gender,Datos2$Age)
TablaCruzada##
## 18 19 20 21 22 23 24
## Female 1 8 12 1 0 3 0
## Male 5 4 0 7 5 1 3TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores2 <- c("#A020F0","darkslategray4")
barplot(TablaCruzada, col = colores2)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores2, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores2)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores2)
mosaicplot(TablaCruzada_propFilas, col = colores2)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores2)
Tablas Género y Horas diarias de uso de redes sociales
TablaCruzada <- table(Datos2$Gender,Datos2$`Daily Usage Hours`)
TablaCruzada##
## 1.5 2 2.1 2.2 2.3 2.5 2.6 2.7 2.8 3 3.1 3.2 3.3 3.5 3.6 3.7 3.8 3.9 4
## Female 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0
## Male 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 2 1 1 1
##
## 4.2 4.4 4.5 4.6 4.7 4.8 4.9 5 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1
## Female 1 1 1 1 1 1 0 1 1 0 0 2 1 1 2 1 1 1
## Male 0 0 1 0 1 2 1 0 0 1 1 1 0 0 0 0 0 0
##
## 6.2 6.3 6.5 7.2
## Female 1 1 1 1
## Male 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores3 <- c("khaki","lightsalmon2")
barplot(TablaCruzada, col = colores3)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores3, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores3)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores3)
mosaicplot(TablaCruzada_propFilas, col = colores3)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores3)
Tablas de Género y Horas de sueño diarias
TablaCruzada <- table(Datos2$Gender,Datos2$`Sleep Hours Per Night`)
TablaCruzada##
## 4.5 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.2 6.5 6.6 6.7 6.8 6.9 7
## Female 1 1 1 2 1 2 2 1 3 2 2 2 0 1 0 0 0 0 1
## Male 0 0 0 0 1 1 1 0 0 2 1 1 1 2 2 3 2 1 1
##
## 7.1 7.2 7.3 7.4 7.5 7.8 8
## Female 1 1 1 0 0 0 0
## Male 0 1 1 1 1 1 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores4 <- c("maroon2","mediumpurple3")
barplot(TablaCruzada, col = colores4)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores4, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores4)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores4)
mosaicplot(TablaCruzada_propFilas, col = colores4)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores4)
Tablas de género y Salud Mental
TablaCruzada <- table(Datos2$Gender,Datos2$`Mental Health Score`)
TablaCruzada##
## 4 5 6 7 8 9
## Female 1 11 9 1 3 0
## Male 0 4 4 11 5 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores5 <- c("slateblue2","tomato2")
barplot(TablaCruzada, col = colores5)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores5, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores5)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores5)
mosaicplot(TablaCruzada_propFilas, col = colores5)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores5)
Tablas de Género y Adicción a las Redes Sociales
TablaCruzada <- table(Datos2$Gender,Datos2$`Addicted Score`)
TablaCruzada##
## 0 1 2 3 4 5
## Female 0 4 2 8 10 1
## Male 4 8 6 4 3 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores7 <- c("forestgreen","lightgoldenrodyellow")
barplot(TablaCruzada, col = colores7)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores7, beside = TRUE)
legend("topleft", legend = c("Femenino", "Masculino"), fill = colores7)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores7)
mosaicplot(TablaCruzada_propFilas, col = colores7)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores7)
Nivel académico
barplot(table(Datos2$Academic_Level),col="plum1",main="Frecuencias de la variable Nivel académico")
library(plotrix)
