Descripción del Problema
Identificación del
problema o fenómeno
Mejorar la forma, hábito de vida y cuidar su salud es lo más acertado
que un estudiante debe aplicar para su propio bienestar y que lo
conseguirá con disciplina y constancia, lo cual en la mayoría de los
casos sería afectado por diversos factores externos al no ser
controlados. En tal sentido, el presente trabajo pretende explorar y
analizar cómo diversas características y hábitos inadecuados de vida
influyen en el rendimiento académico del estudiante.
Variables a tratar
library(readr)
datos <- read_csv("E:/DIPL0MADO UCSP 2024/Semana 04/TAREA/Impact_of_Mobile_Phone_on_Students_Health.csv")
## Rows: 100 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (20): Names, Age, Gender, Mobile Phone, Mobile Operating System, Mobile ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 100 × 20
## Names Age Gender `Mobile Phone` `Mobile Operating System`
## <chr> <chr> <chr> <chr> <chr>
## 1 Ali 21-25 Male Yes Android
## 2 Bilal 21-25 Male Yes Android
## 3 Hammad 21-25 Male Yes IOS
## 4 Abdullah 21-25 Male Yes Android
## 5 Waqar 21-25 Male Yes IOS
## 6 Aammar 21-25 Male Yes Android
## 7 Fatima 21-25 Female Yes IOS
## 8 Jehanzaib 21-25 Male Yes Android
## 9 Shafiq 21-25 Male Yes Android
## 10 Mubashir 21-25 Male Yes Android
## # ℹ 90 more rows
## # ℹ 15 more variables: `Mobile phone use for education` <chr>,
## # `Mobile phone activities` <chr>, `Helpful for studying` <chr>,
## # `Educational Apps` <chr>, `Daily usages` <chr>, `Performance impact` <chr>,
## # `Usage distraction` <chr>, `Attention span` <chr>, `Useful features` <chr>,
## # `Health Risks` <chr>, `Beneficial subject` <chr>, `Usage symptoms` <chr>,
## # `Symptom frequency` <chr>, `Health precautions` <chr>, …
## Names Age Gender Mobile Phone
## Length:100 Length:100 Length:100 Length:100
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Mobile Operating System Mobile phone use for education Mobile phone activities
## Length:100 Length:100 Length:100
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## Helpful for studying Educational Apps Daily usages Performance impact
## Length:100 Length:100 Length:100 Length:100
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Usage distraction Attention span Useful features Health Risks
## Length:100 Length:100 Length:100 Length:100
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Beneficial subject Usage symptoms Symptom frequency Health precautions
## Length:100 Length:100 Length:100 Length:100
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Health rating
## Length:100
## Class :character
## Mode :character
Crítica de datos
##
## 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, union
datos %>% is.na() %>% sum()
## [1] 23
datos %>% is.na() %>% colSums()
## Names Age
## 0 0
## Gender Mobile Phone
## 0 0
## Mobile Operating System Mobile phone use for education
## 0 2
## Mobile phone activities Helpful for studying
## 1 2
## Educational Apps Daily usages
## 2 1
## Performance impact Usage distraction
## 2 2
## Attention span Useful features
## 1 2
## Health Risks Beneficial subject
## 2 1
## Usage symptoms Symptom frequency
## 2 1
## Health precautions Health rating
## 1 1
datos <- datos %>% na.omit()
names(datos) <- c("Nombres","Edad","Genero","Celular","SistemaOperativoCelular","UsoCelularEducacion","ActividadesCelular","UtilParaEstudo","AppsEducativos","UsoDiario","ImpactoDesempeno","DistraccionUso","SpanAtencion","CaracteristicasUtiles","RiesgoSalud","UtilidadBenefica","SintomasUso","FrecuenciaSintoma","PrecaucionesSalud","IndiceSalud")
Respuesta a la pregunta 1
par(mar = c(5,10,4,2) + 0.1)
barplot(table(datos$SintomasUso),
main = "Numero de estudiantes por sintoma de uso",
xlab = "Cantidad",
names.arg = c("Todos", "Ansiedad", "Dolor de Cabeza", "Estres", "Disturbio del sueño", "Insomnio"),
col = "darkred",
las = 2,
horiz = TRUE)
##
## All of these
## 30
## Anxiety or Stress
## 11
## Headache
## 22
## Headache;Sleep disturbance;Anxiety or Stress;All of these
## 1
## Sleep disturbance
## 23
## Sleep disturbance;Anxiety or Stress
## 4
par(mar = c(5,10,4,2) + 0.1)
barplot(table(datos$FrecuenciaSintoma),
main = "Numero de estudiantes por incidencia de sintoma",
xlab = "Cantidad",
names.arg = c("Frecuentemente", "Nunca", "Raramente", "A veces"),
col = "darkred",
las = 2,
horiz = TRUE)
table(datos$FrecuenciaSintoma)
##
## Frequently Never Rarely Sometimes
## 8 13 19 51
par(mar = c(5,10,4,2) + 0.1)
barplot(table(datos$ImpactoDesempeno),
main = "Numero de estudiantes por impacto en su desempeño academico",
xlab = "Cantidad",
names.arg = c("Conforme", "Disconforme", "Neutral", "Totalmente conforme", "Totalmente disconforme"),
col = "darkred",
las = 2,
horiz = TRUE)
table(datos$ImpactoDesempeno)
##
## Agree Disagree Neutral Strongly agree
## 38 7 26 11
## Strongly disagree
## 9
par(mar = c(5,10,4,2) + 0.1)
barplot(table(datos$ActividadesCelular),
main = "Numero de estudiantes segun actividad de uso del movil",
xlab = "Cantidad",
#names.arg = c("Todos", "Ansiedad", "Dolor de Cabeza", "Estres", "Disturbio del sueño", "Insomnio"),
col = "darkred",
las = 2,
horiz = TRUE)
table(datos$ActividadesCelular)
##
## All of these
## 57
## Messaging
## 1
## Social Media
## 20
## Social Media;All of these
## 2
## Social Media;Messaging
## 1
## Social Media;Web-browsing;Messaging;All of these
## 6
## Web-browsing
## 4
Respuesta a la pregunta 2
con1 <- table(datos$ActividadesCelular,datos$ImpactoDesempeno)
con1
##
## Agree Disagree Neutral
## All of these 26 4 14
## Messaging 0 1 0
## Social Media 5 1 10
## Social Media;All of these 1 0 0
## Social Media;Messaging 1 0 0
## Social Media;Web-browsing;Messaging;All of these 3 1 1
## Web-browsing 2 0 1
##
## Strongly agree
## All of these 8
## Messaging 0
## Social Media 2
## Social Media;All of these 0
## Social Media;Messaging 0
## Social Media;Web-browsing;Messaging;All of these 1
## Web-browsing 0
##
## Strongly disagree
## All of these 5
## Messaging 0
## Social Media 2
## Social Media;All of these 1
## Social Media;Messaging 0
## Social Media;Web-browsing;Messaging;All of these 0
## Web-browsing 1
## Warning in chisq.test(con1): Chi-squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: con1
## X-squared = 26.795, df = 24, p-value = 0.3141
mosaicplot(con1,main = "Tabla de contingencia",sub="Actividad del uso movil y el impacto del desempeño del estudiante")