pie3D(table(Datos2$Academic_Level), radius=0.9,main="Nivel Académico",labels=c("Graduados","Bachillerato","No graduados"),explode=0.1,col = hcl.colors(length(table(Datos2$Academic_Level)),"Temps"), border = "black")
Tablas de Nivel Académico y Edad
TablaCruzada <- table(Datos2$Academic_Level,Datos2$Age)
TablaCruzada##
## 18 19 20 21 22 23 24
## Graduate 0 0 0 2 5 4 3
## High School 6 6 0 0 0 0 0
## Undergraduate 0 6 12 6 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores2 <- c("#A020F0","darkslategray4","mediumvioletred")
barplot(TablaCruzada, col = colores2)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores2, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores2)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores2)
mosaicplot(TablaCruzada_propFilas, col = colores2)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores2)
Tablas de Nivel académico y Horas diarias de uso de las redes sociales
TablaCruzada <- table(Datos2$Academic_Level,Datos2$`Daily Usage Hours`)
TablaCruzada##
## 1.5 2 2.1 2.2 2.3 2.5 2.6 2.7 2.8 3 3.1 3.2 3.3 3.5 3.6 3.7 3.8
## Graduate 1 1 1 1 1 1 1 1 1 0 1 1 2 0 0 0 0
## High School 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## Undergraduate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 1
##
## 3.9 4 4.2 4.4 4.5 4.6 4.7 4.8 4.9 5 5.2 5.3 5.4 5.5 5.6 5.7 5.8
## Graduate 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## High School 0 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1
## Undergraduate 1 0 1 1 1 1 2 3 1 0 1 0 0 2 1 1 1
##
## 5.9 6 6.1 6.2 6.3 6.5 7.2
## Graduate 0 0 0 0 0 0 0
## High School 1 0 1 1 1 1 0
## Undergraduate 0 1 0 0 0 0 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores3 <- c("khaki","lightsalmon2","red3")
barplot(TablaCruzada, col = colores3)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores3, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores3)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores3)
mosaicplot(TablaCruzada_propFilas, col = colores3)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores3)
Tablas de contingencias Nivel académico y Horas de sueño Diarias
TablaCruzada <- table(Datos2$Academic_Level,Datos2$`Sleep Hours Per Night`)
TablaCruzada##
## 4.5 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.2 6.5 6.6 6.7 6.8
## Graduate 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
## High School 0 0 1 2 2 2 2 0 1 0 0 0 0 1 0 0 0
## Undergraduate 1 1 0 0 0 1 1 1 2 4 3 2 1 2 2 2 1
##
## 6.9 7 7.1 7.2 7.3 7.4 7.5 7.8 8
## Graduate 1 1 1 2 2 1 1 1 1
## High School 0 1 0 0 0 0 0 0 0
## Undergraduate 0 0 0 0 0 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores4 <- c("maroon2","mediumpurple3","paleturquoise2")
barplot(TablaCruzada, col = colores4)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores4, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores4)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores4)
mosaicplot(TablaCruzada_propFilas, col = colores4)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores4)
Tablas de contingencias Nivel Académico y Salud Mental
TablaCruzada <- table(Datos2$Academic_Level,Datos2$`Mental Health Score`)
TablaCruzada##
## 4 5 6 7 8 9
## Graduate 0 0 1 4 8 1
## High School 0 10 0 2 0 0
## Undergraduate 1 5 12 6 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores5 <- c("maroon","tomato2","darkslateblue")
barplot(TablaCruzada, col = colores5)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores5, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores5)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores5)
mosaicplot(TablaCruzada_propFilas, col = colores5)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores5)
Tablas de contingencias Nivel Académico y Adicción
TablaCruzada <- table(Datos2$Academic_Level,Datos2$`Addicted Score`)
TablaCruzada##
## 0 1 2 3 4 5
## Graduate 4 7 3 0 0 0
## High School 0 2 0 1 9 0
## Undergraduate 0 3 5 11 4 1TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores7 <- c("forestgreen","lightgoldenrodyellow","sienna")
barplot(TablaCruzada, col = colores7)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores7, beside = TRUE)
legend("topleft", legend = c("Graduados","Bachillerato","No graduados"), fill = colores7)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores7)
mosaicplot(TablaCruzada_propFilas, col = colores7)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores7)
Países
barplot(table(Datos2$Country),col="palegreen3",main="Frecuencias de la variable Países")
Aplicaciones más usadas
barplot(table(Datos2$`Most Used Platform`),col="salmon2",main="Frecuencias de la variable Aplicaciones")
library(plotrix)
pie3D(table(Datos2$`Most Used Platform`), radius=0.9,main="Aplicaciones más utilizadas",labels=c(),explode=0.1,col = hcl.colors(length(table(Datos2$`Most Used Platform`)),"Temps"), border = "black")
Tablas de contingencias Aplicaciones más usadas y edad
TablaCruzada <- table(Datos2$`Most Used Platform`,Datos2$Age)
TablaCruzada##
## 18 19 20 21 22 23 24
## Facebook 0 0 0 4 3 0 0
## Instagram 1 11 0 1 0 0 0
## LinkedIn 0 0 0 0 1 4 3
## Snapchat 0 1 4 0 0 0 0
## TikTok 4 0 8 0 0 0 0
## Twitter 0 0 0 0 1 0 0
## YouTube 1 0 0 3 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores2 <- c("#A020F0","darkslategray4","mediumvioletred")
barplot(TablaCruzada, col = colores2)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores2, beside = TRUE)
legend("topleft", legend = c(""), fill = colores2)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores2)
mosaicplot(TablaCruzada_propFilas, col = colores2)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores2)
Tablas Aplicaciones más usadas y Horas diarias de uso
TablaCruzada <- table(Datos2$`Most Used Platform`,Datos2$`Daily Usage Hours`)
TablaCruzada##
## 1.5 2 2.1 2.2 2.3 2.5 2.6 2.7 2.8 3 3.1 3.2 3.3 3.5 3.6 3.7 3.8 3.9
## Facebook 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0
## Instagram 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## LinkedIn 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TikTok 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Twitter 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1
##
## 4 4.2 4.4 4.5 4.6 4.7 4.8 4.9 5 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6
## Facebook 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Instagram 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0 1 0 0
## TikTok 1 1 1 1 0 0 0 0 0 0 1 1 3 1 1 0 0 1
## Twitter 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 6.1 6.2 6.3 6.5 7.2
## Facebook 0 0 0 0 0
## Instagram 1 1 1 1 1
## LinkedIn 0 0 0 0 0
## Snapchat 0 0 0 0 0
## TikTok 0 0 0 0 0
## Twitter 0 0 0 0 0
## YouTube 0 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores3 <- c("khaki","lightsalmon2","red3")
barplot(TablaCruzada, col = colores3)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores3, beside = TRUE)
legend("topleft", legend = c(""), fill = colores3)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores3)
mosaicplot(TablaCruzada_propFilas, col = colores3)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores3)
Tablas Aplicaciones más usadas y Horas de sueño Diarias
TablaCruzada <- table(Datos2$`Most Used Platform`,Datos2$`Sleep Hours Per Night`)
TablaCruzada##
## 4.5 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.2 6.5 6.6 6.7 6.8 6.9
## Facebook 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 3 1 1
## Instagram 1 0 1 2 1 1 1 0 1 2 1 0 0 1 0 0 0 0
## LinkedIn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Snapchat 0 0 0 0 0 0 0 0 2 0 1 1 1 0 0 0 0 0
## TikTok 0 1 0 0 1 2 2 1 0 2 1 1 0 1 0 0 0 0
## Twitter 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## YouTube 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0
##
## 7 7.1 7.2 7.3 7.4 7.5 7.8 8
## Facebook 0 0 0 0 0 0 0 0
## Instagram 1 0 0 0 0 0 0 0
## LinkedIn 0 1 2 2 1 0 1 1
## Snapchat 0 0 0 0 0 0 0 0
## TikTok 0 0 0 0 0 0 0 0
## Twitter 0 0 0 0 0 1 0 0
## YouTube 1 0 0 0 0 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores4 <- c("maroon2","mediumpurple3","paleturquoise2")
barplot(TablaCruzada, col = colores4)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores4, beside = TRUE)
legend("topleft", legend = c(""), fill = colores4)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores4)
mosaicplot(TablaCruzada_propFilas, col = colores4)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores4)
Tablas de Aplicaciones más usadas y Salud Mental
TablaCruzada <- table(Datos2$`Most Used Platform`,Datos2$`Mental Health Score`)
TablaCruzada##
## 4 5 6 7 8 9
## Facebook 0 0 1 6 0 0
## Instagram 1 7 4 1 0 0
## LinkedIn 0 0 0 0 7 1
## Snapchat 0 1 4 0 0 0
## TikTok 0 7 4 1 0 0
## Twitter 0 0 0 0 1 0
## YouTube 0 0 0 4 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores5 <- c("maroon","tomato2","darkslateblue")
barplot(TablaCruzada, col = colores5)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores5, beside = TRUE)
legend("topleft", legend = c(""), fill = colores5)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores5)
mosaicplot(TablaCruzada_propFilas, col = colores5)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores5)
Tablas Aplicaciones más usadas y Adicción
TablaCruzada <- table(Datos2$`Most Used Platform`,Datos2$`Addicted Score`)
TablaCruzada##
## 0 1 2 3 4 5
## Facebook 0 3 4 0 0 0
## Instagram 0 1 0 5 6 1
## LinkedIn 3 5 0 0 0 0
## Snapchat 0 0 1 4 0 0
## TikTok 0 1 1 3 7 0
## Twitter 1 0 0 0 0 0
## YouTube 0 2 2 0 0 0TablaCruzada_propFilas=prop.table(TablaCruzada, margin = 1)
TablaCruzada_propColumnas=prop.table(TablaCruzada, margin = 2)
TablaCruzada100 <- addmargins(TablaCruzada * 100)
par(mfrow = c(1, 2))
colores7 <- c("forestgreen","lightgoldenrodyellow","sienna")
barplot(TablaCruzada, col = colores7)
par(mfrow = c(1, 2))
barplot(TablaCruzada_propFilas, col = colores7, beside = TRUE)
legend("topleft", legend = c(""), fill = colores7)
par(mfrow = c(1, 1))
par(mfrow = c(1, 2))
mosaicplot(TablaCruzada, cex = 1.1, col = colores7)
mosaicplot(TablaCruzada_propFilas, col = colores7)
par(mfrow = c(1, 1))
legend("topleft", legend = c("No", "Si"), fill = colores7)
¿El desempeño académico se ve afectado por el ujso de redes sociales?
library(plotrix)
pie3D(table(Datos2$`Affects Academic Performance`), radius=0.9,main="El uso de aplicaciones afecta el desempeño académico",labels=c("No","Si"),explode=0.1,col = hcl.colors(length(table(Datos2$`Affects Academic Performance`)),"Temps"), border = "black")
barplot(table(Datos2$`Affects Academic Performance`),col="rosybrown1",main="Afecta o no el uso de Redes sociales")
Valor de Densidad de Kernel
Datos totales
Edad
plot(density(Datos$Age))
Horas diarias de uso de redes sociales
plot(density(Datos$`Daily Usage Hours`))
Horas de sueño
plot(density(Datos$`Sleep Hours Per Night`))
Salud mental
plot(density(Datos$`Mental Health Score`))
Adicción por las redes sociales
plot(density(Datos$`Addicted Score`))
Muestra de 50
Edad
plot(density(Datos2$Age))
Horas diarias de uso de redes sociales
plot(density(Datos2$`Daily Usage Hours`))
Horas de sueño
plot(density(Datos2$`Sleep Hours Per Night`))
Salud mental
plot(density(Datos2$`Mental Health Score`))
Adicción por las redes sociales
plot(density(Datos2$`Addicted Score`))